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Sue Barsamian | International Women's Day


 

(upbeat music) >> Hi, everyone. Welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE. As part of International Women's Day, we're featuring some of the leading women in business technology from developer to all types of titles and to the executive level. And one topic that's really important is called Getting a Seat at the Table, board makeup, having representation at corporate boards, private and public companies. It's been a big push. And former technology operating executive and corporate board member, she's a board machine Sue Barsamian, formerly with HPE, Hewlett Packard. Sue, great to see you. CUBE alumni, distinguished CUBE alumni. Thank you for coming on. >> Yes, I'm very proud of my CUBE alumni title. >> I'm sure it opens a lot of doors for you. (Sue laughing) We're psyched to have you on. This is a really important topic, and I want to get into the whole, as women advance up, and they're sitting on the boards, they can implement policy and there's governance. Obviously public companies have very strict oversight, and not strict, but like formal. Private boards have to operate, be nimble. They don't have to share all their results. But still, boards play an important role in the success of scaled up companies. So super important, that representation there is key. >> Yes. >> I want to get into that, but first, before we get started, how did you get into tech? How did it all start for you? >> Yeah, long time ago, I was an electrical engineering major. Came out in 1981 when, you know, opportunities for engineering, if you were kind, I went to Kansas State as an undergrad, and basically in those days you went to Texas and did semiconductors. You went to Atlanta and did communication satellites. You went to Boston or you went to Silicon Valley. And for me, that wasn't too hard a choice. I ended up going west and really, I guess what, embarked on a 40 year career in Silicon Valley and absolutely loved it. Largely software, but some time on the hardware side. Started out in networking, but largely software. And then, you know, four years ago transitioned to my next chapter, which is the corporate board director. And again, focused on technology software and cybersecurity boards. >> For the folks watching, we'll cut through another segment we can probably do about your operating career, but you rose through the ranks and became a senior operating executive at the biggest companies in the world. Hewlett Packard Enterprise, Hewlett Packard Enterprise and others. Very great career, okay. And so now you're kind of like, put that on pause, and you're moving on to the next chapter, which is being a board director. What inspired you to be a board director for multiple public companies and multiple private companies? Well, how many companies are you on? But what's the inspiration? What's the inspiration? First tell me how many board ships you're on, board seats you're on, and then what inspired you to become a board director? >> Yeah, so I'm on three public, and you are limited in terms of the number of publics that you can do to four. So I'm on three public, and I'm on four private from a tech perspective. And those range from, you know, a $4 billion in revenue public company down to a 35 person private company. So I've got the whole range. >> So you're like freelancing, I mean, what is it like? It's a full-time job, obviously. It's a lot of work involved. >> Yeah, yeah, it's. >> John: Why are you doing it? >> Well, you know, so I retired from being an operating executive after 37 years. And, but I loved, I mean, it's tough, right? It's tough these days, particularly with all the pressures out there in the market, not to mention the pandemic, et cetera. But I loved it. I loved working. I loved having a career, and I was ready to back off on, I would say the stresses of quarterly results and the stresses of international travel. You have so much of it. But I wasn't ready to back off from being involved and engaged and continuing to learn new things. I think this is why you come to tech, and for me, why I went to the valley to begin with was really that energy and that excitement, and it's like it's constantly reinventing itself. And I felt like that wasn't over for me. And I thought because I hadn't done boards before I retired from operating roles, I thought, you know, that would fill the bill. And it's honestly, it has exceeded expectations. >> In a good way. You feel good about where you're at and. >> Yeah. >> What you went in, what was the expectation going in and what surprised you? And were there people along the way that kind of gave you some pointers or don't do this, stay away from this. Take us through your experiences. >> Yeah, honestly, there is an amazing network of technology board directors, you know, in the US and specifically in the Valley. And we are all incredibly supportive. We have groups where we get together as board directors, and we talk about topics, and we share best practices and stories, and so I underestimated that, right? I thought I was going to, I thought I was going to enter this chapter where I would be largely giving back after 37 years. You've learned a little bit, right? What I underestimated was just the power of continuing to learn and being surrounded by so many amazing people. When, you know, when you do, you know, multiple boards, your learnings are just multiplied, right? Because you see not just one model, but you see many models. You see not just one problem, but many problems. Not just one opportunity, but many opportunities. And I underestimated how great that would be for me from a learning perspective and then your ability to share from one board to the other board because all of my boards are companies who are also quite close to each other, the executives collaborate. So that has turned out to be really exciting for me. >> So you had the stressful job. You rose to the top of the ranks, quarterly shot clock earnings, and it's hard charging. It's like, it's like, you know, being an athlete, as we say tech athlete. You're a tech athlete. Now you're taking that to the next level, which is now you're juggling multiple operational kind of things, but not with super pressure. But there's still a lot of responsibility. I know there's one board, you got compensation committee, I mean there's work involved. It's not like you're clipping coupons and having pizza. >> Yeah, no, it's real work. Believe me, it's real work. But I don't know how long it took me to not, to stop waking up and looking at my phone and thinking somebody was going to be dropping their forecast, right? Just that pressure of the number, and as a board member, obviously you are there to support and help guide the company and you feel, you know, you feel the pressure and the responsibility of what that role entails, but it's not the same as the frontline pressure every quarter. It's different. And so I did the first type. I loved it, you know. I'm loving this second type. >> You know, the retirement, it's always a cliche these days, but it's not really like what people think it is. It's not like getting a boat, going fishing or whatever. It's doing whatever you want to do, that's what retirement is. And you've chose to stay active. Your brain's being tested, and you're working it, having fun without all the stress. But it's enough, it's like going the gym. You're not hardcore workout, but you're working out with the brain. >> Yeah, no, for sure. It's just a different, it's just a different model. But the, you know, the level of conversations, the level of decisions, all of that is quite high. Which again, I like, yeah. >> Again, you really can't talk about some of the fun questions I want to ask, like what's the valuations like? How's the market, your headwinds? Is there tailwinds? >> Yes, yes, yes. It's an amazing, it's an amazing market right now with, as you know, counter indicators everywhere, right? Something's up, something's down, you know. Consumer spending's up, therefore interest rates go up and, you know, employment's down. And so or unemployment's down. And so it's hard. Actually, I really empathize with, you know, the, and have a great deal of respect for the CEOs and leadership teams of my board companies because, you know, I kind of retired from operating role, and then everybody else had to deal with running a company during a pandemic and then running a company through the great resignation, and then running a company through a downturn. You know, those are all tough things, and I have a ton of respect for any operating executive who's navigating through this and leading a company right now. >> I'd love to get your take on the board conversations at the end if we have more time, what the mood is, but I want to ask you about one more thing real quick before we go to the next topic is you're a retired operating executive. You have multiple boards, so you've got your hands full. I noticed there's a lot of amazing leaders, other female tech athletes joining boards, but they also have full-time jobs. >> Yeah. >> And so what's your advice? Cause I know there's a lot of networking, a lot of sharing going on. There's kind of a balance between how much you can contribute on the board versus doing the day job, but there's a real need for more women on boards, so yet there's a lot going on boards. What's the current state of the union if you will, state of the market relative to people in their careers and the stresses? >> Yeah. >> Cause you left one and jumped in all in there. >> Yeah. >> Some can't do that. They can't be on five boards, but they're on a few. What's the? >> Well, and you know, and if you're an operating executive, you wouldn't be on five boards, right? You would be on one or two. And so I spend a lot of time now bringing along the next wave of women and helping them both in their career but also to get a seat at the table on a board. And I'm very vocal about telling people not to do it the way I do it. There's no reason for it to be sequential. You can, you know, I thought I was so busy and was traveling all the time, and yes, all of that was true, but, and maybe I should say, you know, you can still fit in a board. And so, and what I see now is that your learnings are so exponential with outside perspective that I believe I would've been an even better operating executive had I done it earlier. I know I would've been an even better operating executive had I done it earlier. And so my advice is don't do it the way I did it. You know, it's worked out fine for me, but hindsight's 2020, I would. >> If you can go back and do a mulligan or a redo, what would you do? >> Yeah, I would get on a board earlier, full stop, yeah. >> Board, singular, plural? >> Well, I really, I don't think as an operating executive you can do, you could do one, maybe two. I wouldn't go beyond that, and I think that's fine. >> Yeah, totally makes sense. Okay, I got to ask you about your career. I know technical, you came in at that time in the market, I remember when I broke into the business, very male dominated, and then now it's much better. When you went through the ranks as a technical person, I know you had some blockers and definitely some, probably some people like, well, you know. We've seen that. How did you handle that? What were some of the key pivot points in your journey? And we've had a lot of women tell their stories here on theCUBE, candidly, like, hey, I was going to tell that professor, I'm going to sit in the front row. I'm going to, I'm getting two degrees, you know, robotics and aerospace. So, but they were challenged, even with the aspiration to do tech. I'm not saying that was something that you had, but like have you had experience like that, that you overcome? What were those key points and how did you handle them and how does that help people today? >> Yeah, you know, I have to say, you know, and not discounting that obviously this has been a journey for women, and there are a lot of things to overcome both in the workforce and also just balancing life honestly. And they're all real. There's also a story of incredible support, and you know, I'm the type of person where if somebody blocked me or didn't like me, I tended to just, you know, think it was me and like work harder and get around them, and I'm sure that some of that was potentially gender related. I didn't interpret it that way at the time. And I was lucky to have amazing mentors, many, many, many of whom were men, you know, because they were in the positions of power, and they made a huge difference on my career, huge. And I also had amazing female mentors, Meg Whitman, Ann Livermore at HPE, who you know well. So I had both, but you know, when I look back on the people who made a difference, there are as many men on the list as there are women. >> Yeah, and that's a learning there. Create those coalitions, not just one or the other. >> Yeah, yeah, yeah, absolutely. >> Well, I got to ask you about the, well, you brought up the pandemic. This has come up on some interviews this year, a little bit last year on the International Women's Day, but this year it's resonating, and I would never ask in an interview. I saw an interview once where a host asked a woman, how do you balance it all? And I was just like, no one asked men that. And so it's like, but with remote work, it's come up now the word empathy around people knowing each other's personal situation. In other words, when remote work happened, everybody went home. So we all got a glimpse of the backdrop. You got, you can see what their personal life was on Facebook. We were just commenting before we came on camera about that. So remote work really kind of opened up this personal side of everybody, men and women. >> Yeah. >> So I think this brings this new empathy kind of vibe or authentic self people call it. Is remote work an opportunity or a threat for advancement of women in tech? >> It's a much debated topic. I look at it as an opportunity for many of the reasons that you just said. First of all, let me say that when I was an operating executive and would try to create an environment on my team that was family supportive, I would do that equally for young or, you know, early to mid-career women as I did for early to mid-career men. And the reason is I needed those men, you know, chances are they had a working spouse at home, right? I needed them to be able to share the load. It's just as important to the women that companies give, you know, the partner, male or female, the partner support and the ability to share the love, right? So to me it's not just a woman thing. It's women and men, and I always tried to create the environment where it was okay to go to your soccer game. I knew you would be online later in the evening when the kids were in bed, and that was fine. And I think the pandemic has democratized that and made that, you know, made that kind of an everyday occurrence. >> Yeah the baby walks in. They're in the zoom call. The dog comes in. The leaf blower going on the outside the window. I've seen it all on theCUBE. >> Yeah, and people don't try to pretend anymore that like, you know, the house is clean, the dog's behaved, you know, I mean it's just, it's just real, and it's authentic, and I think that's healthy. >> Yeah. >> I do, you know, I also love, I also love the office, and you know, I've got a 31 year old and a soon to be 27 year old daughter, two daughters. And you know, they love going into the office, and I think about when I was their age, how just charged up I would get from being in the office. I also see how great it is for them to have a couple of days a week at home because you can get a few things done in between Zoom calls that you don't have to end up piling onto the weekend, and, you know, so I think it's a really healthy, I think it's a really healthy mix now. Most tech companies are not mandating five days in. Most tech companies are at two to three days in. I think that's a, I think that's a really good combination. >> It's interesting how people are changing their culture to get together more as groups and even events. I mean, while I got you, I might as well ask you, what's the board conversations around, you know, the old conferences? You know, before the pandemic, every company had like a user conference. Right, now it's like, well, do we really need to have that? Maybe we do smaller, and we do digital. Have you seen how companies are handling the in-person? Because there's where the relationships are really formed face-to-face, but not everyone's going to be going. But now certain it's clearly back to face-to-face. We're seeing that with theCUBE as you know. >> Yeah, yeah. >> But the numbers aren't coming back, and the numbers aren't that high, but the stakeholders. >> Yeah. >> And the numbers are actually higher if you count digital. >> Yeah, absolutely. But you know, also on digital there's fatigue from 100% digital, right? It's a hybrid. People don't want to be 100% digital anymore, but they also don't want to go back to the days when everybody got on a plane for every meeting, every call, every sales call. You know, I'm seeing a mix on user conferences. I would say two-thirds of my companies are back, but not at the expense level that they were on user conferences. We spend a lot of time getting updates on, cause nobody has put, interestingly enough, nobody has put T&E, travel and expense back to pre-pandemic levels. Nobody, so everybody's pulled back on number of trips. You know, marketing events are being very scrutinized, but I think very effective. We're doing a lot of, and, you know, these were part of the old model as well, like some things, some things just recycle, but you know, there's a lot of CIO and customer round tables in regional cities. You know, those are quite effective right now because people want some face-to-face, but they don't necessarily want to get on a plane and go to Las Vegas in order to do it. I mean, some of them are, you know, there are a lot of things back in Las Vegas. >> And think about the meetings that when you were an operating executive. You got to go to the sales kickoff, you got to go to this, go to that. There were mandatory face-to-faces that you had to go to, but there was a lot of travel that you probably could have done on Zoom. >> Oh, a lot, I mean. >> And then the productivity to the family impact too. Again, think about again, we're talking about the family and people's personal lives, right? So, you know, got to meet a customer. All right. Salesperson wants you to get in front of a customer, got to fly to New York, take a red eye, come on back. Like, I mean, that's gone. >> Yeah, and oh, by the way, the customer doesn't necessarily want to be in the office that day, so, you know, they may or may not be happy about that. So again, it's and not or, right? It's a mix. And I think it's great to see people back to some face-to-face. It's great to see marketing and events back to some face-to-face. It's also great to see that it hasn't gone back to the level it was. I think that's a really healthy dynamic. >> Well, I'll tell you that from our experience while we're on the topic, we'll move back to the International Women's Day is that the productivity of digital, this program we're doing is going to be streamed. We couldn't do this face-to-face because we had to have everyone fly to an event. We're going to do hundreds of stories that we couldn't have done. We're doing it remote. Because it's better to get the content than not have it. I mean it's offline, so, but it's not about getting people to the event and watch the screen for seven hours. It's pick your interview, and then engage. >> Yeah. >> So it's self-service. So we're seeing a lot, the new user experience kind of direct to consumer, and so I think there will be an, I think there's going to be a digital first class citizen with events, so that that matches up with the kind of experience, but the offline version. Face-to-face optimized for relationships, and that's where the recruiting gets done. That's where, you know, people can build these relationships with each other. >> Yeah, and it can be asynchronous. I think that's a real value proposition. It's a great point. >> Okay, I want to get, I want to get into the technology side of the education and re-skilling and those things. I remember in the 80s, computer science was software engineering. You learned like nine languages. You took some double E courses, one or two, and all the other kind of gut classes in school. Engineering, you had the four class disciplines and some offshoots of specialization. Now it's incredible the diversity of tracks in all engineering programs and computer science and outside of those departments. >> Yeah. >> Can you speak to the importance of STEM and the diversity in the technology industry and how this brings opportunity to lower the bar to get in and how people can stay in and grow and keep leveling up? >> Yeah, well look, we're constantly working on how to, how to help the incoming funnel. But then, you know, at a university level, I'm on the foundation board of Kansas State where I got my engineering degree. I was also Chairman of the National Action Council for Minorities in Engineering, which was all about diversity in STEM and how do you keep that pipeline going because honestly the US needs more tech resources than we have. And if you don't tap into the diversity of our entire workforce, we won't be able to fill that need. And so we focused a lot on both the funnel, right, that starts at the middle school level, particularly for girls, getting them in, you know, the situation of hands-on comfort level with coding, with robot building, you know, whatever gives them that confidence. And then keeping that going all the way into, you know, university program, and making sure that they don't attrit out, right? And so there's a number of initiatives, whether it's mentoring and support groups and financial aid to make sure that underrepresented minorities, women and other minorities, you know, get through the funnel and stay, you know, stay in. >> Got it. Now let me ask you, you said, I have two daughters. You have a family of girls too. Is there a vibe difference between the new generation and what's the trends that you're seeing in this next early wave? I mean, not maybe, I don't know how this is in middle school, but like as people start getting into their adult lives, college and beyond what's the current point of view, posture, makeup of the talent coming in? >> Yeah, yeah. >> Certain orientations, do you see any patterns? What's your observation? >> Yeah, it's interesting. So if I look at electrical engineering, my major, it's, and if I look at Kansas State, which spends a lot of time on this, and I think does a great job, but the diversity of that as a major has not changed dramatically since I was there in the early 80s. Where it has changed very significantly is computer science. There are many, many university and college programs around the country where, you know, it's 50/50 in computer science from a gender mix perspective, which is huge progress. Huge progress. And so, and to me that's, you know, I think CS is a fantastic degree for tech, regardless of what function you actually end up doing in these companies. I mean, I was an electrical engineer. I never did core electrical engineering work. I went right into sales and marketing and general management roles. So I think, I think a bunch of, you know, diverse CS graduates is a really, really good sign. And you know, we need to continue to push on that, but progress has been made. I think the, you know, it kind of goes back to the thing we were just talking about, which is the attrition of those, let's just talk about women, right? The attrition of those women once they got past early career and into mid-career then was a concern, right? And that goes back to, you know, just the inability to, you know, get it all done. And that I am hopeful is going to be better served now. >> Well, Sue, it's great to have you on. I know you're super busy. I appreciate you taking the time and contributing to our program on corporate board membership and some of your story and observations and opinions and analysis. Always great to have you and call you a contributor for theCUBE. You can jump on on one more board, be one of our board contributors for our analysts. (Sue laughing) >> I'm at capacity. (both laughing) >> Final, final word. What's the big seat at the table issue that's going well and areas that need to be improved? >> So I'll speak for my boards because they have made great progress in efficiency. You know, obviously with interest rates going up and the mix between growth and profitability changing in terms of what investors are looking for. Many, many companies have had to do a hard pivot from grow at all costs to healthy balance of growth and profit. And I'm very pleased with how my companies have made that pivot. And I think that is going to make much better companies as a result. I think diversity is something that has not been solved at the corporate level, and we need to keep working it. >> Awesome. Thank you for coming on theCUBE. CUBE alumni now contributor, on multiple boards, full-time job. Love the new challenge and chapter you're on, Sue. We'll be following, and we'll check in for more updates. And thank you for being a contributor on this program this year and this episode. We're going to be doing more of these quarterly, so we're going to move beyond once a year. >> That's great. (cross talking) It's always good to see you, John. >> Thank you. >> Thanks very much. >> Okay. >> Sue: Talk to you later. >> This is theCUBE coverage of IWD, International Women's Day 2023. I'm John Furrier, your host. Thanks for watching. (upbeat music)

Published Date : Mar 3 2023

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Thank you for coming on. of my CUBE alumni title. We're psyched to have you on. And then, you know, four years ago and then what inspired you And those range from, you know, I mean, what is it like? I think this is why you come to tech, You feel good about where you're at and. that kind of gave you some directors, you know, in the US I know there's one board, you and you feel, you know, It's doing whatever you want to But the, you know, the right now with, as you know, but I want to ask you about of the union if you will, Cause you left one and but they're on a few. Well, and you know, Yeah, I would get on a executive you can do, Okay, I got to ask you about your career. have to say, you know, not just one or the other. Well, I got to ask you about the, So I think this brings and made that, you know, made that They're in the zoom call. that like, you know, the house is clean, I also love the office, and you know, around, you know, and the numbers aren't that And the numbers are actually But you know, also on that you had to go to, So, you know, got to meet a customer. that day, so, you know, is that the productivity of digital, That's where, you know, people Yeah, and it can be asynchronous. and all the other kind all the way into, you know, and what's the trends that you're seeing And so, and to me that's, you know, Well, Sue, it's great to have you on. I'm at capacity. that need to be improved? And I think that is going to And thank you for being a It's always good to see you, John. I'm John Furrier, your host.

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Sue Persichetti & Danielle Greshock | AWS Partner Showcase S1E3


 

(upbeat music) >> Hey everyone! Welcome to the AWS Partner Showcase. This is season one, episode three with a focus on women in tech. I'm your host, Lisa Martin. I've got two guests here with me, Sue Persichetti, the EVP of Global AWS Strategic Alliances at Jefferson Frank. A Tenth Revolution Group company. And Danielle Greshock, one of our own CUBE alumni, joins us, ISV PSA director. Ladies, it's great to have you on the program talking about a topic that is near and dear to my heart, women in tech. >> Thank you, Lisa! >> Great to be here! >> So let's go ahead and start with you. Give the audience an understanding of Jefferson Frank, what does the company do, and about the partnership with AWS. >> Sure, so let's just start, Jefferson Frank is a Tenth Revolution Group company. And if you look at it, it's really talent as a service. So Jefferson Frank provides talent solutions all over the world for AWS clients, partners, and users, et cetera. And we have a sister company called Revolent, which is a talent creation company within the AWS ecosystem. So we create talent and put it out in the ecosystem. Usually underrepresented groups, over half of them are women. And then we also have a company called Rebura, which is a delivery model around AWS technology. So all three companies fall under the Tenth Revolution Group organization. >> Got it, Danielle, talk to me a little bit about from AWS' perspective and the focus on hiring more women in technology and about the partnership. >> Yes, this has definitely been a focus ever since I joined eight years ago, but also just especially in the last few years of we've grown exponentially and our customer base has changed. We want to have an organization interacting with them that reflects our customers, right? And we know that we need to keep pace with that even with our growth. And so we've very much focused on early career talent, bringing more women and underrepresented minorities into the organization, sponsoring those folks, promoting them, giving them paths to grow inside of the organization. I'm an example of that, of course, I've benefited from it. But also, I try to bring that into my organization as well and it's super important. >> Tell me a little bit about how you benefited from that, Danielle. >> I just think that I've been able to get, a seat at the table. I think that. I feel as though I have folks supporting me very deeply and want to see me succeed. And also they put me forth as a representative to bring more women into the organization as well. They give me a platform in order to do that, like this, but also many other spots as well. And I'm happy to do it because I feel that... you always want to feel that you're making a difference in your job. And that is definitely a place where I get that time and space in order to be that representative. To bring more women into benefiting from having careers in technology, which there's a lot of value there. >> Lot of value. Absolutely. So back over to you, what are some of the trends that you are seeing from a gendered diversity perspective in tech? We know the numbers of women in technical positions. >> Right. There's so much data out there that shows when girls start dropping out, but what are some of the trends that you're seeing? >> So that's a really interesting question. And Lisa, I had a whole bunch of data points that I wanted to share with you but just two weeks ago, I was in San Francisco with AWS at The Summit. And we were talking about this, we were talking about how we can collectively together attract more women, not only to AWS, not only to technology, but to the AWS ecosystem in particular. And it was fascinating because I was talking about the challenges that women have, and how hard to believe but about 5% of women who were in the ecosystem have left in the past few years. Which was really, really something that shocked everyone when we were talking about it, because all of the things that we've been asking for, for instance working from home, better pay, more flexibility, better maternity leave. Seems like those things are happening. So we're getting what we want, but people are leaving. And it seemed like the feedback that we got was that a lot of women still felt very underrepresented. The number one thing was that they couldn't be... you can't be what you can't see. So because they... we feel, collectively women, people who identify as women, just don't see enough women in leadership, they don't see enough mentors. I think I've had great mentors, but just not enough. I'm lucky enough to have the president of our company, Zoe Morris is a woman and she does lead by example. So I'm very lucky for that. And Jefferson Frank really quickly we put out a hiring, a salary, and hiring guide. Career and hiring guide every year. And the data points, and that's about 65 pages long, no one else does it. It gives an abundance of information around everything about the AWS ecosystem that a hiring manager might need to know. What I thought was really unbelievable was that only 7% of the people that responded to it were women. So my goal, being that we have such a very big global platform, is to get more women to respond to that survey. So we can get as much information and take action. So... >> Absolutely only 7%. So a long way to go there. Danielle, talk to me about AWS' focus on women in tech. I was watching, Sue, I saw that you shared on LinkedIn the TED Talk that the CEO and founder of Girls Who Code did. And one of the things that she said was that there was a survey that HP did some years back that showed that 60%... that men will apply for jobs if they only meet 60% of the list of requirements. Whereas with females, it's far, far less. We've all been in that imposter syndrome conundrum before. But Danielle, talk to us about AWS' specific focus here to get these numbers up. >> Well, I think it speaks to what Susan was talking about how I think we're approaching it top and bottom, right? We're looking out at who are the women who are currently in technical positions and how can we make AWS an attractive place for them to work? And that's a lot of the changes that we've had around maternity leave and those types of things. But then also, a more flexible working arrangements. But then also early... how can we actually impact early career women and actually women who are still in school. And our training and certification team is doing amazing things to get more girls exposed to AWS, to technology, and make it a less intimidating place. And have them look at employees from AWS and say like, "Oh, I can see myself in those people". And kind of actually growing the viable pool of candidates. I think we're limited with the viable pool of candidates when you're talking about mid-to-late career. But how can we help retrain women who are coming back into the workplace after having a child, and how can we help with military women who want to... or underrepresented minorities who want to move into AWS? We have a great military program but then also just that early high school career getting them in that trajectory. >> Sue, is that something that Jefferson Frank is also able to help with is getting those younger girls before they start to feel... >> Right. "There's something wrong with me, I don't get this." >> Right. >> Talk to us about how Jefferson Frank can help really drive up that in those younger girls. >> Let me tell you one other thing to refer back to that Summit that we did we had breakout sessions and that was one of the topics. Cause that's the goal, right? To make sure that there are ways to attract them. That's the goal. So some of the things that we talked about was mentoring programs from a very young age, some people said high school. But then we said, even earlier, goes back to you can't be what you can't see. So getting mentoring programs established. We also talked about some of the great ideas was being careful of how we speak to women using the right language to attract them. And so there was a teachable moment for me there actually. It was really wonderful because an African American woman said to me, "Sue". And I was talking about how you can't be what you can't see. And what she said was, "Sue, it's really different for me as an African American woman" Or she identified as non-binary but she was relating to African American women. She said, "You're a white woman. Your journey was very different than my journey". And I thought, "This is how we're going to learn". I wasn't offended by her calling me out at all. It was a teachable moment. And I thought I understood that but those are the things that we need to educate people on. Those moments where we think we're saying and doing the right thing, but we really need to get that bias out there. So here at Jefferson Frank we're trying really hard to get that careers and hiring guide out there. It's on our website to get more women to talk to it, but to make suggestions in partnership with AWS around how we can do this. Mentoring. We have a mentor me program. We go around the country and do things like this. We try to get the education out there in partnership with AWS. We have a women's group, a women's leadership group. So much that we do and we try to do it in partnership with AWS. >> Danielle, can you comment on the impact that AWS has made so far regarding some of the trends and and gender diversity that Sue was talking about? What's the impact that's been made so far with this partnership? >> Well, I think just being able to get more of the data and have awareness of leaders on how... it used to be a couple years back, I would feel like sometimes the solving to bring more women into the organization was kind of something that folks thought, "Oh, this is... Danielle is going to solve this." And I think a lot of folks now realize, "Oh, this is something that we all need to solve for." And a lot of my colleagues, who maybe a couple years ago didn't have any awareness or didn't even have the tools to do what they needed to do in order to improve the statistics on their or in their organizations, now actually have those tools and are able to kind of work with companies like Susan's work with Jefferson Frank in order to actually get the data, and actually make good decisions, and feel as though they often... these are not lived experiences for these folks. So they don't know what they don't know. And by providing data, and providing awareness, and providing tooling, and then setting goals, I think all of those things have really turned things around in a very positive way. >> And so you bring up a great point about from a diversity perspective. What is Jefferson Frank doing to get those data points up to get more women of all, well, really underrepresented minorities to be able to provide that feedback so that you can have the data and gleamy insights from it to help companies like AWS on their strategic objectives? >> Right, so when I go back to that careers and hiring guide, that is my focus today really, because the more data that we have and the data takes... we need people to participate in order to accurately get ahold of that data. So that's why we're asking. We're taking the initiative to really expand our focus. We are a global organization with a very, very massive database all over the world. But if people don't take action then we can't get the right... the data will not be as accurate as we'd like it to be, therefore take better action. So what we're doing is we're asking people all over the world to participate on our website jeffersonfrank.com In the survey so we can learn as much as we can. 7% is such a... Danielle and I we've got to partner on this just to sort of get that message out there, get more data so we can execute. Some of the other things that we're doing, we're partnering, as I mentioned, more of these events. We're doing around the Summits, we're going to be having more EDNI events, and collecting more information from women. Like I said, internally, we do practice what we preach and we have our own programs that are out there, that are within our own company where the women who are talking to candidates and clients every single day are trying to get that message out there. So if I'm speaking to a client or one of our internal people are speaking to a client or a candidate, they're telling them, "Listen, we really are trying to get these numbers up. We want to attract as many people as we can. Would you mind going to this hiring guide and offering your own information?" So we've got to get that 7% up. We've got to keep talking. We've got to keep getting programs out there. One other thing I wanted to Danielle's point, she mentioned women in leadership, the number that we gathered was only 9% of women in leadership within the AWS ecosystem. We've got to get that number up as well, because I know for me, when I see people like Danielle or her peers it inspires me. And I feel like I just want to give back. Make sure I send the elevator back to the first floor and bring more women in to this amazing ecosystem. >> Absolutely, we need- >> Love that metaphor. >> I do too! But to your point to get those numbers up not just at AWS, but everywhere else we need It's a help me help you situation. >> Exactly. >> So ladies, underrepresented minorities, if you're watching go to the Jefferson Frank website, take the survey. Help provide the data so that the women here that are doing this amazing work, have it to help make decisions and have more of females in leadership roles or underrepresented minorities. So we can be what we can see. >> Exactly. >> Ladies, thank you so much for joining me today and sharing what you guys are doing together to partner on this important cause. >> Thank you for having me, Lisa! >> Thank you! Thank you! >> My pleasure! For my guests, I'm Lisa Martin. You're watching theCUBES coverage of the AWS partner showcase. Thanks for your time. (gentle xylophone music)

Published Date : Jul 21 2022

SUMMARY :

and dear to my heart, women in tech. and about the partnership with AWS. And then we also have a in technology and about the partnership. in the last few years of about how you benefited a representative to bring more women of the trends that you are seeing that shows when girls start dropping out, is to get more women to And one of the things that she said was and how can we help with to help with is getting with me, I don't get this." Talk to us about So some of the things that we talked about and are able to kind of work to get more women of all, well, because the more data that we have But to your point to get those numbers up so that the women here and sharing what you guys of the AWS partner showcase.

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>>Hey everyone. Welcome to the AWS partner showcase. This is season one, episode three, with a focus on women in tech. I'm your host, Lisa Martin. I've got two guests here with me, Sue Peretti, the EVP of global AWS strategic alliances at Jefferson Frank, a 10th revolution group company, and Danielle GShock. One of our alumni joins us ISV PSA director, ladies. It's great to have you on the program talking about a, a topic that is near and dear to my heart at women in tech. >>Thank you, Lisa. >>So let's go ahead and start with you. Give the audience an understanding of Jefferson Frank, what does the company do and about the partnership with AWS? >>Sure. Um, so let's just start, uh, Jefferson Frank is a 10th revolution group company. And if you look at it, it's really talent as a service. So Jefferson Frank provides talent solutions all over the world for AWS clients, partners and users, et cetera. And we have a sister company called revelent, which is a talent creation company within the AWS ecosystem. So we create talent and put it out in the ecosystem. Usually underrepresented groups over half of them are women. And then we also have, uh, a company called Ruba, which is a delivery model around AWS technology. So all three companies fall under the 10th revolution group organization. >>Got it. Danielle, talk to me a little bit about from AWS's perspective and the focus on hiring more women in technology and about the partnership. >>Yes. I mean, this has definitely been a focus ever since I joined eight years ago, but also just especially in the last few years of we've grown exponentially and our customer base has changed. You know, we wanna have, uh, an organization interacting with them that reflects our customers, right. And, uh, we know that we need to keep pace with that even with our growth. And so we've very much focused on early career talent, uh, bringing more women and underrepresented minorities into the organization, sponsoring those folks, promoting them, uh, giving them paths to grow, to grow inside of the organization. I'm an example of that. Of course I've benefit benefited from it, but also I try to bring that into my organization as well. And it's super important. >>Tell me a little bit about how you be benefited from that, Danielle. >>Um, I just think that, um, you know, I I've been able to get, you know, a seat at the table. I think that, um, I feel as though I have folks supporting me, uh, very deeply and wanna see me succeed. And also they put me forth as, um, you know, a, represent a representative, uh, to bring more women into the organization as well. And I think, um, they give me a platform, uh, in order to do that, um, like this, um, but also many other, uh, spots as well. Um, and I'm happy to do it because I feel that, you know, you always wanna feel that you're making a difference in your job. And that is definitely a place where I get that time and space in order to be that representative to, um, bring more, more women into benefiting from having careers and technology, which there's a lot of value there. >>Lot of value. Absolutely. So back over to you, what are some of the trends that you are seeing from a gender diversity perspective in tech? We know the, the numbers of women in technical positions. Uh, there's so much data out there that shows when girls start dropping up, but what are some of the trends that you are seeing? >>So it's, that's a really interesting question. And, and Lisa, I had a whole bunch of data points that I wanted to share with you, but just two weeks ago, uh, I was in San Francisco with AWS at the, at the summit. And we were talking about this. We were talking about how we can collectively together attract more women, not only to, uh, AWS, not only to technology, but to the AWS ecosystem in particular. And it was fascinating because I was talking about, uh, the challenges that women have and how hard to believe, but about 5% of women who were in the ecosystem have left in the past few years, which was really, really, uh, something that shocked everyone when we, when we were talking about it, because all of the things that we've been asking for, for instance, uh, working from home, um, better pay, uh, more flexibility, uh, better maternity leave. >>It seems like those things are happening. So we're getting what we want, but people are leaving. And it seemed like the feedback that we got was that a lot of women still felt very underrepresented. The number one thing was that they, they couldn't be, you can't be what you can't see. So because they, we feel collectively women, uh, people who identify as women just don't see enough women in leadership, they don't see enough mentors. Um, I think I've had great mentors, but, but just not enough. I'm lucky enough to have a pres a president of our company, the president of our company, Zoe Morris is a woman and she does lead by example. So I'm very lucky for that. And Jefferson, Frank really quickly, we put out a hiring a salary and hiring guide a career and hiring guide every year and the data points. And that's about 65 pages long. No one else does it. Uh, it gives an abundance of information around, uh, everything about the AWS ecosystem that a hiring manager might need to know. But there is what, what I thought was really unbelievable was that only 7% of the people that responded to it were women. So my goal, uh, being that we have such a very big global platform is to get more women to respond to that survey so we can get as much information and take action. So >>Absolutely only 7%. So a long way to go there. Danielle, talk to me about AWS's focus on women in tech. I was watching, um, Sue, I saw that you shared on LinkedIn, the Ted talk that the CEO and founder of girls and co did. And one of the things that she said was that there was a, a survey that HP did some years back that showed that, um, 60%, that, that men will apply for jobs if they only meet 60% of the list of requirements. Whereas with females, it's far, far less, we've all been in that imposter syndrome, um, conundrum before. But Danielle, talk to us about AWS, a specific focus here to get these numbers up. >>Well, I think it speaks to what Susan was talking about, how, you know, I think we're approaching it top and bottom, right? We're looking out at what are the, who are the women who are currently in technical positions and how can we make AWS and attractive place for them to work? And that's all a lot of the changes that we've had around maternity leave and, and those types of things, but then also a more flexible working, uh, can, you know, uh, arrangements, but then also, um, early, how can we actually impact early, um, career women and actually women who are still in school. Um, and our training and certification team is doing amazing things to get, um, more girls exposed to AWS, to technology, um, and make it a less intimidating place and have them look at employees from AWS and say like, oh, I can see myself in those people. >>Um, and kind of actually growing the viable pool of candidates. I think, you know, we're, we're limited with the viable pool of candidates, um, when you're talking about mid to late career. Um, but how can we, you know, help retrain women who are coming back into the workplace after, you know, having a child and how can we help with military women who want to, uh, or underrepresented minorities who wanna move into AWS, we have a great military program, but then also just that early high school, uh, career, you know, getting them in, in that trajectory. >>Sue, is that something that Jefferson Frank is also able to help with is, you know, getting those younger girls before they start to feel there's something wrong with me. I don't get this. Talk to us about how Jefferson Frank can help really drive up that when those younger girls, >>Uh, let me tell you one other thing to refer back to that summit that we did, uh, we had breakout sessions and that was one of the topics. What can cuz that's the goal, right? To make sure that, that there are ways to attract them. That's the goal? So some of the things that we talked about was mentoring programs, uh, from a very young age, some people said high school, but then we said even earlier, goes back to you. Can't be what you can't see. So, uh, getting mentoring programs, uh, established, uh, we also talked about some of the great ideas was being careful of how we speak to women using the right language to attract them. And some, there was a teachable moment for, for me there actually, it was really wonderful because, um, an African American woman said to me, Sue and I, I was talking about how you can't be what you can't see. >>And what she said was Sue, it's really different. Um, for me as an African American woman, uh, or she identified, uh, as nonbinary, but she was relating to African American women. She said, you're a white woman. Your journey was very different than my journey. And I thought, this is how we're going to learn. I wasn't offended by her calling me out at all. It was a teachable moment. And I thought I understood that, but those are the things that we need to educate people on those, those moments where we think we're, we're saying and doing the right thing, but we really need to get that bias out there. So here at Jefferson, Frank, we're, we're trying really hard to get that careers and hiring guide out there. It's on our website to get more women, uh, to talk to it, but to make suggestions in partnership with AWS around how we can do this mentoring, we have a mentor me program. We go around the country and do things like this. We, we try to get the education out there in partnership with AWS. Uh, we have a, a women's group, a women's leadership group, uh, so much that, that we do, and we try to do it in partnership with AWS. >>Danielle, can you comment on the impact that AWS has made so far, um, regarding some of the trends and, and gender diversity that Sue was talking about? What's the impact that's been made so far with this partnership? >>Well, I mean, I think just being able to get more of the data and have awareness of leaders, uh, on how, you know, it used to be a, a couple years back, I would feel like sometimes the, um, solving to bring more women into the organization was kind of something that folks thought, oh, this is Danielle is gonna solve this. You know? And I think a lot of folks now realize, oh, this is something that we all need to solve for. And a lot of my colleagues who maybe a couple years ago, didn't have any awareness or didn't even have the tools to do what they needed to do in order to improve the statistics on their, or in their organizations. Now actually have those tools and are able to kind of work with, um, work with companies like Susan's work with Jefferson Frank in order to actually get the data and actually make good decisions and feel as though, you know, they, they often, these are not lived experiences for these folks. So they don't know what they don't know. And by providing data and providing awareness and providing tooling and then setting goals, I think all of those things have really turned, uh, things around in a very positive way. >>And so you bring up a great point about from a diversity perspective, what is Jefferson Frank doing to, to get those data points up, to get more women of, of all well, really underrepresented minorities to, to be able to provide that feedback so that you can, can have the data and glean the insights from it to help companies like AWS on their strategic objectives. >>Right? So as I, when I go back to that higher that, uh, careers in hiring guide, that is my focus today, really because the more data that we have, I mean, the, and the data takes, uh, you know, we need people to participate in order to, to accurately, uh, get ahold of that data. So that's why we're asking, uh, we're taking the initiative to really expand our focus. We are a global organization with a very, very massive database all over the world, but if people don't take action, then we can't get the right. The, the data will not be as accurate as we'd like it to be. Therefore take better action. So what we're doing is we're asking people all over the, all over the world to participate on our website, Jefferson frank.com, the se the high, uh, in the survey. So we can learn as much as we can. >>7% is such a, you know, Danielle and I we're, we've got to partner on this just to sort of get that message out there, get more data so we can execute, uh, some of the other things that we're doing. We're, we're partnering in. As I mentioned, more of these events, uh, we're, we're doing around the summits, we're gonna be having more ed and I events and collecting more information from women. Um, like I said, internally, we do practice what we preach and we have our own programs that are, that are out there that are within our own company where the women who are talking to candidates and clients every single day are trying to get that message out there. So if I'm speaking to a client or one of our internal people are speaking to a client or a candidate, they're telling them, listen, you know, we really are trying to get these numbers up. >>We wanna attract as many people as we can. Would you mind going to this, uh, hiring guide and offering your own information? So we've gotta get that 7% up. We've gotta keep talking. We've gotta keep, uh, getting programs out there. One other thing I wanted to Danielle's point, she mentioned, uh, women in leadership, the number that we gathered was only 9% of women in leadership within the AWS ecosystem. We've gotta get that number up, uh, as well because, um, you know, I know for me, when I see people like Danielle or, or her peers, it inspires me. And I feel like, you know, I just wanna give back, make sure I send the elevator back to the first floor and bring more women in to this amazing E ecosystem. >>Absolutely. That's that metaphor I do too. But we, but to your point to get that those numbers up, not just at AWS, but everywhere else we need, it's a help me help use situation. So ladies underrepresented minorities, if you're watching go to the Jefferson Frank website, take the survey, help provide the data so that the women here that are doing this amazing work, have it to help make decisions and have more of females in leadership roles or underrepresented minorities. So we can be what we can see. Ladies, thank you so much for joining me today and sharing what you guys are doing together to partner on this important. Cause >>Thank you for having me, Lisa, >>Thank you. My pleasure for my guests. I'm Lisa Martin. You're watching the cubes coverage of the AWS partner showcase. Thanks for your time.

Published Date : May 5 2022

SUMMARY :

It's great to have you on the program talking about a, a topic that is near and So let's go ahead and start with you. And if you look at it, it's really talent as a service. Danielle, talk to me a little bit about from AWS's perspective and the focus on And, uh, we know that we need to And also they put me forth as, um, you know, So back over to you, what are some of the trends that you are seeing from a gender I was talking about, uh, the challenges that women have and how hard And it seemed like the feedback that we got was And one of the things that she said was that there was a, Well, I think it speaks to what Susan was talking about, how, you know, but then also just that early high school, uh, career, you know, Sue, is that something that Jefferson Frank is also able to help with is, you know, So some of the things that we talked about was mentoring And I thought I understood that, but those are the things that we need to educate people on uh, on how, you know, it used to be a, a couple years back, And so you bring up a great point about from a diversity perspective, what is Jefferson Frank doing to, the more data that we have, I mean, the, and the data takes, uh, you know, 7% is such a, you know, Danielle and I we're, And I feel like, you know, I just wanna give back, make sure I send the elevator back to So we can be what we can see. of the AWS partner showcase.

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Sue Waite, SAP | SAP SAPPHIRE NOW 2018


 

>> From Orlando, Florida, it's The Cube. Covering SAP Sapphire Now 2018. Brought to you by NetApp. >> Welcome to The Cube. I'm Lisa Martin with Keith Townsend in Orlando, at SAP Sapphire Now 2018. We're in the NetApp booth and we are having some great conversations, really understanding how SAP and their ecosystem of partners have really helped to transform 390,000 plus customers. We're joined next by Sue Waite, who is one of the directors of the Global Center of Excellence for Database and Data Management at SAP. Sue, thanks for coming to The Cube. >> Thank you very much for letting me join you. >> So SAP, 46 years young company, like I said, 390,000 customers and 25 plus industries. You guys have, probably, many Centers of Excellence. Give us a little bit of understanding of the COE for database and data management. >> I will, happy to do so. So, the team that I'm very, it's my pleasure to be a part of, focuses on helping our customers understand what are the new opportunities that are out there. Many customers are so driven by the day-to-day operations. How do we take that opportunity to step back and look at what perhaps other competitors have done in their space, or in completely different industries. And what are new ways that they could be looking at approaching their business, approaching their engagements with their customers, and helping them grow, as well. And our database and data management solutions are the platform that helps enable that in a truly comprehensive data management way. >> It sounds pretty symbiotic. >> Very much so. >> Where, they're actually, you're helping them, but customers are also helping you. Tell us maybe some examples, like Data Hub, for example, of one of the things that maybe that symbiotic relationship-- >> Love to. >> Helped to evolve. >> Yes, yes. So, a little history in our database and data management solutions. Of course, SAP HANA is a cornerstone to our core platforms. Very much a groundbreaking technology eight years ago in introducing a completely comprehensive platform. But one of the things we've learned as we've worked with our customers over time, so many clients, and in fact SAP itself, has our different pockets of enterprise systems. We have our CRM applications, our ERP, our finance, our, you know, supply chain. But in today's environments, we have so much more information coming at us. There's the whole big data space. Everybody's trying to pull and collect information from, of course, social media feeds. That's the one everybody thinks of. But another new space is internet of things. Collecting information from sensors off their machines, from, you know, telemetry from where their trucks are, to facial recognition as people are coming into our stores, or image recognition as we're manufacturing sheet metal on the plant floor. It's amazing the amount of information that is now available to be collected and mined to bring further insight into business operations. So, great, we can collect all of that fabulous new data and store it in Hadoop or Amazon S3 or object stores, but how do we get at that information? >> Right, and extract valuable insights-- >> Exactly. >> That they can then use to generate new products, new revenue streams, new businesses. >> Completely so. >> Yeah, and a simple example is, well, not example, but S/4 and HANA. The journey to S/4 and HANA starts mostly with BW. So if the original data warehouse and the capability that that brings to organizations, one of the first things that happens when you deploy BW on HANA is other businesses look up, other business units look up and say, "Hey! I want that capability. I want that instant analytics, that instant search." >> Yes, yes. >> Talk to the evolution of that. After we go BW and the focus is still on analytics and data intelligence. >> And it should be, you know. It is about making important decisions, in an instant now. >> Right. >> I mean, everybody looks at their phone when we make deposit. We expect to see that deposit instantaneously. >> Yes. >> Right. >> The business needs to operate just as instantaneously and with BW it has a tremendously powerful system that works hand-in-hand, as you said, with S/4, ERP, and the whole business suite itself. But then the goal was, as well, to bring in this larger context, from these other large-data environments that are being captured in Hadoop or S3. So the genesis of the idea to help address that marrying up of data, stored in our classic enterprise data warehouse, like BW, is the solution that we call Data Hub. And what Data Hub does, what's different about it, is it truly is an umbrella solution that transcends the big data environment as well as the classic enterprise systems. And in doing so, one of the first problems was we have all this fabulous information collected in our data lake. How do we get to the information that's truly useful, to combine with information in BW? Or even feed into S/4 itself? So Data Hub helps pre-process, refine, and enrich that information, and the key is doing so where the data lives. Let's not move petabytes of data around, just trying to derive intelligence from it. So Data Hub allows customers to pre process, refine, and enrich that data in their data lake itself. Get from petabytes of information to, say, gigabytes of data that is useful to combine with information in BW, or within HANA, or S/4, or whatever other systems may be useful to bring that together. And the trick with all of that is having visibility into the information that truly lives within each of those systems, which is also something that Data Hub brings to the table, because it has the ability to collect metadata. So, information about the data that lives within each of those environments, so the data analysts, who are bringing those data sets together, can intelligently know, this is the data set I want, this is how I need to refine it, and I want to combine it here, and they can set that up through pipelines and orchestration within Data Hub. It is tremendously powerful in simplifying that end-to-end scenario, and the whole goal is to make it easier for the business to get to those useful insights. Really help me have a competitive differentiator because of the great set of information I can now bring together, and bubble that up through our analytics tools. >> Yeah, access at speed, that was one of the things that Hasso Plattner-- >> Completely so. >> Plattner talked about this morning, is, everything has to be realtime. We expect it, as consumers, right? >> Yes, yes. >> And then as consumers who are also business people, which many are, you also expect that. One of the things, too, that you reminded me of, that Bill MCDermott talked about yesterday, was customers in every industry need a 360 of their customers, right? But, SAP is moving it's away from the 360 of just sales automation to really having a true, enabling a true 360 of the entire customer experience. And one of the things I liked yesterday was the notion, in a not-creepy way, but we expect that, and customers have to connect. If you can connect finance and procurement and supply chain and marketing and sales and extract those really valuable insights, faster than your competition, that's what today's digital businesses need. >> One of the simplest statements I've heard that I think is so powerful is, "Understand more about your customers, so that you can do more for your customers." That's what it's all about. Truly providing that end service to help them achieve their goals and move to that. >> So let's talk about some of the, from a high level, some of the technology to makes this capable. When you're talking about petabytes and petabytes of data, you can't move all the data, different systems have different capabilities when it comes to data transformation. I love the insight that you provided that data analysts need to be aware of the metadata, so that they can set up the transformations needed to get the reporting that they need. How does Data Hub enable the power of metadata to all these different systems, whether it's Hadoop, unstructured data, systems that we don't even control, such as social media data. How does Data Hub bring all that metadata together? >> So one of the capabilities that enables that visibility into data content is through what we call a data discovery mechanism. And Data Hub includes metadata crawlers. So it literally, anytime Data Hub has a system that it's been authorized to connect to, we can then go out and collect the metadata about the information, the data itself, that lives within those environments. And so it comes back and there's a repository within Data Hub that holds information about the tables, the column names, and then things like data types, as well as, even basic profiling information, such as, you know, minimum, maximum, how often values showing up, cardinality, even the frequency of different values that are there, down to the ability to even preview, literally look at the content within the tables. And that's so powerful for the data analysts, because they no longer have to alright, go, you know literally crack open a file, to look at the content. It's at their fingertips. And that's just an amazing tool, that, once they have that, then they can move on to the truly value-added activity of how they want to refine, enrich, mashup, that information to get to those insights that are at their fingertips. >> With so many, the C-suite, like we've talked about before, is changing so dramatically, the CDO, the CIO, the CMO, the CXO, they all have need, different needs, a need for this data. Your customer conversations, where do you start at the C-suite, in terms of, you know, they've got all of this data that they know, there's golden nuggets in there. How do we find it? And also, exploit insights for marketing, for sales, for finance, for procurement. Where do you start in terms of that conversation within a customer? Do you help unite the C-suite to understand how they can team together? >> That is always the goal, of course. And it's important to understand each customer's individual, you know, what their business is, what their market is, as well as, that company themselves, what their goals are, what they're trying to achieve. So that we can truly be, I know you've heard the term trusted advisor, but we really take that seriously, because understanding what their challenges are and where they're trying to grow their business, along with, you know, the very technical aspects of which technologies they're using today, and what roadblocks are they experiencing that are preventing them from achieving those goals. Of course, our objective is to help them cross those roadblocks, cross those bridges, and if we can help with SAP solutions to achieve those goals, it's not about rip and replace, it's helping them bridge those challenges to reach those goals. And that's the role we play. I love what I do. >> So, the Data Hub is a great example of a platform that can be expanded upon. Can you share about some of the successes that you've had with the ecosystem around Data Hub? To extend, not just the analysts who can interact with Data Hub directly, but what we like to call bolt-on applications, that extend the overall capability of whether it's analytics, AI, machine learning, the examples, or automation, business process automation. What are some of the successes coming out of making Data Hub? I know it's only a year old, but what are making the Data Hub available to your ecosystem of partners? >> Yep, so, some of the successes have been, truly, you know, efficiency, obviously, but in that ability to bring those data sets together. For example, we've been working with one customer who's, we'll just say they're a manufacturer. And they have their own team of data scientists, and they have petabytes of information they've been collecting in their data lake, and we talked with them about Data Hub and what we were seeing, and they're like, "Yeah, love the story. But, you know, our data science team is really good. I think we've got this." They literally came back to us six months later and said, "It's a whole lot more work, than we ever expected it would be." Because in a classic environment, it's a lot of hand coding, it's a lot of scripting, it's creating those predictive models which is the lifeblood, that's why we hire data scientists. But they were spending so much time and data manipulation and trying to find the right information. They're like, "Please, you know, white flag." >> Yeah, the can bring back a lot of data. >> Yeah, it literally seems like a great opportunity for the overall market to start adding value on top the, on top of Data Hub to basically shorten that timeframe for internal data scientists. They should be figuring out what questions to ask, versus figuring out how to organize the data. >> Exactly so, that's why they're being paid the big bucks. Let them do the job that we hired them to do, you know. >> Well, Sue, you said you love your job, and it's evident. Thank you so much for stopping by The Cube and sharing what you're doing within the COE for database and database management, specifically, >> Thank you very much. A pleasure to speak with you this morning. >> With Data Hub, we can't wait to hear what's next for next year. >> Alright, excellent. >> We wanna thank you for watching The Cube. I'm Lisa Martin with Keith Townsend, from SAP Sapphire Now 2018. Thanks for watching. (upbeat music)

Published Date : Jun 8 2018

SUMMARY :

Brought to you by NetApp. of the Global Center of Excellence of the COE for database and data management. So, the team that I'm very, of one of the things that maybe that is now available to be collected and mined to generate new products, and the capability that that brings to organizations, and the focus is still on analytics and data intelligence. And it should be, you know. We expect to see that deposit instantaneously. because it has the ability to collect metadata. everything has to be realtime. One of the things, too, that you reminded me of, so that you can do more for your customers." some of the technology to makes this capable. because they no longer have to alright, go, you know at the C-suite, in terms of, you know, And that's the role we play. that extend the overall capability but in that ability to bring those data sets together. for the overall market to start adding value Let them do the job that we hired them to do, you know. and sharing what you're doing A pleasure to speak with you this morning. With Data Hub, we can't wait to hear We wanna thank you for watching The Cube.

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Sue Morrow, United Methodist Homes | VTUG Winter Warmer 2018


 

>> Narrator: From Gillette Stadium in Foxborough, Massachusets, it's theCUBE, covering VTUG Winter Warmer 2018. Presented by SiliconANGLE. (upbeat music) >> I'm Stu Miniman and this is theCUBE's fifth year at the VTUG Winter Warmer. 2018 is the 12th year of this event, always love when we get to talk to some of the users at the conference which's why I'm really happy to introduce to our audience Sue Morrow, who is a network manager at United Methodist Homes. Thanks for joining me Sue. >> No problem. >> First, tell me a little bit about yourself and what brings you all the way from Upstate New York to come to the VTUG. >> Well, I like to go to conferences whenever I can continue my education in IT. I grew up with computers in my house in the '80s. My dad was a physics teacher and a scientist so we always had a Commodore 64 or an Amiga in our house, growing up, when most people had Atari, we had computers. >> Totally, so Commodore 64, classic. I myself was a Tandy Radioshack, the TRS-80 Model III. So, in a similar era. >> Yep, I actually took a basic coding class on a TRS-80 when I was around 10, I think. Anyway, grew up with computers and somehow stumbled into IT later in life. So, that's why I'm here. >> United Methodist Homes, tell us just a little bit about what the mission of the company is. >> United Methodist Homes is a longterm care corporation. We have four facilities, two in the Binghamton area and two in Northeastern Pennsylvania. We have all levels of care from nursing homes, skilled care, up to independent living, and everything in between. >> Okay, and as network manager, what's under your purview? >> Well, it's kind of a silly title, actually. In longterm care or in healthcare or nonprofits, as we are, you often wear many hats and so that's, sort of, a weird title for me, but I supervise our help desk which we serve centrally from our corporate office. We serve about 600 actual computer users and, all in total, about 1200 employees who interface with the technology, in some way. So, I supervise the help desk, I make sure our network is running well. IT has changed over the years so that we're now providing more of a service and making sure that everything is up and running, network-wise, for everyone instead of keeping our servers running all the time. >> Yeah, reminds me of the old saying, it was like oh, the network is the computer, things like that, so you've got both ends of it. >> Sue: Yes. >> What kind of things are you looking at from a technology standpoint when you come to event like this? Did you catch some of the keynotes this morning, there was a broad spectrum? >> Yes. >> What are the kind of things that you're digging in to and find interesting? >> Yeah, the keynotes are really interesting. I think the first one that I went to with Luigi and Chris was great just to, kind of, expand your thinking about your own career personally, and where you want to go with your life was really interesting. I also watched Randall do his coding which is completely outside of what I do everyday, but was fascinating. And then the last major keynote was fantastic. I think that from my perspective in my company, we're kind of small and we don't do a whole lot of, we don't run apps and things like that, so the things that we have ritualized is mostly storage, so I'm looking at better ways that we can manage our storage and stuff. Most of the applications that we run now are SAS applications hosted by somebody else and their cloud, or a public cloud, or wherever, so I'm not so much looking at the cloud technologies like more businesses are that are providing an application for their company. >> It sounds like cloud and SAS's being a part of the overall strategy, have you been seeing that dynamic change in your company? How does it impact what you're doing or is it just a separate organization. >> It's definitely been a shift in the last few years, we used to run all of our applications in-house. Longterm care has caught up now, with the hospitals, so we have our electronic medical record which is a hosted application, whereas, up until five years ago, that was an on-premises application that we hosted and had to run and maintain, and update and upgrade, and make sure was available. That is definitely been a shift, that everything is now hosted. So we just make sure that our network is up and running and support our users and all of their issues when they break things, flip their screens, drop something, provide hardware for them all that sorts of stuff. >> The constant pace of innovation change. On the news this week they were saying, okay, medical records on your iPhone is up for debate. Does regulation impact your day to day activities and what are some of the challenges in that area? >> Absolutely. One of the other things we have to do is interface with the providers. We have medical providers that come in from the outside and they need to access our EMR also, so we need to provide access for them on, sometimes, whatever device they bring in, which is not always compatible, so we have a whole other set of challenges there. Where we can manage our computers for our employees by pushing out policies and things that are required for the application. When someone comes in from the outside, it isn't, necessarily, setup right, so we have that other set of challenges, and regulation-wise, yes. The government is always pushing out new and updated regulations for healthcare and we have to keep on top of that too. Of course, we have HIPAA concerns and things like that, which is also comes into play when you're talking about cloud host, and any hosted application. We have to be concerned about HIPAA, as well. >> Yeah, wondering when I look at the space that you're in, the ultimate goal is you want the patients, the people at your company, be able to spend more time, help them, not be caught up in the technology of things. Could you, maybe, talk a little bit about that dynamic? >> Yeah, one of the things that I always say is, we need to give our employees the tools that they need to do their job most efficiently. A nurse needs to be ready to go at the beginning of her shift on her laptop, ready to pass meds, and when they can't remember their password or that computer isn't working, my team needs to work as quickly as we can to get them back to work. We serve our users, really. We're not there being all techy. They want us to fix them and get them back to work, and that's what we do. We put tools in their hands, any device that they need to make them more efficient. I try hard to provide a variety of devices, people have different preferences on how they do their work. Some people prefer a laptop, some people prefer to stand at a wall-mounted touchscreen and document, some people want to carry a tablet with them. I try to provide a range of devices so that they can have whatever suits them and makes them most comfortable to get their job done. >> Love that, it's not, necessarily, about the cool or trendier thing, it's about getting business done, helping, and in you're case, enabling your employees to really help the people that are there. Anything you want to highlight as to things you're excited to look at this show, or just technology in general? >> I'm just kind of here for the general nature of it. I enjoy the networking and getting to talk to people, and keeping current in what's happening in the industry and my career, so that's why I come. >> Alright, well Sue Morrow, really appreciate you coming, sharing with our audience. >> Absolutely. >> User groups like this, all about the users. Happy to have lots of them on the program, so big thanks to the VTUG group for bringing us some great guests. We'll be back with more coverage here. I'm Stu Miniman, you're watching theCUBE. (upbeat music)

Published Date : Jan 30 2018

SUMMARY :

in Foxborough, Massachusets, 2018 is the 12th year of this event, and what brings you all the way so we always had a Commodore 64 the TRS-80 Model III. and somehow stumbled into IT later in life. about what the mission of the company is. and everything in between. and making sure that everything is up and running, Yeah, reminds me of the old saying, so the things that we have ritualized is mostly storage, being a part of the overall strategy, and had to run and maintain, and update and upgrade, On the news this week they were saying, One of the other things we have to do the ultimate goal is you want the patients, any device that they need to make them more efficient. the people that are there. I enjoy the networking and getting to talk to people, really appreciate you coming, so big thanks to the VTUG group

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Krista Satterthwaite | International Women's Day


 

(upbeat music) >> Hello, welcome to the Cube's coverage of International Women's Day 2023. I'm John Furrier, host of the CUBE series of profiles around leaders in the tech industry sharing their stories, advice, best practices, what they're doing in their jobs their vision of the future, and more importantly, passing it on and encouraging more and more networking and telling the stories that matter. Our next guest is a great executive leader talking about how to lead in challenging times. Krista Satterthwaite, who is Senior Vice President and GM of Mainstream Compute. Krista great to see you're Cube alumni. We've had you on before talking about compute power. And by the way, congratulations on your BPT and Black Professional Tech Network 2023 Black Tech Exec of the Year Award. >> Thank you very much. Appreciate it. And thanks for having me. >> I knew I liked you the first time we were doing interviews together. You were so smart and so on top of it. Thanks for coming on. >> No problem. >> All kidding aside, let's get into it. You know, one of the things that's coming out on these interviews is leadership is being showcased and there's a network effect happening in the industry and you're starting to see people look and hear stories that they may or may not have heard before or news stories are coming out. So, one of the things that's interesting is that also in the backdrop of post pandemic, there's been a turn in the industry a little bit, there's a little bit of headwind in certain areas, some tailwinds in cloud and other areas. Compute, your area is doing very well. It could be challenging. And as a leader, has the conversation changed? And where are you at right now in the network of folks you're working with? What's the mood? >> Yeah, so actually I, things are much better. Obviously we had a chip shortage last year. Things are much, much better. But I learned a lot when it came to going through challenging times and leadership. And I think when we talk to customers, a lot of 'em are in challenging situations. Sometimes it's budget, sometimes it's attracting and retaining talent and sometimes it's just demands because, it's really exciting that technology is behind everything. But that means the demands on IT are bigger than ever before. So what I find when it comes to challenging times is that there's really three qualities that are game changers when it comes to leading and challenging times. And the first one is positivity. People have to feel like there's a light at the end of the tunnel to make sure that, their attitudes stay up, that they stay working really really hard and they look to the leader for that. The second one is communication. And I read somewhere that communication is leadership. And we had a great example from our CEO Antonio Neri when the pandemic hit and everything shut down. He had an all employee meeting every week for a month and we have tens of thousands of employees. And then even after that month, we had 'em very regularly. But he wanted to make sure that everybody heard from, him his thoughts had all the updates, knew how their peers were doing, how we were helping customers. And I really learned a lot from that in terms of communicating and communicating more during tough times. And then I would say the third one is making sure that they are informed and they feel empowered. So I would say a leader who is able to do that really, really stands out in a challenging time. >> So how do you get yourself together? Obviously you the chip shortage everyone knows in the industry and for the folks not in the tech industry, it was an economic potential disaster, because you don't get the chips you need. You guys make servers and technology, chips power everything. If you miss a shipment, it could cause a lot of backlash. So Cisco had an earnings impact. It has impact to the business. When do you have that code red moment where it's like, okay, we have to kind of put the pause and go into emergency mode. And how do you handle that? >> Well, you know, it is funny 'cause when it, when we have challenges, I come to learn that people can look at challenges and hard work as a burden or a mission and they behave totally different. If they see it as a burden, then they're doing the bare minimum and they're pointing fingers and they're complaining and they're probably not getting a whole lot done. If they see it as a mission, then all of a sudden they're going above and beyond. They're working really hard, they're really partnering. And if it affects customers for HPE, obviously we, HPE is a very customer centric company, so everyone pays attention and tries to pitch in. But when it comes to a mission, I started thinking, what are the real ingredients for a mission? And I think it's important. I think it's, people feel like they can make an impact. And then I think the third one is that the goal is clear, even if the path isn't, 'cause you may have to pivot a lot if it's a challenge. And so when it came to the chip shortage, it was a mission. We wanted to make sure that we could ship to customers as quickly as possible. And it was a mission. Everybody pulled together. I learned how much our team could pull off and pull together through that challenge. >> And the consequences can be quantified in economics. So it's like the burn the boats example, you got to burn the boats, you're stuck. You got to figure out a solution. How does that change the demands on people? Because this is, okay, there's a mission it they're not, it's not normal. What are some of those new demands that arise during those times and how do you manage that? How do you be a leader? >> Yeah, so it's funny, I was reading this statement from James White who used to be the CEO of Jamba Juice. And he was talking about how he got that job. He said, "I think it was one thing I said that really convinced them that I was the right person." And what he said was something like, "I will get more out of people than nine out of 10 leaders on the planet." He said, "Because I will look at their strengths and their capabilities and I will play to their passions." and their capabilities and I will play their passions. and getting the most out people in difficult times, it is all about how much you can get out of people for their own sake and for the company's sake. >> That's great feedback. And to people watching who are early in their careers, leading is getting the best out of your team, attitude. Some of the things you mentioned. What advice would you give folks that are starting to get into the workforce, that are starting to get into that leadership track or might have a trajectory or even might have an innate ability that they know they have and they want to pursue that dream? >> Yeah so. >> What advice would you give them? >> Yeah, what I would say, I say this all the time that, for the first half of my career I was very job conscious, but I wasn't very career conscious. So I'd get in a role and I'd stay in that role for long periods of time and I'd do a good job, but I wasn't really very career conscious. And what I would say is, everybody says how important risk taking is. Well, risk taking can be a little bit of a scary word, right? Or term. And the way I see it is give it a shot and see what happens. You're interested in something, give it a shot and see what happens. It's kind of a less intimidating way of looking at risk because even though I was job conscious, and not career conscious, one thing I did when people asked me to take something on, hey Krista, would you like to take on more responsibility here? The answer was always yes, yes, yes, yes. So I said yes because I said, hey I'll give it a shot and see what happens. And that helped me tremendously because I felt like I am giving it a try. And the more you do that, the the better it is. >> It's great. >> And actually the the less scary it is because you do that, a few times and it goes well. It's like a muscle that builds. >> It's funny, a woman executive was on the program. I said, the word balance comes up a lot. And she stopped and said, "Let's just talk about balance for a second." And then she went contrarian and said, "It's about not being unbalanced. It's about being, taking a chance and being a little bit off balance to put yourself outside your comfort zone to try new things." And then she also came up and followed and said, "If you do that alone, you increase your risk. But if you do it with people, a team that you trust and you're authentic and you're vulnerable and you're communicating, that is the chemistry." And that was a really good point. What's your reaction? 'Cause you were talking about authentic conversations good communications with Antonio. How does someone get, feel, find that team and do you agree with it? And what was your, how would you react to that? >> Yes, I agree with that. And when it comes to being authentic, that's the magic and when someone isn't, if someone's not really being themselves, it's really funny because you can feel it, you can sense it. There's kind of a wall between you and them. And over time people won't be able to put their finger on it, but they'll feel a distance from you. But when you're authentic and you share who you are, what you find is you find things in common with other people. 'Cause you're sharing more of who you are and it's like, oh, I do that too. Oh, I'm interested in that too. And build the bonds between people and the authenticity. And that's what people crave. They want people to be authentic and people can tell when you're authentic and when you're not. >> Is managing and leading through a crisis a born talent or can you learn it? >> Oh, definitely learned. I think that we're born knowing nothing and I once read people are nurtured into greatness and I think that's true. So yeah, definitely learned. >> What are some examples that can come out of a tough time as folks may look at a crisis and be shy away from it? How do they lean into it? What advice would you give folks? How do you handle it? I mean, everyone's got different personality. Okay, they get to a position but stepping through that door. >> Yeah, well, I do this presentation called, "10 things I Wish I Knew Earlier in my Career." And one of those things is about the growth mindset and the growth mindset. There's a book called "Mindset" by Carol Dweck and the growth mindset is all about learning and not always having to know everything, but really the winning is in the learning. And so if you have a growth mindset it makes you feel better about everything because you can't lose. You're winning because you're learning. So when I've learned that, I started looking at things much differently. And when it comes to going through tough times, what I find is you're exercising muscles that you didn't even know you had, which makes you stronger when the crisis is over, obviously. And I also feel like you become a lot a much more creative when you're in challenging times. You're forced to do things that you hadn't had to do before. And it also bonds the team. It's almost like going through bootcamp together. When you go through a challenge together it bonds you for life. >> I mean, you could have bonding, could be trauma bonding or success bonding. People love to be on the success side because that's positive and that's really the key mindset. You're always winning if you have that attitude. And learnings is also positive. So it's not, it's never a failure unless you make it. >> That's right, exactly. As long as you learn from it. And that's the name of the game. So, learning is the goal. >> So I have to ask you, on your job now, you have a really big responsibility HPE compute and big division. What's the current mindset that you have right now in your career, where you're at? What are some of the things on your mind that you think about? We had other, other seniors leaders say, hey, you know I got the software as my brain and the hardware's my body. I like to keep software and hardware working together. What is your current state of your career and how you looking at it, what's next and what's going on in your mind right now? >> Yeah, so for me, I really want to make sure that for my team we're nurturing the next generation of leadership and that we're helping with career development and career growth. And people feel like they can grow their careers here. Luckily at HPE, we have a lot of people stay at HPE a long time, and even people who leave HPE a lot of times they come back because the culture's fantastic. So I just want to make sure I'm contributing to that culture and I'm bringing up the next generation of leaders. >> What's next for you? What are you looking at from a career personal standpoint? >> You know, it's funny, I, I love what I'm doing right now. I'm actually on a joint venture board with H3C, which is HPE Joint Venture Company. And so I'm really enjoying that and exploring more board service opportunities. >> You have a focus of good growth mindset, challenging through, managing through tough times. How do you stay focused on that North star? How do you keep the reinforcement of the mission? How do you nurture the team to greatness? >> Yeah, so I think it's a lot of clarity, providing a lot of clarity about what's important right now. And it goes back to some of the communication that I mentioned earlier, making sure that everybody knows where the North Star is, so everybody's focused on the same thing, because I feel like with the, I always felt like throughout my career I was set up for success if I had the right information, the right guidance and the right goals. And I try to make sure that I do that with my team. >> What are some of the things that you could share as we wrap up here for the folks watching, as the networks increase, as the stories start to unfold more and more on digital like we're doing here, what do you hope people walk away with? What's working, what needs work, and what is some things that people aren't talking about that should be discussed publicly? >> Do you mean from a career standpoint or? >> For career? For growing into tech and into leadership positions. >> Okay. >> Big migration tech is now a wide field. I mean, when I grew up, broke into the eighties, it was computer science, software engineering, and three degrees in engineering, right? >> I see huge swath of AI coming. So many technical careers. There's a lot more women. >> Yeah. And that's what's so exciting about being in a technical career, technical company, is that everything's always changing. There's always opportunity to learn something new. And frankly, you know, every company is in the business of technology right now, because they want to closer to their customers. Typically, they're using technology to do that. Everyone's digitally transforming. And so what I would say is that there's so much opportunity, keep your mind open, explore what interests you and keep learning because it's changing all the time. >> You know I was talking with Sue, former HP, she's on a lot of boards. The balance at the board level still needs a lot of work and the leaderships are getting better, but the board at the seats at the table needs work. Where do you see that transition for you in the future? Is that something on your mind? Maybe a board seat? You mentioned you're on a board with HPE, but maybe sitting on some other boards? Any, any? >> Yes, actually, actually, we actually have a program here at HPE called the Board Ready Now program that I'm a part of. And so HPE is very supportive of me exploring an independent board seat. And so they have some education and programming around that. And I know Sue well, she's awesome. And so yes, I'm looking into those opportunities right now. >> She advises do one no more than two. The day job. >> Yeah, I would only be doing one current job that I have. >> Well, kris, it was great to chat with you about these topics and leadership and challenging times. Great masterclass, great advice. As SVP and GM of mainstream compute for HPE, what's going on in your job these days? What's the most exciting thing happening? Share some of your work situations. >> Sure, so the most exciting thing happening right now is HPE Gen 11, which we just announced and started shipping, brings tremendous performance benefit, has an intuitive operating experience, a trusted security by design, and it's optimized to run workloads so much faster. So if anybody is interested, they should go check it out on hpe.com. >> And of course the CUBE will be at HPE Discover. We'll see you there. Any final wisdom you'd like to share as we wrap up the last minute here? >> Yeah, so I think the last thing I'll say is that when it comes to setting your sights, I think, expecting it, good things to happen usually happens when you believe you deserve it. So what happens is you believe you deserve it, then you expect it and you get it. And so sometimes that's about making sure you raise your thermostat to expect more. And I always talk about you don't have to raise it all up at once. You could do that incrementally and other people can set your thermostat too when they say, hey, you should be, you should get a level this high or that high, but raise your thermostat because what you expect is what you get. >> Krista, thank you so much for contributing to this program. We're going to do it quarterly. We're going to do getting more stories out there, so we'll have you back and if you know anyone with good stories, send them our way. And congratulations on your BPTN Tech Executive of the Year award for 2023. Congratulations, great prize there and great recognition for your hard work. >> Thank you so much, John, I appreciate it. >> Okay, this is the Cube's coverage of National Woodman's Day. I'm John Furrier, stories from the front lines, management ranks, developers, all there, global coverage of international events with theCUBE. Thanks for watching. (soft music)

Published Date : Mar 3 2023

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And by the way, Thank you very much. I knew I liked you And where are you at right now And the first one is positivity. And how do you handle that? that the goal is clear, And the consequences can and for the company's sake. Some of the things you mentioned. And the more you do that, And actually the the less scary it is find that team and do you agree with it? and you share who you are, and I once read What advice would you give folks? And I also feel like you become a lot I mean, you could have And that's the name of the game. that you have right now of leadership and that we're helping And so I'm really enjoying that How do you nurture the team to greatness? of the communication For growing into tech and broke into the eighties, I see huge swath of AI coming. And frankly, you know, every company is Where do you see that transition And so they have some education She advises do one no more than two. one current job that I have. great to chat with you Sure, so the most exciting And of course the CUBE So what happens is you and if you know anyone with Thank you so much, from the front lines,

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AWS Partner Showcase S1E3 | Full Segment


 

>>Hey, everyone. Welcome to the AWS partner, showcase women in tech. I'm Lisa Martin from the cube. And today we're gonna be looking into the exciting evolution of women in the tech industry. I'm going to be joined by Danielle GShock, the ISP PSA director at AWS. And we have the privilege of speaking with some wicked smart women from Teradata NetApp. JFI a 10th revolution group, company and honeycomb.io. We're gonna look at some of the challenges and biases that women face in the tech industry, especially in leadership roles. We're also gonna be exploring how are these tech companies addressing diversity, equity and inclusion across their organizations? How can we get more young girls into stem earlier in their careers? So many questions. So let's go ahead and get started. This is the AWS partner showcase women in tech. Hey, everyone. Welcome to the AWS partner showcase. This is season one, episode three. And I'm your host, Lisa Martin. I've got two great guests here with me to talk about women in tech. Hillary Ashton joins us the chief product officer at Terry data. And Danielle Greshaw is back with us, the ISV PSA director at AWS ladies. It's great to have you on the program talking through such an important topic, Hillary, let's go ahead and start with you. Give us a little bit of an intro into you, your background, and a little bit about Teradata. >>Yeah, absolutely. So I'm Hillary Ashton. I head up the products organization. So that's our engineering product management office of the CTO team. Um, at Teradata I've been with Terra data for just about three years and really have spent the last several decades. If I can say that in the data and analytics space, um, I spent time, uh, really focused on the value of, of analytics at scale, and I'm super excited to be here at Teradata. I'm also a mom of two teenage boys. And so as we talk about women in tech, I think there's, um, uh, lots of different dimensions and angles of that. Um, at Teradata, we are partnered very deeply with AWS and happy to talk a little bit more about that, um, throughout this discussion as well. >>Excellent. A busy mom of two teen boys. My goodness. I don't know how you do it. Let's now look, Atter data's views of diversity, equity and inclusion. It's a, the, it's a topic that's important to everyone, but give us a snapshot into some of the initiatives that Terra data has there. >>Yeah, I have to say, I am super proud to be working at Teradata. We have gone through, uh, a series of transformations, but I think it starts with culture and we are deeply committed to diversity, equity and inclusion. It's really more than just a statement here. It's just how we live our lives. Um, and we use, uh, data to back that up. Um, in fact, we were named one of the world's most ethical companies for the 13th year in a row. Um, and all of our executive leadership team has taken an oath around D E and I that's available on LinkedIn as well. So, um, in fact, our leadership team reporting into the CEO is just about 50 50, um, men and women, which is the first time I've worked in a company where that has been the case. And I think as individuals, we can probably appreciate what a huge difference that makes in terms of not just being a representative, but truly being on a, on a diverse and equitable, uh, team. And I think it really, uh, improves the behaviors that we can bring, um, to our office. >>There's so much value in that. It's I impressive to see about a 50 50 at the leadership level. That's not something that we see very often. Tell me how you, Hillary, how did you get into tech? Were you an engineering person by computer science, or did you have more of a zigzaggy path to where you are now? >>I'm gonna pick door number two and say more zigzaggy. Um, I started off thinking, um, that I started off as a political science major or a government major. Um, and I was probably destined to go into, um, the law field, but actually took a summer course at Harvard. I did not go to Harvard, but I took a summer course there and learned a lot about multimedia and some programming. And that really set me on a trajectory of how, um, data and analytics can truly provide value and, and outcomes to our customers. Um, and I have been living that life ever since. Um, I graduated from college, so, um, I was very excited and privileged in my early career to, uh, work in a company where I found after my first year that I was managing, um, uh, kids, people who had graduated from Harvard business school and from MIT Sloan school. Um, and that was super crazy, cuz I did not go to either of those schools, but I sort of have always had a natural knack for how do you take technology and, and the really cool things that technology can do, but because I'm not a programmer by training, I'm really focused on the value that I'm able to help, um, organizations really extract value, um, from the technology that we can create, which I think is fantastic. >>I think there's so much value in having a zigzag path into tech. You bring Danielle, you and I have talked about this many times you bring such breadth and such a wide perspective. That really is such a value. Add to teams. Danielle, talk to us from AWS's perspective about what can be done to encourage more young women to get and under and underrepresented groups as well, to get into stem and stay. >>Yeah, and this is definitely a challenge as we're trying to grow our organization and kind of shift the numbers. And the reality is, especially with the more senior folks in our organization, unless you bring folks with a zigzag path, the likelihood is you won't be able to change the numbers that you have. Um, but for me, it's really been about, uh, looking at that, uh, the folks who are just graduating college, maybe in other roles where they are adjacent to technology and to try to spark their interest and show that yes, they can do it because oftentimes it's really about believing in themselves and, and realizing that we need folks with all sorts of different perspectives to kind of come in, to be able to help really, um, provide both products and services and solutions for all types of people inside of technology, which requires all sorts of perspectives. >>Yeah, the diverse perspectives. There's so much value and there's a lot of data that demonstrates how much value revenue impact organizations can make by having diversity, especially at the leadership level. Hillary, let's go back to you. We talked about your career path. You talked about some of the importance of the focus on de and I at Tarana, but what are, what do you think can be done to encourage, to sorry, to recruit more young women and under groups into tech, any, any carrot there that you think are really important that we need to be dangling more of? >>Yeah, absolutely. And I'll build on what Danielle just said. I think the, um, bringing in diverse understandings, um, of, of customer outcomes, I mean, I, the we've really moved from technology for technology's sake and I know AWS and entirety to have had a lot of conversations on how do we drive customer outcomes that are differentiated in the market and really being customer centric and technology is wonderful. You can do wonderful things with it. You can do not so wonderful things with it as well, but unless you're really focused on the outcomes and what customers are seeking, um, technology is not hugely valuable. And so I think bringing in people who understand, um, voice of customer who understand those outcomes, and those are not necessarily the, the, the folks who are PhD in mathematics or statistics, um, those can be people who understand a day in the life of a data scientist or a day in the life of a citizen data scientist. And so really working to bridge the high impact technology with the practical kind of usability, usefulness of data and analytics in our cases, I think is something that we need more of in tech and sort of demystifying tech and freeing technology so that everybody can use it and having a really wide range of people who understand not just the bits and bites and, and how to program, but also the value in outcomes that technology through data and analytics can drive. >>Yeah. You know, we often talk about the hard skills, but this, their soft skills are equally, if not more important that even just being curious, being willing to ask questions, being not afraid to be vulnerable, being able to show those sides of your personality. I think those are important for, for young women and underrepresented groups to understand that those are just as important as some of the harder technical skills that can be taught. >>That's right. >>What do you think about from a bias perspective, Hillary, what have you seen in the tech industry and how do you think we can leverage culture as you talked about to help dial down some of the biases that are going on? >>Yeah. I mean, I think first of all, and, and there's some interesting data out there that says that 90% of the population, which includes a lot of women have some inherent bias in their day, day behaviors when it comes to to women in particular. But I'm sure that that is true across all kinds of, of, um, diverse and underrepresented folks in, in the world. And so I think acknowledging that we have bias and actually really learning how, what that can look like, how that can show up. We might be sitting here and thinking, oh, of course I don't have any bias. And then you realize that, um, as you, as you learn more about, um, different types of bias, that actually you do need to kind of, um, account for that and change behaviors. And so I think learning is sort of a fundamental, um, uh, grounding for all of us to really know what bias looks like, know how it shows up in each of us. >>Um, if we're leaders know how it shows up in our teams and make sure that we are constantly getting better, we're, we're not gonna be perfect anytime soon. But I think being on a path to improvement to overcoming bias, um, is really, is really critical. And part of that is really starting the dialogue, having the conversations, holding ourselves and each other accountable, um, when things aren't going in, in a, in a Coptic way and being able to talk openly about that, that felt, um, like maybe there was some bias in that interaction and how do we, um, how do we make good on that? How do we change our, our behavior? Fundamentally of course, data and analytics can have some bias in it as well. And so I think as we look at the, the technology aspect of bias, um, looking at at ethical AI, I think is a, a really important, uh, additional area. And I'm sure we could spend another 20 minutes talking about that, but I, I would be remiss if I didn't talk more about sort of the bias, um, and the over the opportunity to overcome bias in data and analytics as well. >>Yeah. The opportunity to overcome it is definitely there you bring up a couple of really good points, Hillary. It, it starts with awareness. We need to be aware that there are inherent biases in data in thought. And also to your other point, hold people accountable ourselves, our teammates, that's critical to being able to, to dial that back down, Daniel, I wanna get your perspective on, on your view of women in leadership roles. Do you think that we have good representation or we still have work to do in there? >>I definitely think in both technical and product roles, we definitely have some work to do. And, you know, when I think about, um, our partnership with Teradata, part of the reason why it's so important is, you know, Teradata solution is really the brains of a lot of companies. Um, you know, the what, how, what they differentiate on how they figure out insights into their business. And it's, it's all about the product itself and the data and the same is true at AWS. And, you know, we really could do some work to have some more women in these technical roles, as well as in the product, shaping the products. Uh, just for all the reasons that we just kind of talked about over the last 10 minutes, um, in order to, you know, move bias out of our, um, out of our solutions and also to just build better products and have, uh, better, you know, outcomes for customers. So I think there's a bit of work to do still. >>I agree. There's definitely a bit of work to do, and it's all about delivering those better outcomes for customers at the end of the day, we need to figure out what the right ways are of doing that and working together in a community. Um, we've had obviously a lot had changed in the last couple of years, Hillary, what's your, what have you seen in terms of the impact that the pandemic has had on this status of women in tech? Has it been a pro is silver lining the opposite? What are you seeing? >>Yeah, I mean, certainly there's data out there that tells us factually that it has been, um, very difficult for women during COVID 19. Um, women have, uh, dropped out of the workforce for a wide range of, of reasons. Um, and, and that I think is going to set us back all of us, the, the Royal us or the Royal we back, um, years and years. Um, and, and it's very unfortunate because I think we we're at a time when we're making great progress and now to see COVID, um, setting us back in, in such a powerful way. I think there's work to be done to understand how do we bring people back into the workforce. Um, how do we do that? Understanding work life balance, better understanding virtual and remote, working better. I think in the technology sector, um, we've really embraced, um, hybrid virtual work and are, are empowering people to bring their whole selves to work. >>And I think if anything, these, these zoom calls have, um, both for the men and the women on my team. In fact, I would say much more. So for the men on my team, I'm seeing, I was seeing more kids in the background, more kind of split childcare duties, more ability to start talking about, um, other responsibilities that maybe they had, uh, especially in the early days of COVID where maybe daycares were shut down. And, um, you had, you know, maybe a parent was sick. And so we saw quite a lot of, um, people bringing their whole selves to the office, which I think was, was really wonderful. Um, uh, even our CEO saw some of that. And I think, um, that that really changes the dialogue, right? It changes it to maybe scheduling meetings at a time when, um, people can do it after daycare drop off. >>Um, and really allowing that both for men and for women makes it better for, for women overall. So I would like to think that this hybrid working, um, environment and that this, um, uh, whole view into somebody's life that COVID has really provided for probably for white collar workers, if I'm being honest for, um, people who are in a, at a better point of privilege, they don't necessarily have to go into the office every day. I would like to think that tech can lead the way in, um, you know, coming out of the, the old COVID. I don't know if we have a new COVID coming, but the old COVID and really leading the way for women and for people, um, to transform how we do work, um, leveraging data and analytics, but also, um, overcoming some of the, the disparities that exist for women in particular in the workforce. >>Yeah, I think there's, there's like we say, there's a lot of opportunity there and I like your point of hopefully tech can be that guiding light that shows us this can be done. We're all humans at the end of the day. And ultimately if we're able to have some sort of work life balance, everything benefits, our work or more productive, higher performing teams impacts customers, right? There's so much value that can be gleaned from, from that hybrid model and embracing for humans. We need to be able to, to work when we can, we've learned that you don't have to be, you know, in an office 24, 7 commuting, crazy hours flying all around the world. We can get a lot of things done in a ways that fit people's lives rather than taking command over it. Wanna get your advice, Hillary, if you were to talk to your younger self, what would be some of the key pieces of advice you would say? And Danielle and I have talked about this before, and sometimes we, we would both agree on like, ask more questions. Don't be afraid to raise your hand, but what advice would you give your younger self and that younger generation in terms of being inspired to get into tech >>Oh, inspired and being in tech? You know, I think looking at technology as, in some ways, I feel like we do a disservice to, um, inclusion when we talk about stem, cuz I think stem can be kind of daunting. It can be a little scary for people for younger people. When I, when I go and talk to folks at schools, I think stem is like, oh, all the super smart kids are over there. They're all like maybe they're all men. And so, um, it's, it's a little, uh, intimidating. Um, and stem is actually, you know, especially for, um, people joining the workforce today. It's actually how you've been living your life since you were born. I mean, you know, stem inside and out because you walk around with a phone and you know how to get your internet working and like that is technology right. >>Fundamentally. And so demystifying stem as something that is around how we, um, actually make our, our lives useful and, and, and how we can change outcomes. Um, through technology I think is maybe a different lens to put on it. So, and there's absolutely for, for hard sciences, there's absolutely a, a great place in the world for folks who wanna pursue that and men and women can do that. So I, I don't want to be, um, uh, setting the wrong expectations, but I, I think stem is, is very holistic in, um, in the change that's happening globally for us today across economies, across global warming, across all kinds of impactful issues. And so I think everybody who's interested in, in some of that world change can participate in stem. It just may be through a different, through a different lens than how we classically talk about stem. >>So I think there's great opportunity to demystify stem. I think also, um, what I would tell my younger self is choose your bosses wisely. And that sounds really funny. That sounds like inside out almost, but I think choose the person that you're gonna work for in your first five to seven years. And it might be more than one person, but be, be selective, maybe be a little less selective about the exact company or the exact title. I think picking somebody that, you know, we talk about mentors and we talk about sponsors and those are important. Um, but the person you're gonna spend in your early career, a lot of your day with a lot, who's gonna influence a lot of the outcomes for you. That is the person that you, I think want to be more selective about, um, because that person can set you up for success and give you opportunities and set you on course to be, um, a standout or that person can hold you back. >>And that person can put you in the corner and not invite you to the meetings and not give you those opportunities. And so we're in an economy today where you actually can, um, be a little bit picky about who you go and work for. And I would encourage my younger self. I actually, I just lucked out actually, but I think that, um, my first boss really set me, um, up for success, gave me a lot of feedback and coaching. Um, and some of it was really hard to hear, but it really set me up for, for, um, the, the path that I've been on ever since. So it, that would be my advice. >>I love that advice. I it's brilliant. I didn't think it choose your bosses wisely. Isn't something that we primarily think about. I think a lot of people think about the big name companies that they wanna go after and put on a resume, but you bring up a great point. And Danielle and I have talked about this with other guests about mentors and sponsors. I think that is brilliant advice and also more work to do to demystify stem. But luckily we have great family leaders like the two of you helping us to do that. Ladies, I wanna thank you so much for joining me on the program today and talking through what you're seeing in de and I, what your companies are doing and the opportunities that we have to move the needle. Appreciate your time. >>Thank you so much. Great to see you, Danielle. Thank you Lisa, to see you. >>My pleasure for my guests. I'm Lisa Martin. You're watching the AWS partner showcase season one, episode three. Hey everyone. Welcome to the AWS partner showcase. This is season one, episode three, with a focus on women in tech. I'm your host, Lisa Martin. I've got two guests here with me, Sue Peretti, the EVP of global AWS strategic alliances at Jefferson Frank, a 10th revolution group company, and Danielle brushoff. One of our cube alumni joins us ISV PSA director, ladies. It's great to have you on the program talking about a, a topic that is near and dear to my heart at women in tech. >>Thank you, Lisa. >>So let's go ahead and start with you. Give the audience an understanding of Jefferson Frank, what does the company do and about the partnership with AWS? >>Sure. Um, so let's just start, uh, Jefferson Frank is a 10th revolution group company. And if you look at it, it's really talent as a service. So Jefferson Frank provides talent solutions all over the world for AWS clients, partners and users, et cetera. And we have a sister company called revelent, which is a talent creation company within the AWS ecosystem. So we create talent and put it out in the ecosystem. Usually underrepresented groups over half of them are women. And then we also have, uh, a company called rubra, which is a delivery model around AWS technology. So all three companies fall under the 10th revolution group organization. >>Got it. Danielle, talk to me a little bit about from AWS's perspective and the focus on hiring more women in technology and about the partnership. >>Yes. I mean, this has definitely been a focus ever since I joined eight years ago, but also just especially in the last few years we've grown exponentially and our customer base has changed. You know, we wanna have, uh, an organization interacting with them that reflects our customers, right. And, uh, we know that we need to keep pace with that even with our growth. And so we've very much focused on early career talent, um, bringing more women and underrepresented minorities into the organization, sponsoring those folks, promoting them, uh, giving them paths to growth, to grow inside of the organization. I'm an example of that. Of course I benefit benefited from it, but also I try to bring that into my organization as well. And it's super important. >>Tell me a little bit about how you benefited from that, Danielle. >>Um, I just think that, um, you know, I I've been able to get, you know, a seat at the table. I think that, um, I feel as though I have folks supporting me, uh, very deeply and wanna see me succeed. And also they put me forth as, um, you know, a, represent a representative, uh, to bring more women into the organization as well. And I think, um, they give me a platform, uh, in order to do that, um, like this, um, but also many other, uh, spots as well. Um, and I'm happy to do it because I feel that, you know, if you always wanna feel that you're making a difference in your job, and that is definitely a place where I get that time and space in order to be that representative to, um, bring more, more women into benefiting from having careers in technology, which there's a lot of value there, >>A lot of value. Absolutely. So back over to you, what are some of the trends that you are seeing from a gender diversity perspective in tech? We know the, the numbers of women in technical positions, uh, right. There's so much data out there that shows when girls start dropping up, but what are some of the trends that you are seeing? >>So it's, that's a really interesting question. And, and Lisa, I had a whole bunch of data points that I wanted to share with you, but just two weeks ago, uh, I was in San Francisco with AWS at the, at the summit. And we were talking about this. We were talking about how we can collectively together attract more women, not only to, uh, AWS, not only to technology, but to the AWS ecosystem in particular. And it was fascinating because I was talking about, uh, the challenges that women have and how hard to believe, but about 5% of women who were in the ecosystem have left in the past few years, which was really, really, uh, something that shocked everyone when we, when we were talking about it, because all of the things that we've been asking for, for instance, uh, working from home, um, better pay, uh, more flexibility, uh, better maternity leave seems like those things are happening. >>So we're getting what we want, but people are leaving. And it seemed like the feedback that we got was that a lot of women still felt very underrepresented. The number one thing was that they, they couldn't be, you can't be what you can't see. So because they, we feel collectively women, uh, people who identify as women just don't see enough women in leadership, they don't see enough mentors. Um, I think I've had great mentors, but, but just not enough. I'm lucky enough to have a pres a president of our company, the president of our company, Zoe Morris is a woman and she does lead by example. So I'm very lucky for that. And Jefferson, Frank really quickly, we put out a hiring a salary and hiring guide a career and hiring guide every year and the data points. And that's about 65 pages long. No one else does it. Uh, it gives an abundance of information around, uh, everything about the AWS ecosystem that a hiring manager might need to know. But there is what, what I thought was really unbelievable was that only 7% of the people that responded to it were women. So my goal, uh, being that we have such a very big global platform is to get more women to respond to that survey so we can get as much information and take action. So >>Absolutely 7%. So a long way to go there. Danielle, talk to me about AWS's focus on women in tech. I was watching, um, Sue, I saw that you shared on LinkedIn, the Ted talk that the CEO and founder of girls and co did. And one of the things that she said was that there was a, a survey that HP did some years back that showed that, um, 60%, that, that men will apply for jobs if they only meet 60% of the list of requirements. Whereas with females, it's far, far less, we've all been in that imposter syndrome, um, conundrum before. But Danielle, talk to us about AWS, a specific focus here to get these numbers up. >>I think it speaks to what Susan was talking about, how, you know, I think we're approaching it top and bottom, right? We're looking out at what are the, who are the women who are currently in technical positions and how can we make AWS an attractive place for them to work? And that's all a lot of the changes that we've had around maternity leave and, and those types of things, but then also, um, more flexible working, uh, can, you know, uh, arrangements, but then also, um, early, how can we actually impact early, um, career women and actually women who are still in school. Um, and our training and certification team is doing amazing things to get, um, more girls exposed to AWS, to technology, um, and make it a less intimidating place and have them look at employees from AWS and say like, oh, I can see myself in those people. >>Um, and kind of actually growing the viable pool of candidates. I think, you know, we're, we're limited with the viable pool of candidates, um, when you're talking about mid to late career. Um, but how can we, you know, help retrain women who are coming back into the workplace after, you know, having a child and how can we help with military women who want to, uh, or underrepresented minorities who wanna move into AWS, we have a great military program, but then also just that early high school, uh, career, you know, getting them in, in that trajectory. >>Sue, is that something that Jefferson Frank is also able to help with is, you know, getting those younger girls before they start to feel there's something wrong with me. I don't get this. Talk to us about how Jefferson Frank can help really drive up that in those younger girls. >>Uh, let me tell you one other thing to refer back to that summit that we did, uh, we had breakout sessions and that was one of the topics. What can cuz that's the goal, right? To make sure that, that there are ways to attract them. That's the goal? So some of the things that we talked about was mentoring programs, uh, from a very young age, some people said high school, but then we said even earlier, goes back to you. Can't be what you can't see. So, uh, getting mentoring programs, uh, established, uh, we also talked about some of the great ideas was being careful of how we speak to women using the right language to attract them. And some, there was a teachable moment for, for me there actually, it was really wonderful because, um, an African American woman said to me, Sue and I, I was talking about how you can't be what you can't see. >>And what she said was Sue, it's really different. Um, for me as an African American woman, uh, or she identified, uh, as nonbinary, but she was relating to African American women. She said, your white woman, your journey was very different than my journey. And I thought, this is how we're going to learn. I wasn't offended by her calling me out at all. It was a teachable moment. And I thought I understood that, but those are the things that we need to educate people on those, those moments where we think we're, we're saying and doing the right thing, but we really need to get that bias out there. So here at Jefferson, Frank, we're, we're trying really hard to get that careers and hiring guide out there. It's on our website to get more women, uh, to talk to it, but to make suggestions in partnership with AWS around how we can do this mentoring, we have a mentor me program. We go around the country and do things like this. We, we try to get the education out there in partnership with AWS. Uh, we have a, a women's group, a women's leadership group, uh, so much that, that we do, and we try to do it in partnership with AWS. >>Danielle, can you comment on the impact that AWS has made so far, um, regarding some of the trends and, and gender diversity that Sue was talking about? What's the impact that's been made so far with this partnership? >>Well, I mean, I think just being able to get more of the data and have awareness of leaders, uh, on how <laugh>, you know, it used to be a, a couple years back, I would feel like sometimes the, um, uh, solving to bring more women into the organization was kind of something that folks thought, oh, this is Danielle is gonna solve this. You know? And I think a lot of folks now realize, oh, this is something that we all need to solve for. And a lot of my colleagues who maybe a couple years ago, didn't have any awareness or didn't even have the tools to do what they needed to do in order to improve the statistics on their, or in their organizations. Now actually have those tools and are able to kind of work with, um, work with companies like Susan's work with Jefferson Frank in order to actually get the data and actually make good decisions and feel as though, you know, they, they often, these are not lived experiences for these folks, so they don't know what they don't know. And by providing data and providing awareness and providing tooling and then setting goals, I think all of those things have really turned, uh, things around in a very positive way. >>And so you bring up a great point about from a diversity perspective, what is Jefferson Frank doing to, to get those data points up, to get more women of, of all well, really underrepresented minorities to, to be able to provide that feedback so that you can, can have the data and gleamy insights from it to help companies like AWS on their strategic objectives. >>Right? So as I, when I go back to that higher that, uh, careers in hiring guide, that is my focus today, really because the more data that we have, I mean, the, and the data takes, uh, you know, we need people to participate in order to, to accurately, uh, get a hold of that data. So that's why we're asking, uh, we're taking the initiative to really expand our focus. We are a global organization with a very, very massive database all over the world, but if people don't take action, then we can't get the right. The, the, the data will not be as accurate as we'd like it to be. Therefore take better action. So what we're doing is we're asking people all over the, all over the world to participate on our website, Jefferson frank.com, the se the high, uh, in the survey. So we can learn as much as we can. >>7% is such a, you know, Danielle and I we're, we've got to partner on this just to sort of get that message out there, get more data so we can execute, uh, some of the other things that we're doing. We're, we're partnering in. As I mentioned, more of these events, uh, we're, we're doing around the summits, we're gonna be having more ed and I events and collecting more information from women. Um, like I said, internally, we do practice what we preach and we have our own programs that are, that are out there that are within our own company where the women who are talking to candidates and clients every single day are trying to get that message out there. So if I'm speaking to a client or one of our internal people are speaking to a client or a candidate, they're telling them, listen, you know, we really are trying to get these numbers up. >>We wanna attract as many people as we can. Would you mind going to this, uh, hiring guide and offering your own information? So we've gotta get that 7% up. We've gotta keep talking. We've gotta keep, uh, getting programs out there. One other thing I wanted to Danielle's point, she mentioned, uh, women in leadership, the number that we gathered was only 9% of women in leadership within the AWS ecosystem. We've gotta get that number up, uh, as well because, um, you know, I know for me, when I see people like Danielle or, or her peers, it inspires me. And I feel like, you know, I just wanna give back, make sure I send the elevator back to the first floor and bring more women in to this amazing ecosystem. >>Absolutely. That's not that metaphor I do too, but we, but to your point to get that those numbers up, not just at AWS, but everywhere else we need, it's a help me help use situation. So ladies underrepresented minorities, if you're watching go to the Jefferson Frank website, take the survey, help provide the data so that the woman here that are doing this amazing work, have it to help make decisions and have more of females and leadership roles or underrepresented minorities. So we can be what we can see. Ladies, thank you so much for joining me today and sharing what you guys are doing together to partner on this important. Cause >>Thank you for having me, Leah, Lisa, >>Thank you. My pleasure for my guests. I'm Lisa Martin. You're watching the cubes coverage of the AWS partner showcase. Thanks for your time. Hey everyone. Welcome to the AWS partner showcase season one, episode three women in tech. I'm your host, Lisa Martin. We've got two female rock stars here with me next. Stephanie Curry joins us the worldwide head of sales and go to market strategy for AWS at NetApp and Danielle GShock is back one of our QM ISV PSA director at AWS. Looking forward to a great conversation, ladies, about a great topic, Stephanie, let's go ahead and start with you. Give us an overview of your story, how you got into tech and what inspired you. >>Thanks so much, Lisa and Danielle. It's great to be on this show with you. Um, thank you for that. Uh, my name's Stephanie cur, as Lisa mentioned, I'm the worldwide head of sales for, uh, AWS at NetApp and run a global team of sales people that sell all things AWS, um, going back 25 years now, uh, when I first started my career in tech, it was kind of by accident. Um, I come from a different background. I have a business background and a technical background from school, um, but had been in a different career and I had an opportunity to try something new. Um, I had an ally really that reached out to me and said, Hey, you'd be great for this role. And I thought, I'd take a chance. I was curious. Um, and, uh, it, it turned out to be a 25 year career, um, that I'm really, really excited about and, and, um, really thankful for that person, for introducing me to the, to the industry >>25 years in counting. I'm sure Danielle, we've talked about your background before. So what I wanna focus on with you is the importance of diversity for high performance. I know what a machine AWS is, and Stephanie'll come back to you with the same question, but talk about that, Danielle, from your perspective, that importance, um, for diversity to drive the performance. >>Yeah. Yeah. I truly believe that, you know, in order to have high performing teams, that you have to have people from all different types of backgrounds and experiences. And we do find that oftentimes being, you know, field facing, if we're not reflecting our customers and connecting with them deeply, um, on, on the levels that they're at, we, we end up missing them. And so for us, it's very important to bring people of lots of different technical backgrounds experiences. And of course, both men, women, and underrepresented minorities and put that forth to our customers, um, in order to make that connection and to end up with better outcomes. So >>Definitely it's all about outcomes, Stephanie, your perspective and NetApp's perspective on diversity for creating highly performant teams and organizations. >>I really aligned with Danielle on the comment she made. And in addition to that, you know, just from building teams in my, um, career know, we've had three times as many women on my team since we started a year ago and our results are really showing in that as well. Um, we find the teams are stronger, they're more collaborative and to Danielle's point really reflective, not only our partners, but our customers themselves. So this really creates connections, which are really, really important to scale our businesses and, and really, uh, meet the customer where they're at as well. So huge proponent of that ourselves, and really finding that we have to be intentional in our hiring and intentional in how we attract diversity to our teams. >>So Stephanie let's stay with you. So a three X increase in women on the team in a year, especially the kind of last year that we've had is really incredible. I, I like your, I, your thoughts on there needs to be a, there needs to be focus and, and thought in how teams are hired. Let's talk about attracting and retaining those women now, especially in sales roles, we all know the number, the percentages of women in technical roles, but what are some of the things that, that you do Stephanie, that NetApp does to attract and retain women in those sales roles? >>The, the attracting part's really interesting. And we find that, you know, you, you read the stats and I'd say in my experience, they're also true in the fact that, um, a lot of women would look at a job description and say, I can't do a hundred percent of that, that, so I'm not even going to apply with the women that we've attracted to our team. We've actually intentionally reached out and targeted those people in a good way, um, to say, Hey, we think you've got what it takes. Some of the feedback I've got from those women are, gosh, I didn't think I could ever get this role. I didn't think I had the skills to do that. And they've been hired and they are doing a phenomenal job. In addition to that, I think a lot of the feedback I've got from these hires are, Hey, it's an aggressive sales is aggressive. Sales is competitive. It's not an environment that I think I can be successful in. And what we're showing them is bring those softer skills around collaboration, around connection, around building teams. And they do, they do bring a lot of that to the team. Then they see others like them there and they know they can be successful cuz they see others like them on the team, >>The whole concept of we can't be what we can't see, but we can be what we can't see is so important. You said a couple things, Stephanie, that really stuck with me. And one of them was an interview on the Cub I was doing, I think a couple weeks ago, um, about women in tech. And the stat that we talked about was that women will apply will not apply for a job unless they meet 100% of the skills and the requirements that it's listed, but men will, if they only meet 60. And I, that just shocked me that I thought, you know, I, I can understand that imposter syndrome is real. It's a huge challenge, but the softer skills, as you mentioned, especially in the last two years, plus the ability to communicate, the ability to collaborate are incredibly important to, to drive that performance of any team of any business. >>Absolutely. >>Danielle, talk to me about your perspective and AWS as well for attracting and retaining talent. And, and, and particularly in some of those challenging roles like sales that as Stephanie said, can be known as aggressive. >>Yeah, for sure. I mean, my team is focused on the technical aspect of the field and we definitely have an uphill battle for sure. Um, two things we are focused on first and foremost is looking at early career women and that how we, how can we bring them into this role, whether in they're in support functions, uh, cl like answering the phone for support calls, et cetera, and how, how can we bring them into this organization, which is a bit more strategic, more proactive. Um, and then the other thing that as far as retention goes, you know, sometimes there will be women who they're on a team and there are no other women on that team. And, and for me, it's about building community inside of AWS and being part of, you know, we have women on solution architecture organizations. We have, uh, you know, I just personally connect people as well and to like, oh, you should meet this person. Oh, you should talk to that person. Because again, sometimes they can't see someone on their team like them and they just need to feel anchored, especially as we've all been, you know, kind of stuck at home, um, during the pandemic, just being able to make those connections with women like them has been super important and just being a, a long tenured Amazonian. Um, that's definitely one thing I'm able to, to bring to the table as well. >>That's so important and impactful and spreads across organizations in a good way. Daniel let's stick with you. Let's talk about some of the allies that you've had sponsors, mentors that have really made a difference. And I said that in past tense, but I also mean in present tense, who are some of those folks now that really inspire you? >>Yeah. I mean, I definitely would say that one of my mentors and someone who, uh, ha has been a sponsor of my career has, uh, Matt YK, who is one of our control tower GMs. He has really sponsored my career and definitely been a supporter of mine and pushed me in positive ways, which has been super helpful. And then other of my business partners, you know, Sabina Joseph, who's a cube alum as well. She definitely has been, was a fabulous partner to work with. Um, and you know, between the two of us for a period of time, we definitely felt like we could, you know, conquer the world. It's very great to go in with a, with another strong woman, um, you know, and, and get things done, um, inside of an organization like AWS. >>Absolutely. And S I've, I've agreed here several times. So Stephanie, same question for you. You talked a little bit about your kind of, one of your, uh, original early allies in the tech industry, but talk to me about allies sponsors, mentors who have, and continue to make a difference in your life. >>Yeah. And, you know, I think it's a great differentiation as well, right? Because I think that mentors teach us sponsors show us the way and allies make room for us at the table. And that is really, really key difference. I think also as women leaders, we need to make room for others at the table too, and not forget those softer skills that we bring to the table. Some of the things that Danielle mentioned as well about making those connections for others, right. And making room for them at the table. Um, some of my allies, a lot of them are men. Brian ABI was my first mentor. Uh, he actually is in the distribution, was in distribution, uh, with advent tech data no longer there. Um, Corey Hutchinson, who's now at Hashi Corp. He's also another ally of mine and remains an ally of mine, even though we're not at the same company any longer. Um, so a lot of these people transcend careers and transcend, um, um, different positions that I've held as well and make room for us. And I think that's just really critical when we're looking for allies and when allies are looking for us, >>I love how you described allies, mentors and sponsors Stephanie. And the difference. I didn't understand the difference between a mentor and a sponsor until a couple of years ago. Do you talk with some of those younger females on your team so that when they come into the organization and maybe they're fresh outta college, or maybe they've transitioned into tech so that they can also learn from you and understand the importance and the difference between the allies and the sponsors and the mentors? >>Absolutely. And I think that's really interesting because I do take, uh, an extra, uh, approach an extra time to really reach out to the women that have joined the team. One. I wanna make sure they stay right. I don't want them feeling, Hey, I'm alone here and I need to, I need to go do something else. Um, and they are located around the world, on my team. They're also different age groups, so early in career, as well as more senior people and really reaching out, making sure they know that I'm there. But also as Danielle had mentioned, connecting them to other people in the community that they can reach out to for those same opportunities and making room for them >>Make room at the table. It's so important. And it can, you never know what a massive difference and impact you can make on someone's life. And I, and I bet there's probably a lot of mentors and sponsors and allies of mine that would be surprised to know, uh, the massive influence they've had Daniel back over. Let's talk about some of the techniques that you employ, that AWS employees to make the work environment, a great place for women to really thrive and, and be retained as Stephanie was saying. Of course that's so important. >>Yeah. I mean, definitely I think that the community building, as well as we have a bit more programmatic mentorship, um, we're trying to get to the point of having a more programmatic sponsorship as well. Um, but I think just making sure that, um, you know, both everything from, uh, recruit to onboard to ever boarding that, uh, they they're the women who come into the organization, whether it's they're coming in on the software engineering side or the field side or the sales side that they feel as that they have someone, uh, working with them to help them drive their career. Those are the key things that were, I think from an organizational perspective are happening across the board. Um, for me personally, when I run my organization, I'm really trying to make sure that people feel that they can come to me at any time open door policy, make sure that they're surfacing any times in which they are feeling excluded or anything like that, any challenges, whether it be with a customer, a partner or with a colleague. Um, and then also of course, just making sure that I'm being a good sponsor, uh, to, to people on my team. Um, that is key. You can talk about it, but you have to start with yourself as well. >>That's a great point. You you've got to, to start with yourself and really reflect on that. Mm-hmm <affirmative> and look, am I, am I embodying what it is that I need? And not that I know they need that focused, thoughtful intention on that is so importants, let's talk about some of the techniques that you use that NetApp uses to make the work environment a great place for those women are marginalized, um, communities to really thrive. >>Yeah. And I appreciate it and much like Danielle, uh, and much like AWS, we have some of those more structured programs, right around sponsorship and around mentorship. Um, probably some growth there, opportunities for allies, because I think that's more of a newer concept in really an informal structure around the allies, but something that we're growing into at NetApp, um, on my team personally, I think, um, leading by example's really key. And unfortunately, a lot of the, um, life stuffs still lands on the women, whether we like it or not. Uh, I have a very, uh, active husband in our household, but I still carry when it push comes to shove it's on me. Um, and I wanna make sure that my team knows it's okay to take some time and do the things you need to do with your family. Um, I'm I show up as myself authentically and I encourage them to do the same. >>So it's okay to say, Hey, I need to take a personal day. I need to focus on some stuff that's happening in my personal life this week now, obviously to make sure your job's covered, but just allowing some of that softer vulnerability to come into the team as well, so that others, um, men and women can feel they can do the same thing. And that it's okay to say, I need to balance my life and I need to do some other things alongside. Um, so it's the formal programs, making sure people have awareness on them. Um, I think it's also softly calling people out on biases and saying, Hey, I'm not sure if you know, this landed that way, but I just wanted to make you aware. And usually the feedback is, oh my gosh, I didn't know. And could you coach me on something that I could do better next time? So all of this is driven through our NetApp formal programs, but then it's also how you manifest it on the teams that we're leading. >>Absolutely. And sometimes having that mirror to reflect into can be really eye-opening and, and allow you to, to see things in a completely different light, which is great. Um, you both talked about, um, kind of being what you, uh, can see, and, and I know both companies are upset customer obsessed in a good way. Talk to me a little bit, Danielle, go back over to you about the AWS NetApp partnership. Um, some of that maybe alignment on, on performance on obviously you guys are very well aligned, uh, in terms of that, but also it sounds like you're quite aligned on diversity and inclusion. >>Well, we definitely do. We have the best partnerships with companies in which we have these value alignments. So I think that is a positive thing, of course, but just from a, from a partnership perspective, you know, from my five now plus years of being a part of the APN, this is, you know, one of the most significant years with our launch of FSX for NetApp. Um, with that, uh, key key service, which we're making available natively on AWS. I, I can't think of a better Testament to the, to the, um, partnership than that. And that's doing incredibly well and it really resonates with our customers. And of course it started with customers and their need for NetApp. Uh, so, you know, that is a reflection, I think, of the success that we're having together. >>And Stephanie talk to, uh, about the partnership from your perspective, NetApp, AWS, what you guys are doing together, cultural alignment, but also your alignment on really bringing diversity into drive performance. >>Yeah, I think it's a, a great question. And I have to say it's just been a phenomenal year. Our relationship has, uh, started before our first party service with FSX N but definitely just, um, uh, the trajectory, um, between the two companies since the announcement about nine months ago has just taken off to a, a new level. Um, we feel like an extended part of the family. We worked together seamlessly. A lot of the people in my team often say we feel like Amazonians. Um, and we're really part of this transformation at NetApp from being that storage hardware company into being an ISV and a cloud company. And we could not do this without the partnership with AWS and without the, uh, first party service of Fs XM that we've recently released. Um, I think that those joint values that Danielle referred to are critical to our success, um, starting with customer obsession and always making sure that we are doing the right thing for the customer. >>We coach our team teams all the time on if you are doing the right thing for the customers, you cannot do anything wrong. Just always put the customer at the, in the center of your decisions. And I think that there is, um, a lot of best practice sharing and collaboration as we go through this change. And I think a lot of it is led by the diverse backgrounds that are on the team, um, female, male, um, race and so forth, and just to really, uh, have different perspectives and different experiences about how we approach this change. Um, so we definitely feel like a part of the family. Uh, we are absolutely loving, uh, working with the AWS team and our team knows that we are the right place, the right time with the right people. >>I love that last question for each of you. And I wanna stick with you Stephanie advice to your younger self, think back five years. What advice would you seen what you've accomplished and maybe the thet route that you've taken along the way, what would you advise your youngest Stephanie self. >>Uh, I would say keep being curious, right? Keep being curious, keep asking questions. And sometimes when you get a no, it's not a bad thing, it just means not right now and find out why and, and try to get feedback as to why maybe that wasn't the right opportunity for you. But, you know, just go for what you want. Continue to be curious, continue to ask questions and find a support network of people around you that wanna help you because they are there and they, they wanna see you be successful too. So never be shy about that stuff. >><laugh> absolutely. And I always say failure does not have to be an, a bad F word. A no can be the beginning of something. Amazing. Danielle, same question for you. Thinking back to when you first started in your career, what advice would you give your younger self? >>Yeah, I think the advice I'd give my younger self would be, don't be afraid to put yourself out there. Um, it's certainly, you know, coming from an engineering background, maybe you wanna stay behind the scenes, not, not do a presentation, not do a public speaking event, those types of things, but back to what the community really needs, this thing. Um, you know, I genuinely now, uh, took me a while to realize it, but I realized I needed to put myself out there in order to, um, you know, allow younger women to see what they could be. So that would be the advice I would give. Don't be afraid to put yourself out there. >>Absolutely. That advice that you both gave are, is so fantastic, so important and so applicable to everybody. Um, don't be afraid to put yourself out there, ask questions. Don't be afraid of a, no, that it's all gonna happen at some point or many points along the way. That can also be good. So thank you ladies. You inspired me. I appreciate you sharing what AWS and NetApp are doing together to strengthen diversity, to strengthen performance and the advice that you both shared for your younger selves was brilliant. Thank you. >>Thank you. >>Thank you >>For my guests. I'm Lisa Martin. You're watching the AWS partner showcase. See you next time. Hey everyone. Welcome to the AWS partner showcase season one, episode three women in tech. I'm your host, Lisa Martin. I've got two female rock stars joining me. Next Vero Reynolds is here engineering manager, telemetry at honeycomb, and one of our cube alumni, Danielle Ock ISV PSA director at AWS. Join us as well. Ladies. It's great to have you talking about a very important topic today. >>Thanks for having us. >>Yeah, thanks for having me. Appreciate it. >>Of course, Vera, let's go ahead and start with you. Tell me about your background and tech. You're coming up on your 10th anniversary. Happy anniversary. >>Thank you. That's right. I can't believe it's been 10 years. Um, but yeah, I started in tech in 2012. Um, I was an engineer for most of that time. Uh, and just recently as a March, switched to engineering management here at honeycomb and, um, you know, throughout my career, I was very much interested in all the things, right. And it was a big FOMO as far as trying a few different, um, companies and products. And I've done things from web development to mobile to platforms. Um, it would be apt to call me a generalist. Um, and in the more recent years I was sort of gravitating more towards developer tool space. And for me that, uh, came in the form of cloud Foundry circle CI and now honeycomb. Um, I actually had my eye on honeycomb for a while before joining, I came across a blog post by charity majors. >>Who's one of our founders and she was actually talking about management and how to pursue that and whether or not it's right, uh, for your career. And so I was like, who is this person? I really like her, uh, found the company. They were pretty small at the time. So I was sort of keeping my eye on them. And then when the time came around for me to look again, I did a little bit more digging, uh, found a lot of talks about the product. And on the one hand they really spoke to me as the solution. They talked about developers owning their coding production and answering questions about what is happening, what are your users seeing? And I felt that pain, I got what they were trying to do. And also on the other hand, every talk I saw at the time was from, uh, an amazing woman <laugh>, which I haven't seen before. Uh, so I came across charity majors again, Christine Y our other founder, and then Liz Jones, who's our principal developer advocate. And that really sealed the deal for me as far as wanting to work here. >>Yeah. Honeycomb is interesting. This is a female founded company. You're two leaders. You mentioned that you like the technology, but you were also attracted because you saw females in the leadership position. Talk to me a little bit about what that's like working for a female led organization at honeycomb. >>Yeah. You know, historically, um, we have tried not to over index on that because there was this, uh, maybe fear awareness of, um, it taking away from our legitimacy as an engineering organization, from our success as a company. Um, but I'm seeing that, uh, rhetoric shift recently because we believe that with great responsibility, uh, with great power comes great responsibility, and we're trying to be more intentional as far as using that attribute of our company. Um, so I would say that for me, it was, um, a choice between a few offers, right. And that was a selling point for sure, because again, I've never experienced it and I've really seen how much they walk that walk. Um, even me being here and me moving into management, I think were both, um, ways in which they really put a lot of trust and support in me. And so, um, I it's been a great ride. >>Excellent. Sounds like it. Before we bring Danielle in to talk about the partnership. I do wanna have you there talk to the audience a little bit about honeycomb, what technology it's delivering and what are its differentiators. >>Yeah, absolutely. Um, so honeycomb is an observability tool, uh, that enables engineers to answer questions about the code that runs in production. And, um, we work with a number of various customers. Some of them are Vanguards, slack. Hello, fresh, just to name a couple, if you're not familiar with observability tooling, it's akin to traditional application performance monitoring, but we believe that observability is succeeding APM because, uh, APM tools were built at the time of monoliths and they just weren't designed to help us answer questions about complex distributed systems that we work with today, where things can go wrong anywhere in that chain. And you can't predict what you're gonna need to ask ahead of time. So some of the ways that we are different is our ability to store and query really rich data, which we believe is the key to understanding those complex systems. >>What I mean by rich data is, um, something that has a lot of attributes. So for example, when an error happens, knowing who it happened to, which user ID, which, um, I don't know, region, they were in, um, what, what, what they were doing at the time and what was happening at the rest of your system. And our ingest engine is really fast. You can do it in as little as three seconds and we call data like this. I said, kind of rich data, contextual data. We refer it as having high ality and high dimensionality, which are big words. But at the end of the day, what that means is we can store and we can query the data. We can do it really fast. And to give you an example of how that looks for our customers, let's say you have a developer team who are using comb to understand and observe their system. >>And they get a report that a user is experiencing a slowdown or something's wrong. They can go into comb and figure out that this only happens to users who are using a particular language pack with their app. And they operated their app last week, that it only happens when they are trying to upload a file. And so it's this level of granularity and being able to zoom in and out, um, under your data that allows you to understand what's happening, especially when you have an incident going on, right. Or your really important high profile customer is telling you that something's wrong. And we can do that. Even if everything else in your other tools looks fine, right? All of your dashboards are okay. You're not actually getting paged on it, but your customers are telling you that something's wrong. Uh, and we believe that's where we shine in helping you there. >>Excellent. It sounds like that's where you really shine that real time visibility is so critical these days. Danielle, Danielle, wanna bring you into the conversation. Talk to us a little bit about the honeycomb partnership from the AWS lens. >>Yeah. So excuse me, observability is obviously a very important, uh, segment in the cloud space, very important to AWS, um, because a lot of all of our customers, uh, as they build their systems distributed, they need to be able to see where, where things are happening in the complex systems that they're building. And so honeycomb is a, is an advanced technology partner. Um, they've been working with us for quite some time and they have a, uh, their solution is listed on the marketplace. Um, definitely something that we see a lot of demand with our customers and they have many integrations, uh, which, you know, we've seen is key to success. Um, being able to work seamlessly with the rest of the services inside of the AWS platform. And I know that they've done some, some great things with people who are trying to develop games on top of AWS, uh, things in that area as well. And so, uh, very important partner in the observa observability market that we have >>Back to you, let's kind of unpack the partnership, the significance that honeycomb ha is getting from being partners with an organization as potent and pivotal as AWS. >>Yeah, absolutely. Um, I know this predates me to some extent, but I know for a long time, AWS and honeycomb has really pushed the envelope together. And, um, I think it's a beneficial relationship for both ends. There's kind of two ways of looking at it. On the one side, there is our own infrastructure. So honeycomb runs on AWS and actually one of our critical workloads that supports that fast query engine that I mentioned uses Lambda. And it does so in a pretty Orthodox way. So we've had a longstanding conversation with the AWS team as far as drawing outside those lines and kind of figuring out how to use this technology in a way that works for us and hopefully will work for other customers of theirs as well. Um, that also allows us to ask for early access for certain features when they become available. >>And then that way we can be sort of the Guinea pigs and try things out, um, in a way that migrates our system and optimizes our own performance, but also allows again, other customers of AWS to follow in that path. And then the other side of that partnership is really supporting our customers who are both honeycomb users and AWS users, because it's, as you imagine, quite a big overlap, and there are certain ways in which we can allow our customers to more easily get their data from AWS to honeycomb. So for example, last year we built a tool, um, based on the new Lambda extension capability that allowed our users who run their applications in Lambdas to get that telemetry data out of their applications and into honeycomb. And it man was win, win. >>Excellent. So I'm hearing a lot of synergies from a technology perspective, you're sticking with you, and then Danielle will bring you in, let's talk about how honeycomb supports D and I across its organization. And how is that synergistic with AWS's approach? Yeah, >>Yeah, absolutely. So I sort of alluded to that hesitancy to over index on the women led aspect of ourselves. Um, but again, a lot of things are shifting, we're growing a lot. And so we are recognizing that we need to be more intentional with our DEI initiatives, and we also notice that we can do better and we should do better. And to that, and we're doing a few things differently, um, that are pretty recent initiatives. We are partnering with organizations that help us target specific communities that are underrepresented in tech. Um, some examples would be after tech hu Latinas in tech among, um, a number of others. And another initiative is DEI head start. That's something that is an internal, um, practice that we started that includes reaching out to underrepresented applicants before any new job for honeycomb becomes live. So before we posted to LinkedIn, before it's even live on our job speech, and the idea there is to kind of balance our pipeline of applicants, which the hope is will lead to more diverse hires in the long term. >>That's a great focus there. Danielle, I know we've talked about this before, but for the audience, in terms of the context of the honeycomb partnership, the focus at AWS for D E and I is really significant, unpack that a little bit for us. >>Well, let me just bring it back to just how we think about it, um, with the companies that we work with, but also in, in terms of, you know, what we want to be able to do, excuse me, it's very important for us to, you know, build products that reflect, uh, the customers that we have. And I think, you know, working with, uh, a company like honeycomb that is looking to differentiate in a space, um, by, by bringing in, you know, the experiences of many different types of people I genuinely believe. And I'm sure Vera also believes that by having those diverse perspectives, that we're able to then build better products for our customers. Um, and you know, it's one of, one of our leadership principles, uh, is, is rooted in this. I write a lot, it asks for us to seek out diverse perspectives. Uh, and you can't really do that if everybody kind of looks the same and thinks the same and has the same background. So I think that is where our de and I, um, you know, I thought process is rooted and, you know, companies like honeycomb that give customers choice and differentiate and help them, um, to do what they need to do in their unique, um, environments is super important. So >>The, the importance of thought diversity cannot be underscored enough. It's something that is, can be pivotal to organizations. And it's very nice to hear that that's so fundamental to both companies, Barry, I wanna go back to you for a second. You, I think you mentioned this, the DEI head start program, that's an internal program at honeycomb. Can you shed a little bit of light on that? >>Yeah, that's right. And I actually am in the process of hiring a first engineer for my team. So I'm learning a lot of these things firsthand, um, and how it works is we try to make sure to pre-load our pipeline of applicants for any new job opening we have with diverse candidates to the best of our abilities, and that can involve partnering with the organizations that I mentioned or reaching out to our internal network, um, and make sure that we give those applicants a head start, so to speak. >>Excellent. I like that. Danielle, before we close, I wanna get a little bit of, of your background. We've got various background in tag, she's celebrating her 10th anniversary. Give me a, a short kind of description of the journey that you've navigated through being a female in technology. >>Yeah, thanks so much. I really appreciate, uh, being able to share this. So I started as a software engineer, uh, back actually in the late nineties, uh, during the, the first.com bubble and, uh, have, have spent quite a long time actually as an individual contributor, um, probably working in software engineering teams up through 2014 at a minimum until I joined AWS, uh, as a customer facing solutions architect. Um, I do think spending a lot of time, hands on definitely helped me with some of the imposter syndrome, um, issues that folks suffer from not to say I don't at all, but it, it certainly helped with that. And I've been leading teams at AWS since 2015. Um, so it's really been a great ride. Um, and like I said, I'm very happy to see all of our engineering teams change, uh, as far as their composition. And I'm, I'm grateful to be part of it. >>It's pretty great to be able to witness that composition change for the better last question for each of you. And we're almost out of time and Danielle, I'm gonna stick with you. What's your advice, your recommendations for women who either are thinking about getting into tech or those who may be in tech, maybe they're in individual positions and they're not sure if they should apply for that senior leadership position. What do you advise them to do? >>I mean, definitely for the individual contributors, tech tech is a great career, uh, direction, um, and you will always be able to find women like you, you have to maybe just work a little bit harder, uh, to join, have community, uh, in that. But then as a leader, um, representation is very important and we can bring more women into tech by having more leaders. So that's my, you just have to take the lead, >>Take the lead, love that there. Same question for you. What's your advice and recommendations for those maybe future female leaders in tech? >>Yeah, absolutely. Um, Danielle mentioned imposter syndrome and I think we all struggle with it from time to time, no matter how many years it's been. And I think for me, for me, the advice would be if you're starting out, don't be afraid to ask, uh, questions and don't be afraid to kind of show a little bit of ignorance because we've all been there. And I think it's on all of us to remember what it's like to not know how things work. And on the flip side of that, if you are a more senior IC or, uh, in a leadership role, also being able to model just saying, I don't know how this works and going and figuring out answers together because that was a really powerful shift for me early in my career is just to feel like I can say that I don't know something. >>I totally agree. I've been in that same situation where just ask the question because you I'm guaranteed, there's a million outta people in the room that probably has the, have the same question and because of imposter syndrome, don't wanna admit, I don't understand that. Can we back up, but I agree with you. I think that is, um, one of the best things. Raise your hand, ask a question, ladies. Thank you so much for joining me talking about honeycomb and AWS, what you're doing together from a technology perspective and the focus efforts that each company has on D E and I, we appreciate your insights. Thank you so much for having us great talking to you. My pleasure, likewise for my guests, I'm Lisa Martin. You're watching the AWS partner showcase women in check. Welcome to the AWS partner showcase I'm Lisa Martin, your host. This is season one, episode three, and this is a great episode that focuses on women in tech. I'm pleased to be joined by Danielle Shaw, the ISV PSA director at AWS, and the sponsor of this fantastic program. Danielle, it's great to see you and talk about such an important topic. >>Yes. And I will tell you, all of these interviews have just been a blast for me to do. And I feel like there has been a lot of gold that we can glean from all of the, um, stories that we heard on these interviews and good advice that I myself would not have necessarily thought of. So >>I agree. And we're gonna get to set, cuz advice is one of the, the main things that our audience is gonna hear. We have Hillary Ashton, you'll see from TETA there, Reynolds joins us from honeycomb, Stephanie Curry from NetApp and Sue Paris from Jefferson Frank. And the topics that we dig into are first and foremost, diversity equity and inclusion. That is a topic that is incredibly important to every organization. And some of the things Danielle that our audiences shared were really interesting to me. One of the things that I saw from a thematic perspective over and over was that like D Reynolds was talking about the importance of companies and hiring managers and how they need to be intentional with de and I initiatives. And that intention was a, a, a common thing that we heard. I'm curious what your thoughts are about that, that we heard about being intentional working intentionally to deliver a more holistic pool of candidates where de I is concerned. What are your, what were some of the things that stuck out to you? >>Absolutely. I think each one of us is working inside of organizations where in the last, you know, five to 10 years, there's been a, you know, a strong push in this direction, mostly because we've really seen, um, first and foremost, by being intentional, that you can change the, uh, the way your organization looks. Um, but also just that, you know, without being intentional, um, there was just a lot of, you know, outcomes and situations that maybe weren't great for, um, you know, a healthy, um, and productive environment, uh, working environment. And so, you know, a lot of these companies have made a big investments and put forth big initiatives that I think all of us are involved in. And so we're really excited to get out here and talk about it and talk about, especially as these are all partnerships that we have, how, you know, these align with our values. So >>Yeah, that, that value alignment mm-hmm <affirmative> that you bring up is another thing that we heard consistently with each of the partners, there's a cultural alignment, there's a customer obsession alignment that they have with AWS. There's a D E and I alignment that they have. And I, I think everybody also kind of agreed Stephanie Curry talked about, you know, it's really important, um, for diversity on it, on, on impacting performance, highly performant teams are teams that are more diverse. I think we heard that kind of echoed throughout the women that we talked to in >>This. Absolutely. And I absolutely, and I definitely even feel that, uh, with their studies out there that tell you that you make better products, if you have all of the right input and you're getting all many different perspectives, but not just that, but I can, I can personally see it in the performing teams, not just my team, but also, you know, the teams that I work alongside. Um, arguably some of the other business folks have done a really great job of bringing more women into their organization, bringing more underrepresented minorities. Tech is a little bit behind, but we're trying really hard to bring that forward as well to in technical roles. Um, but you can just see the difference in the outcomes. Uh, at least I personally can just in the adjacent teams of mine. >>That's awesome. We talked also quite a bit during this episode about attracting women and underrepresented, um, groups and retaining them. That retention piece is really key. What were some of the things that stuck out to you that, um, you know, some of the guests talked about in terms of retention? >>Yeah. I think especially, uh, speaking with Hillary and hearing how, uh, Teradata is thinking about different ways to make hybrid work work for everybody. I think that is definitely when I talk to women interested in joining AWS, oftentimes that might be one of the first, uh, concerns that they have. Like, am I going to be able to, you know, go pick my kid up at four o'clock at the bus, or am I going to be able to, you know, be at my kids' conf you know, conference or even just, you know, have enough work life balance that I can, um, you know, do the things that I wanna do outside of work, uh, beyond children and family. So these are all very important, um, and questions that especially women come and ask, but also, um, you know, it kind of is a, is a bellwether for, is this gonna be a company that allows me to bring my whole self to work? And then I'm also gonna be able to have that balance that I need need. So I think that was something that is, uh, changing a lot. And many people are thinking about work a lot differently. >>Absolutely. The pandemic not only changed how we think about work, you know, initially it was, do I work from home or do I live at work? And that was legitimately a challenge that all of us faced for a long time period, but we're seeing the hybrid model. We're seeing more companies be open to embracing that and allowing people to have more of that balance, which at the end of the day, it's so much better for product development for the customers, as you talked about there's, it's a win-win. >>Absolutely. And, you know, definitely the first few months of it was very hard to find that separation to be able to put up boundaries. Um, but I think at least I personally have been able to find the way to do it. And I hope that, you know, everyone is getting that space to be able to put those boundaries up to effectively have a harmonious, you know, work life where you can still be at home most of the time, but also, um, you know, have that cutoff point of the day or at least have that separate space that you can feel that you're able to separate the two. >>Yeah, absolutely. And a lot of that from a work life balance perspective leads into one of the next topics that we covered in detail with, and that's mentors and sponsors the differences between them recommendations from, uh, the women on the panel about how to combat imposter syndrome, but also how to leverage mentors and sponsors throughout your career. One of the things that, that Hillary said that I thought was fantastic, advice were mentors and sponsors are concerned is, is be selective in picking your bosses. We often see people, especially younger folks, not necessarily younger folks. I shouldn't say that that are attracted to a company it's brand maybe, and think more about that than they do the boss or bosses that can help guide them along the way. But I thought that was really poignant advice that Hillary provided something that I'm gonna take into consideration myself. >>Yeah. And I honestly hadn't thought about that, but as I reflect through my own career, I can see how I've had particular managers who have had a major impact on helping me, um, with my career. But, you know, if you don't have the ability to do that, or maybe that's not a luxury that you have, I think even if you're able to, you know, find a mentor for a period of time or, um, you know, just, just enable for you to be able to get from say a point a to point B just for a temporary period. Um, just so you can grow into your next role, have a, have a particular outcome that you wanna drive, have a particular goal in mind find that person who's been there and done that and can really help you get through. If you don't have the luxury of picking your manager mentor, who can help you get to the next step. >>Exactly. That, that I thought that advice was brilliant and something that I hadn't really considered either. We also talked with several of the women about imposter syndrome. You know, that's something that everybody, I think, regardless of gender of your background, everybody feels that at some point. So I think one of the nice things that we do in this episode is sort of identify, yes, imposter syndrome is real. This is, this is how it happened to me. This is I navigated around or got over it. I think there's some great advice there for the audience to glean as well about how to dial down the imposter syndrome that they might be feeling. >>Absolutely. And I think the key there is just acknowledging it. Um, but also just hearing all the different techniques on, on how folks have dealt with it because everybody does, um, you know, even some of the smartest, most confident men I've, I've met in, uh, industry still talk to me about how they have it and I'm shocked by it oftentimes, but, um, it is very common and hopefully we, we talk about some good techniques to, to deal with that. >>I think we do, you know, one of the things that when we were asking the, our audience, our guests about advice, what would they tell their younger selves? What would they tell young women or underrepresented groups in terms of becoming interested in stem and in tech and everybody sort of agreed on me, don't be afraid to raise your hand and ask questions. Um, show vulnerabilities, not just as the employee, but even from a leadership perspective, show that as a leader, I, I don't have all the answers. There are questions that I have. I think that goes a long way to reducing the imposter syndrome that most of us have faced at some point in our lives. And that's just, don't be afraid to ask questions. You never know, oh, how can people have the same question sitting in the room? >>Well, and also, you know, for folks who've been in industry for 20, 25 years, I think we can just say that, you know, it's a, it's a marathon, it's not a sprint and you're always going to, um, have new things to learn and you can spend, you know, back to, we talked about the zing and zagging through careers, um, where, you know, we'll have different experiences. Um, all of that kind of comes through just, you know, being curious and wanting to continue to learn. So yes, asking questions and being vulnerable and being able to say, I don't know all the answers, but I wanna learn is a key thing, uh, especially culturally at AWS, but I'm sure with all of these companies as well, >>Definitely I think it sounded like it was really ingrained in their culture. And another thing too, that we also talked about is the word, no, doesn't always mean a dead end. It can often mean not right now or may, maybe this isn't the right opportunity at this time. I think that's another important thing that the audience is gonna learn is that, you know, failure is not necessarily a bad F word. If you turn it into opportunity, no isn't necessarily the end of the road. It can be an opener to a different door. And I, I thought that was a really positive message that our guests, um, had to share with the, the audience. >>Yeah, totally. I can, I can say I had a, a mentor of mine, um, a very, uh, strong woman who told me, you know, your career is going to have lots of ebbs and flows and that's natural. And you know that when you say that, not right now, um, that's a perfect example of maybe there's an ebb where it might not be the right time for you now, but something to consider in the future. But also don't be afraid to say yes, when you can. <laugh> >>Exactly. Danielle, it's been a pleasure filming this episode with you and the great female leaders that we have on. I'm excited for the audience to be able to learn from Hillary Vera, Stephanie Sue, and you so much valuable content in here. We hope you enjoy this partner showcase season one, episode three, Danielle, thanks so much for helping >>Us with it's been a blast. I really appreciate it >>All audience. We wanna enjoy this. Enjoy the episode.

Published Date : Jul 21 2022

SUMMARY :

It's great to have you on the program talking And so as we talk about women I don't know how you do it. And I think it really, uh, improves the behaviors that we can bring, That's not something that we see very often. from the technology that we can create, which I think is fantastic. you and I have talked about this many times you bring such breadth and such a wide perspective. be able to change the numbers that you have. but what are, what do you think can be done to encourage, just the bits and bites and, and how to program, but also the value in outcomes that technology being not afraid to be vulnerable, being able to show those sides of your personality. And so I think learning is sort of a fundamental, um, uh, grounding And so I think as we look at the, And also to your other point, hold people accountable I definitely think in both technical and product roles, we definitely have some work to do. What are you seeing? and that I think is going to set us back all of us, the, the Royal us or the Royal we back, And I think, um, that that really changes I would like to think that tech can lead the way in, um, you know, coming out of the, but what advice would you give your younger self and that younger generation in terms I mean, you know, stem inside and out because you walk around And so demystifying stem as something that is around how I think picking somebody that, you know, we talk about mentors and we talk And that person can put you in the corner and not invite you to the meetings and not give you those opportunities. But luckily we have great family leaders like the two of you helping us Thank you Lisa, to see you. It's great to have you on the program talking about So let's go ahead and start with you. And if you look at it, it's really talent as a service. Danielle, talk to me a little bit about from AWS's perspective and the focus on You know, we wanna have, uh, an organization interacting with them Um, I just think that, um, you know, I I've been able to get, There's so much data out there that shows when girls start dropping up, but what are some of the trends that you are And we were talking about only 7% of the people that responded to it were women. I was watching, um, Sue, I saw that you shared on LinkedIn, the Ted talk that I think it speaks to what Susan was talking about, how, you know, I think we're approaching I think, you know, we're, we're limited with the viable pool of candidates, um, Sue, is that something that Jefferson Frank is also able to help with is, you know, I was talking about how you can't be what you can't see. And I thought I understood that, but those are the things that we need uh, on how <laugh>, you know, it used to be a, a couple years back, I would feel like sometimes And so you bring up a great point about from a diversity perspective, what is Jefferson Frank doing to, more data that we have, I mean, the, and the data takes, uh, you know, 7% is such a, you know, Danielle and I we're, And I feel like, you know, I just wanna give back, make sure I send the elevator back to but to your point to get that those numbers up, not just at AWS, but everywhere else we need, Welcome to the AWS partner showcase season one, episode three women Um, I had an ally really that reached out to me and said, Hey, you'd be great for this role. So what I wanna focus on with you is the importance of diversity for And we do find that oftentimes being, you know, field facing, if we're not reflecting Definitely it's all about outcomes, Stephanie, your perspective and NetApp's perspective on diversity And in addition to that, you know, just from building teams that you do Stephanie, that NetApp does to attract and retain women in those sales roles? And we find that, you know, you, you read the stats and I'd say in my And I, that just shocked me that I thought, you know, I, I can understand that imposter syndrome is real. Danielle, talk to me about your perspective and AWS as well for attracting and retaining I mean, my team is focused on the technical aspect of the field and we And I said that in past tense, a period of time, we definitely felt like we could, you know, conquer the world. in the tech industry, but talk to me about allies sponsors, mentors who have, And I think that's just really critical when we're looking for allies and when allies are looking I love how you described allies, mentors and sponsors Stephanie. the community that they can reach out to for those same opportunities and making room for them Let's talk about some of the techniques that you employ, that AWS employees to make Um, but I think just making sure that, um, you know, both everything is so importants, let's talk about some of the techniques that you use that NetApp take some time and do the things you need to do with your family. And that it's okay to say, I need to balance my life and I need to do Talk to me a little bit, Danielle, go back over to you about the AWS APN, this is, you know, one of the most significant years with our launch of FSX for And Stephanie talk to, uh, about the partnership from your perspective, NetApp, And I have to say it's just been a phenomenal year. And I think that there is, um, a lot of best practice sharing and collaboration as we go through And I wanna stick with you Stephanie advice to your younger And sometimes when you get a no, it's not a bad thing, And I always say failure does not have to be an, a bad F word. out there in order to, um, you know, allow younger women to I appreciate you sharing what AWS It's great to have you talking about a very important topic today. Yeah, thanks for having me. Of course, Vera, let's go ahead and start with you. Um, and in the more recent years I And on the one hand they really spoke to me as the solution. You mentioned that you like the technology, but you were also attracted because you saw uh, rhetoric shift recently because we believe that with great responsibility, I do wanna have you there talk to the audience a little bit about honeycomb, what technology And you can't predict what you're And to give you an example of how that looks for Uh, and we believe that's where we shine in helping you there. It sounds like that's where you really shine that real time visibility is so critical these days. Um, definitely something that we see a lot of demand with our customers and they have many integrations, Back to you, let's kind of unpack the partnership, the significance that Um, I know this predates me to some extent, And then that way we can be sort of the Guinea pigs and try things out, um, And how is that synergistic with AWS's approach? And so we are recognizing that we need to be more intentional with our DEI initiatives, Danielle, I know we've talked about this before, but for the audience, in terms of And I think, you know, working with, uh, a company like honeycomb that to hear that that's so fundamental to both companies, Barry, I wanna go back to you for a second. And I actually am in the process of hiring a first engineer for my Danielle, before we close, I wanna get a little bit of, of your background. And I'm, I'm grateful to be part of it. And we're almost out of time and Danielle, I'm gonna stick with you. I mean, definitely for the individual contributors, tech tech is a great career, uh, Take the lead, love that there. And on the flip side of that, if you are a more senior IC or, Danielle, it's great to see you and talk about such an important topic. And I feel like there has been a lot of gold that we can glean from all of the, And the topics that we dig the last, you know, five to 10 years, there's been a, you know, a strong push in this direction, I think everybody also kind of agreed Stephanie Curry talked about, you know, it's really important, um, Um, but you can just see the difference in the outcomes. um, you know, some of the guests talked about in terms of retention? um, you know, it kind of is a, is a bellwether for, is this gonna be a company that allows The pandemic not only changed how we think about work, you know, initially it was, And I hope that, you know, everyone is getting that space to be able to put those boundaries up I shouldn't say that that are attracted to a company it's brand maybe, Um, just so you can grow into your next role, have a, have a particular outcome I think there's some great advice there for the audience to glean on, on how folks have dealt with it because everybody does, um, you know, I think we do, you know, one of the things that when we were asking the, our audience, I think we can just say that, you know, it's a, it's a marathon, it's not a sprint and you're always going the audience is gonna learn is that, you know, failure is not necessarily a bad F word. uh, strong woman who told me, you know, your career is going to have lots of ebbs and flows and Danielle, it's been a pleasure filming this episode with you and the great female I really appreciate it Enjoy the episode.

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AWS Partner Showcase S1E3 Wrap


 

(bright music) >> Welcome to the AWS Partner Showcase. I'm Lisa Martin, your host. This is season one episode three and this is a great episode that focuses on women in tech. I'm pleased to be joined by Danielle Greshock, the ISV PSA director at AWS, and a sponsor of this fantastic program. Danielle, it's great to see you and talk about such an important topic. >> Yes, and I will tell you all of these interviews have just been a blast for me to do and I feel like there has been a lot of gold that we can glean from all of the stories that we heard on these interviews and good advice that I myself would not have necessarily thought of. >> I agree, and we're going to get to (indistinct) 'cause advice is one of the the main things that our audience is going to hear. We have Hillary Ashton, you'll see from Teradata. Vera Reynolds joins us from Honeycomb. Stephanie Curry from NetApp. And Sue Persichetti from Jefferson Frank and the topics that we dig into are, first and foremost, diversity equity and inclusion, that is a topic that is incredibly important to every organization. And some of the things, Danielle, that our audiences shared were really interesting to me. One of the things that I saw, from a thematic perspective, over and over, was that, like Vera Reynolds was talking about, the importance of companies and hiring managers and how they need to be intentional with DE&I initiatives and that intention was a common theme that we heard. I'm curious what your thoughts are about that, that we heard about being intentional, working intentionally to deliver a more holistic pool of candidates where DE&I is concerned. What were some of the things that stuck out to you? >> Absolutely, I think each one of us is working inside of organizations where, in the last five to 10 years, there's been a strong push in this direction, mostly because we've really seen, first and foremost by being intentional, that you can change the way your organization looks. But also just that without being intentional there was just a lot of outcomes and situations that maybe weren't great for a healthy and productive environment, working environment. And so a lot of these companies have made big investments and put forth big initiatives that I think all of us are involved in and so we're really excited to get out here and talk about it and talk about, especially as these are all partnerships that we have, how these align with our values. >> Yeah, that value alignment that you bring up is another theme that we heard consistently with each of the partners. There's a cultural alignment. There's a customer obsession alignment that they have with AWS. There's a DE&I alignment that they have and I think everybody also kind of agreed, Stephanie Curry talked about, it's really important for diversity on impacting performance, highly performant teams are teams that are more diverse. I think we heard that kind of echoed throughout the women that we talked to in this episode. >> Absolutely, and I definitely even feel that there are studies out there that tell you that you make better products if you have all of the right input and you're getting many different perspectives. But not just that, I can personally see it in the performing teams, not just my team, but also the teams that I work alongside. Arguably some of the other business folks have done a really great job of bringing more women into their organization, bringing more underrepresented minorities, tech is a little bit behind but we're trying really hard to bring that forward as well in technical roles. But you can just see the difference in the outcomes. At least I personally can, just in the adjacent teams of mine. >> That's awesome, we talked also quite a bit during this episode about attracting women and underrepresented groups and retaining them. That retention piece is really key. What were some of the things that stuck out to you that some of the guests talked about in terms of retention? >> Yeah, I think, especially speaking with Hillary and hearing how Teradata is thinking about different ways to make hybrid work work for everybody, I think that is definitely, when I talk to women interested in joining AWS, oftentimes that might be one of the first concerns that they have. Like, am I going to be able to go pick my kid up at four o'clock at the bus? Or, am I going to be able to be at my kid's conference? Or even just have enough work life balance that I can do the things that I want to do outside of work, beyond children and family. So these are all very important questions that especially women come and ask, but also it kind of is a bellwether for, is this going to be a company that allows me to bring my whole self to work and then I'm also going to be able to have that balance that I need. So I think that was something that is changing a lot and many people are thinking about work a lot differently. >> Absolutely, the pandemic not only changed how we think about work. You know, initially it was, do I work from home or do I live at work, and that was legitimately a challenge that all of us faced for a long time period, but we're seeing the hybrid model, we're seeing more companies be open to embracing that and allowing people to have more of that balance, which, at the end of the day, it's so much better for product development for the customers, as you talked about, it's a win-win. >> Absolutely, and definitely the first few months of it was very hard to find that separation, to be able to put up boundaries, but I think, at least I personally, have been able to find the way to do it and I hope that everyone is getting that space to be able to put those boundaries up, to effectively have a harmonious work life where you can still be at home most of the time, but also have that cutoff point of the day or at least have that separate space that you can feel that you're able to separate the two. >> Yeah absolutely, and a lot of that, from a work life balance perspective, bleeds into one of the next topics that we covered in detail and that's mentors and sponsors, the differences between them, recommendations from the women on the panel about how to combat imposter syndrome, but also how to leverage mentors and sponsors throughout your career. One of the things that Hillary said that I thought was fantastic advice, where mentors and sponsors are concerned, is be selective in picking your bosses. We often see people, especially younger folks, not necessarily younger folks, I shouldn't say that, that are attracted to a company, it's brand maybe, and think more about that than they do the boss or bosses that can help guide them along the way, but I thought that was really poignant advice that Hillary provided, something that I'm going to take into consideration myself. >> Yeah, and I honestly hadn't thought about that but as I reflect through my own career I can see how I've had particular managers who have had a major impact on helping me with my career. But if you don't have the ability to do that or maybe that's not a luxury that you have, I think even if you're able to find a mentor for a period of time or just enable for you to be able to get from, say a point A to point B, just for a temporary period, just so you can grow into your next role. Have a particular outcome that you want to drive. Have a particular goal in mind. Find that person who's been there and done that and they can really help you get through. If you don't have the luxury of picking your manager, at least be able to pick a mentor who can help you get to the next step. >> Exactly, I thought that advice was brilliant and it's something that I hadn't really considered either. We also talked with several of the women about imposter syndrome. You know that's something that everybody, I think regardless of gender, of your background, everybody feels that at some point. So I think one of the nice things that we do in this episode is sort of identify, yes, imposter syndrome is real, this is how it happened to me, this is how I navigated around or got over it. I think there's some great advice there for the audience to glean as well, about how to dial down the imposter syndrome that they might be feeling. >> Absolutely and I think the key there is just acknowledging it but also just hearing all the different techniques on how folks have dealt with it because everybody does. Even some of the smartest, most confident men I've met in industry still talk to me about how they have it and I'm shocked by it oftentimes, but it is very common and hopefully we talk about some good techniques to deal with that. >> I think we do. You know, one of the things that, when we were asking our guests about advice, what would they tell their younger selves, what would they tell young women or underrepresented groups in terms of becoming interested in STEM and in tech, and everybody sort of agreed on the, don't be afraid to raise your hand and ask questions. Show vulnerabilities, not just as the employee, but even from a leadership perspective, show that as a leader. I don't have all the answers. There are questions that I have. I think that goes a long way to reducing the imposter syndrome that most of us have faced at some point in our lives and that's just, don't be afraid to ask questions. You never know how many people have the same question sitting in the room. >> Well and also, for folks who've been in industry for 20, 25 years, I think we can just say that it's a marathon, it's not a sprint, and you're always going to have new things to learn and you can spend, back to we talked about the zigging and zagging through careers where we'll have different experiences, all of that kind of comes through just being curious and wanting to continue to learn. So yes, asking questions and being vulnerable and being able to say, "I don't know all the answers but I want to learn," is a key thing, especially culturally at AWS, but I'm sure with all of these companies as well. >> Definitely I think it sounded like it was really ingrained in their culture. And another thing too that we also talked about is the word no doesn't always mean a dead end. It can often mean, not right now, or maybe this isn't the right opportunity at this time. I think that's another important thing that the audience is going to learn is that failure is not necessarily a bad F word if you turn it into opportunity. No isn't necessarily the end of the road. It can be an opener to a different door and I thought that was a really positive message that our guests had to share with the audience. >> Yeah totally, I can say I had a mentor of mine, a very strong woman who told me, your career is going to have lots of ebbs and flows and that's natural and that when you say that, not right now, that's a perfect example of maybe there's an ebb where it might not be the right time for you now, but something to consider in the future. But also don't be afraid to say yes, when you can. >> Exactly, Danielle, it's been a pleasure filming this episode with you and the great female leaders that we have on. I'm excited for the audience to be able to learn from Hillary, Vera, Stephanie, Sue, and you. So much valuable content in here. We hope you enjoy this Partner Showcase. Season one episode three. Danielle, thank you so much for helping us. >> Thank you. Thank you, it's been a blast. I really appreciate it. >> All right, audience, we want to thank you. Enjoy the episode. (upbeat music)

Published Date : Jul 20 2022

SUMMARY :

Danielle, it's great to see you and good advice that I myself and how they need to be in the last five to 10 years, alignment that you bring up that you make better products that some of the guests talked that I can do the things that and allowing people to but also have that cutoff point of the day that are attracted to a the ability to do that and it's something that I Absolutely and I think the key there I don't have all the answers. and being able to say, that our guests had to that when you say that, and the great female I really appreciate it. Enjoy the episode.

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AWS Partner Showcase S1E3 Intro


 

(bright music) >> Everyone, it's nice to see you. Welcome to the "AWS Partner Showcase". I'm Lisa Martin, your host. This is season one, episode three, and this is a great episode that focuses on women in tech. I'm pleased to be joined by Danielle Greshock, the ISV PSA Director at AWS, and the sponsor of this fantastic program. Danielle, it's great to see you, and talk about such an important topic. >> Yes, and I will tell you all of these interviews have just been a blast for me to do, and I feel like there has been a lot of gold that we can glean from all of the stories that we heard on these interviews and good advice that I myself would not have necessarily thought of. So-- >> I agree, and we're going to get to that. 'Cause advice is one of the main things that our audience is going to hear. We have Hillary Ashton, you'll see from Teradata, Vera Reynolds joins us from Honeycomb, Stephanie Curry from NetApp and Sue Persichetti from Jefferson Frank. And the topics that we dig into are first and foremost, diversity, equity and inclusion. That is a topic that is incredibly important to every organization. And some of the things, Danielle, that our audiences shared were really interesting to me. One of the things that I saw from a thematic perspective over and over was that like Vera Reynolds was talking about the importance of companies and hiring managers and how they need to be intentional with DE&I initiatives. And that intention was a common thing that we heard. I'm curious what your thoughts are about that, that we heard about being intentional, working intentionally to deliver a more holistic pool of candidates where DE&I is concerned. What were some of the things that stuck out to you? >> Absolutely, I think each one of us is working in the inside of organizations where in the last five to 10 years there's been a strong push in this direction. Mostly because we've really seen first and foremost by being intentional, that you can change the way your organization looks. But also just that without being intentional, there was just a lot of outcomes and situations that maybe weren't great for a healthy and productive working environment. And so a lot of these companies have made big investments and put forth big initiatives that I think all of us are involved in. And so we're really excited to get out here and talk about it and talk about, especially as these are all partnerships that we have, how these align with our values. >> Yeah, that value alignment that you bring up is another thing that we heard consistently with each of the partners. There's a cultural alignment, there's a customer obsession alignment that they have with AWS, there's a DE&I alignment that they have. And I think everybody also kind of agreed. Stephanie Curry talked about it's really important for diversity on impacting performance. Highly performing teams are teams that are more diverse. I think we heard that kind of echoed throughout the women that we talked to in this episode. >> Absolutely, and I definitely even feel that there are studies out there that tell you that you make better products if you have all of the right input and you're getting many different perspectives. But not just that, but I can personally see it in the performing teams, not just my team, but also the teams that I work alongside. Arguably some of the other business folks have done a really great job of bringing more women into their organization, bringing more underrepresented minorities. Tech is a little bit behind, but we're trying really hard to bring that forward as well in technical roles. But you can just see the difference in the outcomes. At least I personally can just in the adjacent teams of mine. >> That's awesome. We talked also quite a bit during this episode about attracting women and underrepresented groups and retaining them. That retention piece is really key. What were some of the things that stuck out to you that some of the guests talked about in terms of retention? >> Yeah, I think especially speaking with Hillary and hearing how Teradata is thinking about different ways to make hybrid work work for everybody. I think that is definitely... When I talk to women interested in joining AWS, oftentimes that might be one of the first concerns that they have. Like, am I going to be able to go pick my kid up at four o'clock at the bus? Or am I going to be able to be at my kids' conference, or even just have enough work-life balance that I can do the things that I want to do outside of work beyond children and family. So these are all very important questions that especially women come and ask, but also it kind of is a bellwether for, is this going to be a company that allows me to bring my whole self to work? And then I'm also going to be able to have that balance that I need. So I think that was something that is changing a lot and many people are thinking about work a lot differently. >> Absolutely, the pandemic not only changed how we think about work. Initially it was, do I work from home or do I live at work? And that was legitimately a challenge that all of us faced for a long time period. But we're seeing the hybrid model, we're seeing more companies be open to embracing that and allowing people to have more of that balance which at the end of the day it's so much better for product development for the customers as you talked about, it's a win-win. >> Absolutely. And definitely the first few months of it was very hard to find that separation to be able to put up boundaries. But I think at least I personally have been able to find the way to do it and I hope that everyone is getting that space to be able to put those boundaries up to effectively have a harmonious work life. Where you can still be at home most of the time, but also have that cutoff point of the day or at least have that separate space that you can feel that you're able to separate the two. >> Yeah, absolutely. And a lot of that from a work-life balance perspective leads into one of the next topics that we covered in detail. And that's mentors and sponsors, the differences between them, recommendations from the women on the panel about how to combat imposter syndrome, but also how to leverage mentors and sponsors throughout your career. One of the things that Hillary said that I thought was fantastic advice where mentors and sponsors are concerned is be selective in picking your bosses. We often see people, especially younger folks, not necessarily younger folks, I shouldn't say that, that are attracted to a company, its brand maybe, and think more about that than they do the boss or bosses that can help guide them along the way. But I thought that was really poignant advice that Hillary provided, something that I'm going to take into consideration myself. >> Yeah, and I honestly hadn't thought about that, but as I reflect through my own career, I can see how I've had particular managers who have had a major impact on helping me with my career. But if you don't have the ability to do that or maybe that's not a luxury that you have, I think even if you're able to find a mentor for a period of time or just enable for you to be able to get from say a point A to point B just for a temporary period, just so you can grow into your next role, have a particular outcome that you want to drive, have a particular goal in mind. Find that person who's been there and done that and they can really help you get through if you don't have the luxury of picking your manager, at least be able to pick a mentor who can help you get to the next step. >> Exactly, I thought that advice was brilliant and something that I hadn't really considered either. We also talked with several other women about imposter syndrome. That's something that everybody, I think regardless of gender, of your background, everybody feels that at some point. So I think one of the nice things that we do in this episode is sort of identify, yes, imposter syndrome is real. This is how it happened to me, this is how I navigated around it or got over it. I think there's some great advice there for the audience to glean as well about how to dial down the imposter syndrome that they might be feeling. >> Absolutely. And I think the key there is just acknowledging it, but also just hearing all the different techniques on how folks have dealt with it, because everybody does. Even some of the smartest, most confident men I've met in industry still talk to me about how they have it. And I'm shocked by it oftentimes, but it is very common. And hopefully we talk about some good techniques to deal with that. >> I think we do. One of the things that when we were asking our guests about advice, what would they tell their younger selves, what would they tell young women or underrepresented groups in terms of becoming interested in STEM and in tech. And everybody sort of agreed on the don't be afraid to raise your hand and ask questions. Show vulnerabilities, not just as the employee, but even from a leadership perspective, show that as a leader, I don't have all the answers. There are questions that I have. I think that goes a long way to reducing the imposter syndrome that most of us have faced at some point in our lives. And that's just, don't be afraid to ask questions. You never know how many people have the same question sitting in the room. >> Well, and also for folks who've been in industry for 20, 25 years, I think we can just say that it's a marathon, it's not a sprint, and you're always going to have new things to learn. And you can spend... Back to we talked about the zigging and zagging through careers where we'll have different experiences. All of that kind of comes through just being curious and wanting to continue to learn. So yes, asking questions and being vulnerable and being able to say, I don't know all the answers but I want to learn is a key thing, especially culturally at AWS, but I'm sure with all of these companies as well. >> Definitely I think it sounded like it was really ingrained in their culture. And another thing too that we also talked about is the word, no, doesn't always mean a dead end, it can often mean, not right now or maybe this isn't the right opportunity at this time. I think that's another important thing that the audience is going to learn is that failure is not necessarily a bad F-word if you turn it into opportunity. No isn't necessarily the end of the road. It can be an opener to a different door. And I thought that was a really positive message that our guests had to share with the audience. >> Yeah, totally. I can say I had a mentor of mine, a very strong woman who told me, "Your career is going to have lots of ebbs and flows, and that's natural." And that when you say that, not right now, that's a perfect example of maybe there's an ebb where it might not be the right time for you now, but something to consider in the future. But also don't be afraid to say yes when you can. >> Exactly. Danielle, it's been a pleasure filming this episode with you and the great female leaders that we have on. I'm excited for the audience to be able to learn from Hillary, Vera, Stephanie, Sue and you. So much valuable content in here. We hope you enjoy this partner showcase season one episode three. Danielle, thanks so much for helping us with this. >> Thank you. Thank you, it's been a blast, I really appreciate it. >> All right. Audience, we want to thank you, enjoy the episode. (gentle music)

Published Date : Jul 18 2022

SUMMARY :

and the sponsor of this fantastic program. that we heard on these that our audience is going to hear. that you can change the way alignment that you bring up that you make better products that some of the guests talked that I can do the things that And that was legitimately a but also have that cutoff point of the day something that I'm going to the ability to do that and something that I hadn't to deal with that. on the don't be afraid to raise and being able to say, I that the audience is going to learn And that when you say that, not right now, leaders that we have on. I really appreciate it. Audience, we want to thank

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Sunil Senan, Infosys & Chris Degnan, Snowflake | Snowflake Summit 2022


 

>>mhm. >>Good morning. Live from Las Vegas. That snowflake Summit 22. Lisa Martin With Day Volonte David's Great. We have three wall to wall days of coverage at Snowflake Summit 22 this year. >>Yeah, it's all about data and bringing data to applications. And we've got some big announcements coming this week. Super exciting >>collaboration around data. We are excited to welcome our first two guests before the keynote. We have seen Nielsen in S V. P of data and Analytics Service offering head at emphasis. And Chris Dignan alumni is back with us to chief revenue officer at stuff like guys. Great to have you on the programme. Thanks for having us. Thank you very much. So he'll tell us what's going on with emphasis and snowflake and the partnership. Give us all that good stuff. >>Yeah, No, I think with the convergence of, uh, data digital and computing economy, um, you know that convergence is creating so much possibilities for for customers, uh, snowflake and emphases working together to help our customers realise the vision and these possibilities that are getting driven. We share a very strategic partnership where we are thinking ahead for our customers in terms of what, uh, we can do together in order to build solutions in order to bring out the expertise that is needed for such transformations and also influencing the thinking, Um, and the and the point of view in the market together so that, you know there is there is cohesive approach to doing this transformation and getting to those business outcomes. So it's a It's a partnership that's very successful and its strategic for for our customers, and we continue to invest for the market. >>Got some great customer. Some of my favourite CVS, Nike, William Sanoma. Gotta love that one. Chris talked to us about the snowflake data cloud. What makes it so unique and compelling in the market? >>Well, I think our customers, really they are going through digital transformation today, and they're moving from on premise to the cloud and historically speaking, there just hasn't been the right tool set to help them do that. I think snowflake brings to the table an opportunity for them to take all of their data and take it and and allow it to go from one cloud to the other so they can sit on a W s it can sit on Azure can sit on G, C, P and I can move around from cloud to cloud, and they can do analytics on top of that. >>So data has been traditionally really hard. And we saw that in the big data movement. But we learned a lot. Uh, and AI has been, you know, challenging. So what are you seeing with with customers? What are they struggling with? And how are you guys helping them? >>Yeah. So if you look at the customer journey, they have invested in a number of technologies in the past and are now at a juncture where they need to transform that landscape. They have the challenges of legacy debt that they need to, you know, get rid of or transform. They have the challenges of really bringing, you know, a cohesive understanding within the enterprise as to what these possibilities are for their business. Given the strategy that they are pursuing, um, business and I t cycles are not necessarily aligned. Um, you have the challenge of very fragmented data landscape that they have created over a period of time. How do you, you know, put all these together and work with a specific outcome in mind so that you're not doing transformation for the purpose of transformation. But to be able to actually drive new business models, new data driven products and services ability for you to collaborate with your partners and create unique competitive advantage in the market. And how do you bring those purposes together with the transformation that that's really happening? And and that's where you know our our customers, um, you know, grapple with the challenges of bringing it together. So, >>Chris, how do you see? Because it was talking about, uh, legacy that I think technical debt. Um, you kind of started out making the data warehouse easier. Then this data cloud thing comes out. You're like, Oh, that's an interesting vision and all of a sudden it's way more than vision. You get this huge ecosystem you're extending, we're gonna hear the announcements this morning. We won't. We won't spill the beans, but but really expanding the data cloud. So it's hard to keep up with with where you're at. So I think modernisation, right? So how do you think about modernisation? How are your customers thinking about it? And what's the scope of Snowflake. >>Well, you know, I think historically, you asked about AI and Ml and, you know, in the A I world historically, they've lacked data, and I think because we're the data cloud, we're bringing data, you know, and making it available and democratising it for everybody. And then, you know, partners like emphasis are actually helping us bring, you know, applications and new business models to to the table to our customers and their innovating on top of the data that we already have in the Snowflake Data Club. >>Chris, can you talk about some of the verticals where you guys are successful with emphasis that the three that I mentioned are retailers, But I know that finance, healthcare and life sciences are are huge for smooth, like talk to me, give us a perspective of the verticals that are coming to you. Guys saying help us out with transport. >>You know, I'll give you just an example. So So in the in the retail space, for example, Kraft Heinz is a is a joint customer of ours. And, you know, they've been all in on on snowflakes, Data Cloud and one of our big customers as well it is is Albertsons, and Albertans realises, Oh my gosh, I have all this information around the consumer in in the grocery stores and Kraft Heinz. They want access to that, and they actually can make supply chain decisions a lot faster if they have access to it. So with snowflakes data sharing, we can actually allow them to share data. Albertans share data directly with Kraft, Heinz and Kraft. Heinz can actually make supply chain decisions in real time so that these are some of the stuff that emphasis and stuff like help our customers self. >>So traditionally, the data pipeline goes through some very highly specialised individuals, whether the data engineer, the data scientists and data analyst. So that example that you just gave our organisation you mentioned before democratisation. So democratisation needs to be as a businessperson, I actually can get access to the data. So in that example that you gave between Kraft, Heinz and and and Albertson, is it the the highly hyper specialised teams sharing that data? Or is it actually extending into the line of business focus? >>That's so that's the interesting part for us is I think, snowflake, we just recently reorganise my sales team this year into verticals, and the reason we did that is customers no longer want to talk to us about speeds and feeds of how fast my database goes. They want to actually talk about business outcomes. How do I solve for demand forecasting? How do I supply fix my supply chain issues? Those are things. Those are the. That's how we're aligning with emphasis. So well is they've been doing this for a long time, Can only we haven't. And so we need their help on getting us to the next level of of the sales motion and talking to our customers on solving these business challenges in >>terms of that next level. So no question for you. Where are the customer conversations happening? At what level? I mean, we've seen such dramatic changes in the market in the last couple of years. Now we're dealing with inflation rising interest rates. Ukraine. Are you seeing the conversations in terms of building data platforms rising up the C suite? As every company recognises, we're going to be a data company. We're not gonna be a business. >>Absolutely. And I think all the macroeconomic forces that you talked about that's working on the enterprises globally is actually leading them to think about how to future proof their business models. Right? And there are tonnes of learning that they've hired in the last two or three years and digitising in embracing more digital models. The conversation with the customers have really pivoted towards business outcome. It is a C suite conversation. It is no longer just an incremental change for the for the companies they recognise. That data has been touted as a strategic asset for a long time, but I think it's taking a purpose and a meaning as to what it does for for the customers, the conversations are around industry verticals. You know, what are the specific challenges and opportunities that the the enterprises have, uh, and how you realise those and these cuts across multiple different layers. You know, we're talking about how your democratised data, which in our point of view, is absolute, must in terms of putting a foundation that doesn't take super specialised people to be able to run every operation and every bit of data that you process we have invested in building autonomous data and a state that can process data as it comes in without any manual intervention and take it all the way to consumption but also investing in those industry solutions. Along with snowflake, we launched the healthcare and life Sciences solution. We launched the only channel for retail and CPG. And these are great examples of how Snowflake Foundation enables democratisation on one side but also help solve business problems. In fact, with Snowflake, we have a very, uh, special partnership because our point of view on data economy is about how you connect with the network partners externally, and snowflake brings native capabilities. On this, we leverage that to Dr Exchanges for our customers and one of the services company in the recycling business. Uh, we're actually building and in exchange, which will allow the data points from multiple different sources and partners to come together. So they have a better understanding of their customers, their operations, the field operations and things >>like building a data ecosystem. Yes. Alright, They they Is it a two sided market place where you guys are observers and providing the the technology and the process, you know, guidance. What's your role in that? >>Yeah. So, um, we were seeing their revolution coming? Uh, two stages. Maybe even more. Um, customers are comfortable building an ecosystem. That's kind of private for them. Which means that they know who they are sharing data with. They know what the data is getting used for. And how do you really put governance on this? So that on one side you can trust it on the other side. There is a good use of that data, Uh, and not, uh, you know, compromise on their quality or privacy and some of the other regulations. But we do see this opening up to the two sided market places as well. Uh, some of the industry's lend themselves extremely well for that kind of play. We have seen that happening in trading area. We've seen that happen. And, uh, you know, the credit checks and things like that which are usually open for, you know, those kind of ecosystem. But the conversations and the and the programmes are really leading towards towards that in the market. >>You know, Lisa, one of things I wrote about this weekend is I was decided to come to stuff like summit and and see one of the, you know, thesis I have is that we're going to move not just beyond analytics, including analytics, but also building data products that can be monetised and and I'm hoping we're going to see some of that here. Are you seeing that Christian in the customer? It's It's >>a great question, David. So So we have You know, I just thought of it as as he was talking about. We have a customer who's a very large customer of ours who's in the financial services space, and they handle roughly 40% of the credit card transactions that happen in the US and they're coming to us and saying they want to go from zero in data business today to a $2 billion business over the next five years, and they're leaning on us to help them do that. And one of the things that's exciting for me is they're coming to us not saying Hey, how do you do it? You know, they're saying, Hey, we want to build a consumption model on top of snowflake and we want to use you as the delivery mechanism and the billing mechanism to help us actually monetise that data. So yes, the answer is. You know, I I used to sell to, you know, chief Data Officers and and see IOS. Now I'm talking to VPs of sales and I'm talking to chief operating officers and I'm talking to CEOs about how do we actually create a new revenue stream? And that's just I mean, it's exhilarating to have those conversations. That's >>data products. They don't have to worry about the infrastructure that comes from the cloud. They don't have to worry about the governance, as Senior was saying, Just put >>it in stuff like Just >>put stuff like that. So I call it The super cloud is kind of a, you know, a funny little tongue in cheek. But it's happening. It's this layer. It's not just multiple clouds. You see a lot of your critical competitors adjacent competitors saying, Hey, we're now running in in Google or we're running in Azure. We've been running on AWS. This is different. This is different, isn't it? It's a cloud that floats above the The infrastructure of the hyper scale is, and that's that's a new era. I think >>it's a new error. I think they're you know, I think the hyper scholars want to, you know, keep us as a as a data warehouse and and we're not. The customers are not letting them so So I think that's you know where emphasis kind of saw the light early on. And they were our innovation partner of the year, uh, this past year and they're helping us in our customers innovate, >>but you're uniquely qualified to do that where? I don't think it's the hyper scholars agenda. At least I never say never with the hyper scale is, but yeah, they have focused on providing infrastructure. And, yeah, they have databases and other tools. But that that cross cloud that continuum to your point, talking to VPs of sales and how do you generate revenue? That maybe, is a conversation that they have, but not explicitly as to how to actually do it in a data >>cloud. That's right. I mean, those and those are the Those are the fun conversations because you're you're saying, Hey, we can actually create a new revenue stream. And how can we actually help you solve our joint customers problems? So, yes, it is. Well, >>that's competitive differentiation for businesses. I mean, this is, as I mentioned Every company has to be a data company. If they're not, they're probably not going to be around much longer. They've got to be able to to leverage a data platform like snowflake, to find insights, be able to act on them and create value new services, new products to stay competitive, to stay ahead of the competition. That's no longer nice to have >>100%. I mean, I think they're they're all scared. I mean, you know, like if you look in the financial services space, they look at some of the fintech, as you know, the giant £800 gorillas look at the small fintech has huge threats to the business, and they're coming to us and say, How can we innovate our business now? And they're looking at us as the the innovator, and they're looking at emphasis to help them do that. So I think these are These are incredible times. >>So the narrative on Wall Street, of course, this past earnings season was consumption and who has best visibility and and they they were able to snowflake had a couple of large customers dial down consumption, some consumer facing. Here's the thing. If you're selling a data product for more than it costs you to make. If you dial down consumption in the future, you're gonna dial down revenue. So that's it's going to become less and less discretionary over time. And that, to me, is the next error. That's really exciting. >>The key, The key there is understanding the unit of measure. I think that's the number. One question that we get from customers is what is the unit of measure that we care about, that we want to monetise because to your point, it costs you more to make the product. You're not going to sell it right? And so I think that those are the things that the energy that we're spending with customers today is advising them, jointly advising them on how to actually monetise the specific, you know, unit of measure that they care >>about because when they get the Amazon bill or the snowflake bill, the CFO starts knocking the door. The answer has to be well, look at all the revenue that we generated and all the operating profit and the free cash flow that we drove, and then it's like, Oh, I get it. Keep doing it well, if I'm >>if I'm going on sales calls with the VP of sales and his their sales team, fantastic, right generated helping them generate revenue, right? That's a great conversation >>dynamic. And I think the adoption is really driven through the value, uh, that they can drive in their ecosystem. Their products are similar to products and services that these companies sell. And if you're embedding data inside Syria into your products services, that makes you that much more competitive in the market and drive value for your stakeholders. And that's essentially the future business model that we're talking about. On one side, the other one is the agility. Things aren't remaining constant, they are constantly changing, and we talked about some of those forces earlier. All of this is changing. The landscape is changing the the needs in the economy and things like that, and how you adapt to those kind of models in the future and pivoted on data capabilities that lets you identify new opportunities and and create new value. >>Speaking of creating new value last question guys, before we wrap, what's the go to market approach here between the two companies working customers go to get engaged. I imagine both sides. >>Yeah. I mean, the way that partnership looks good to me is is sell with co selling. So So I think, you know, we look at developing joint solutions with emphasis. They've done a wonderful job of leading into our partnership. So, you know, Sue Neill and I have a regular cadence where we talked every quarter, and our sales teams and our partner teams are are all leaning in and co selling. I don't know if you >>have Absolutely, um, you know, we we proactively identify, you know, the opportunities for our customers. And we work together at all levels within, you know, between the two companies to be able to bring a cohesive solution and a proposition for the customers. Really help them understand how to, you know, what is it that they can, um, get to and how you get that journey actually executed. And it's a partnership that works very seamlessly through that entire process, not just upstream when we're selling, but also downstream and we're executing. And we've had tremendous success together and look forward to more. >>Congratulations on that success, guys. Thank you so much for coming on talking about new possibilities with data and AI and sharing some of the impact that the technologies are making. We appreciate your insights. >>Thank you. Thank >>you. Thank you So much >>for our guests and a Volonte. I'm Lisa Martin. You're watching the Cube live in Las Vegas from Snowflake Summit 22 back after the keynote with more breaking news. Mhm, mhm.

Published Date : Jun 14 2022

SUMMARY :

We have three wall to wall days of coverage Yeah, it's all about data and bringing data to applications. Great to have you on the programme. Um, and the and the point of view in the market together so that, you know there is there is cohesive Chris talked to us about the snowflake data cloud. I think snowflake brings to the table an opportunity for them to Uh, and AI has been, you know, challenging. And and that's where you know our our customers, um, you know, grapple with the challenges So how do you think about modernisation? and I think because we're the data cloud, we're bringing data, you know, and making it available and democratising Chris, can you talk about some of the verticals where you guys are successful with emphasis that the three that I mentioned are And, you know, they've been all in on on So in that example that you gave between Kraft, of the sales motion and talking to our customers on solving these business challenges in Are you seeing the conversations in terms and opportunities that the the enterprises have, uh, and how you realise those you know, guidance. Uh, and not, uh, you know, compromise on their quality or privacy and some and and see one of the, you know, thesis I have is that we're going to move not just me is they're coming to us not saying Hey, how do you do it? They don't have to worry about the infrastructure that comes from the cloud. So I call it The super cloud is kind of a, you know, a funny little tongue in cheek. I think they're you know, I think the hyper scholars want to, you know, keep us as a as a data warehouse talking to VPs of sales and how do you generate revenue? And how can we actually help you solve our joint customers problems? I mean, this is, as I mentioned Every company has to be a data company. space, they look at some of the fintech, as you know, the giant £800 gorillas look at the small fintech If you dial down consumption in the future, on how to actually monetise the specific, you know, unit of measure that they care The answer has to be well, look at all the revenue that we generated and all the operating profit and the free and how you adapt to those kind of models in the future and pivoted on data Speaking of creating new value last question guys, before we wrap, what's the go to market approach here between the two companies So So I think, you know, we look at developing joint solutions with emphasis. have Absolutely, um, you know, we we proactively identify, and AI and sharing some of the impact that the technologies are making. Thank you. Thank you So much Summit 22 back after the keynote with more breaking news.

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Danielle Cook & John Forman | KubeCon CloudNativeCon NA 2021


 

>>I want to welcome back to the cubes coverage. We're here at another event in person I'm John furrier, host of the cube. We've got to CNCF coop con cloud native con for in-person 2021. And we're back. It's a hybrid event and we're streaming lives on all channels, as well as all the folks watching a great guest kicking off the show here from the co-chairs from cataract coast. Is that right? Danielle Cook. Who's the vice president at Fairwinds and John Foreman director at Accenture. Thanks for coming on your co-chair. Your third co-chair is not here, but you guys are here to talk about the cloud maturity model. Pretty mature funding is flowing tons of announcements. We're going to have a startup on $200 million. They're announcing in funding and observability of all of all hot spaces. Um, so the maturity is it's the journey in the cloud native space now is crossed over to mainstream. That's the we've been telling that story for a couple of years. Now, you guys have been working on this. Tell us about the cloud maturity model you guys worked on. >>So we got together earlier this year because we, um, four of us had been working on maturity models. So Simon Forester, who is one of the co-chairs, who isn't here, he had worked on a maturity model that looked at your legacy journey, all the way to cloud native, um, myself, I had been part of the Fairwinds team working on the Kubernetes maturity model. So, and then, um, we have Robbie, who's not here. And John Foreman, who we all got together, they had worked on a maturity model and we put it together and I've been working since February to go, what is cloud native maturity and what are the stages you need to go through to achieve maturity. So put this together and now we have this great model that people can use to take them from. I have no idea what cloud native is to the steps they can take to actually be a mature organization. >>And, you know, you've made it when you have a book here. So just hold that up to the camera real quick. So you can see it. It's very much in spirit of the community, but in all seriousness, it book's great, but this is a real need. What was the pain point? What was jumping out at you guys on the problem? Was it just where people like trying to get more cloud native, they want to go move faster. It was a confusing, what were the problems you solve in? >>Well, and if anything is, if we start at the beginning, right, there was during the cloud journey DevSecOps and the Kootenays being a thing that then there's journeys to DevSecOps tributaries as well. But everything is leading to cloud native. It's about the journey to cloud native. So everybody, you know, we're taught to go John, the ecosystem's an eyesore man. If I look at, you know, landscape, >>The whole map I >>Need, it's just like in trend map, it's just so confusing what we do. So every time we go to, I revert the wheel and I get them from zero to hero. So we just put together a model instead that we can re reuse yeah. As a good reference architecture. So from that is a primary, how we built because the native trademark you have with us today. So it's a five scale model from one to five what's twice today, or how to, to, you know, what our job is getting to a five where they could optimize a really rocket rolling. >>You know, it's interesting. I love these inflection points and, you know, being a student of history and the tech business there's moments where things are the new thing, and they're really truly new things like first-time operationalized dev ops. I mean the hardcore dev ops or early adopters we've been doing that, you know, we know that, but now mainstream, like, okay, this is a real disruption in a positive way. So the transformation is happening and it's new, new roles, new, new workflows, new, uh, team formations. So there's a, it's complicated in the sense of getting it up and running so I can see the need. How can you guys share your data on where people are? Because now you have more data coming in, you have more people doing dev ops, more cloud native development, and you mentioned security shepherds shifting left. Where's the data tell you, is it, as you said, people are more like a two or more. What's the, what's the data say? >>So we've had, so part of pulling this model together was your experience at Accenture, helping clients, the Fairwinds, um, experience, helping people manage Kubernetes. And so it's from out dozens of clusters that people have managed going, okay, where are people? And they don't even know where they are. So if we provide the guidelines from them, they can read it and go, oh, I am at about two. So the data is actually anecdotal from our experiences at our different companies. Um, but we, you know, we we've made it so that you can self identify, but we've also recognized that you might be at stage two for one application, but five for another application. So just because you're on this journey, doesn't mean everything is in, >>It's not boiler plate. It's really unique to every enterprise because they everyone's different >>Journey. Put you in journey with these things. A big part of this also torn apart one to five, your clients wants to in denial, you know? So, so Mr. CX level, you are level two. We are not, there's no way we would deal with this stuff for years. You've got to be a five. No, sorry. You're too. >>So >>There's use denial also about this. People think they do a cloud-native director rolling, and I'm looking at what they're doing and go, okay, do you do workups security? And they go, what's that? I go, exactly. So we really need to peel back the onion, start from seed year out and we need to be >>All right. So I want to ask more about the, um, the process and how that relates to the themes are involved. What are some of the themes around the maturity model that you guys can share that you see that people can look at and say, how do I self identify? What's the process will come to expect? >>Well, one of the things we did when we were putting it together was we realized that there were themes coming out amongst the maturity model itself. So we realized there's a whole people layer. There's a whole policy layer process and technology. So this maturity model does not just look at, Hey, this is the tech you need to do. It looks at how you introduce cloud native to your organization. How do you take the people along with it? What policies you need to put in place the process. So we did that first and foremost, but one of the things that was super important to all of us was that security was ever present throughout it. Because as everything is shifting left, you need to be looking at security from day one and considering how it's going to happen and roll out from your developers all the way to your compliance people. Um, it's super important. And one of the themes throughout. >>So, so it would be safe to say, then that security was a catalyst for the maturity models because you gotta be mature. I mean, security, you don't fool around security. >>About the last year when I created the program for, since I worked with Cheryl Holland, from CCF, we put together the community certification, her special program. I saw a need where security was a big gap in communities. Nobody knew anything about it. They wanted to use the old rack and stack ways of doing it. They wanted to use their tray micro tombs from yesteryear, and that doesn't work anymore. You need a new set of tools for Kubernetes. It's the upgrade system. It's different way of doing things. So that knowledge is critical. So I think you're part of this again, on this journey was getting certifications out there for people to understand how to do better. Now, the next phase of that now it's how do we put all these pieces together and built this roadmap? >>Well, it's a great group. You guys have the working groups hard to pronounce the name, but, uh, it's a great effort because one of the things I'm hearing and we've been reporting this one, the Cubans looking angle is the modern software developers want speed, and they don't want to wait for the old slow groups now and security, and it are viewed as blockers and like slow things down. And so you start to see a trend where those groups could provide policy and then start putting, feeding up, uh, data models that allow the developers in real time to do their coding, to shift left and to be efficient and move on and code not be waiting for weeks or days >>Comes to play. So today is the age of Caleb's right now, get up this emerging we're only to have now where everything is code policies, code, securities, code policies, cookie figures, code. That is the place for, and then again, walk a fusion more need for a cargo office. >>Okay. What's your thoughts on that? >>So I think what's really important is enabling service ownership, right? You need the developers to be able to do security, see policy, see it live and make sure that, you know, you're not your configuration, isn't stopping the build or getting into production. So, you know, we made sure that was part of the maturity model. Like you need to be looking continuous scanning throughout checking security checking policy. What is your process? Um, and we, you know, we made that ever present so that the developers are the ones who are making sure that you're getting to Kubernetes, you're getting to cloud native and you're doing it. >>Well, the folks watching, if you don't know the cloud native landscape slide, that ecosystem slide, it's getting bigger and bigger. There's more new things emerging. You see role of software abstractions coming in, automation and AI are coming in. So it makes it very challenging if you want to jump right in lifting and shifting to the clouds, really easy check, been there, done that, but companies want to refactor their applications, not just replatform refactoring means completely taking advantage of these higher level services. So, so it's going to be hard to navigate. So I guess with all that being said, what you guys advice to people who are saying, I need the navigation. I need to have the blueprint. What do I do? How do I get involved? And how do I leverage this? >>We want people to, you can go on to get hub and check out our group and read the maturity model. You can understand it, self identify where you're at, but we want people to get involved as well. So if they're seeing something that like, actually this needs to be adjusted slightly, please join the group. The cardiograph is group. Um, you can also get copies of our book available on the show. So if you, um, if you know, you can read it and it takes you line by line in a really playful way as to where you should be at in the maturity model. >>And on top of that, if you come Thursday was Sonia book. And of course, a lot of money, one day, I promise >>You guys are good. I gotta ask, you know, the final question is like more and more, just more personal commentary. If you don't mind, as teams start to change, this is obviously causing a lot of positive transformation if done, right? So the roles and the teams are starting to change. Hearing SRS are now not just the dev ops guys provisioning they're part of the, of the scale piece, the developers shifting left, new kind of workflows, the role of certain engineers and developers now, new team formations. Why were you guys seeing that evolve? Is there any trends that you see around how people are reconfiguring their team makeup? >>I think a lot of things is going to a single panic last tonight, where I'm taking dev and ops and putting them one panel where I can see everything going on in my environment, which is very critical. So right now we're seeing a pre-training where every client wants to be able to have the holy grail of a secret credit class to drive to that. But for you to get there, there's a lot of work you've got to do overnight that will not happen. And that's where this maturity model, I think again, will enhance that ability to do that. >>There's a cultural shift happening. I mean, people are changing there's new skillsets and you know, obviously there's a lot of people who don't have the skill. So it's super important that people work with Kubernetes, get certified, use the maturity model to help them know what skills they need. >>And it's a living document too. It's not, I mean, a book and I was living book. It's going to evolve. Uh, what areas you think are going to come next? So you guys have to predict if you had to see kind of where the pieces are going. Uh, obviously with cloud, everything's getting, you know, more Lego blocks to play with more coolness you have in the, in this world. What's coming next with Sue. Do you guys see any, any, uh, forecasts or >>We're working with each one of the tag groups within the CNCF to help us build it out and come up with what is next based on their expertise in the area. So we'll see lots more coming. Um, and we hope that the maturity grows and because of something that everybody relies on and that they can use alongside the landscape and the trail map. And, um, >>It's super valuable. I think you guys need a plug for any people want to, how they join. If I want to get involved, how do I, what do I do? >>Um, you can join the Carter Garfish group. You can check us out on, get hub and see all the information there. Um, we have a slack channel within the CNCF and we have calls every other Tuesday that people can see the pools. >>Awesome. Congratulations, we'll need it. And super important as people want to navigate and start building out, you know, you've got to edge right around the corner there it's happening real fast. Data's at the edge. You got cloud at the edge. Azure, AWS, Google. I mean, they're pushing really hardcore 5g, lot changes. >>Everybody wants to cloud today. Now one client is, one is more cloud. At least both the cloud is comfortable playing everywhere. One pump wife had DevOps. >>It's distributed computing back in the modern era. Thank you so much for coming on the keep appreciating. Okay. I'm Jennifer here for cube con cloud native con 2021 in person. It's a hybrid event. We're here live on the floor show floor, bringing you all the coverage. Thanks for watching station all day. Next three days here in Los Angeles. Thanks for watching. >>Thank you.

Published Date : Oct 26 2021

SUMMARY :

but you guys are here to talk about the cloud maturity model. are the stages you need to go through to achieve maturity. So you can see it. It's about the journey to cloud native. So from that is a primary, how we built because the native trademark you have with us I mean the hardcore dev ops or early adopters we've been doing that, you know, So the data is actually anecdotal from our It's not boiler plate. so Mr. CX level, you are level two. and I'm looking at what they're doing and go, okay, do you do workups security? What are some of the themes around the maturity model that you guys can share that you see that people can look at and say, So this maturity model does not just look at, Hey, this is the tech you need to I mean, security, you don't fool around security. Now, the next phase of that now it's how do we put all these pieces together and built this roadmap? And so you start to see a trend where those groups could provide policy and then start putting, feeding up, So today is the age of Caleb's right now, get up this emerging we're only to have now where everything Um, and we, you know, we made that ever present so that the developers So I guess with all that being said, what you guys advice to We want people to, you can go on to get hub and check out our group and read the maturity And on top of that, if you come Thursday was Sonia book. So the roles and the teams are starting to change. But for you to get there, there's a lot of work you've got to do overnight that will not happen. new skillsets and you know, obviously there's a lot of people who don't have the skill. So you guys have to predict if you had to see kind of where the pieces are going. landscape and the trail map. I think you guys need a plug for any people want to, how they join. Um, you can join the Carter Garfish group. you know, you've got to edge right around the corner there it's happening real fast. At least both the cloud is comfortable playing everywhere. We're here live on the floor show floor, bringing you all the coverage.

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Intermission 1 | DockerCon 2021


 

>>Hey, everyone. I want to welcome you back. This is our intermission. And let me tell you what a morning we've had for those of you that don't know. I'm, Hayma Ganapati, I'm in product marketing at Docker. And I would just want to quote, actually someone who was in one of the chat rooms and this, I think encapsulates exactly how I feel today, because this is my first Docker con and the quote was from. And he said, I feel like a kid in an ice cream store where I don't know which flavor to choose. I want to go to all of the sessions and I got to tell you that's how I felt. And, you know, um, I want to just do some specific call-ups. Um, first of all, Dana way to keep it real in your interview. I love the cube interview. If you miss that, um, it was really great. >>She talks a lot about, uh, CI CD pipeline and you know, what to do with GoodHub. It was great. Um, I also want to say that I was, uh, slipping back and forth between the community rooms and way to go Brazil obrigado until all of the people who participate in the Brazil room, we had about 250 plus people in that room. And the, the chat window was just going crazy and in the French community room, Vive left hall. So if you've a uncle funny, uh, we had about 150 plus people in that room. So I just want to say that, you know, we've been seeing a lot of participation and I just want to thank everyone for attending and for participating on people have been so kind in the chat rooms, we just want to remind you to stay kind, you know, presenters put a lot of effort into their presentations, so just, you know, offer some positive and supportive critique to them. >>And the other thing I want to mention is all of the countries that we're seeing, all of the participation. So I'm just going to call out a few. We have people from the Netherlands, from Canada, from South Africa, Akron, Ohio, Belgium, Austria, yeah, Ecuador, New Zealand. And he cut up Westchester. Like, I mean, it just goes list goes on and on and on. And I think this really speaks to the power of Docker community. And it's a real testimony to how people from all over the world are in love with Docker technology and are excited to be here. And so I just wanted to thank everyone again and want to remind you that we want to leverage the power of community. And we have a fundraising campaign going on to help, uh, people who are affected by COVID. And you know, some of our big communities, especially in India and Brazil are, have been really affected by COVID. >>So we're asking you to contribute and we'd really like you to participate. Um, we have, uh, the, the link you can see here, Docker donates, you can tweet about it and would love to see the numbers go up for those donations, because, you know, I've personally been affected, had some family members pass away from COVID in India, and I'm sure other people may have stories that firsthand or secondhand. So please do that and let's show what the power of Docker community can do. And before I hand over to, to Peter, I'm just going to read out some of the tweets we've been getting, okay, this Brett and Peter, these are great. Uh, one of the, one of the tweets said dev environments is one of the most exciting features in the past few years. Super excited to try this out. Great, great, great tweet. Yeah. >>I agree to, um, another loving the content that was not your tweets. You can, you can slip me the 20 bucks later. Um, there's another tweet that says loving the content from hashtag Docker con so far fascinating use cases and interesting progress and future directions love that. And then there's another one I'm trying to find it here. Uh, I've been waiting for this so long Docker builds now work on Intel and M one. So keep those tweets coming. We love getting this kind of feedback and we love reading the chat room. So, um, Peter, you know, I attended your, your panel and I love that we were talking about a security and that moving, moving it left. So how did that go for you? >>Uh, it was, it was, uh, it was extremely fun. And for those that are, uh, I think my parents might be watching, so they probably watched it and were like, w this is the most boring thing I've ever seen, but, um, you know, you get a bunch of geeks and, uh, Brett has told me I should use geek instead of nerd, but I, I liked, uh, geek. So you get a bunch of geeks talking about security and coding and, um, what, what, what containers actually are, what vulnerabilities are. Yeah, it was, it was extremely fun. The panel was fantastic. They were very engaging the chat. I mean, I couldn't keep up with the chat. Right. It would just kept flying by, uh, luckily I had a helper to pull off questions, but, um, yeah, it's super exciting. You can, I know we're all remote, but you can just feel that energy, right. It was, it was great. It was great. Yeah. Yeah, for sure. It's super >>Connected. I felt that with your panel to Brett as well, sorry to talk over you there, but yeah. How did, how did it go for you? I, there was a lot of engagement in your session. >>Uh, ditto, like it was just, uh, there was so many questions. We only got to get a fraction of them. I tried to pick themes because, uh, when you talk about continuous testing and integration and all the things that take a part of that, um, you, you end up with lots of, well, what I like is the discussion around opinions, because so much of these pipelines from code on your machine, into production and everything in between, it's really, uh, it's a culture. It turns out to be the description of your culture and how you all perceive testing, how you, what you value in testing. And so that really started to come out as a theme, um, throughout that show. And we, we ran at a time. I was also watching Peters and it was fantastic, but like you think an hour is enough time to cover a topic, but it's just tipping tip of the iceberg kind of stuff. So I think it was super helpful. I learned some things, um, I really enjoyed watching Peters and, uh, yeah, can't wait for the next one. There's >>More than that. And likewise, great. I mean, I know, I know we're w maybe we pat chose it, but it, it was, it was super exciting to watch your panel. They were very Nikos, one of my favorite people in the world, uh, a fellow Austinite, but, um, yeah, I love that too. How you, uh, you were talking about opinions right. And playing off each other. It's, it's always interesting to hear smart people, uh, how they think, right. Yeah. I learned from how they think, right. Yeah. A hundred percent. >>So, all right. So we're, we're, um, what's next? Like, we, we gotta keep this thing going, so I've got to remember that. >>I want to, so I want to talk a bit about some of the panels that are, or the sessions that are coming up and just want to remind people that happened this afternoon. I'm all about use cases. You know, I was a developer for many decades, and it's great to hear how other developers are using the tools. But, uh, as a developer, I always wanted to know how are, what are the end user applications? And so we have two exciting sessions at 1:00 PM. We have sneak and red ventures, and they're going to be talking about how they used Docker containers. The title of the, uh, uh, session is great. An ounce of prevention, curing, insecure, container images. So check that out. And we also have another one at one 30 with Massimo, from AWS and Dexter Legaspi from Sirius XM. And they're going to be talking about a real world application using Docker containers. So I really want you to, to encourage you to attend those. >>Yeah. Um, can I say one really quick? Cause I'm Sue and a shout out to Eric Smalling. He's giving the red ventures talk with our partners. He's awesome. Go check out his, uh, but I'm really excited about Matt. Jarvis's sneak talk around. Uh, I think we might've talked about it earlier. My container image has 500 vulnerabilities vulnerabilities now what, right. I mean, I think as developers, as we're coming into this and dev ops and everybody right. You scan and then you see all these vulnerabilities just shoot by. And you're like, well, what do I do? So Matt, Matt will be addressing that. And he is fantastic. I can go on. There's a bunch of them. >>Yeah. There's a whole bunch of coming up and right up after this, I'm on a live stream with a bunch of panels on get ops. And then after that, Peter will be back. And so stay tuned and thanks for watching during the intermission. And we'll see you soon. >>I'm also leading the women in tech panel attend that. Don't forget to do that. >>Absolutely. Yep. All right. Ciao. Ciao >>For me like my first, oh, I get it about Docker was when I used a SQL server container on my neck book for the first time >>Being able to install Docker desktop, which was the first thing that I did and be able to build this without worrying about any of my software versions that I currently had on my machine. It was >>Awesome. One of the things, because I love the most about Docker is because I write books and I do video training courses to help a lot of people take their first steps with Docker and containers and to get a connection with those people and for them to come back to me and say, do you know what this is so cool, so easy, and it's going to change both my job. And, but also my organization, my team, all of that kind of stuff, change the experience that our customers have with our applications and what our business really puts a smile on my face. If >>You want to use containers, then Docker is the first toys, especially with tools like the mark Docker, compose, you can, uh, easily do your day-to-day job as a developer, or even if you're an ops person, then there are the books of the cloud and other things. So yeah, the idea is that we can go the simplicity one simple task, uh, to, uh, Daugherty mate and make that reuse as many times. Uh, that is one of the cool things I like about my >>Favorite part about Docker is using it as a developer tool. I using Docker desktop, really easy to install, really easy to run. >>Every time I come back to DACA, I love the simplicity of the way that it works, especially on things like security, which I find frustrating and hard. It's just done so seamlessly. And so my favorite thing about DACA is not just that it changed the world in the way that we develop in and ship and build applications and put that. It's just so easy that even the guy, like, I think >>It really is all about finding that aha moment, that hook where Docker really makes sense to you because once you have that moment, then all of a sudden, you, you know, you are on your way to being a Docker power user. >>We need for people to understand this technology better before they can, uh, actually dive deep into that. And Docker makes it easier to explain things, to explain the concept of containers, to explain how containers will work, how you can split your environments, how you can, uh, standardize all your pipelines and so on. It's important that we also take the time to help other people. And I think it's very important that we also give back and that's part of the motto of open sources. How do we give back to other people and how we help other people learn? And I think that's what I'm really passionate about. This whole thing is continuing, uh, giving back to the community. I just >>Hope and has fun at Docker con. And I know that there's a lot of great speakers coming and I will be watching the talks, even though they're happening at 3:00 AM and in my local time zone, um, I'm pretty excited to watch and, uh, hopefully watch more than later on streaming or YouTube or wherever they're going to be. So I hope everyone has fun and learn something and yeah, I don't see how you couldn't have fun.

Published Date : May 28 2021

SUMMARY :

I want to welcome you back. She talks a lot about, uh, CI CD pipeline and you know, what to do with GoodHub. And I think this really speaks to So we're asking you to contribute and we'd really like you to participate. I agree to, um, another loving the content that was not your tweets. thing I've ever seen, but, um, you know, you get a bunch of geeks and, I felt that with your panel to Brett as well, sorry to talk over you there, And so that really started to come out as a theme, um, throughout that show. And likewise, great. So we're, we're, um, what's next? So I really want you to, to encourage you to attend those. You scan and then you see all these vulnerabilities just shoot by. And we'll see you soon. I'm also leading the women in tech panel attend that. Being able to install Docker desktop, which was the first thing that I did and be able to to get a connection with those people and for them to come back to me and say, do you know what this the mark Docker, compose, you can, uh, easily do your day-to-day job as a developer, really easy to install, really easy to run. It's just so easy that even the guy, like, I think really makes sense to you because once you have that moment, And I think it's very important that we also give back and that's part of the motto of open sources. And I know that there's a lot of great speakers coming and I

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HPE Accelerating Next | HPE Accelerating Next 2021


 

momentum is gathering [Music] business is evolving more and more quickly moving through one transformation to the next because change never stops it only accelerates this is a world that demands a new kind of compute deployed from edge to core to cloud compute that can outpace the rapidly changing needs of businesses large and small unlocking new insights turning data into outcomes empowering new experiences compute that can scale up or scale down with minimum investment and effort guided by years of expertise protected by 360-degree security served up as a service to let it control own and manage massive workloads that weren't there yesterday and might not be there tomorrow this is the compute power that will drive progress giving your business what you need to be ready for what's next this is the compute power of hpe delivering your foundation for digital transformation welcome to accelerating next thank you so much for joining us today we have a great program we're going to talk tech with experts we'll be diving into the changing economics of our industry and how to think about the next phase of your digital transformation now very importantly we're also going to talk about how to optimize workloads from edge to exascale with full security and automation all coming to you as a service and with me to kick things off is neil mcdonald who's the gm of compute at hpe neil always a pleasure great to have you on it's great to see you dave now of course when we spoke a year ago you know we had hoped by this time we'd be face to face but you know here we are again you know this pandemic it's obviously affected businesses and people in in so many ways that we could never have imagined but in the reality is in reality tech companies have literally saved the day let's start off how is hpe contributing to helping your customers navigate through things that are so rapidly shifting in the marketplace well dave it's nice to be speaking to you again and i look forward to being able to do this in person some point the pandemic has really accelerated the need for transformation in businesses of all sizes more than three-quarters of cios report that the crisis has forced them to accelerate their strategic agendas organizations that were already transforming or having to transform faster and organizations that weren't on that journey yet are having to rapidly develop and execute a plan to adapt to this new reality our customers are on this journey and they need a partner for not just the compute technology but also the expertise and economics that they need for that digital transformation and for us this is all about unmatched optimization for workloads from the edge to the enterprise to exascale with 360 degree security and the intelligent automation all available in that as a service experience well you know as you well know it's a challenge to manage through any transformation let alone having to set up remote workers overnight securing them resetting budget priorities what are some of the barriers that you see customers are working hard to overcome simply per the organizations that we talk with are challenged in three areas they need the financial capacity to actually execute a transformation they need the access to the resource and the expertise needed to successfully deliver on a transformation and they have to find the way to match their investments with the revenues for the new services that they're putting in place to service their customers in this environment you know we have a data partner called etr enterprise technology research and the spending data that we see from them is it's quite dramatic i mean last year we saw a contraction of roughly five percent of in terms of i.t spending budgets etc and this year we're seeing a pretty significant rebound maybe a six to seven percent growth range is the prediction the challenge we see is organizations have to they've got to iterate on that i call it the forced march to digital transformation and yet they also have to balance their investments for example at the corporate headquarters which have kind of been neglected is there any help in sight for the customers that are trying to reduce their spend and also take advantage of their investment capacity i think you're right many businesses are understandably reluctant to loosen the purse strings right now given all of the uncertainty and often a digital transformation is viewed as a massive upfront investment that will pay off in the long term and that can be a real challenge in an environment like this but it doesn't need to be we work through hpe financial services to help our customers create the investment capacity to accelerate the transformation often by leveraging assets they already have and helping them monetize them in order to free up the capacity to accelerate what's next for their infrastructure and for their business so can we drill into that i wonder if we could add some specifics i mean how do you ensure a successful outcome what are you really paying attention to as those sort of markers for success well when you think about the journey that an organization is going through it's tough to be able to run the business and transform at the same time and one of the constraints is having the people with enough bandwidth and enough expertise to be able to do both so we're addressing that in two ways for our customers one is by helping them confidently deploy new solutions which we have engineered leveraging decades of expertise and experience in engineering to deliver those workload optimized portfolios that take the risk and the complexity out of assembling some of these solutions and give them a pre-packaged validated supported solution intact that simplifies that work for them but in other cases we can enhance our customers bandwidth by bringing them hpe point next experts with all of the capabilities we have to help them plan deliver and support these i.t projects and transformations organizations can get on a faster track of modernization getting greater insight and control as they do it we're a trusted partner to get the most for a business that's on this journey in making these critical compute investments to underpin the transformations and whether that's planning to optimizing to safe retirement at the end of life we can bring that expertise to bayer to help amplify what our customers already have in-house and help them accelerate and succeed in executing these transformations thank you for that neil so let's talk about some of the other changes that customers are seeing and the cloud has obviously forced customers and their suppliers to really rethink how technology is packaged how it's consumed how it's priced i mean there's no doubt in that to take green lake it's obviously a leading example of a pay as pay-as-you-scale infrastructure model and it could be applied on-prem or hybrid can you maybe give us a sense as to where you are today with green lake well it's really exciting you know from our first pay-as-you-go offering back in 2006 15 years ago to the introduction of green lake hpe has really been paving the way on consumption-based services through innovation and partnership to help meet the exact needs of our customers hpe green lake provides an experience that's the best of both worlds a simple pay-per-use technology model with the risk management of data that's under our customers direct control and it lets customers shift to everything as a service in order to free up capital and avoid that upfront expense that we talked about they can do this anywhere at any scale or any size and really hpe green lake is the cloud that comes to you like that so we've touched a little bit on how customers can maybe overcome some of the barriers to transformation what about the nature of transformations themselves i mean historically there was a lot of lip service paid to digital and and there's a lot of complacency frankly but you know that covered wrecking ball meme that so well describes that if you're not a digital business essentially you're going to be out of business so neil as things have evolved how is hpe addressed the new requirements well the new requirements are really about what customers are trying to achieve and four very common themes that we see are enabling the productivity of a remote workforce that was never really part of the plan for many organizations being able to develop and deliver new apps and services in order to service customers in a different way or drive new revenue streams being able to get insights from data so that in these tough times they can optimize their business more thoroughly and then finally think about the efficiency of an agile hybrid private cloud infrastructure especially one that now has to integrate the edge and we're really thrilled to be helping our customers accelerate all of these and more with hpe compute i want to double click on that remote workforce productivity i mean again the surveys that we see 46 percent of the cios say that productivity improved with the whole work from home remote work trend and on average those improvements were in the four percent range which is absolutely enormous i mean when you think about that how does hpe specifically you know help here what do you guys do well every organization in the world has had to adapt to a different style of working and with more remote workers than they had before and for many organizations that's going to become the new normal even post pandemic many it shops are not well equipped for the infrastructure to provide that experience because if all your workers are remote the resiliency of that infrastructure the latencies of that infrastructure the reliability of are all incredibly important so we provide comprehensive solutions expertise and as a service options that support that remote work through virtual desktop infrastructure or vdi so that our customers can support that new normal of virtual engagements online everything across industries wherever they are and that's just one example of many of the workload optimized solutions that we're providing for our customers is about taking out the guesswork and the uncertainty in delivering on these changes that they have to deploy as part of their transformation and we can deliver that range of workload optimized solutions across all of these different use cases because of our broad range of innovation in compute platforms that span from the ruggedized edge to the data center all the way up to exascale and hpc i mean that's key if you're trying to affect the digital transformation and you don't have to fine-tune you know be basically build your own optimized solutions if i can buy that rather than having to build it and rely on your r d you know that's key what else is hpe doing you know to deliver things new apps new services you know your microservices containers the whole developer trend what's going on there well that's really key because organizations are all seeking to evolve their mix of business and bring new services and new capabilities new ways to reach their customers new way to reach their employees new ways to interact in their ecosystem all digitally and that means app development and many organizations of course are embracing container technology to do that today so with the hpe container platform our customers can realize that agility and efficiency that comes with containerization and use it to provide insights to their data more and more that data of course is being machine generated or generated at the edge or the near edge and it can be a real challenge to manage that data holistically and not have silos and islands an hpe esmerald data fabric speeds the agility and access to data with a unified platform that can span across the data centers multiple clouds and even the edge and that enables data analytics that can create insights powering a data-driven production-oriented cloud-enabled analytics and ai available anytime anywhere in any scale and it's really exciting to see the kind of impact that that can have in helping businesses optimize their operations in these challenging times you got to go where the data is and the data is distributed it's decentralized so i i i like the esmerel of vision and execution there so that all sounds good but with digital transformation you get you're going to see more compute in in hybrid's deployments you mentioned edge so the surface area it's like the universe it's it's ever-expanding you mentioned you know remote work and work from home before so i'm curious where are you investing your resources from a cyber security perspective what can we count on from hpe there well you can count on continued leadership from hpe as the world's most secure industry standard server portfolio we provide an enhanced and holistic 360 degree view to security that begins in the manufacturing supply chain and concludes with a safeguarded end-of-life decommissioning and of course we've long set the bar for security with our work on silicon root of trust and we're extending that to the application tier but in addition to the security customers that are building this modern hybrid are private cloud including the integration of the edge need other elements too they need an intelligent software-defined control plane so that they can automate their compute fleets from all the way at the edge to the core and while scale and automation enable efficiency all private cloud infrastructures are competing with web scale economics and that's why we're democratizing web scale technologies like pinsando to bring web scale economics and web scale architecture to the private cloud our partners are so important in helping us serve our customers needs yeah i mean hp has really upped its ecosystem game since the the middle of last decade when when you guys reorganized it you became like even more partner friendly so maybe give us a preview of what's coming next in that regard from today's event well dave we're really excited to have hp's ceo antonio neri speaking with pat gelsinger from intel and later lisa sue from amd and later i'll have the chance to catch up with john chambers the founder and ceo of jc2 ventures to discuss the state of the market today yeah i'm jealous you guys had some good interviews coming up neil thanks so much for joining us today on the virtual cube you've really shared a lot of great insight how hpe is partnering with customers it's it's always great to catch up with you hopefully we can do so face to face you know sooner rather than later well i look forward to that and uh you know no doubt our world has changed and we're here to help our customers and partners with the technology the expertise and the economics they need for these digital transformations and we're going to bring them unmatched workload optimization from the edge to exascale with that 360 degree security with the intelligent automation and we're going to deliver it all as an as a service experience we're really excited to be helping our customers accelerate what's next for their businesses and it's been really great talking with you today about that dave thanks for having me you're very welcome it's been super neal and i actually you know i had the opportunity to speak with some of your customers about their digital transformation and the role of that hpe plays there so let's dive right in we're here on the cube covering hpe accelerating next and with me is rule siestermans who is the head of it at the netherlands cancer institute also known as nki welcome rule thank you very much great to be here hey what can you tell us about the netherlands cancer institute maybe you could talk about your core principles and and also if you could weave in your specific areas of expertise yeah maybe first introduction to the netherlands institute um we are one of the top 10 comprehensive cancers in the world and what we do is we combine a hospital for treating patients with cancer and a recent institute under one roof so discoveries we do we find within the research we can easily bring them back to the clinic and vis-a-versa so we have about 750 researchers and about 3 000 other employees doctors nurses and and my role is to uh to facilitate them at their best with it got it so i mean everybody talks about digital digital transformation to us it all comes down to data so i'm curious how you collect and take advantage of medical data specifically to support nki's goals maybe some of the challenges that your organization faces with the amount of data the speed of data coming in just you know the the complexities of data how do you handle that yeah it's uh it's it's it's challenge and uh yeah what we we have we have a really a large amount of data so we produce uh terabytes a day and we we have stored now more than one petabyte on data at this moment and yeah it's uh the challenge is to to reuse the data optimal for research and to share it with other institutions so that needs to have a flexible infrastructure for that so a fast really fast network uh big data storage environment but the real challenge is not not so much the i.t bus is more the quality of the data so we have a lot of medical systems all producing those data and how do we combine them and and yeah get the data fair so findable accessible interoperable and reusable uh for research uh purposes so i think that's the main challenge the quality of the data yeah very common themes that we hear from from other customers i wonder if you could paint a picture of your environment and maybe you can share where hpe solutions fit in what what value they bring to your organization's mission yeah i think it brings a lot of flexibility so what we did with hpe is that we we developed a software-defined data center and then a virtual workplace for our researchers and doctors and that's based on the hpe infrastructure and what we wanted to build is something that expect the needs of doctors and nurses but also the researchers and the two kind of different blood groups blood groups and with different needs so uh but we wanted to create one infrastructure because we wanted to make the connection between the hospital and the research that's that's more important so um hpe helped helped us not only with the the infrastructure itself but also designing the whole architecture of it and for example what we did is we we bought a lot of hardware and and and the hardware is really uh doing his his job between nine till five uh dennis everything is working within everyone is working within the institution but all the other time in evening and and nights hours and also the redundant environment we have for the for our healthcare uh that doesn't do nothing of much more or less uh in in those uh dark hours so what we created together with nvidia and hpe and vmware is that we we call it video by day compute by night so we reuse those those servers and those gpu capacity for computational research jobs within the research that's you mentioned flexibility for this genius and and so we're talking you said you know a lot of hard ways they're probably proliant i think synergy aruba networking is in there how are you using this environment actually the question really is when you think about nki's digital transformation i mean is this sort of the fundamental platform that you're using is it a maybe you could describe that yeah it's it's the fundamental platform to to to work on and and and what we see is that we have we have now everything in place for it but the real challenge is is the next steps we are in so we have a a software defined data center we are cloud ready so the next steps is to to make the connection to the cloud to to give more automation to our researchers so they don't have to wait a couple of weeks for it to do it but they can do it themselves with a couple of clicks so i think the basic is we are really flexible and we have a lot of opportunities for automation for example but the next step is uh to create that business value uh really for for our uh employees that's a great story and a very important mission really fascinating stuff thanks for sharing this with our audience today really appreciate your time thank you very much okay this is dave vellante with thecube stay right there for more great content you're watching accelerating next from hpe i'm really glad to have you with us today john i know you stepped out of vacation so thanks very much for joining us neil it's great to be joining you from hawaii and i love the partnership with hpe and the way you're reinventing an industry well you've always excelled john at catching market transitions and there are so many transitions and paradigm shifts happening in the market and tech specifically right now as you see companies rush to accelerate their transformation what do you see as the keys to success well i i think you're seeing actually an acceleration following the covet challenges that all of us faced and i wasn't sure that would happen it's probably at three times the paces before there was a discussion point about how quickly the companies need to go digital uh that's no longer a discussion point almost all companies are moving with tremendous feed on digital and it's the ability as the cloud moves to the edge with compute and security uh at the edge and how you deliver these services to where the majority of applications uh reside are going to determine i think the future of the next generation company leadership and it's the area that neil we're working together on in many many ways so i think it's about innovation it's about the cloud moving to the edge and an architectural play with silicon to speed up that innovation yes we certainly see our customers of all sizes trying to accelerate what's next and get that digital transformation moving even faster as a result of the environment that we're all living in and we're finding that workload focus is really key uh customers in all kinds of different scales are having to adapt and support the remote workforces with vdi and as you say john they're having to deal with the deployment of workloads at the edge with so much data getting generated at the edge and being acted upon at the edge the analytics and the infrastructure to manage that as these processes get digitized and automated is is so important for so many workflows we really believe that the choice of infrastructure partner that underpins those transformations really matters a partner that can help create the financial capacity that can help optimize your environments and enable our customers to focus on supporting their business are all super key to success and you mentioned that in the last year there's been a lot of rapid course correction for all of us a demand for velocity and the ability to deploy resources at scale is more and more needed maybe more than ever what are you hearing customers looking for as they're rolling out their digital transformation efforts well i think they're being realistic that they're going to have to move a lot faster than before and they're also realistic on core versus context they're they're their core capability is not the technology of themselves it's how to deploy it and they're we're looking for partners that can help bring them there together but that can also innovate and very often the leaders who might have been a leader in a prior generation may not be on this next move hence the opportunity for hpe and startups like vinsano to work together as the cloud moves the edge and perhaps really balance or even challenge some of the big big incumbents in this category as well as partners uniquely with our joint customers on how do we achieve their business goals tell me a little bit more about how you move from this being a technology positioning for hpe to literally helping your customers achieve their outcomes they want and and how are you changing hpe in that way well i think when you consider these transformations the infrastructure that you choose to underpin it is incredibly critical our customers need a software-defined management plan that enables them to automate so much of their infrastructure they need to be able to take faster action where the data is and to do all of this in a cloud-like experience where they can deliver their infrastructure as code anywhere from exascale through the enterprise data center to the edge and really critically they have to be able to do this securely which becomes an ever increasing challenge and doing it at the right economics relative to their alternatives and part of the right economics of course includes adopting the best practices from web scale architectures and bringing them to the heart of the enterprise and in our partnership with pensando we're working to enable these new ideas of web scale architecture and fleet management for the enterprise at scale you know what is fun is hpe has an unusual talent from the very beginning in silicon valley of working together with others and creating a win-win innovation approach if you watch what your team has been able to do and i want to say this for everybody listening you work with startups better than any other company i've seen in terms of how you do win win together and pinsando is just the example of that uh this startup which by the way is the ninth time i have done with this team a new generation of products and we're designing that together with hpe in terms of as the cloud moves to the edge how do we get the leverage out of that and produce the results for your customers on this to give the audience appeal for it you're talking with pensano alone in terms of the efficiency versus an amazon amazon web services of an order of magnitude i'm not talking 100 greater i'm talking 10x greater and things from throughput number of connections you do the jitter capability etc and it talks how two companies uniquely who believe in innovation and trust each other and have very similar cultures can work uniquely together on it how do you bring that to life with an hpe how do you get your company to really say let's harvest the advantages of your ecosystem in your advantages of startups well as you say more and more companies are faced with these challenges of hitting the right economics for the infrastructure and we see many enterprises of various sizes trying to come to terms with infrastructures that look a lot more like a service provider that require that software-defined management plane and the automation to deploy at scale and with the work we're doing with pinsando the benefits that we bring in terms of the observability and the telemetry and the encryption and the distributed network functions but also a security architecture that enables that efficiency on the individual nodes is just so key to building a competitive architecture moving forwards for an on-prem private cloud or internal service provider operation and we're really excited about the work we've done to bring that technology across our portfolio and bring that to our customers so that they can achieve those kind of economics and capabilities and go focus on their own transformations rather than building and running the infrastructure themselves artisanally and having to deal with integrating all of that great technology themselves makes tremendous sense you know neil you and i work on a board together et cetera i've watched your summarization skills and i always like to ask the question after you do a quick summary like this what are the three or four takeaways we would like for the audience to get out of our conversation well that's a great question thanks john we believe that customers need a trusted partner to work through these digital transformations that are facing them and confront the challenge of the time that the covet crisis has taken away as you said up front every organization is having to transform and transform more quickly and more digitally and working with a trusted partner with the expertise that only comes from decades of experience is a key enabler for that a partner with the ability to create the financial capacity to transform the workload expertise to get more from the infrastructure and optimize the environment so that you can focus on your own business a partner that can deliver the systems and the security and the automation that makes it easily deployable and manageable anywhere you need them at any scale whether the edge the enterprise data center or all the way up to exascale in high performance computing and can do that all as a service as we can at hpe through hpe green lake enabling our customers most critical workloads it's critical that all of that is underpinned by an ai powered digitally enabled service experience so that our customers can get on with their transformation and running their business instead of dealing with their infrastructure and really only hpe can provide this combination of capabilities and we're excited and committed to helping our customers accelerate what's next for their businesses neil it's fun i i love being your partner and your wingman our values and cultures are so similar thanks for letting me be a part of this discussion today thanks for being with us john it was great having you here oh it's friends for life okay now we're going to dig into the world of video which accounts for most of the data that we store and requires a lot of intense processing capabilities to stream here with me is jim brickmeyer who's the chief marketing and product officer at vlasics jim good to see you good to see you as well so tell us a little bit more about velocity what's your role in this tv streaming world and maybe maybe talk about your ideal customer sure sure so um we're leading provider of carrier great video solutions video streaming solutions and advertising uh technology to service providers around the globe so we primarily sell software-based solutions to uh cable telco wireless providers and broadcasters that are interested in launching their own um video streaming services to consumers yeah so this is this big time you know we're not talking about mom and pop you know a little video outfit but but maybe you can help us understand that and just the sheer scale of of the tv streaming that you're doing maybe relate it to you know the overall internet usage how much traffic are we talking about here yeah sure so uh yeah so our our customers tend to be some of the largest um network service providers around the globe uh and if you look at the uh the video traffic um with respect to the total amount of traffic that that goes through the internet video traffic accounts for about 90 of the total amount of data that uh that traverses the internet so video is uh is a pretty big component of um of how people when they look at internet technologies they look at video streaming technologies uh you know this is where we we focus our energy is in carrying that traffic as efficiently as possible and trying to make sure that from a consumer standpoint we're all consumers of video and uh make sure that the consumer experience is a high quality experience that you don't experience any glitches and that that ultimately if people are paying for that content that they're getting the value that they pay for their for their money uh in their entertainment experience i think people sometimes take it for granted it's like it's like we we all forget about dial up right those days are long gone but the early days of video was so jittery and restarting and and the thing too is that you know when you think about the pandemic and the boom in streaming that that hit you know we all sort of experienced that but the service levels were pretty good i mean how much how much did the pandemic affect traffic what kind of increases did you see and how did that that impact your business yeah sure so uh you know obviously while it was uh tragic to have a pandemic and have people locked down what we found was that when people returned to their homes what they did was they turned on their their television they watched on on their mobile devices and we saw a substantial increase in the amount of video streaming traffic um over service provider networks so what we saw was on the order of 30 to 50 percent increase in the amount of data that was traversing those networks so from a uh you know from an operator's standpoint a lot more traffic a lot more challenging to to go ahead and carry that traffic a lot of work also on our behalf and trying to help operators prepare because we could actually see geographically as the lockdowns happened [Music] certain areas locked down first and we saw that increase so we were able to help operators as as all the lockdowns happened around the world we could help them prepare for that increase in traffic i mean i was joking about dial-up performance again in the early days of the internet if your website got fifty percent more traffic you know suddenly you were you your site was coming down so so that says to me jim that architecturally you guys were prepared for that type of scale so maybe you could paint a picture tell us a little bit about the solutions you're using and how you differentiate yourself in your market to handle that type of scale sure yeah so we so we uh we really are focused on what we call carrier grade solutions which are designed for that massive amount of scale um so we really look at it you know at a very granular level when you look um at the software and and performance capabilities of the software what we're trying to do is get as many streams as possible out of each individual piece of hardware infrastructure so that we can um we can optimize first of all maximize the uh the efficiency of that device make sure that the costs are very low but one of the other challenges is as you get to millions and millions of streams and that's what we're delivering on a daily basis is millions and millions of video streams that you have to be able to scale those platforms out um in an effective in a cost effective way and to make sure that it's highly resilient as well so we don't we don't ever want a consumer to have a circumstance where a network glitch or a server issue or something along those lines causes some sort of uh glitch in their video and so there's a lot of work that we do in the software to make sure that it's a very very seamless uh stream and that we're always delivering at the very highest uh possible bit rate for consumers so that if you've got that giant 4k tv that we're able to present a very high resolution picture uh to those devices and what's the infrastructure look like underneath you you're using hpe solutions where do they fit in yeah that's right yeah so we uh we've had a long-standing partnership with hpe um and we work very closely with them to try to identify the specific types of hardware that are ideal for the the type of applications that we run so we run video streaming applications and video advertising applications targeted kinds of video advertising technologies and when you look at some of these applications they have different types of requirements in some cases it's uh throughput where we're taking a lot of data in and streaming a lot of data out in other cases it's storage where we have to have very high density high performance storage systems in other cases it's i gotta have really high capacity storage but the performance does not need to be quite as uh as high from an io perspective and so we work very closely with hpe on trying to find exactly the right box for the right application and then beyond that also talking with our customers to understand there are different maintenance considerations associated with different types of hardware so we tend to focus on as much as possible if we're going to place servers deep at the edge of the network we will make everything um maintenance free or as maintenance free as we can make it by putting very high performance solid state storage into those servers so that uh we we don't have to physically send people to those sites to uh to do any kind of maintenance so it's a it's a very cooperative relationship that we have with hpe to try to define those boxes great thank you for that so last question um maybe what the future looks like i love watching on my mobile device headphones in no distractions i'm getting better recommendations how do you see the future of tv streaming yeah so i i think the future of tv streaming is going to be a lot more personal right so uh this is what you're starting to see through all of the services that are out there is that most of the video service providers whether they're online providers or they're your traditional kinds of paid tv operators is that they're really focused on the consumer and trying to figure out what is of value to you personally in the past it used to be that services were one size fits all and um and so everybody watched the same program right at the same time and now that's uh that's we have this technology that allows us to deliver different types of content to people on different screens at different times and to advertise to those individuals and to cater to their individual preferences and so using that information that we have about how people watch and and what people's interests are we can create a much more engaging and compelling uh entertainment experience on all of those screens and um and ultimately provide more value to consumers awesome story jim thanks so much for keeping us helping us just keep entertained during the pandemic i really appreciate your time sure thanks all right keep it right there everybody you're watching hpes accelerating next first of all pat congratulations on your new role as intel ceo how are you approaching your new role and what are your top priorities over your first few months thanks antonio for having me it's great to be here with you all today to celebrate the launch of your gen 10 plus portfolio and the long history that our two companies share in deep collaboration to deliver amazing technology to our customers together you know what an exciting time it is to be in this industry technology has never been more important for humanity than it is today everything is becoming digital and driven by what i call the four key superpowers the cloud connectivity artificial intelligence and the intelligent edge they are super powers because each expands the impact of the others and together they are reshaping every aspect of our lives and work in this landscape of rapid digital disruption intel's technology and leadership products are more critical than ever and we are laser focused on bringing to bear the depth and breadth of software silicon and platforms packaging and process with at scale manufacturing to help you and our customers capitalize on these opportunities and fuel their next generation innovations i am incredibly excited about continuing the next chapter of a long partnership between our two companies the acceleration of the edge has been significant over the past year with this next wave of digital transformation we expect growth in the distributed edge and age build out what are you seeing on this front like you said antonio the growth of edge computing and build out is the next key transition in the market telecommunications service providers want to harness the potential of 5g to deliver new services across multiple locations in real time as we start building solutions that will be prevalent in a 5g digital environment we will need a scalable flexible and programmable network some use cases are the massive scale iot solutions more robust consumer devices and solutions ar vr remote health care autonomous robotics and manufacturing environments and ubiquitous smart city solutions intel and hp are partnering to meet this new wave head on for 5g build out and the rise of the distributed enterprise this build out will enable even more growth as businesses can explore how to deliver new experiences and unlock new insights from the new data creation beyond the four walls of traditional data centers and public cloud providers network operators need to significantly increase capacity and throughput without dramatically growing their capital footprint their ability to achieve this is built upon a virtualization foundation an area of intel expertise for example we've collaborated with verizon for many years and they are leading the industry and virtualizing their entire network from the core the edge a massive redesign effort this requires advancements in silicon and power management they expect intel to deliver the new capabilities in our roadmap so ecosystem partners can continue to provide innovative and efficient products with this optimization for hybrid we can jointly provide a strong foundation to take on the growth of data-centric workloads for data analytics and ai to build and deploy models faster to accelerate insights that will deliver additional transformation for organizations of all types the network transformation journey isn't easy we are continuing to unleash the capabilities of 5g and the power of the intelligent edge yeah the combination of the 5g built out and the massive new growth of data at the edge are the key drivers for the age of insight these new market drivers offer incredible new opportunities for our customers i am excited about recent launch of our new gen 10 plus portfolio with intel together we are laser focused on delivering joint innovation for customers that stretches from the edge to x scale how do you see new solutions that this helping our customers solve the toughest challenges today i talked earlier about the superpowers that are driving the rapid acceleration of digital transformation first the proliferation of the hybrid cloud is delivering new levels of efficiency and scale and the growth of the cloud is democratizing high-performance computing opening new frontiers of knowledge and discovery next we see ai and machine learning increasingly infused into every application from the edge to the network to the cloud to create dramatically better insights and the rapid adoption of 5g as i talked about already is fueling new use cases that demand lower latencies and higher bandwidth this in turn is pushing computing to the edge closer to where the data is created and consumed the confluence of these trends is leading to the biggest and fastest build out of computing in human history to keep pace with this rapid digital transformation we recognize that infrastructure has to be built with the flexibility to support a broad set of workloads and that's why over the last several years intel has built an unmatched portfolio to deliver every component of intelligent silicon our customers need to move store and process data from the cpus to fpgas from memory to ssds from ethernet to switch silicon to silicon photonics and software our 3rd gen intel xeon scalable processors and our data centric portfolio deliver new core performance and higher bandwidth providing our customers the capabilities they need to power these critical workloads and we love seeing all the unique ways customers like hpe leverage our technology and solution offerings to create opportunities and solve their most pressing challenges from cloud gaming to blood flow to brain scans to financial market security the opportunities are endless with flexible performance i am proud of the amazing innovation we are bringing to support our customers especially as they respond to new data-centric workloads like ai and analytics that are critical to digital transformation these new requirements create a need for compute that's warlord optimized for performance security ease of use and the economics of business now more than ever compute matters it is the foundation for this next wave of digital transformation by pairing our compute with our software and capabilities from hp green lake we can support our customers as they modernize their apps and data quickly they seamlessly and securely scale them anywhere at any size from edge to x scale but thank you for joining us for accelerating next today i know our audience appreciated hearing your perspective on the market and how we're partnering together to support their digital transformation journey i am incredibly excited about what lies ahead for hp and intel thank you thank you antonio great to be with you today we just compressed about a decade of online commerce progress into about 13 or 14 months so now we're going to look at how one retailer navigated through the pandemic and what the future of their business looks like and with me is alan jensen who's the chief information officer and senior vice president of the sawing group hello alan how are you fine thank you good to see you hey look you know when i look at the 100 year history plus of your company i mean it's marked by transformations and some of them are quite dramatic so you're denmark's largest retailer i wonder if you could share a little bit more about the company its history and and how it continues to improve the customer experience well at the same time keeping costs under control so vital in your business yeah yeah the company founded uh approximately 100 years ago with a department store in in oahu's in in denmark and i think in the 60s we founded the first supermarket in in denmark with the self-service and combined textile and food in in the same store and in beginning 70s we founded the first hyper market in in denmark and then the this calendar came from germany early in in 1980 and we started a discount chain and so we are actually building department store in hyber market info in in supermarket and in in the discount sector and today we are more than 1 500 stores in in three different countries in in denmark poland and germany and especially for the danish market we have a approximately 38 markets here and and is the the leader we have over the last 10 years developed further into online first in non-food and now uh in in food with home delivery with click and collect and we have done some magnetism acquisition in in the convenience with mailbox solutions to our customers and we have today also some restaurant burger chain and and we are running the starbuck in denmark so i can you can see a full plate of different opportunities for our customer in especially denmark it's an awesome story and of course the founder's name is still on the masthead what a great legacy now of course the pandemic is is it's forced many changes quite dramatic including the the behaviors of retail customers maybe you could talk a little bit about how your digital transformation at the sawing group prepared you for this shift in in consumption patterns and any other challenges that that you faced yeah i think uh luckily as for some of the you can say the core it solution in in 19 we just roll out using our computers via direct access so you can work from anywhere whether you are traveling from home and so on we introduced a new agile scrum delivery model and and we just finalized the rolling out teams in in in january february 20 and that was some very strong thing for suddenly moving all our employees from from office to to home and and more or less overnight we succeed uh continuing our work and and for it we have not missed any deadline or task for the business in in 2020 so i think that was pretty awesome to to see and for the business of course the pandemic changed a lot as the change in customer behavior more or less overnight with plus 50 80 on the online solution forced us to do some different priorities so we were looking at the food home delivery uh and and originally expected to start rolling out in in 2022 uh but took a fast decision in april last year to to launch immediately and and we have been developing that uh over the last eight months and has been live for the last three months now in the market so so you can say the pandemic really front loaded some of our strategic actions for for two to three years uh yeah that was very exciting what's that uh saying luck is the byproduct of great planning and preparation so let's talk about when you're in a company with some strong financial situation that you can move immediately with investment when you take such decision then then it's really thrilling yeah right awesome um two-part question talk about how you leverage data to support the solid groups mission and you know drive value for customers and maybe you could talk about some of the challenges you face with just the amount of data the speed of data et cetera yeah i said data is everything when you are in retail as a retailer's detail as you need to monitor your operation down to each store eats department and and if you can say we have challenge that that is that data is just growing rapidly as a year by year it's growing more and more because you are able to be more detailed you're able to capture more data and for a company like ours we need to be updated every morning as a our fully updated sales for all unit department single sku selling in in the stores is updated 3 o'clock in the night and send out to all top management and and our managers all over the company it's actually 8 000 reports going out before six o'clock every day in the morning we have introduced a loyalty program and and you are capturing a lot of data on on customer behavior what is their preferred offers what is their preferred time in the week for buying different things and all these data is now used to to personalize our offers to our cost of value customers so we can be exactly hitting the best time and and convert it to sales data is also now used for what we call intelligent price reductions as a so instead of just reducing prices with 50 if it's uh close to running out of date now the system automatically calculate whether a store has just enough to to finish with full price before end of day or actually have much too much and and need to maybe reduce by 80 before as being able to sell so so these automated [Music] solutions built on data is bringing efficiency into our operation wow you make it sound easy these are non-trivial items so congratulations on that i wonder if we could close hpe was kind enough to introduce us tell us a little bit about the infrastructure the solutions you're using how they differentiate you in the market and i'm interested in you know why hpe what distinguishes them why the choice there yeah as a when when you look out a lot is looking at moving data to the cloud but we we still believe that uh due to performance due to the availability uh more or less on demand we we still don't see the cloud uh strong enough for for for selling group uh capturing all our data we have been quite successfully having one data truth across the whole con company and and having one just one single bi solution and having that huge amount of data i think we have uh one of the 10 largest sub business warehouses in global and but on the other hand we also want to be agile and want to to scale when needed so getting close to a cloud solution we saw it be a green lake as a solution getting close to the cloud but still being on-prem and could deliver uh what we need to to have a fast performance on on data but still in a high quality and and still very secure for us to run great thank you for that and thank alan thanks so much for your for your time really appreciate your your insights and your congratulations on the progress and best of luck in the future thank you all right keep it right there we have tons more content coming you're watching accelerating next from hpe [Music] welcome lisa and thank you for being here with us today antonio it's wonderful to be here with you as always and congratulations on your launch very very exciting for you well thank you lisa and we love this partnership and especially our friendship which has been very special for me for the many many years that we have worked together but i wanted to have a conversation with you today and obviously digital transformation is a key topic so we know the next wave of digital transformation is here being driven by massive amounts of data an increasingly distributed world and a new set of data intensive workloads so how do you see world optimization playing a role in addressing these new requirements yeah no absolutely antonio and i think you know if you look at the depth of our partnership over the last you know four or five years it's really about bringing the best to our customers and you know the truth is we're in this compute mega cycle right now so it's amazing you know when i know when you talk to customers when we talk to customers they all need to do more and and frankly compute is becoming quite specialized so whether you're talking about large enterprises or you're talking about research institutions trying to get to the next phase of uh compute so that workload optimization that we're able to do with our processors your system design and then you know working closely with our software partners is really the next wave of this this compute cycle so thanks lisa you talk about mega cycle so i want to make sure we take a moment to celebrate the launch of our new generation 10 plus compute products with the latest announcement hp now has the broadest amd server portfolio in the industry spanning from the edge to exascale how important is this partnership and the portfolio for our customers well um antonio i'm so excited first of all congratulations on your 19 world records uh with uh milan and gen 10 plus it really is building on you know sort of our you know this is our third generation of partnership with epic and you know you are with me right at the very beginning actually uh if you recall you joined us in austin for our first launch of epic you know four years ago and i think what we've created now is just an incredible portfolio that really does go across um you know all of the uh you know the verticals that are required we've always talked about how do we customize and make things easier for our customers to use together and so i'm very excited about your portfolio very excited about our partnership and more importantly what we can do for our joint customers it's amazing to see 19 world records i think i'm really proud of the work our joint team do every generation raising the bar and that's where you know we we think we have a shared goal of ensuring that customers get the solution the services they need any way they want it and one way we are addressing that need is by offering what we call as a service delivered to hp green lake so let me ask a question what feedback are you hearing from your customers with respect to choice meaning consuming as a service these new solutions yeah now great point i think first of all you know hpe green lake is very very impressive so you know congratulations um to uh to really having that solution and i think we're hearing the same thing from customers and you know the truth is the compute infrastructure is getting more complex and everyone wants to be able to deploy sort of the right compute at the right price point um you know in in terms of also accelerating time to deployment with the right security with the right quality and i think these as a service offerings are going to become more and more important um as we go forward in the compute uh you know capabilities and you know green lake is a leadership product offering and we're very very you know pleased and and honored to be part of it yeah we feel uh lisa we are ahead of the competition and um you know you think about some of our competitors now coming with their own offerings but i think the ability to drive joint innovation is what really differentiate us and that's why we we value the partnership and what we have been doing together on giving the customers choice finally you know i know you and i are both incredibly excited about the joint work we're doing with the us department of energy the oak ridge national laboratory we think about large data sets and you know and the complexity of the analytics we're running but we both are going to deliver the world's first exascale system which is remarkable to me so what this milestone means to you and what type of impact do you think it will make yes antonio i think our work with oak ridge national labs and hpe is just really pushing the envelope on what can be done with computing and if you think about the science that we're going to be able to enable with the first exascale machine i would say there's a tremendous amount of innovation that has already gone in to the machine and we're so excited about delivering it together with hpe and you know we also think uh that the super computing technology that we're developing you know at this broad scale will end up being very very important for um you know enterprise compute as well and so it's really an opportunity to kind of take that bleeding edge and really deploy it over the next few years so super excited about it i think you know you and i have a lot to do over the uh the next few months here but it's an example of the great partnership and and how much we're able to do when we put our teams together um to really create that innovation i couldn't agree more i mean this is uh an incredible milestone for for us for our industry and honestly for the country in many ways and we have many many people working 24x7 to deliver against this mission and it's going to change the future of compute no question about it and then honestly put it to work where we need it the most to advance life science to find cures to improve the way people live and work but lisa thank you again for joining us today and thank you more most importantly for the incredible partnership and and the friendship i really enjoy working with you and your team and together i think we can change this industry once again so thanks for your time today thank you so much antonio and congratulations again to you and the entire hpe team for just a fantastic portfolio launch thank you okay well some pretty big hitters in those keynotes right actually i have to say those are some of my favorite cube alums and i'll add these are some of the execs that are stepping up to change not only our industry but also society and that's pretty cool and of course it's always good to hear from the practitioners the customer discussions have been great so far today now the accelerating next event continues as we move to a round table discussion with krista satrathwaite who's the vice president and gm of hpe core compute and krista is going to share more details on how hpe plans to help customers move ahead with adopting modern workloads as part of their digital transformations krista will be joined by hpe subject matter experts chris idler who's the vp and gm of the element and mark nickerson director of solutions product management as they share customer stories and advice on how to turn strategy into action and realize results within your business thank you for joining us for accelerate next event i hope you're enjoying it so far i know you've heard about the industry challenges the i.t trends hpe strategy from leaders in the industry and so today what we want to do is focus on going deep on workload solutions so in the most important workload solutions the ones we always get asked about and so today we want to share with you some best practices some examples of how we've helped other customers and how we can help you all right with that i'd like to start our panel now and introduce chris idler who's the vice president and general manager of the element chris has extensive uh solution expertise he's led hpe solution engineering programs in the past welcome chris and mark nickerson who is the director of product management and his team is responsible for solution offerings making sure we have the right solutions for our customers welcome guys thanks for joining me thanks for having us krista yeah so i'd like to start off with one of the big ones the ones that we get asked about all the time what we've been all been experienced in the last year remote work remote education and all the challenges that go along with that so let's talk a little bit about the challenges that customers have had in transitioning to this remote work and remote education environment uh so i i really think that there's a couple of things that have stood out for me when we're talking with customers about vdi first obviously there was a an unexpected and unprecedented level of interest in that area about a year ago and we all know the reasons why but what it really uncovered was how little planning had gone into this space around a couple of key dynamics one is scale it's one thing to say i'm going to enable vdi for a part of my workforce in a pre-pandemic environment where the office was still the the central hub of activity for work uh it's a completely different scale when you think about okay i'm going to have 50 60 80 maybe 100 of my workforce now distributed around the globe um whether that's in an educational environment where now you're trying to accommodate staff and students in virtual learning uh whether that's uh in the area of things like uh formula one racing where we had uh the desire to still have events going on but the need for a lot more social distancing not as many people able to be trackside but still needing to have that real-time experience this really manifested in a lot of ways and scale was something that i think a lot of customers hadn't put as much thought into initially the other area is around planning for experience a lot of times the vdi experience was planned out with very specific workloads or very specific applications in mind and when you take it to a more broad-based environment if we're going to support multiple functions multiple lines of business there hasn't been as much planning or investigation that's gone into the application side and so thinking about how graphically intense some applications are one customer that comes to mind would be tyler isd who did a fairly large roll out pre-pandemic and as part of their big modernization effort what they uncovered was even just changes in standard windows applications had become so much more graphically intense with windows 10 with the latest updates with programs like adobe that they were really needing to have an accelerated experience for a much larger percentage of their install base than than they had counted on so in addition to planning for scale you also need to have that visibility into what are the actual applications that are going to be used by these remote users how graphically intense those might be what's the login experience going to be as well as the operating experience and so really planning through that experience side as well as the scale and the number of users uh is is kind of really two of the biggest most important things that i've seen yeah mark i'll i'll just jump in real quick i think you you covered that pretty comprehensively there and and it was well done the couple of observations i've made one is just that um vdi suddenly become like mission critical for sales it's the front line you know for schools it's the classroom you know that this isn't a cost cutting measure or a optimization nit measure anymore this is about running the business in a way it's a digital transformation one aspect of about a thousand aspects of what does it mean to completely change how your business does and i think what that translates to is that there's no margin for error right you really need to deploy this in a way that that performs that understands what you're trying to use it for that gives that end user the experience that they expect on their screen or on their handheld device or wherever they might be whether it's a racetrack classroom or on the other end of a conference call or a boardroom right so what we do in in the engineering side of things when it comes to vdi or really understand what's a tech worker what's a knowledge worker what's a power worker what's a gp really going to look like what's time of day look like you know who's using it in the morning who's using it in the evening when do you power up when do you power down does the system behave does it just have the it works function and what our clients can can get from hpe is um you know a worldwide set of experiences that we can apply to making sure that the solution delivers on its promises so we're seeing the same thing you are krista you know we see it all the time on vdi and on the way businesses are changing the way they do business yeah and it's funny because when i talk to customers you know one of the things i heard that was a good tip is to roll it out to small groups first so you could really get a good sense of what the experience is before you roll it out to a lot of other people and then the expertise it's not like every other workload that people have done before so if you're new at it make sure you're getting the right advice expertise so that you're doing it the right way okay one of the other things we've been talking a lot about today is digital transformation and moving to the edge so now i'd like to shift gears and talk a little bit about how we've helped customers make that shift and this time i'll start with chris all right hey thanks okay so you know it's funny when it comes to edge because um the edge is different for for every customer in every client and every single client that i've ever spoken to of hp's has an edge somewhere you know whether just like we were talking about the classroom might be the edge but but i think the industry when we're talking about edge is talking about you know the internet of things if you remember that term from not to not too long ago you know and and the fact that everything's getting connected and how do we turn that into um into telemetry and and i think mark's going to be able to talk through a couple of examples of clients that we have in things like racing and automotive but what we're learning about edge is it's not just how do you make the edge work it's how do you integrate the edge into what you're already doing and nobody's just the edge right and and so if it's if it's um ai mldl there's that's one way you want to use the edge if it's a customer experience point of service it's another you know there's yet another way to use the edge so it turns out that having a broad set of expertise like hpe does to be able to understand the different workloads that you're trying to tie together including the ones that are running at the at the edge often it involves really making sure you understand the data pipeline you know what information is at the edge how does it flow to the data center how does it flow and then which data center uh which private cloud which public cloud are you using i think those are the areas where where we really sort of shine is that we we understand the interconnectedness of these things and so for example red bull and i know you're going to talk about that in a minute mark um uh the racing company you know for them the the edge is the racetrack and and you know milliseconds or partial seconds winning and losing races but then there's also an edge of um workers that are doing the design for for the cars and how do they get quick access so um we have a broad variety of infrastructure form factors and compute form factors to help with the edge and this is another real advantage we have is that we we know how to put the right piece of equipment with the right software we also have great containerized software with our esmeral container platform so we're really becoming um a perfect platform for hosting edge-centric workloads and applications and data processing yeah it's uh all the way down to things like our superdome flex in the background if you have some really really really big data that needs to be processed and of course our workhorse proliance that can be configured to support almost every um combination of workload you have so i know you started with edge krista but but and we're and we nail the edge with those different form factors but let's make sure you know if you're listening to this this show right now um make sure you you don't isolate the edge and make sure they integrate it with um with the rest of your operation mark you know what did i miss yeah to that point chris i mean and this kind of actually ties the two things together that we've been talking about here but the edge uh has become more critical as we have seen more work moving to the edge as where we do work changes and evolves and the edge has also become that much more closer because it has to be that much more connected um to your point uh talking about where that edge exists that edge can be a lot of different places but the one commonality really is that the edge is is an area where work still needs to get accomplished it can't just be a collection point and then everything gets shipped back to a data center or back to some some other area for the work it's where the work actually needs to get done whether that's edge work in a use case like vdi or whether that's edge work in the case of doing real-time analytics you mentioned red bull racing i'll i'll bring that up i mean you talk about uh an area where time is of the essence everything about that sport comes down to time you're talking about wins and losses that are measured as you said in milliseconds and that applies not just to how performance is happening on the track but how you're able to adapt and modify the needs of the car uh adapt to the evolving conditions on the track itself and so when you talk about putting together a solution for an edge like that you're right it can't just be here's a product that's going to allow us to collect data ship it back someplace else and and wait for it to be processed in a couple of days you have to have the ability to analyze that in real time when we pull together a solution involving our compute products our storage products our networking products when we're able to deliver that full package solution at the edge what you see are results like a 50 decrease in processing time to make real-time analytic decisions about configurations for the car and adapting to to real-time uh test and track conditions yeah really great point there um and i really love the example of edge and racing because i mean that is where it all every millisecond counts um and so important to process that at the edge now switching gears just a little bit let's talk a little bit about some examples of how we've helped customers when it comes to business agility and optimizing their workload for maximum outcome for business agility let's talk about some things that we've done to help customers with that mark yeah give it a shot so when we when we think about business agility what you're really talking about is the ability to to implement on the fly to be able to scale up to scale down the ability to adapt to real time changing situations and i think the last year has been has been an excellent example of exactly how so many businesses have been forced to do that i think one of the areas that that i think we've probably seen the most ability to help with customers in that agility area is around the space of private and hybrid clouds if you take a look at the need that customers have to to be able to migrate workloads and migrate data between public cloud environments app development environments that may be hosted on-site or maybe in the cloud the ability to move out of development and into production and having the agility to then scale those application rollouts up having the ability to have some of that some of that private cloud flexibility in addition to a public cloud environment is something that is becoming increasingly crucial for a lot of our customers all right well i we could keep going on and on but i'll stop it there uh thank you so much uh chris and mark this has been a great discussion thanks for sharing how we helped other customers and some tips and advice for approaching these workloads i thank you all for joining us and remind you to look at the on-demand sessions if you want to double click a little bit more into what we've been covering all day today you can learn a lot more in those sessions and i thank you for your time thanks for tuning in today many thanks to krista chris and mark we really appreciate you joining today to share how hpe is partnering to facilitate new workload adoption of course with your customers on their path to digital transformation now to round out our accelerating next event today we have a series of on-demand sessions available so you can explore more details around every step of that digital transformation from building a solid infrastructure strategy identifying the right compute and software to rounding out your solutions with management and financial support so please navigate to the agenda at the top of the page to take a look at what's available i just want to close by saying that despite the rush to digital during the pandemic most businesses they haven't completed their digital transformations far from it 2020 was more like a forced march than a planful strategy but now you have some time you've adjusted to this new abnormal and we hope the resources that you find at accelerating next will help you on your journey best of luck to you and be well [Music] [Applause] [Music] [Applause] [Music] [Applause] [Music] [Applause] [Music] [Applause] [Music] [Applause] [Music] [Music] [Applause] [Music] [Applause] [Music] [Applause] so [Music] [Applause] [Music] you

Published Date : Apr 19 2021

SUMMARY :

and the thing too is that you know when

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A Day in the Life of Data with the HPE Ezmeral Data Fabric


 

>>Welcome everyone to a day in the life of data with HPE as well. Data fabric, the session is being recorded and will be available for replay at a later time. When you want to come back and view it again, feel free to add any questions that you have into the chat. And Chad and I joined stark. We'll, we'll be more than willing to answer your questions. And now let me turn it over to Jimmy Bates. >>Thanks. Uh, let me go ahead and share my screen here and we'll get started. >>Hey everyone. Uh, once again, my name is Jimmy Bates. I'm a director of solutions architecture here for HPS Merle in the Americas. Uh, today I'd like to walk you through a journey on how our everyday life is evolving, how everything about our world continues to grow more connected about, and about how here at HPE, how we support the data that represents that digital evolution for our customers, with the HPE as rural data fabric to start with, let's define that term data. The concept of that data can be simplified to a record of life's events. No matter if it's personal professional or mechanical in nature, data is just records that represent and describe what has happened, what is happening or what we think will happen. And it turns out the more complete record we have of these events, the easier it is to figure out what comes next. >>Um, I like to refer to that as the omnipotence protocol. Um, let's look at this from a personal perspective of two very different people. Um, let me introduce you to James. He's a native citizen of the digital world. He's, he's been, he's been a citizen of this, uh, an a career professional in the it world for years. He's always on always connected. He loves to get all the information he needs on a smartphone. He works constantly with analytics. He predicts what his customers need, what they want, where they are, uh, and how best to reach them. Um, he's fully embraced the use of data in his life. This is Sue SCA. She's, she's a bit of a, um, of an opposite to James. She's not yet immigrated to our digital world. She's been dealing with the changes that are prevalent in our times. And she started a new business that allows her customers, the option of, um, of expressing their personalities and the mask that they wear. She wants to make sure her customers can upload images, logos, and designs in order to deliver that customized mask, uh, to brighten their interactions with others while being safe as they go about their day. But she needs a crash course in digital and the digital journey. She's recently as, as most of us have as transitioned from an office culture to a work from home culture, and she wants to continue to grow that revenue venture on the side >>At the core of these personalities is a journey that is, that is representative common challenge that we're all facing today. Our world has been steadily shrinking as our ability to reach out to one another has steadily increased. We're all on that journey together to know more about what is happening to be connected to what our business is doing to be instantly responsive to our customer needs and to deliver that personalized service to every individual. And it as moral, we see this across every industry, the challenge of providing tailored experiences to potential customers in a connected world to provide constant information on deliveries that we requested or provide an easier commute to our destination to, to change the inventories, um, to the just-in-time arrival for our fabrications to identify quality issues in real time to alter the production of each product. So it's tailored to the request of the end user to deliver energy in, in smarter, more efficient ways, uh, without injury w while protecting the environment and to identify those, those, uh, medical emerging threats, and to deliver those personalized treatments safely. >>And at the core of all of these changes, all of these different industries is data. Um, if you look at the major technology trends, um, they've been evolving down this path for some time now, we're we're well into our cloud journey. The mobile platform world is, is now just part of our core strategies. IOT is feeding constant streams of data often over those mobile, uh, platforms. And the edge is increasingly just part of our core, all of this combined with the massive amounts of data that's becoming, becoming available through it is driving autonomous solutions with machine learning and AI. Uh, this is, this is just one aspect of this, this data journey that we're on, but for success, it's got, uh, sorry for success. It's got to be paired. Um, it's gotta be paired with action. >>Um, >>Well, when you look at the, uh, um, if we take a look at James and Cisco, right, we can start to see, um, with the investments in those actions, um, how their travel they're realizing >>Their goals, >>Services, efforts, you know, uh, focused, deliver new data-driven applications are done in new ways that are smaller in nature and kind of rapidly iterate, um, to respond to the digital needs of, of our new world, um, containerization to deploy and manage those apps anywhere in our connected world, they need to be secure we'll time streaming architecture, um, from, from the, from the beginning to allow for continual interactions with our changing customer demands and all of this, especially in our current environment, while running cost reduction initiatives. This is just the current world that, that our solutions must live in. Um, with that framework in mind, um, I'd like to take the remainder of our time and kind of walk through some of the use cases where, where we at HPE helped organizations through this journey with, with, with the ASML data fabrics, >>Let's >>Start with what's happening in the mobile world. In fact, the HPE as moral data fabric is being used by a number of companies to provide infinitely personalized experiences. In this case, it could be James could be sushi. It could be anyone that opens up their smartphone in the morning, uh, quickly checking what's transpiring in the world with a selection of curated, relative relevant articles, images, and videos provided by data-driven algorithm workloads, all that data, the logs, the recommendations, and the delivery of those recommendations are done through a variety of companies using HP as rural software, um, that provides a very personalized experience for our users. In addition, other companies monitor the service quality of those mobile devices to ensure optimize connectivity as they move throughout their day. The same is true for digital communication for that video communication, what we're doing right now, especially in these days where it's our primary method of connecting as we deal with limited physical engagements. Um, there's been a clear spike in the usage of these types of services. HPE, as Merle is helping a number of these companies deliver on real time telemetry analysis, predicting demand, latency, monitoring, user experience, and analyzing in real time, responding with autonomous adjustments to maintain pleasant experiences for all participants involved. >>Um, >>Another area, um, we're eight or HBS ML data fabric is playing a crucial role in the daily experience inside our automobiles. We invest a lot of ourselves in our cars. We expect tailored experiences that help us stay safe and connected as we move from one destination to another, in the areas of autonomous driving connected car, a number of major car companies in the world are using our data fabric to take autonomous driving to the next level where it should be effectively collecting all data from sensors and cameras, and then feeding that back into a global data fabric. So that engineers that develop cars can train next generation, future driving algorithms that make our driving experience safer and more autonomy going forward. >>Now let's take a look at a different mode of travel. Uh, the airline industry is being impaired. Varied is being impacted very differently today from, from the car companies, with our software, uh, we help airlines travel agencies, and even us as consumers deal with pricing, calculations and challenges, uh, with, um, air traffic services. We, we deal with, um, um, uh, delivering services around route predictions on time arrivals, weather patterns, and tagging and tracking luggage. We help people with flight connections and finding out what the figuring out what the best options are for your, for your travel. Uh, we collect mountains of data, secure it in a global data fabric, so it can provide, be provided back in an analyzed form with it. The stressed industry can contain some very interesting insights, provide competitive offerings and better services to us as travelers. >>This is also true for powering biometrics. At scale, we work with the biggest biometrics databases in the world, providing the back end for their enormous biometric authentication pursuit. Just to kind of give you a rough idea. A biometric authentication is done with a number of different data points from fingerprints. I re scans numerous facial features. All of these data points are captured for every individual and uploaded into the database, such that when the user is requesting services, their biometric metrics can be pooled and validated in seconds. From a scale perspective, they're onboarding 1 million people a day more than 200 million a year with a hundred percent business continuity and the options do multi-master and a global data fabric as needed ensuring that users will have no issues in securely accessing their pension payouts medical services or what other types of services. They may be guaranteed >>Pivoting >>To a very different industry. Even agriculture was being impacted in digital ways. Using HPE as well, data fabric, we help farmers become more digital. We help them predict weather patterns, optimize sea production. We even helped see producers create custom seed for very specific weather and ground conditions. We combine all of these things to help optimize production and ensure we can feed future generations. In some cases, all of these data sources collected at the edge can be provided back to insurance companies to help farmers issue claims when micro patterns affect farmers in negative ways, we all benefit from optimized farming and the HBS Modena fabric is there to assist in that journey. We provide the framework and the workload guidance to collect relevant data, analyze it and optimize food production. Our customers demonstrate the agricultural industry is most definitely my immigrating to our digital world. >>Now >>That we've got the food, we need to ship it along with everything else, all over the world, as well as offer can be found in action in many of the largest logistics companies in the world. I mean, just tracking things with greater efficiency can lead to astounding insights. What flights and ships did the package take? What Hans held it along its journey, what weather conditions did it encounter? What, what customs office did it go through and, and how much of it's requested and being delivered this along with hundreds of other telemetry points can be used to provide very accurate trade and economic predictions around what's going on with trade in the world. These data sets are being used very intensively to understand economy conditions and plan for future event consequences. We also help answer, uh, questions for shipping containers that are, that are more basic. Uh, like where is my container located at is my container still on the correct ship? Uh, surprisingly, uh, this helps cut down on those pesky little events like lost containers. >>Um, it's astounding the amount of data that's in DNA, and it's not just the pairs. It's, it's the never ending patterns found with other patterns that none of it can be fully understood unless the micro is maintained in context to the macro. You can't really understand these small patterns unless you maintain that overall understanding of the entire DNA structure to help the HVS mold data fabric can be found across every aspect of the medical field. Most recently was there providing the software framework to collect genomic sequencing, landing it in the data fabric, empowering connected availability for analysis to predict and find patterns of significance to shorten the effort it takes to identify those potential triggers and make things like vaccines become becoming available. In record time. >>Data is about people at HPE asthma. We keep people connected all around the world. We do this in a variety of ways. We we've already looked at several of the ways that that happens. We help you find data. You need, we help you get from point a to point B. We help make sure those birthday gifts show up on time. Some other interesting ways we connect people via recipes, through social platforms and online services. We help people connect to that new recipe that is unexpected, but may just be the kind of thing you need for dinner tonight at HPDs where we provide our customers with the power to deliver services that are tailored to the individual from edge to core, from containers to cloud. Many of the services you encounter everyday are delivered to you through an HV as oral global data fabric. You may not see it, but we're there in the morning in the morning when you get up and we're there in the evening. Um, when you wind down, um, at HPE as role, we make data globally available across everywhere that your business needs to go. Um, I'd like to thank everyone, uh, for the time that you've given us today. And I'd like to turn it back over and open up the floor for questions at this time, >>Jimmy, here's a question. What are the ways consumers can get started with HPS >>The fabric? Well, um, uh, there's several ways to get started, right? We, we, uh, first off we have software available that you can download that there's extensive documentation and use cases posted on our website. Um, uh, we have services that we offer, like, um, assessment services that can come in and help you assess the, the data challenges that you're having, whether you're, you're just dealing with a scale issue, a security issue, or trying to migrate to a more containerized approach. We have a services to help you come in, assess that aspect. Um, we have a getting started bundles, um, and we have, um, so there's all kinds of services that, that help you get started on your journey. So what >>Does a typical first deployment look like? >>Well, that's, that's a very, very interesting question. Um, a typical first deployment, it really kind of varies depending on where you're at in the material. Are you James? Are you, um, um, Cisco, right? It really depends on, on where you're at in your journey. Um, but a typical deployment, um, is, is, is involved. Uh, we, we like to come in, we we'd like to do workshops, really understand your specific challenges and problems so that we can determine what solutions are best for you. Um, that to take a look at when we kind of settle on that we, we, um, the first deployment, uh, is, um, there's typically, um, a deployment of, uh, a, uh, a service offering, um, w with a software to kind of get you started along the way we kind of bundle that aspect. Um, as you move forward, if you're more mature and you already have existing container solutions, you already have existing, large scale data aspects of it. Um, it's really about the specific use case of your current problem that you're dealing with. Um, every solution, um, is tailored towards the individual challenges and problems that, that each one of us are facing. >>I break, they mentioned as part of the asthma family. So how does data fabric pair with the other solutions within Israel? >>Well, so I like to say there's, um, there, there's, there's three main areas, um, from a software standpoint, um, for when you count some of our, um, offerings with the GreenLake solution, but there are, so there are really four main areas with ESMO. There's the data fabric offering, which is really focused on, on, on, on delivering that data at scale for AI ML workloads for big data workloads for containerized workloads. There is the ESMO container platform, which really solves a lot of, um, some of the same problems, but really focus more on a compute delivery, uh, and a hundred percent Kubernetes environment. We also have security offerings, um, which, which help you take in this containerized world, uh, that help you take the different aspects of, um, securing those applications. Um, so that when the application, the containerized applications move from one framework or one infrastructure from one to the other, it really helps those, the security go with those applications so that they can operate in a zero trust environment. And of course, all of this, uh, options of being available to you, where everything has a service, including the hardware through some of our GreenLake offerings. So those are kind of the areas that, uh, um, that pair with the HPE, um, data fabric, uh, when you look at the entire ESMO pro portfolio. >>Well, thanks, Jimmy really appreciate it. That's all the questions we have right now. So is there anything that you'd like to close with? >>Uh, you know, the, um, I I'm, I find it I'm very, uh, I'm honored to be here at HPE. Um, I, I really find it, it's amazing. Uh, as we work with our customers solving some really challenging problems that are core to their business, um, it's, it's always an interesting, um, interesting, um, day in the office because, uh, every problem is different because every problem is tailored to the specific challenges that our customers face. Um, while they're all will well, we will, what we went over today is a lot of the general areas and the general concepts that we're all on together in a journey, but the devil's always in the details. It's about understanding the specific challenges in the organization and, and as moral software is designed to help adapt, um, and, and empower your growth in your, in your company. So that you're focused on your business, in the complexity of delivering services across this connected world. That's what as will takes off your plate so that you don't have to worry about that. It just works, and you can focus on the things that impact your business more directly. >>Okay. Well, we really thank everyone for coming today and hope you learned, uh, an idea about how data fabric can begin to help your business with it. All of a sudden analytics, thank you for coming. Thanks.

Published Date : Mar 17 2021

SUMMARY :

Welcome everyone to a day in the life of data with HPE as well. Uh, let me go ahead and share my screen here and we'll get started. that digital evolution for our customers, with the HPE as rural data fabric to and designs in order to deliver that customized mask, uh, to brighten their interactions with others while protecting the environment and to identify those, those, uh, medical emerging threats, all of this combined with the massive amounts of data that's becoming, becoming available through it is This is just the current world that, that our solutions must live in. the service quality of those mobile devices to ensure optimize connectivity as they move a number of major car companies in the world are using our data fabric to take autonomous uh, we help airlines travel agencies, and even us as consumers deal with pricing, Just to kind of give you a rough idea. from optimized farming and the HBS Modena fabric is there to assist in that journey. and how much of it's requested and being delivered this along with hundreds of other telemetry points landing it in the data fabric, empowering connected availability for analysis to Many of the services you encounter everyday are delivered to you through What are the ways consumers can get started with HPS We have a services to help you uh, a service offering, um, w with a software to kind of get you started with the other solutions within Israel? uh, um, that pair with the HPE, um, data fabric, uh, when you look at the entire ESMO pro portfolio. That's all the questions we have right now. in the organization and, and as moral software is designed to help adapt, an idea about how data fabric can begin to help your business with it.

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Fireside Chat Innovating at Allianz Benelux with the Data Cloud


 

>>Hey, Sue, my great to see you. Welcome to the Data Cloud Summit. Super excited to have you welcome. >>Hey, Chris. Very nice to be there. Thank you for having me >>tell us a little bit about alien spending lakhs. Tell us a little bit about yourself and your role. Italy and Benelux >>aliens, Benelux zits. Basically the aliens business in the region. Belgium, Netherlands and Luxembourg. We serve the needs of the customer here by securing the future. We actually do both PNC asses. We call it properly and casualities in life investment management and health. We do retail, uh, small and medium enterprises. I am a regional chief Data and Biggs, officer for aliens. Benelux. I report directly to the regional CEO my job here in alliance to basically drive the data and analytics agenda for aliens. Vanilla, >>cinnamon. I understand you're getting your PhD in data science. It would be great for the audience to learn a little bit more about what's driving you to do that. And kind of what? What's most interesting to you about data science? A I m l >>the reason why I started to do this because there's so much relevance. Push that which is basically driving the agenda. We need to really look at the theoretical part off it as well. To kind of concrete eyes, Andi toe bring in a certain develop dependency, consistency, timelessness, etcetera. And obviously that which we're doing is very innovative. Here, Italians, monologues driven again by relevance and which is very good for the business. But the timelessness needs to also be the sustainability the scalability needs also has to be given to this particular relevance driven topic so that we don't just create superficial impact. But we create a long lasting and everlasting impact in our competitive intelligence intelligence that building against monologues. >>That's awesome. I mean, thanks for sharing that. So So I think. Cinnamon. When when you and I met back in March 1 of the big things that you were you were considering is, you know, uh, signing up with snowflake and becoming a customer. But part of that journey was convincing Ali on spent lakhs to move to the cloud in your journey. So kind of it would be great for you to explain to the audience. You know what that journey has been like. Was it hard to convince your organization moved to the cloud, What hurdles might you have seen in your journey to the cloud? >>It was not very different to any kind of a change on the kind of effort that you need to put in a change for a normal status go set up that which exists today. So, of course, in any kind of a change, your status could change or challenge that which you bring in. There is a considerable, uh, effort that you need to put in. And it's also your responsibility to basically do that because if you don't have that energy or if you don't have that commitment and you are not able to sustain the energy of the commitment that you show in the new agenda that you bring in, then probably you're not gonna be there to see the change through. Of course, it waas difficult, obviously, because, uh, there is already existing status. Go. And there we have a lot of benefits by moving to cloud, and obviously the benefits seems very interesting. But there is skepticism, and we s alliance is from a group perspective, and Benelux perspective is full of very, very clear on a point that we cannot take advantage off the data that which we have. We want to ensure that privacy is by design. Security is by design. And we give utmost care to our customer data. Um, mhm. And all of this basically brings in tow the concept off. Okay, what is it about moving to the cloud and where are we getting exposed? Where should we basically put together? A security by design privacy with some kind of concepts before we do it and etc. Are you ready? Can be ensured that we still keep the customers data A to a place where we basically can't bust. Well, those are the things that which had to be explained. A certain level of sensitization had to be created. A certain level of awareness. Uh, then the consideration part. Yeah, all of this basically takes its own cycle. >>Awesome. Thanks for sharing that. So we're super excited to call Ali on spending lakhs of customer. Now, what are you excited about with snowflake? And I know that you're you're looking at snowflake. Is this kind of data cloud and data cloud transformation project. Tell us a little bit more about, you know, What? What excites you about Snowflake? How you think you might use stuff like, um, in this kind of transformation of Ali on spending lakhs? >>I know that snowflake is brought to us as a product by you guys, but we look at snowflake is a kind off message. We are breaking down the silos. Literally. Onda. We look at snowflake as a kind often agent to do this. Uh, this is something that which is very important to understand that whatever you do with the organizational level, you still end up with a situation where you kind of reinforce the silos. But, snowflake, we have an opportunity here to even challenge that on break the data silos. Once the data silos is broke, you basically improve the find ability of data. You basically improve the understand ability of the data accessibility of the data interpret ability on everyone sees pretty much the same truth. And that's how the silos disappear. We're very, very excited about the journey that which, which we have in front of us because we're pretty new in it. In the sense that we are going toe haven't very exciting journey as we progress, we are also looking forward to see how Snowflakes road map is going to take us to the point off arrival, as I would call it in our own data revenge in >>today we live in this kind of multi cloud, multi cloud application world. What are some of the concerns you have as you transition from, you know, having stuff in a data center to using multiple clouds to using multiple tools? You know, what's what's some of the challenges you for? See having? What are the things that you're looking for from Snowflake to help you? Um, in that journey, >>there is always a reason why we basically make a change. And the reason is always mostly towards more efficiency, effectiveness and so on and so forth, right? I mean, basically, we have Catholics challenges on this. Catholic challenges can also be addressed with this move to the cloud, except but what We should be careful and should avoid us that the cost that which we have in terms of Camp X is just does not get re attributed into another cost called articulation, cost or arbitration cost. So having a multi cloud is definitely a challenge until you have a kind off orchestrator because we are doing a business here and we don't want to care about pretty much the orchestration. The are part off it on. This needs to be taken taken into account because there is this application cloud and there is this infrastructure cloud. You can have as many clothes as you want, whatever function that which is is supporting you. But that has to be encapsulate, er abstracted away from us so that we're able to focus on the business that we're here to do. And these are certain constraints that I really had as I was thinking about multi cloud or hybrid cloud and I was even focusing on how am I going toe orchestrate all of these different things Eso that you know, you kind of feel abstracted from those things. So well, those are the constraints that I think we still have toe conquer as we progress. I think we are evolving very fastly in that area. And you are the experts in that area, and you know exactly what you're doing there. But for me, what is very important is that uh, yeah, it gets abstracted away from us, and we just get the scalability that we need the elasticity that which we need the security by design the privacy by design on. Then I think this is perfect for us. >>Awesome. So? So I think a lot of customers that are listening to this are about to jump on the same journey that you're you're embarking on. What, is there a specific use case that you decided to kind of go? You know, you know, all in on Snowflake. What was the what was the kind of the initial driver for you to say? Hey, then the business driver on you saying, Hey, I'm gonna use this use case to drive transformation within within Ali and spend lakhs, >>I think virtualization, uh, it's the keep point that comes up the top of my head the moment you speak about what even did drive me to think about snowflake as an option, right? Why virtualization? Because obviously I don't want to move huge amount of data from left, right and center, because you know that when you start optimizing such a kind of an architectural, you end up creating pockets silos, which is totally against what we want to do. We want to break silos. But in the end, just because off the infrastructure needs in the computational needs, etcetera on the response rates and stuff like that, you start to create silos, bring with virtualization and especially with the performance that with Snowflake and provide us in that area. Now it seems like a possibility that we will be able to do that. I mean, it was not something that we just thought about, let's say, a few years back, but now it's definitely possible virtualization. It's one of the key points, but when you talk in the terms of use cases, we Italians monologues do not look at use cases. Actually, we look at business initiatives, so the reason why we don't look at it as use cases is because use cases used, kind off a start and stop. But we were not in the game. Off use cases were in the game off delivering future, that which our customer really wants to be secured. That's what the business we are in and that there are no use cases. There are initiatives there that which matches to the agenda for our customer. So when you start thinking about like that one of the most important things that snowflake offices is an opportunity is to obviously create on environment, so to say, on elastic scalable, uh, situation with the computer that which we need that which basically matches one on one with the agenda for our customer. So what I mean is the data warehousing on the cloud through data warehousing on the cloud is what waas on off our driving thought processes for We did not want to go and say that we will just do, uh, do Data Lake. We will just do data hub way don't belong toe religion. So to say, we basically are very opportunistic in this approach where we say we will have a data lake. We will have a data warehouse. We will have a data hub on. We will integrate it, you know, very a semantic way that which will match to the agenda of the customer and treat the customer as a sort of centric point. >>That's great. I appreciate that. So So, um, Suderman, thank you so much for for, you know, joining us today. Um, And again, thank you for your partnership. We snowflake is super excited. I'm I'm super excited Thio participate in this journey with you. Is there anything that you kind of like to let the audience know before we wrap up? >>Very happy about the way we started Toe talk. Converse. I think the proof of value as we did was a very good engagement with you guys. I mean, you guys were really there. I really appreciate the way that you took the proof of what I've worked with many other windows in terms of proof of value. But I think you had a marked difference in the way you you brought Snowflake. Tow us. Thank you so much and keep doing the good work. >>Thanks so much cinnamon for the partnership and were super pumped on, you know, making you very successful in your project. So thank you so much. >>Thank you.

Published Date : Nov 19 2020

SUMMARY :

Super excited to have you welcome. Thank you for having me Tell us a little bit about yourself and your I report directly to the regional CEO my job to learn a little bit more about what's driving you to do that. But the timelessness needs to also be the sustainability the scalability back in March 1 of the big things that you were you were considering is, you know, are not able to sustain the energy of the commitment that you show in the new agenda that you bring in, Tell us a little bit more about, you know, What? I know that snowflake is brought to us as a product by you guys, but we look at snowflake is a kind off What are some of the concerns you have as you transition from, you know, Eso that you know, you kind of feel abstracted from those things. of the initial driver for you to say? computational needs, etcetera on the response rates and stuff like that, you start to create silos, Is there anything that you kind of like to let the audience know before we wrap up? I really appreciate the way that you took the proof of what I've worked with many other windows in terms of proof Thanks so much cinnamon for the partnership and were super pumped on, you know,

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Stewart Knox V1


 

>>from around the globe. It's the Cube covering space and cybersecurity. Symposium 2020 hosted by Cal Poly. Yeah, Lauren, Welcome to the Space and Cybersecurity Symposium 2020 put on by Cal Poly and hosted with Silicon Angle acute here in Palo Alto, California for a virtual conference. Couldn't happen in person this year. I'm John for a year. Host the intersection of space and cybersecurity. I'll see critical topics, great conversations. We got a great guest here to talk about the addressing the cybersecurity workforce gap, and we have a great guest, a feature speaker. Stewart Knox, the undersecretary with California's Labor and Workforce Development Office. Stewart Thanks for joining us today. >>Thank you so much, John. Appreciate your time today and listening to a little bit of our quandaries with making sure that we have the security that's necessary for the state of California and making sure that we have the work force that is necessary for cybersecurity in space. >>Great, I'd love to get started. I got a couple questions for you, but first take a few minutes for an opening statement to set the stage. >>Sure, realizing that in California we lead the nation in much of cybersecurity based on Department of Defense contractors within the Santa California leading the nation with over $160 billion within the industry just here in California alone and having over 800,000 bus workers. Full time employment in the state of California is paramount for us to make sure that we face, um, defense manufacturers approximate 700,000 jobs that are necessary to be filled. There's over 37,000 vacancies that we know of in California, just alone in cybersecurity. And so we look forward to making sure that California Workforce Development Agency is leading the charge to make sure that we have equity in those jobs and that we are also leading in a way that brings good jobs to California and to the people of California, a good education system that is developed in a way that those skills are necessarily met for the for the employers here in California and the nation, >>One of the exciting things about California is obviously look at Silicon Valley, Hewlett Packard in the garage, storied history space. It's been a space state. Many people recognize California. You mentioned defense contractors. It's well rooted with with history, um, just breakthroughs bases, technology companies in California. And now you've got technology. This is the cybersecurity angle. Um, take >>them into >>Gets more commentary to that because that's really notable. And as the workforce changes, these two worlds are coming together, and sometimes they're in the same place. Sometimes they're not. This is super exciting and a new dynamic that's driving opportunities. Could you share, um, some color commentary on that dynamic? >>Absolutely. And you're so correct. I think in California we lead the nation in the way that we developed programs that are companies lead in the nation in so many ways around, uh, cyberspace cybersecurity, Uh, in so many different areas for which in the Silicon Valley is just, uh, such a leader in those companies are good qualified companies to do so. Obviously, one of the places we play a role is to make sure that those companies have a skilled workforce. Andi, also that the security of those, uh, systems are in place for our defense contractors onda For the theater companies, those those outlying entities that are providing such key resource is to those companies are also leading on the cutting edge for the future. Also again realizing that we need to expand our training on skills to make sure that those California companies continue to lead is just, um, a great initiative. And I think through apprenticeship training programs on By looking at our community college systems, I think that we will continue to lead the nation as we move forward. >>You know, we've had many conversations here in this symposium, virtually certainly around. The everyday life of consumer is impacted by space. You know, we get our car service Uber lyft. We have maps. We have all this technology that was born out of defense contracts and r and D that really changed generations and create a lot of great societal value. Okay, now, with space kind of on the next generation is easier to get stuff into space. The security of the systems is now gonna be not only paramount for quality of life, but defending that and the skills are needed in cybersecurity to defend that. And the gap is there. What >>can we >>do to highlight the opportunities for career paths? It used to be the day when you get a mechanical engineering degree or aerospace and you graduated. You go get a job. Not anymore. There's a variety of of of paths career wise. What can we do to highlight this career path? >>Absolutely correct. And I think it starts, you know, k through 12 system on. I know a lot of the work that you know, with this bow and other entities we're doing currently, uh, this is where we need to bring our youth into an age where they're teaching us right as we become older on the uses of technology. But it's also teaching, um, where the levels of those education can take them k through 12. But it's also looking at how the community college system links to that, and then the university system links above and beyond. But it's also engage in our employers. You know, One of the key components, obviously, is the employers player role for which we can start to develop strategies that best meet their needs quickly. I think that's one of the comments we hear the most labor agency is how we don't provide a change as fast as we should, especially in technology. You know, we buy computers today, and they're outdated. Tomorrow it's the same with the technology that's in those computers is that those students are going to be the leaders within that to really develop how those structures are in place. S O. K. Through 12 is probably primary place to start, but also continuing. That passed the K 12 system and I bring up the employers and I bring them up in a way, because many times when we've had conversations with employers around what their skills needs were and how do we develop those better? One of the pieces that of that that I think is really should be recognized that many times they recognized that they wanted a four year degree, potentially or five year, six year degree. But then, when we really looked at the skill sets, someone coming out of the community college system could meet those skill sets. And I think we need to have those conversations to make sure not that they shouldn't be continue their education. They absolutely should. Uh, but how do we get those skill sets built into this into 12 plus the two year plus the four year person? >>You know, I love the democratization of these new skills because again. There's no pattern matching because they weren't around before, right? So you gotta look at the exposure to your point K through 12 exposure. But then there's an exploration piece of whether it's community, college or whatever progression. And sometimes it's nonlinear, right? I mean, people are learning different ways, combining the exposure and the exploration. That's a big topic. Can you share your view on this because this now opens up mawr doors for people choice. You got new avenues. You got online clock and get a cloud computing degree now from Amazon and walk in and help. I could be, you know, security clearance, possibly in in college. So you know you get exposure. Is there certain things you see? Is it early on middle school? And then I'll see the exploration Those air two important concepts. Can you unpack that a little bit exposure and exploration of skills? >>Absolutely. And I think this takes place, you know, not only in in the K 12 because somebody takes place in our community colleges and universities is that that connection with those employers is such a key component that if there's a way we could build in internships where experiences what we call on the job training programs apprenticeship training pre apprenticeship training programs into a design where those students at all levels are getting an exposure to the opportunities within the Space and Cybersecurity Avenue. I think that right there alone will start to solve a problem of having 37 plus 1000 openings at any one time in California. Also, I get that there's there's a burden on employers. Thio do that, and I think that's a piece that we have to acknowledge. And I think that's where education to play a larger role That's a place we had. Labor, Workforce, Development Agency, player role With our apprenticeship training programs are pre apprenticeship training programs. I could go on all day of all of our training programs that we have within the state of California. Many of the list of your partners on this endeavor are partners with Employment Training Panel, which I used to be the director of the Brown administration of um, That program alone does incumbent worker training on DSO. That also is an exposure place where ah worker, maybe, you know, you know, use the old adage of sweeping the floors one day and potentially, you know, running a large portion of the business, you know, within years. But it's that exposure that that employee gets through training programs on band. Acknowledging those skill sets and where their opportunities are, is what's valid and important. I think that's where our students we need to play a larger role in the K 12. That's a really thio Get that pushed out there. >>It's funny here in California you're the robotics clubs in high school or like a varsity sport. You're seeing kids exposed early on with programming. But you know, this whole topic of cybersecurity in space intersection around workforce and the gaps and skills is not just for the young. Certainly the young generations gotta be exposed to the what the careers could be and what the possible jobs and societal impact and contributions what they could be. But also it's people who are already out there. You know, you have retraining re Skilling is plays an important role. I know you guys do a lot of thinking on this is the under secretary. You have to look at this because you know you don't wanna have a label old and antiquated um systems. And then a lot of them are, and they're evolving and they're being modernized by digital transformation. So what does the role of retraining and skill development these programs play? Can you share what you guys are working on in your vision for that? >>Absolutely. That's a great question. And I think that is where we play a large role, obviously in California and with Kobe, 19 is we're faced with today that we've never seen before, at least in my 27 years of running program. Similar Thio, of course, in economic development, we're having such a large number of people displaced currently that it's unprecedented with unemployment rates to where we are. We're really looking at How do we take? And we're also going to see industries not return to the level for which they stood at one point in time. Uh, you know, entertainment industries, restaurants, all the alike, uh, really looking at how do we move people from those jobs that were middle skill jobs, topper skilled jobs? But the pay points maybe weren't great, potentially, and there's an opportunity for us to skill people into jobs that are there today. It may take training, obviously, but we have dollars to do that generally, especially within our K 12 and are que 14 systems and our universities. But we really wanna look at where those skill sets are are at currently. And we want to take people from that point in time where they said today, and try to give them that exposure to your point. Earlier question is, how do we get them exposed to a system for which there are job means that pay well with benefit packages with companies that care about their employees? Because that's what our goal is. >>You know. You know, I don't know if you have some visibility on this or ah opinion, but one observation that I've had and talking to whether it's a commercial or public sector is that with co vid uh, there have been a lot of awareness of the situation. We're adequately prepared. There's, um, readiness. But as everyone kind of deals with it, they're also starting to think about what to do. Post covert as we come out of it, Ah, growth strategy for a company or someone's career, um, people starting to have that on the top of their minds So I have to ask you, Is there anything that you see that they say? Okay, certain areas, maybe not doubling down on other areas. We're gonna double down on because we've seen some best practices on a trajectory of value for coming out of co vid with, you know, well, armed skills or certain things because you because that's what a lot of people are thinking right now. It's probably cyber is I mean, how many jobs are open? So you got well, that that's kind of maybe not something double down on here are areas we see that are working. Can you share your current visibility to that dynamic? >>Absolutely. Another great question. One of the key components that we look at Labor Workforce Development Agency. And so look at industries and growth modes and ones that are in decline boats. Now Kobe has changed that greatly. We were in a growth rate for last 78 years. We saw almost every industry might miss a few. You know that we're all in growth in one way or enough, obviously, that has changed. Our landscape is completely different than we saw 67 months ago. So today we're looking at cybersecurity, obviously with 30 plus 1000 jobs cos we're looking at Defense Department contractor is obviously with federal government contracts. We were looking at the supply chains within those we're looking at. Health care, which has always been one, obviously are large one of our large entities that has has grown over the years. But it's also changed with covered 19. We're looking at the way protective equipment is manufactured in the way that that will continue to grow over time. We're looking at the service industry. I mean, it will come back, but it won't come back the way we've seen it, probably in the past, but where the opportunities that we develop programs that we're making sure that the skill sets of those folks are transferrable to other industries with one of the issues that we face constant labor and were forced moment programs is understanding that over the period of time, especially in today's world again, with technology that people skill sets way, don't see is my Parents Day that you worked at a job for 45 years and you retired out of one job. Potentially, that is, that's been gone for 25 years, but now, at the pace for which we're seeing systems change. This is going to continue to amp up. I will stay youth of today. My 12 year old nephew is in the room next door to me on a classroom right now online. And so you know, there. It's a totally different atmosphere, and he's, you know, enjoying actually being in helping learning from on all online system. I would not have been able to learn that way, but I think we do see through the K Through 12 system where we're moving, um, people's interest will change, and I think that they will start to see things in a different way than we have in the past. They were forced systems. We are an old system been around since the thirties. Some even will say prior to the thirties came out of the Great Depression in some ways, and that system we have to change the way we develop our programs are should not be constant, and it should be an evolving system. >>It's interesting a lot of the conversation between the private and public partnerships and industry. You're seeing an agile mind set where it's a growth mindset. It's also reality based mindset and certainly space kind of forces. This conversation with cyber security of being faster, faster, more relevant, more modern. You mentioned some of those points, and with co vid impact the workforce development, it's certainly going to put a lot of pressure on faster learning. And then you mentioned online learning. This has become a big thing. It's not just putting education online per se. There's new touch points. You know you got APS, you got digital. This digital transformation is also accelerating. How do you guys view the workforce development? Because it's going to be open. It's gonna be evolving. There's new data coming in, and maybe kids don't want to stare at a video conference. Is there some game aspect to it? Is there how do you integrate thes new things that are coming really fast? And it's happening kind of in real time in front of our eyes. So I love to get your thoughts on how you guys see that, because it will certainly impact their ability to compete for jobs and or to itself learn. >>I think one of the key components of California's our innovation right and So I think one of the things that we pride ourselves in California is around that, um that said, that is the piece that I think the Silicon Valley and there's many areas in California that that have done the same, um, or trying to do the same, at least in their economy, is to build in innovation. And I think that's part of the K through 12 system with our with our our state universities and our UCS is to be able to bridge that. I think that you we see that within universities, um, that really instill an innovative approach to teaching but also instill innovation within their students. I'm not sure there yet with our fully with our K 12 system. And I think that's a place that either our community colleges could be a bridge, too, as well. Eso that's one component of workforce development I think that we look at as being a key. A key piece you brought up something that's really interesting to me is when you talk about agile on day, one of the things that even in state government on this, is gonna be shocking to you. But we have not been an agile system, Aziz. Well, I think one of the things that the Newsome administration Governor Newsom's administration has brought is. And when I talk about agile systems, I actually mean agile systems. We've gone from Kobol Systems, which are old and clunky, still operating. But at the same time, we're looking at upgrading all of our systems in a way that even our technology in the state of California should be matching the technology that our great state has within our our state. So, um, there in lies. It's also challenges of finding the qualified staff that we need in the state of California for all of our systems and servers and everything that we have. Um, currently. So you know, not only are we looking at external users, users of labor, workforce development, but we're looking at internal users that the way we redevelop our systems so that we are more agile in two different ways. >>You just got me. I triggered with COBOL. I programmed in the eighties with COBOL is only one credit lab in college. Never touched it again. Thank God. But this. But this >>is the >>benefit of cloud computing. I think this is at the heart, and this is the undertone of the conference and symposium is cloud computing. You can you can actually leverage existing resource is whether there legacy systems because they are running. They're doing a great job, and they do a certain work load extremely well. Doesn't make sense to replace what does a job, but you can integrate it in this. What cloud does this is Opening up? Can mawr more and more capabilities and workloads? This is kind of the space industry is pointing to when they say we need people that can code. And that could solve data problems. Not just a computer scientist, but a large range of people. Creative, um, data, science, everything. How does California's workforce solve the needs of America's space industry? This is because it's a space state. How do you see that? Let your workforce meeting those needs. >>Yeah, I think I think it's an investment. Obviously, it's an investment on our part. It's an investment with our college partners. It's an investment from our K 12 system to make sure that that we are allocating dollars in a way through meeting the demand of industry Onda, we do look at industry specific around there needs. Obviously, there's a large one. We wanna be very receptive and work with our employers and our employee groups to make sure that we need that demand. I think it's putting our money where our mouth is and and designing and working with employer groups to make sure that the training meets their needs. Um, it's also working with our employer groups to make sure that the employees are taken care of. That equity is built within the systems, Um, that we keep people employed in California on their able to afford a home, and they're able to afford a life here in California. But it's also again, and I brought up the innovation component. I think it's building an innovation within systems for which they are employers but are also our incoming employees are incumbent workers. And you brought this up earlier. People that already employed and people that are unemployed currently with the skill set that might match up, is how do we bridge those folks into employment that they maybe have not thought about. We have a whole career network of systems out throughout the city, California with the Americans job Centers of California on day will be working, and they already are working with a lot of dislocated workers on day. One of the key components of that is to really look at how do we, um, take what their current skills that might be and then expose them to a system for which we have 37 plus 1000 job openings to Andi? How do we actually get those books employed? It's paying for potentially through those that local Workforce Innovation Opportunity Act, funding for Americans job centers, um, to pay for some on the job, training it Z to be able to pay for work experiences. It's to be able to pay for internships for students, um, to get that opportunity with our employers and also partner with our employers that they're paying obviously a percentage of that, too. >>You know, one of the things I've observed over my, um, career 54 times around the sun is you know, in the old days when I was in college in school, you had career people have longer jobs, as you mentioned. Not like that anymore. But also I knew someone I'm gonna be in line to get that job, maybe nepotism or things of that nature. Now the jobs have no historical thing or someone worked longer in a job and has more seniority. Ah, >>lot of these >>jobs. Stewart don't HAVA requirements like no one's done them before. So the ability for someone who, um, is jumping in either from any college, there's no riel. It's all level set. It's like complete upside down script here. It's not like, Oh, I went to school. Therefore I get the job you could be Anyone could walk into these careers because the jobs air so new. So it's not where you came from or what school you went to or your nationality or gender. The jobs have been democratized. They're not discriminating against people with skills. So this opens up mawr. How >>do you >>see that? Because this really is an opportunity for this next generation to be more diverse and to be mawr contributed because diversity brings expertise and different perspectives. Your thoughts on that? >>Absolutely. And that was one of the things we welcome. Obviously we want to make sure that that everybody is treated equally and that the employers view everyone as employer employer of choice but an employee of choices. Well, we've also been looking at, as I mentioned before on the COVITZ situation, looking at ways that books that are maybe any stuck in jobs that are don't have a huge career pathway or they don't have a pathway out of poverty. I mean, we have a lot of working for people in the state of California, Um, that may now do to cope and lost their employment. Uh, this, you know, Let's let's turn back to the old, you know? Let's try, eliminate, eliminate, eliminate. How do we take those folks and get them employed into jobs that do have a good career pathway? And it's not about just who you knew or who you might have an in with to get that job. It is based on skills, I think, though that said there we need to have a better way to actually match those jobs up with those employers. And I think those are the long, ongoing conversations with those employer groups to make sure that one that they see those skill sets is valid and important. Um, they're helping design this crew sets with us, eh? So that they do match up and that were quickly matching up those close skills. That so that we're not training people for yesterday skills. >>I think the employer angles super important, but also the educators as well. One of the things that was asked in another question by the gas they they said. She said The real question to ask is, how early do you start exposing the next generation? You mentioned K through 12. Do you have any data or insight into or intuition or best practice of where that insertion point is without exposure? Point is, is that middle school is a elementary, obviously high school. Once you're in high school, you got your training. Wheels are off, you're off to the races. But is there a best practice? What's your thoughts? Stewart On exposure level to these kinds of new cyber and technical careers? >>Sure, absolutely. I I would say kindergarten. We San Bernardino has a program that they've been running for a little bit of time, and they're exposing students K through 12 but really starting in kindergarten. One is the exposure Thio. What a job Looks like Andi actually have. I've gone down to that local area and I've had three opportunity to see you know, second graders in a health care facility, Basically that they have on campus, built in on dear going from one workstation as a second grader, Uh, looking at what those skills would be and what that job would entail from a nurse to a Dr Teoh physician's assistant in really looking at what that is. Um you know, obviously they're not getting the training that the doctor gets, but they are getting the exposure of what that would be. Andi, I think that is amazing. And I think it's the right place to start. Um, it was really interesting because I left. This was pre covet, but I jumped on the plane to come back up north. I was thinking to myself, How do we get this to all school district in California, where we see that opportunity, um, to expose jobs and skill sets to kids throughout the system and develop the skill set so that they do understand that they have an opportunity. >>We're here at Cal Poly Space and Cybersecurity Symposium. We have educators. We have, um, students. We have industry and employers and government together. What's your advice to them all watching and listening about the future of work. Let's work force. What can people do? What do you think you're enabling? What can maybe the private sector help with And what are you trying to do? Can you share your thoughts on that? Because we have a range from the dorm room to the boardroom here at this event. Love to get your thoughts on the workforce development view of this. >>Yeah, absolutely. I think that's the mix. I mean, I think it's going to take industry to lead A in a lot of ways, in terms of understanding what their needs are and what their needs are today and what they will be tomorrow. I think it takes education, toe listen, and to understand and labor and workforce development also listen and understand what those needs will look like. And then how do we move systems? How do we move systems quickly? How do we move systems in a way that meets those needs? How do we, uh, put money into systems where the most need is, but also looking at trends? What is that trend going to look like in two years? What does that train gonna look like in five years. But that's again listening to those employers. Um, it's also the music community based organizations. I think, obviously some of our best students are also linked to CBS. And one way or another, it may be for services. It maybe for, uh, faith based. It may be anything, but I think we also need to bring in the CBS is Well, ah, lot of outreach goes through those systems in conjunction with, but I think that's the key component is to make sure that our employers are heard on. But they sit at the table like you said to the boardroom of understanding, and I think bringing students into that so that they get a true understanding of what that looks like a well, um, is a key piece of this. >>So one of the things I want to bring up with you is maybe a bit more about the research side of it. But, um, John Markoff, who was a former New York Times reporter with author of the book What the Dormouse, said It was a book about the counter culture of the sixties and the computer revolution, and really there was about how government defense spending drove the computer revolution that we now saw with Apple and PC, and then the rest is history in California has really participated. Stanford, uh, Berkeley and the University of California School system and all the education community colleges around it. That moment, the enablement. And now you're seeing space kind of bringing that that are a lot of research coming in and you eat a lot of billionaires putting money in. You got employers playing a role. You have this new focus space systems, cybersecurity, defending and making it open and and not congested and peaceful is going to enable quickly new inflection points for opportunities. E want to get your thoughts on that? Because California is participate in drove these revolutions that created massive value This next wave seems to be coming upon us. >>Yeah, absolutely. And again, Nazis covered again as too much of ah starting point to this. But I think that is also an opportunity to actually, because I think one of the things that we were seeing seven months ago was a skill shortage, and we still see the skills shortage, obviously. But I think a key piece to that is we saw people shortage. Not only was it skills shortage, but we didn't have enough people really to fill positions in addition to and I think that people also felt they were already paying the bills and they were making ends meet and they didn't have the opportunities. Thio get additional skills This again is where we're looking at. You know that our world has changed. It changed in the sixties based on what you're you're just expressing in terms of California leading the way. Let's like California lead the way again in developing a system from which labor, workforce development with our universities are, you know, are amazing universities and community college system and structure of how do we get students back into school? You know, a lot of graduates may already have a degree, but how do they now take a skill so that they already have and develop that further with the idea that they those jobs have changed? Whales have a lot of folks that don't have a degree, and that's okay. But how do we make that connection to a system that may have failed? Ah, lot of our people over the years, um, and our students who didn't make it through the school system. How do we develop in adult training school? How do we develop contract education through our community college system with our employer sets that we developed cohorts within those systems of of workers that have amazing talents and abilities to start to fill these needs? And I think that's the key components of hearing Agency, Labor, Workforce Development Agency. We work with our community. Colleges are UCS in our state universities t develop and figure that piece out, and I think it is our opportunity for the future. >>That's such a great point. I want to call that out This whole opportunity to retrain people that are out there because these air new jobs, I think that's a huge opportunity, and and I hope you keep building and investing in those programs. That's that's really worth calling out. Thank you for doing that. And, yeah, it's a great opportunity. Thes jobs they pay well to cyber security is a good job, and you don't really need to have that classical degree. You can learn pretty quickly if you're smart. So again, great call out there question for you on geography, Um, mentioned co vid we're talking about Covic. Virtualization were virtual with this conference. We couldn't be in person. People are learning virtually, but people are starting to relocate virtually. And so one observation that I have is the space state that California is there space clusters of areas where space people hang out or space spaces and whatnot. Then you got, like, the tech community cybersecurity market. You know, Silicon Valley is a talented in these hubs, and sometimes cyber is not always in the same hubs of space. Maybe Silicon Valley has some space here, Um, and some cyber. But that's not generally the case. This is an opportunity potentially to intersect. What's your thoughts on this? Because this is This is something that we're seeing where your space has historical, you know, geography ease. Now, with borderless communication, the work boat is not so much. You have to move the space area. You know what I'm saying? So okay. What's your thoughts on this? How do you guys look at this? Is on your radar On how you're viewing this this dynamic? >>It's absolute on our radar, Like you said, you know, here we are talking virtually on and, you know, 75% of all of our staff currently in some of our department that 80% of our staff are now virtual. Um you know, seven months ago, uh, we were not were government again being slow move, we quickly transitioned. Obviously, Thio being able to have a tele work capacity. We know employers move probably even quickly, more quickly than we did, but we see that as an opportunity for our rural areas. Are Central Valley are north state um, inland Empire that you're absolutely correct. I mean, if you didn't move to a city or to a location for which these jobs were really housed, um, you didn't have an opportunity like you do today. I think that's a piece that we really need to work with our education partners on of to be able to see how much this has changed. Labor agency absolutely recognizes this. We are investing funding in the Central Valley. We're investing funding in the North State and empire to really look a youth populations of how the new capacity that we have today is gonna be utilized for the future for employers. But we also have to engage our universities around. This is well, but mostly are employers. I know that they're already very well aware. I know that a lot of our large employers with, um, Silicon Valley have already done their doing almost 100% tele work policies. Um, but the affordability toe live in rural areas in California. Also, it enables us to have, ah, way thio make products more affordable is, well, potentially in the future. But we want to keep California businesses healthy and whole in California. Of course, on that's another way we can We can expand and keep California home to our 40 plus million people, >>most to a great, great work. And congratulations for doing such a great job. Keep it up. I gotta ask about the governor. I've been following his career since he's been office. A za political figure. Um, he's progressive. He's cutting edge. He likes toe rock the boat a little bit here and there, but he's also pragmatic. Um, you're starting to see government workers starting to get more of a tech vibe. Um um just curious from your perspective. How does the governor look at? I mean, the old, almost the old guard. But like you know, used to be. You become a lawyer, become a lawmaker Now a tech savvy lawmaker is a premium candidates, a premium person in government, you know, knowing what COBOL is. A start. I mean, these are the things. As we transform and evolve our society, we need thinkers who can figure out which side the streets, self driving cars go on. I mean, who does that? I mean, it's a whole another generation off thinking. How does the Governor how do you see this developing? Because this is the challenge for society. How does California lead? How do you guys talk about the leadership vision of Why California and how will you lead the future? >>Absolutely no governor that I'm aware of that I've been around for 26 27 years of workforce development has led with an innovation background, as this governor has a special around technology and the use of technology. Uh, you know, he's read a book about the use of technology when he was lieutenant governor, and I think it's really important for him that we, as his his staff are also on the leading edge of technology. I brought a badge. I'll systems. Earlier, when I was under the Brown administration, we had moved to where I was at a time employment training panel. We moved to an agile system and deported that one of the first within within the state to do that and coming off of an old legacy system that was an antique. Um, I will say it is challenging. It's challenging on a lot of levels. Mostly the skill sets that are folks have sometimes are not open to a new, agile system to an open source system is also an issue in government. But this governor, absolutely. I mean, he has established three Office of Digital Innovation, which is part of California and department technology, Um, in partnership with and that just shows how much he wants. Thio push our limits to make sure that we are meeting the needs of Californians. But it's also looking at, you know, Silicon Valley being at the heart of our state. How do we best utilize systems that already there? How do we better utilize the talent from those those folks is well, we don't always pay as well as they dio in the state. But we do have great benefit packages. Everybody does eso If anybody's looking for a job, we're always looking for technology. Folks is well on DSO I would say that this governor, absolute leads in terms of making sure that we will be on cutting edge of technology for the nation, >>you know, and, you know, talk about pay. I mean, I know it's expensive to live in some parts of California, but there's a huge young population that wants a mission driven job and serving, um, government for the governments. Awesome. Ah, final parting question for you, Stuart, is, as you look at, um, workforce. Ah, lot of people are passionate about this, and it's, you know, you you can't go anywhere without people saying, You know, we got to do education this way and that way there's an opinion everywhere you go. Cybersecurity is a little bit peaked and focused, but there are people who are paying attention to education. So I have to ask you, what creative ways can people get involved and contribute to workforce development? Whether it's stem underrepresented minorities, people are looking for new, innovative ways to contribute. What advice would you give these people who have the passion to contribute to the next cyber workforce. >>Yeah, I appreciate that question, because I think is one of the key components. But my secretary, Julie Sue, secretary of Labor and Workforce Development Agency, talks about often, and a couple of us always have these conversations around. One is getting people with that passion to work in government one or on. I brought it up community based organizations. I think I think so many times, um, that we didn't work with our CBS to the level of in government we should. This administration is very big on working with CBS and philanthropy groups to make sure that thing engagement those entities are at the highest level. So I would say, You know, students have opportunities. Thio also engage with local CBS and be that mission what their values really drives them towards Andi. That gives them a couple of things to do right. One is to look at what ways that we're helping society in one way or another through the organizations, but it also links them thio their own mission and how they could develop those skills around that. But I think the other piece to that is in a lot of these companies that you are working with and that we work with have their own foundations. So those foundations are amazing. We work with them now, especially in the new administration. More than we ever have, these foundations are really starting to help develop are strategies. My secretary works with a large number of foundations already. Andi, when we do is well in terms of strategy, really looking at, how do we develop young people's attitudes towards the future but also skills towards the future? >>Well, you got a pressure cooker of a job. I know how hard it is. I know you're working hard, appreciate you what you do and and we wish you the best of luck. Thank you for sharing this great insight on workforce development. And you guys working hard. Thank you for what you do. Appreciate it. >>Thank you so much. Thistle's >>three cube coverage and co production of the space and cybersecurity supposed in 2020 Cal Poly. I'm John for with silicon angle dot com and the Cube. Thanks for watching

Published Date : Oct 1 2020

SUMMARY :

We got a great guest here to talk about the addressing the cybersecurity workforce sure that we have the work force that is necessary for cybersecurity in space. the stage. leading the charge to make sure that we have equity in those jobs and that we are One of the exciting things about California is obviously look at Silicon Valley, Hewlett Packard in the garage, And as the workforce changes, I think that we will continue to lead the nation as we move forward. of life, but defending that and the skills are needed in cybersecurity to defend that. What can we do to highlight this career path? I know a lot of the work that you know, with this bow and other entities we're doing currently, I could be, you know, security clearance, possibly in in is such a key component that if there's a way we could build in internships where experiences I know you guys do a lot of thinking on this is the under secretary. And I think that is where we play a large role, obviously in California and with Kobe, but one observation that I've had and talking to whether it's a commercial or public sector is One of the key components that we look at Labor Workforce Development Agency. It's interesting a lot of the conversation between the private and public partnerships and industry. challenges of finding the qualified staff that we need in the state of California I programmed in the eighties with COBOL is only one credit lab in This is kind of the space industry is pointing to when they say we need people that can code. One of the key components of that is to really look at how do we, um, take what their current skills around the sun is you know, in the old days when I was in college in school, Therefore I get the job you could be Anyone could walk into Because this really is an opportunity for this next generation to be more diverse and And I think those are the long, ongoing conversations with those employer groups to make sure One of the things that was asked And I think it's the right place to start. What can maybe the private sector help with And what are you trying to do? I mean, I think it's going to take industry to lead So one of the things I want to bring up with you is maybe a bit more about the research side of it. But I think a key piece to that is we saw And so one observation that I have is the space state that California is there I think that's a piece that we really need to work with our education partners on of How does the Governor how do you see this developing? But it's also looking at, you know, You know, we got to do education this way and that way there's an opinion everywhere you go. But I think the other piece to that is in a lot of these companies that you are working with and that we work And you guys working hard. Thank you so much. I'm John for with silicon angle dot com and the Cube.

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Sheng Liang, Rancher Labs & Murli Thirumale, Portworx | KubeCon + CloudNativeCon Europe - Virtual


 

>>from around the globe. It's the Cube with coverage of Coop con and cloud, native con Europe 2020 Virtual brought to you by Red Hat, The Cloud Native Computing Foundation and its ecosystem partners >>Welcome back. This is the Cube coverage of Cube Con Cloud, native con, the European show for 2020. I'm your host to Minuteman. And when we talk about the container world, we talk about what's happening in cloud. Native storage has been one of those sticking points. One of those things that you know has been challenging, that we've been looking to mature and really happy to welcome back to the program two of our cube alumni to give us the update on the state of storage for the container world. Both of them are oh, founders and CEOs. First of all, we have Xiang Yang from Rancher Labs, of course, was recently acquired by Sue Save it and the intention to acquire on and also joining us from early the relay. Who is with port works? Shang Amerli. Thanks so much for joining us. Thank you. Thank you. Alright. So early. I actually I'm going to start with you just cause you know we've seen, you know, a couple of waves of companies working on storage. In this environment, we know storage is difficult. Um, And when we change how we're building things, there's architectural things that can happen. Eso maybe if you could just give us a snapshot, you know, Port works, you know, was created to help unpack this. You know, straight on here in 2020 you know, where you see things in the overall kind of computer storage landscape? >>Absolutely. Still, before I kind of jump into port works. I just want to take a minute to publicly congratulate the the whole rancher team, and and Shang and Shannon And will China have known those folks for a while there? They're kind of true entrepreneurs. They represent the serial entrepreneur spirit that that so many folks know in the valley, and so, you know, great outcome for them. We're very happy for them and ah, big congrats and shout out to the whole team. What works is is a little over five years old, and we've been kind of right from the inception of the company recognized that to put containers in production, you're gonna have to solve, not just the orchestration problem. But the issue of storage and data orchestration and so in a natural kubernetes orchestrates containers and what works orchestrates storage and data. And more specifically, by doing that, what we enable is enterprises to be able to take APS that are containerized into production at scale and and have high availability. Disaster recovery, backup all of the things that for decades I t has had to do and has done to support application, reliability and availability. But essentially we're doing it for purpose with the purpose build solution for containerized workloads. >>Alright, shaming. Of course, storage is a piece of the overall puzzle that that ranchers trying to help with. Maybe if you could just refresh our audience on Longhorn, which your organization has its open source. It's now being managed by the CN. CF is my understanding. So help us bring Longhorn into the discussion >>thanks to. So I'm really glad to be here. We've I think rancher and port work started about the same time, and we started with a slightly different focus. More is exactly right to get containers going, you really need both so that the computer angle orchestrating containers as well as orchestrating the storage and the data. So rancher started with, ah, it's slightly stronger focus on orchestrating containers themselves, but pretty quickly, we realized, as adoption of containers grow, we really need it to be able to handle ah, storage feather. And like any new technology, you know, uh, Kubernetes and containers created some interesting new requirements and opportunities, and at the time, really, they weren't. Ah, a lot of good technologies available, you know, technologies like rook and SEF at the time was very, very premature, I think, Ah, the You know, we actually early on try to incorporate ah, the cluster technology. And it was just it was just not easy. And And at the time I think port Works was, ah, very busy developing. Ah, what turned out to be there flagship product, which we end up, end up, uh, partnering very, very closely. But but early on, we really had no choice but to start developing our own storage technology. So Long horn. As a piece of container storage technology, it's actually almost as oh, there's rancher itself. When about funding engineers, we hired he he ended up, you know, working on it and Then over the years, you know the focus shift that I think the original version was written in C plus plus, and over the years it's now being completely re written in Golan. It was originally written more for Docker workload. Now, of course, everything is kubernetes centric. And last year we you know, we we decided to donate the Longhorn Open Source project to CN CF. And now it's a CN CF sandbox project, and the adoption is just growing really quickly. And just earlier this year, we we finally ah decided to we're ready to offer a commercial support for it. So So that's that's where rancher is. And with longhorn and container storage technology. >>Yeah, it has been really interesting to watch in this ecosystem. A couple of years ago, one of the Q con shows I was talking to people coming out of the Believe It was the Sigs, the special interest group for storage, and it was just like, Wow, it was heated. Words were, you know, back and forth. There's not a lot of agreement there. Anybody that knows the storage industry knows that you know standards in various ways of doing things often are contentious and there's there's differences of opinion. Look at the storage industry. You know, there's a reason why there's so many different solutions out there. So maybe it love to hear from early. From your standpoint, things are coming to get a little bit more. There are still a number of options out there. So you know, why is this kind of coop petition? I actually good for the industry? >>Yeah, I think this is a classic example of Coop petition. Right? Let's let's start with the cooperation part right? The first part of time the you know, the early days of CN, CF, and even sort of the Google Communities team, I think, was really very focused on compute and and subsequent years. In the last 34 years, there's been a greater attention to making the whole stack works, because that's what it's going to take to take a the enterprise class production and put it in, you know, enterprise class application and put it in production. So extensions like C and I for networking and CS I container storage interface. We're kind of put together by a working group and and ah ah you know both both in the CN CF, but also within the kubernetes Google community. That's you talked about six storage as an example. And, you know, as always happens, right? Like it It looks a little bit in the early days. Like like a polo game, right where folks are really? Ah, you know, seemingly, uh, you know, working with each other on on top of the pool. But underneath they're kicking each other furiously. But that was a long time back, and we've graduated from then into really cooperating. And I think it's something we should all be proud of. Where now the CS I interface is really a A really very, very strong and complete solution tow, allowing communities to orchestrate storage and data. So it's really strengthened both communities and the kubernetes ecosystem. Now the competition part. Let's kind of spend. I want to spend a couple of minutes on that too, right? Um, you know, one of the classic things that people sometimes confuse is the difference between an overlay and an interface. CSC is wonderful because it defines how the two layers off essentially kind of old style storage. You know, whether it's a san or ah cloud, elastic storage bucket or all of those interact with community. So the the definition of that interface kind of lay down some rules and parameters for how that interaction should happen. However, you still always need an overlay like Port Works that that actually drives that interface and enables Kubernetes to actually manage that storage. And that's where the competition is. And, you know, she mentioned stuff and bluster and rook and kind of derivatives of those. And I think those have been around really venerable and and really excellent products for born in a different era for a different time open stack, object storage and all of that not really meant for kind of primary workloads. And they've been they've been trying to be adapted for, for for us, for this kind of workload. Port Works is really a built from right from the inception to be designed for communities and for kubernetes workloads at enterprise scale. And so I think, you know, as I as I look at the landscape, we welcome the fact that there are so many more people acknowledging that there is a vital need for data orchestration on kubernetes right, that that's why everybody and their brother now has a CS I interface. However, I think there's a big difference between having an interface. This is actually having the software that provides the functionality for H. A, D R. And and for backup, as as the kind of life cycle matures and doing it not just at scale, but in a way that allows kind of really significant removal or reduction off the storage admin role and replaces it with self service that is fully automated within communities. Yeah, if I >>can, you know, add something that that I completely agree. I mean, over the Longhorns been around for a long time. Like I said, I'm really happy that over the years it hasn't really impacted our wonderful collaborative partnership with what works. I mean, Poll works has always been one of our premier partners. We have a lot of, ah, common customers in this fight. I know these guys rave about what works. I don't think they'll ever get out for works. Ah, home or not? Uh huh. Exactly. Like Morissette, you know, in the in the storage space, there's interface, which a lot of different implementations can plugging, and that's kind of how rancher works. So we always tell people Rancher works with three types of storage implementations. One is let we call legacy storage. You know, your netapp, your DMC, your pure storage and those are really solid. But they were not suddenly not designed to work with containers to start with, but it doesn't matter. They've all written CS I interfaces that would enable containers to take advantage of. The second type is some of the cloud a block storage or file storage services like EBS, GFS, Google Cloud storage and support for these storage back and the CS I drivers practically come with kubernetes itself, so those are very well supported. But there's still a huge amount of opportunities for the third type of you know, we call container Native Storage. So that is where Port Works and the Longhorn and other solutions like open EBS storage OS. All these guys fitting is a very vibrant ecosystem of innovation going on there. So those solutions are able to create basically reliable storage from scratch. You know, when you from from just local disks and they're actually also able to add a lot of value on top of whatever traditional or cloud based, persistent storage you already have. So so the whole system, the whole ecosystem, is developing very quickly. A lot of these solutions work with each other, and I think to me it's really less of a competition or even Coop petition. It's really more off raising the bar for for the capabilities so that we can accelerate the amount of workload that's been moved onto this wonderful kubernetes platform in the end of the benefit. Everyone, >>Well, I appreciate you both laying out some of the options, you know, showing just a quick follow up on that. I think back if you want. 15 years ago was often okay. I'm using my GMC for my block. I'm using my netapp for the file. I'm wondering in the cloud native space, if we expect that you might have multiple different data engine types in there you mentioned you know, I might want port works for my high performance. You said open EBS, very popular in the last CN CF survey might be another one there. So is do we think some of it is just kind of repeating itself that storage is not monolithic and in a micro service architecture. You know, different environments need different storage requirements. >>Yeah, I mean quick. I love to hear more is view as well, especially about you know, about how the ecosystem is developing. But from my perspective, just just the range of capabilities that's now we expect out of storage vendors or data management vendors is just increased tremendously. You know, in the old days, if you can store blocks to object store file, that's it. Right. So now it's this is just table stakes. Then then what comes after that? There will be 345 additional layers of requirements come all the way from backup, restore the our search indexing analytics. So I really think all of this potentially off or in the in the bucket of the storage ecosystem, and I just can't wait to see how this stuff will play out. I think we're still very, very early stages, and and there, you know what? What, what what containers did is they made fundamentally the workload portable, but the data itself still holds a lot of gravity. And then just so much work to do to leverage the fundamental work load portability. Marry that with some form of universal data management or data portability. I think that would really, uh, at least the industry to the next level. Marie? >>Yeah. Shanghai Bean couldn't. Couldn't have said it better. Right? Let me let me let me kind of give you Ah, sample. Right. We're at about 160 plus customers now, you know, adding several by the month. Um, just with just with rancher alone, right, we are. We have common customers in all common video expedient Roche March X, Western Asset Management. You know, charter communications. So we're in production with a number off rancher customers. What are these customers want? And why are they kind of looking at a a a Port works class of solution to use, You know, Xiang's example of the multiple types, right? Many times, people can get started with something in the early days, which has a CS I interface with maybe say, $10 or 8 to 10 nodes with a solution that allows them to at least kind of verify that they can run the stack up and down with, say, you know, a a rancher type orchestrator, workloads that are containerized on and a network plug in and a storage plugging. But really, once they start to get beyond 20 notes or so, then there are problems that are very, very unique to containers and kubernetes that pop up that you don't see in a in a non containerized environment, right? Some. What are some of these things, right? Simple examples are how can you actually run 10 to hundreds of containers on a server, with each one of those containers belonging to a different application and having different requirements? How do you actually scale? Not to 16 nodes, which is sort of make typically, maybe Max of what a San might go to. But hundreds and thousands of notes, like many of our customers, are doing like T Mobile Comcast. They're running this thing at 600 thousands of notes or scale is one issue. Here is a critical critical difference that that something that's designed for Kubernetes does right. We are providing all off the storage functions that Shang just described at container granted, granularity versus machine granularity. One way to think about this is the old Data center was in machine based construct. Construct everything you know. VM Ware is the leader, sort of in that all of the way. You think of storage as villains. You think of compute and CPUs, everything. Sub sub nets, right? All off. Traditional infrastructure is very, very machine centric. What kubernetes and containers do is move it into becoming an app defined control plane, right? One of the things were super excited about is the fact that Kubernetes is really not just a container orchestrator, but actually a orchestrator for infrastructure in an app defined way. And by doing that, they have turned, uh, you know, control off the infrastructure via communities over to a kubernetes segment. The same person who uses rancher uses port works at NVIDIA, for example to manage storage as they use it, to manage the compute and to manage containers. And and that's marvellous, because now what has happened is this thing is now fully automated at scale and and actually can run without the intervention off a storage admin. No more trouble tickets, right? No more requests to say, Hey, give me another 20 terabytes. All of that happens automatically with the solution like port works. And in fact, if you think about it in the world of real time services that we're all headed towards right Services like uber now are expected in enterprises machine learning. Ai all of these things analytics that that change talk about are things that you expect to run in a fully automated way across vast amounts of data that are distributed sometimes in the edge. And you can't do that unless you're fully automated and and not really the storage admin intervention. And that's kind of the solution that we provide. >>Alright, well, we're just about out of time. If I could just last piece is, you know, early and saying to talk about where we are with long for and what we should expect to see through the rest of this year and get some early for you to you know, what differentiates port works from Just, you know, the open source version. So And maybe if we start with just kind of long or in general and then really from from your standpoint, >>yeah, so it's so so the go along one is really to lower the bar for folks to run state for workloads on on kubernetes we want you know, the the Longhorn is 100% open source and it's owned by CN cf now. So we in terms of features and functionalities is obviously a small subset of what a true enterprise grade solution like Port Works or, um, CEO on that that could provide. So there's just, you know, the storage role. Ah, future settle. The roadmap is very rich. I don't think it's not really Ranchers go Oh, our Longhorns goal to, you know, to try to turn itself into a into a plug in replacement for these enterprise, great storage or data management solutions. But But they're you know, there's some critical critical feature gaps that we need address. And that's what the team is gonna be focusing on, perhaps for the rest of the year. >>Yeah, uh, still, I would I would kind of, you know, echo what Chang said, right? I think folks make it started with solutions, like longer or even a plug in connector plug in with one of their existing storage vendors, whether it's pure netapp or or EMC from our viewpoint, that's wonderful, because that allows them to kind of graduate to where they're considering storage and data as part of the stack. They really should that's the way they're going to succeed by by looking at it as a whole and really with, You know, it's a great way to get started on a proof of concept architecture where your focus initially is very much on the orchestration and the container ization part. But But, as Xiang pointed out, you know what what rancher did, what I entered it for Kubernetes was build a simple, elegant, robust solution that kind of democratized communities. We're doing the same thing for communities storage right? What Port works does is have a solution that is simple, elegant, fully automated, scalable and robust. But more importantly, it's a complete data platform, right? We we go where all these solutions start, but don't kind of venture forward. We are a full, complete lifecycle management for data across that whole life cycle. So there's many many customers now are buying port works and then adding deal right up front, and then a few months later they might come back and I'd backup from ports. So two shanks point right because of the uniqueness of the kubernetes workload, because it is an app defined control plane, not machine to find what is happening is it's disrupting, Just like just like virtualization day. VM exist today because because they focused on a VM version off. You know, the their backup solution. So the same thing is happening. Kubernetes workloads are district causing disruption of the D r and backup and storage market with solutions like sports. >>Wonderful. Merlin Chang. Thank you so much for the updates. Absolutely. The promise of containers A Z you were saying? Really, is that that Atomic unit getting closer to the application really requires storage to be a full and useful solution. So great to see the progress that's being made. Thank you so much for joining us. >>Welcome, Shannon. We look forward to ah, working with you as you reach for the stars. Congratulations again. We look >>forward to the containing partnership morally and thank you. Still for the opportunity here. >>Absolutely great talking to both of you And stay tuned. Lots more coverage of the Cube Cube Con cloud, native con 2020 Europe. I'm stew minimum. And thank you for watching the Cube. Yeah, yeah, yeah, yeah, yeah, yeah

Published Date : Aug 18 2020

SUMMARY :

and cloud, native con Europe 2020 Virtual brought to you by Red Hat, I actually I'm going to start with you just cause you know we've seen, of the things that for decades I t has had to do and has done to Of course, storage is a piece of the overall puzzle that that ranchers trying to help Ah, a lot of good technologies available, you know, Anybody that knows the storage industry knows that you know standards in various ways And so I think, you know, the third type of you know, we call container Native Storage. I think back if you want. I love to hear more is view as well, especially about you know, And that's kind of the solution that we provide. the rest of this year and get some early for you to you know, to run state for workloads on on kubernetes we want you know, causing disruption of the D r and backup and storage market with solutions like sports. Thank you so much for the updates. We look forward to ah, working with you as you reach for the stars. Still for the opportunity here. Absolutely great talking to both of you And stay tuned.

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Tom Broderick, Commvault | Commvault FutureReady


 

>>From around the globe. It's the cube with digital coverage of Convolt future. Ready? 2020 brought to you by Convolt. >>Hi, I'm Stu Miniman and this is the cubes coverage of Convolt future. Ready to help me wrap up our coverage of the event? Happy to welcome back to the program. Trump Geraldo key is the vice president of strategy and chief of staff with Convolt. Tom. Thanks so much for joining us. Thanks to glad to be here. All right. So before we get into the news, Tom, and I love when I get to talk to strategy people, cause you know, you have your 90 day plans, you have your quarterly plans, you have your yearly plans. You're a, you know, year, year and a half into the new regime, of course, Sanjay you're chief of staff for and yourself. So, um, I'm sure coming into 2020, um, a few things probably hit in 2020 that weren't on your initial plan. So, you know, global pandemic and the like tell us a little bit about, you know, how current events, uh, have been impacting, you know, both, both, both of your plans and, and, and, and led to what we saw today. >>Yeah, sure. Great, Stu um, good question. You know, you know, what's interesting is, um, obviously we're in a different world today than where we were last time we spoke at the, at our go conference in the, um, in the fall and, uh, and like everyone else, you know, we're, we're dealing with this new reality. And, um, you know, we're fortunate because we are a software company, which means, you know, from an operational standpoint, um, you know, we're, we're able to, we're able to continue to build products, deliver great software to our customers. And the, like I'd say from a strategy perspective, you know, we're still on course, we, uh, as you saw at the go conference last, last fall, and as you see today, um, we've been building new products, new capabilities, um, you know, under the mantra of intelligent data management, which really is a new direction for the company or a new way that we're framing, uh, the, the, uh, what we're bringing to our customers. >>And while we've had to make some tactical changes inside our business, the fundamental strategy is strong. In fact, I think in a lot of ways it's actually sped up, sped things up. Uh, I think it was a, as we talked about today, um, quite a bit the, uh, digital transformation for organizations is happening, even pastoring them, or people are saying, Hey, you know, where I thought I might've had five, 10 years to, you know, to on this journey, it's actually compressing quite a bit. And we feel like we're in a really good spot. And we feel like we've been building towards, um, the delivering the kinds of capabilities that customers need. So, um, fundamentally things aren't changing for us. And I think, um, I think we're in a good spot today. And hopefully, you know, that is gonna resonate with the marketplace with this series of announcements that we're making, uh, making today. >>Yeah, absolutely. Tom huge forcing function in the marketplace, by what we're seeing today. Um, you, you mentioned, uh, the user conference go from last year. One of the big themes at the show last year was the intelligent data management. So help us understand, you know, connect the dots if you would, uh, between the, the vision and now really it's a whole portfolio refresh, uh, that we're seeing from Convolt today. >>Yeah. So we're, you know, we're building off of 20 years of development in this space, right? We it's so much more than backup. And, uh, you know, I think traditionally Combolt may have gotten a little bit pigeonholed as a backup provider, but really when you look under the covers and you look at our platform, right, it provides true data awareness, automation, agility across your entire estate. And we have almost a thousand patents in this space. So, um, you know, what that platform offers for us is the ability to support lots of different data management services that can be consumed in different ways. And so when we think about intelligent data management, right it's data management services that are delivered in that intelligent way, right through that awareness automation and agility, you know, obviously to the way that we deliver it to customers is, you know, either it's flexible traditional software, right downloadable software, or, um, as an integrated appliance. >>Um, and now even as software, as a service through our metallic offering, because when you look at it, you know, that foundation that metallic is built upon is that same core platform that, you know, our current customers know and enjoy. So, um, you know, when we talk about intelligent data management, that frames the whole story for us. So when we think about, you know, our roadmap going forward as well, it's about what are the new types of data management services that customers are demanding and how can we build that right on that platform, that core platform that we've already been, uh, had developed for so many years, >>You know, Tom, Tom, I'm glad you brought up, uh, some of those different models, uh, the way that people can consume, uh, and purchase, uh, these solutions. Uh, if we look at, uh, you know, the overhaul of the portfolio, they are, uh, one of the pieces I like to understand from you is if you look at pricing, uh, if you look at licensing, um, you know, obviously Convolt has been going along this journey, uh, from, uh, T to be more in the, uh, uh, that subscription model, if you will. So help us understand how the, how the updates fit there. >>Yeah. I, you know, it it's, I'll tell you, it starts with, if you, if you had a chance to see, I'm sure you did see Sanjay's presentation today. You know, a lot of what we're talking about here is simplification and simplifying, not just the user experience, but also the commercial experience, right? So simplifying the usage of the product, but also simplifying the usage. So when we think about it, um, you know, and customers are demanding this more than ever right now, right? Um, obviously this goes back to the new world that we're living in. And, um, if you take that and also consider our expanding portfolio, we need to make sure that we're doing it in a way that makes it easy for customers to consume, um, the different elements of it. So we've taken an approach that makes, um, licensing simple. So first we're, we're aligning licensing to the workload that's being either protected or managed in some other way. >>Right? So for instance, if it's a virtual machine workload, the licensing is by virtual machine. If it's mailbox oriented, it's by user, right. It's aligned to the workload and the procurement methodology that organizations are used to buying for that particular workload. I think in the past things that might've gotten a little bit convoluted, is this terabyte based, is it VM based? Is it user based? Is it instance based? Does it right. And what we've done is we've taken a drastic simplification approach. Uh, I'm looking at that and looking at that, so that as organizations think about their workloads, uh, they don't have to do like math derivatives to figure out what their cost is going to be. The second thing is we wanted to make it more predictable. And so, um, you know, we've had subscription licensing as an option for a few years now, but it's really just been an option, right? >>And now we're going to be more aggressive in terms of how we're offering that. We want to incentivize prescriptions even further. You know, if a customer wants a one year term, we'll provide a one year term, right? If they want a multi year term, we'll provide a multi-year term, it's up to them, but whatever it is, it's going to help them buy what they need rather than be forced to make some sort of massive upfront perpetual license decision. Right? Well, we'll incentivize that sort of predictability. And then lastly, you know, we want to make sure that we're being aggressive and flexible in how we're providing these options to customers. So, you know, if you go back to the intelligent data management comments and being able to deliver different data management services, we want to make sure that we're aggressive when it comes to the solitary use case. >>If I want to back up the thing, I'm going to be very aggressive, right? Combolt's going to have aggressive pricing, uh, for that particular use case. Um, but then what we also want to do is we want to make it attractive to customers to bring their data in environment, even further with, you know, other types of data management capabilities. So we're going to make it easier for customers to do that. And you see that through the portfolio approach that we've announced today. Um, we've really rebuilt our portfolio, I think for, you know, uh, the more modern enterprise and, uh, and we're incentivizing and making it worth customers while to really look at all the different offerings that we have. >>So, Tom, I wonder if you could speak a little bit to the ripple effect of that change that you have to, to the pricing and, and, and the like, um, I thinking about go to market your channel, uh, and you know, compensation for, for along the value chain there, how does that happen? >>So, um, obviously, you know, as we make any kind of change to our portfolio, be it, um, you know, be it adding products or changing the licensing. There's a lot of work that has to go into it behind the scenes. And, you know, this frankly is a big part of what we've changed in the last 18 months. Um, the, uh, and we talked about this a little bit last time. You and I, you and I spoke, you know, it's hard to get an organization to be aligned end to end and even harder when you think about all the downstream effects of that, right. When you've got, you know, distribution layers, and in of course your, your customer base, um, how do you get everybody in sync and aligned with what it is that we're doing? And we've made sure that throughout this process, as we've been leading to today, which is really a combination of a lot of this effort, that, you know, the folks in the organization both internally and externally, that need to be up to speed and need to be aligned to what it is that we're doing are there. >>Right? So it's part of that. Um, part of that process, we've been working on this for months to make sure that we're in a good place. You know, I, I have a saying inside the company, which is sometimes you have to go slow to go fast and it, it relates to, it actually relates to, um, to like motorcycle racing. You know, when you, you know, a motorcycle race, isn't one on the straightaway is when everybody has their throttle wide open it's one, um, before they enter the turns as they're slowing down, because the whole idea is you slow down when you get the bike settled, you get your, you enter the curve, you know, the right trajectory so that you can hit the apex and accelerate out. So when it comes to aligning a big organization and in our, um, distribution and channel infrastructure behind that, going slow means making sure everybody's aligned upfront in the process. And then we can build the plans going forward to make sure that we've got the right information in the right place at the right time, so that the customer can have a great experience at the end of the line. >>Alright, Tom, you, you've now been with Convolt a little over a year, you know, bring us inside a little bit, you know, some of the cultural changes, what surprises you've seen, uh, and, uh, you know, H help us, you know, bring a close to, uh, the Convolt future. Ready? >>Yeah. I'll tell you a stew. It's been, so I've been with, Convolt now a little bit over a year, but a year and a quarter. And, um, it has been a tremendous experience, obviously, we've we have, we've transformed the company. And, you know, if you look at the leadership team, it's, you know, it's a, it's a mixture of new folks and folks that have a lot of experience with the company. Um, and you look throughout the organization between how we operate as I was just talking about, and, um, the kind of innovation that we're delivering to the marketplace, it's founded on a really ideal, you know, we want to, um, we wanted to simplify the business. We wanted to drive innovation and we want to make sure that we're executing and we've been able to rally the company around those three components. And I'll tell you, um, the, the biggest pleasant surprise that I've had inside the organization is the attitude of our people and the openness of the Convolt teams to the kinds of change that we've been making. >>Um, it's been tremendous because without, without their support, there's no way that we would have been able to do this. Um, you know, from employee one to employee, you know, 2,500, you know, we're all like rowing in the same direction to use another analogy. And, um, without that, it would be impossible to make the kinds of changes that we're making now, you know, I I'd also say, you know, it takes it's, um, it takes time and that's one of the things that we've all needed to make sure that we, that we had, you know, getting, getting the whole company aligned, um, and then having the patients to, to make sure that we've got a nice follow-through, um, has been really important. And I think, you know, today is a great example of how far we've taken things at Humboldt and where we think things are headed going forward. And I expect lots more of us, more great things as we move forward through the rest of our, our calendar and fiscal year and into the out years as well. >>All right, Tom, uh, one more final word. Uh, w w what do you want really the customers to have as their takeaway from Convolt future ready? >>Well, I mean, I hope a customer see, you know, the kind of a positive change in momentum that we're making between, you know, what we're trying to, to deliver in terms of value for our customers and how much they can derive out of that. Um, you know, for our PR, you know, when you're, when you're a hammer, everything looks like a nail. So for us, you know, it all comes down to the data and data, you know, is so fundamental to any organization, how they derive value from it. And, you know, we play, I think, an important part in that landscape, and it's our hope that we're delivering the types of services to customers that allow them to extract the value of the data to make them successful. And at the end of the day, that's what we're all about. And I hope that this has been a good, um, experience for customers to see this and, um, and that they can, um, you know, see from it that we we've got momentum behind us. And that we're just going to continue to move this forward. >>All right. Well, Tom, thank you so much data, absolutely huge opportunity for customers as well as Convolt for you and, and all of your ecosystem there, Tom Broderick. Thanks so much pleasure catching up with you. >>Thank you, Sue. Really appreciate it. >>All right. And that brings to a close the cubes coverage of Convolt future. Ready? Check out the cube.net for lots more. You can go back and see the Convolt go that Tom and I have referenced and stay tuned for lots more events that we have coming up. I'm Stu Miniman. And thank you for watching the cube.

Published Date : Jul 21 2020

SUMMARY :

2020 brought to you by Convolt. get to talk to strategy people, cause you know, you have your 90 day plans, you have your quarterly plans, um, you know, we're, we're able to, we're able to continue to build products, And hopefully, you know, that is gonna resonate with the marketplace you know, connect the dots if you would, uh, between the, the vision and now really it's obviously to the way that we deliver it to customers is, you know, either it's flexible traditional So, um, you know, when we talk about intelligent data management, that frames the whole story Uh, if we look at, uh, you know, the overhaul of the portfolio, So when we think about it, um, you know, and customers are demanding this more than ever right now, And so, um, you know, And then lastly, you know, we want to make sure that we're being aggressive and flexible you know, uh, the more modern enterprise and, uh, and we're incentivizing and making be it, um, you know, be it adding products or changing the licensing. you know, a motorcycle race, isn't one on the straightaway is when everybody has their throttle wide open it's one, uh, and, uh, you know, H help us, you know, bring a close to, uh, you know, if you look at the leadership team, it's, you know, it's a, it's a mixture of new folks to make the kinds of changes that we're making now, you know, Uh, w w what do you want really the customers and that they can, um, you know, see from it that we we've got momentum behind us. for you and, and all of your ecosystem there, Tom Broderick. And thank you for watching the cube.

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Joep Piscaer, TLA Tech | Cloud Native Insights


 

>>from the >>Cube Studios in Palo Alto in Boston, connecting with thought leaders around the globe. >>These are cloud native insights. Hi, I'm stupid, man. And welcome to Episode one of Cloud Native Insights. So this is a new program brought to you by Silicon Angle Media's The Cube. I am your host stew minimum, and we're going to be digging in to cloud native and, of course, cloud native like cloud before kind of a generic term. If you look at it online, there's a lot of buzzwords. There's a lot of jargon out there, and so we want to help. Understand what? This is what This isn't on And really happy to welcome back to the program to help me kick it off you piss car. He is an industry analyst. His company is T l A Tech. You. Thanks so much for joining us. >>Thanks, Dave. Glad we're >>all right. And one of the reasons I wanted you to help me kick this off. Not only have you been on the Cube, you know your background. I met you when you were the cto of a service provider over there in Europe, where you're Netherlands based. You were did strategy for a very large ah, supermarket chain also. And you've been on the program that shows like docker con in the past. You work in the cloud native space you've done consulting for. Some of the companies will be talking about today. But you help me kick this off a little bit. When you heard here the term cloud native. Does that mean anything to you? Did that mean anything back in your previous roles? You know, help us tee that up. >>So, you know, it kind of gives off a certain direction and where people are going. Right. Um so to me, Cloud native is more about the way you use cloud, not necessarily about the cloud services themselves. So, you know, for instance, I'll take the example of the supermarket. They had a big e commerce presence. And so we were come getting them to a place where they could, in smaller teams, deploy software in a faster, more often and in a safer way so that teams could work independently of each other, work on, you know, adding business value, whatever that may be for any kind of different company. That's a cloud native to me, Connie means using that to the fullest extent, using those services available to you in a way organizationally and culturally. That makes sense to you, you know, Go wherever you need to go. Be that release every hour or, you know, transform your s AP environment to something that is more nimble, more flexible, literally more agile. So what cloud native means so many things to so many people? Because it's immediately is not directly about the technology, but how you actually use it. >>Um, and u Pua and I are in, you know, strong agreement on this thing. One is you've noticed we haven't said kubernetes yet. We haven't talked about containers because cloud native is not about the tooling. We're, you know, strong participants in you know, the CN CF activities. The Cloud Native Computing Foundation, cube con and cloud native is a huge show. Great momentum one. We're big fans of too often people would conflate and they'd say, Oh, cloud native equals. I'm doing containers and I've, you know, deployed kubernetes one of the challenges out there. You talk about companies, you know? Well, you know, I had a cloud first initiative and I'm using multi cloud and all this stuff. It's like, Well, are you actually leveraging these capabilities, or did I shove things in something I'd railed about for the last couple of years? You talk about repatriation, and repatriation is often I went to go do cloud. I didn't really understand what I was doing. I didn't understand how to leverage that stuff. And I crawled back to what I was doing before because I knew how to do that. Well, so, you know, I think you said it really well. Cloud native means I'm taking advantage of the services. I'm doing things in a much more modern way. The thing I've loved talking to practitioners and one of things I want to do on this program absolutely is talk to practitioners is how have you gone through things organizationally, there are lots of things right now. Talk about like, thin ops. And, of course, all the spin off from Dev Ops and Dev SEC ops. And, like, how are we breaking through silos? How we're modernizing our environments, how we're taking advantage of new ways of doing things and new services. So yeah, I guess you You know, there are some really cool tools out there. Those are awesome things. But, you know, I love your viewpoint. Your perspective on often people in tech are like, Hey, I have this really cool new tool that I can use, you know? Can I take advantage of that? You know, do I do things in a new way, or do I just kind of take my old way and just make things maybe a little incrementally better? Hopefully with some new tooling. >>Oh, yeah. I mean, I totally agree. Um, you know, tooling is cool. Let me let me start by saying that I You know, I'm an engineer by heart, so I love tinkering with new new stuff. So I love communities I love. Um, you know that a new terra form released, for instance, I love seeing competition in the container orchestration space. I love driving into K native server lists. You know, all those technologies I like, But it is a matter of, you know, what can you do with them, right. So, for instance, has she corporate line of mine? I work on their hashtag off. Even they offer kind of Ah, not necessarily an alternative, but kind of adjacent approach to you what the CNC F is doing, and even in those cases, and I'm up specifically calling out Hashi Corp. But I'm kind of giving. The broader overview is, um, it doesn't actually matter what to use, Even though it'll help me. It'll make me happy just to play around with them. But those new tools have to mean something. They have to solve a particular problem. You have either in speed of delivery or consistency of delivery or quality of service, the thing you are building for your customers. So it has to mean something. So back in the day when I started out in engineering 15 years ago, a lot of the engineering loss for the sake of engineering just because, you know you could create a piece of infrastructure a little faster, but there was no actual business value to be out there. That's a lot of the engineering kind of was stuck inside of its own realm, or as what you see now is, if you can use terraform and actually get all of you know the potential out of you, it allow you to release offer more quickly because you're able to stand up infrastructure for that software more quickly. And so you know, we've kind of shifted from back in the in the attic or in the basement doing I t. Stuff that no one really understands. The one kind of perceives the business value of it into the realm of okay, If we can deploy this faster or we don't even need to use a server, we can use server lists. Then we have an advantage in the marketplace. You know, whatever marketplace that is, whatever application we're talking about. And so that's the difference to me. And that was that. You know, that's what CN CF is doing to me. That is what has she Corpus is helping build. That is what you know. A lot of companies that built, for instance, a managed kubernetes service. But from nine spectral crowd, all those kinds of companies, they will help, you know, a given customer to speed up their delivery, to not care about the underlying infrastructure anymore. And that's what this is all about to me. And that is what cloud native means use it in a way that I don't actually have to do the toil off the engineering anymore. There's loads of smart people working for, you know, the Big Three cloud vendors. There's loads of people working for those manage service providers, but he's used them so that you can speed up your delivery, create better software created faster, make customers happy. >>Yeah, it's a lot to unpack there. I want to talk a little bit about that landscape, right When you talk about, you know, cloud native, maybe a little compare contrast I think about, you know, the wave of Dev ops and for often people like, you know, Dev Ops. You know, that's a cultural movement. But there's also tooling that I could buy to help me along that weighs automation, you know, going agile methodology. See, I CD are all things that you're like. Well, is this part of Dev Ops, isn't it? There's lots of companies out there that we saw rows rode that wave of Dev ops. And if you talk about cloud native, you know the first thing you know, you start with the cloud providers. So when I hear you talking about, how do we get rid of things that we don't need to worry about? Well, for years, we heard Amazon Web services talk about getting rid of undifferentiated heavy lifting. And it's something that we're huge fans off you talk about. What is the business outcome? It's not. Hey, I went from, you know, a stand alone server to I did virtualized environments. And now I'm looking container ization or serverless. What can I get rid of? How do I take advantage of native services and all of those cloud platforms? One of the huge values there is, it isn't Hey, I deployed this and maybe it's a little bit cheaper and maybe a little better. But there's that that is really the center of where innovation is happening not only from the platform providers they're setting themselves, but from that ecosystem. And I guess I'll put it out there. One of the things I would like to see from Cloud Native should be that I should be able to take care of take advantage of innovation wherever it is. So Cloud Native does not mean it must live in the public cloud. It does not necessarily mean that I'm going, you know, full bore, multi cloud everywhere. I've had some great debates with Corey Quinn, on the Cube Online and the like, because if you look at customer environments today, you know, yes, they absolutely have their data centers. They're leveraging, typically more than one public cloud. SAS is a big part of the picture and then edge computing and pulls everything away into a much more distributed architecture. So, you know, I'm glad you brought up. You know, Hashi, a company you're working with really interesting. And if you talk about cloud native, it's there. They're not trying to get people to, oh, use multiple clouds because it's good for us. It's they. Hey, the reality is that you're probably using multiple clouds, and whether it's one cloud or many clouds or even in your data center, we have a set of tools that we can offer you. So you know, Hashi, you mentioned, you know, terra form vault. You know, the various tooling is that they have open source, you know, big play in this environment, both under the CN CF umbrella and beyond. Give us a little bit as to, you know, where are the interesting places where you see either vendors and technology today, or opportunity to make these solutions better for users. >>So that's an interesting question, because I literally don't know where to begin. The spectrum is so so broad, it's all start off with a joke on this, right? You cannot buy that helps. But the vendors were sure try and sell it to you. So it's kind of where you know, the battle is is raging on its getting foothold into an organization. Um, and you see that? You know, you see companies like, how is she doing that? Um, they started out with open source tooling that kind of move into the enterprise realm. Um, you solve the issues that enterprises usually have, and that's what the club defenders will trying to you as although you know, the kind of kick start you with a free service and then move you up into their their stack. And that's you know, that's where Cloud native is kind of risky because the landscape is so fragmented, it is really hard to figure out. Okay, this tool, it actually solves my use case versus this one doesn't. But again, it's in the ecosystem in this ecosystem already, so let's let's still use it just because it's easier. Um, but it does boil the disk a lot of the discussion down into. Basically, it's a friction. How much effort does it take to start using something? Because that's where and that's basically the issues enterprises are trying to solve. It's around friction, and it used to be friction around, you know, buying servers and then kind of being stuck with him for 4 to 5 years. But now it is the vendor lock in where people in organizations have to make tough decisions. You know, what ecosystems am I going to buy into it? It's It's also where a lot of the multi cloud marketing comes from on the way down to get you into a specific ecosystem on your end companies kind of filling that gap, helping you manage that complexity and how she corpus is one of those examples in my book that help you manage that multi cloud ah challenge. So but yeah, But it is all part of that discussion around friction. >>Yeah, and I guess I would start if you say, as you said, it is such a broad spectrum out there. If you look in the developer tooling marketplace is, there's lots of people that have, you know, landscapes out there. So CN cf even has a great landscape. And you know, things like Security, you no matter wherever I am and everywhere that I am. And there's a lot of effort to try to make sure that I can have something that spans across the environment. Of course, Security, you know, huge issue in general. And right now, Cohen, 19. The global pandemic coming on has been, you know, putting a spotlight on it even more. We know shared responsibility models where security needs to be. Data is at the center of what we're talking about when we've been talking for years about companies going through their transformation, I hadn't talked about, you know, digital transformation. What that means is, at the end of the day, you need to be data driven. So there's lots of companies, you know, big movement and things like ml ops. How can I actually harness my data? I said one of the things I think we got out of the whole big data wave. It was that bit flip from, Oh my God, their data everywhere. And maybe that's a challenge for me. It now becomes an opportunity and often times somewhere that I can have new value or even new business models that we can create around data. So, you know, data security on and everyone is modernizing. So, you know, worry a bit that there is sometimes, you know, cloud native washing. You know, just like everything else. It's, you know, cloud enabled. You know, ai ready from an infrastructure standpoint, you know, how much are you actually leveraging Cloud native? The bar, we always said, is, you know, if you're putting something in your data center, how does that compare against what I could get if I'm doing aws azure or Google type of environment? So I have seen good progress over the last couple of years in what we used to call it Private Cloud. And now it's more Ah, hybrid environment or multi cloud. And it looks and acts and is managed much more like the public cloud at a lot of that. Is that driver for developers? So you know Palmer, you know, developers, developers, developers, you know, absolutely. He was right as to how important that is. And one of the things I've been a little bit hardened at is it used to be. You talked about the enterprise and while the developers were off in the corner and, you know, we need to think about them and help enable them. But now, like the Dev Ops movement, we're trying to break down those silos. You know, developers are much more in the workflow. When I look at tools out there not only get hub, you know, you talked about Hashi, you know, get lab answerable and others. Often they have ways to have nothing to developers. The product owners and others all get visibility into it. Because if you can get, you know, people in the organization all accessing the same work stream the way that they need to have it there. There's goodness there. So I guess final question I have for you is you know, what advice do we have for practitioners themselves? Often, the question is, how do I get from where I've been? So where I'm going, This whole discussion of Cloud native is you know, we spent more than a decade talking about cloud, and it was often the kind of where in the movement and the like So what? I want to tee up with cloud native is discussion, really for the next decade. And you know, if I'm, you know, a c i o If I'm in, i t how do I make sure that I'm ready for these next opportunities while still managing? You know what I have in my own environment. >>So that kind of circles back to where we started this discussion, right? Cloud native and Dev ops and a couple of those methodologies they're not actually about the tooling. They are about what to do with them. Can you leverage them to achieve a goal? And so my biggest advice is Look for that goal. First, have something toward towards because if you have a problem, the solution will present itself. Um, and I'm not saying go look for a problem. The problems, they're already It's a matter of, um, you know, articulating that problem in a way that your developers will actually understand what to do. And then they will go and find the tools that are needed to solve that particular problem. And so we turn this around in a sense that so finally, we are at a point where we can have business problems. Actually, solved by I t in a way that doesn't require, you know, millions of upfront investment or, you know, consultants from an outside company. Your developers are now able to start solving those problems, and it will maybe take a while. They may need some outside help Teoh to figure some stuff out, But the point is, we can now use you know, these cloud resource is these cloud native services in such a small, practical way that we can actually start solving these business problems in a real way. >>Yeah, you actually, earlier this year I've done a series of interviews getting ready for this type of environment. You know, one of the areas I spent a bunch of time trying to dig in. And to be frank, understand has been server lists. So, you know, people very excited about server lists. You know, one of the dynamics always is, You know, everything we're talking about with containers and kubernetes driving them to think about that. I always looked as container ization was kind of moving up the stack in making infrastructure easier. The work for applications, but something like serverless it comes, top down. It's it's more of not the tooling, but how do I build those applications in those environments and not need to think at least as much about the infrastructure? So server lists Absolutely something we will cover, you know, containers, kubernetes what I'm looking for. Always love practitioners love to somebody. You you've been, you know, in that end, user it before startups. Absolutely. We'll be talking to as well as other people you know, in the ecosystem that you want to help, have discussions, have debates. You know, we don't have, you know, a strong. You know, this is the agenda that we have for cloud native, but I really want to help facilitate the dialogue. So I'll give you a final word here. Anything You know, what's exciting you these days when you talk to your peers out there, you know, in general, you know, it can be some tools, even though we understand tools are only a piece of it or any other final tips that you have in this market >>space. Well, I want to kind of go go forward on on your statement earlier about server lists without calling, You know, any specific serverless technology out there specifically, but you're looking at those technologies you'll see, But we're now able to solve those business problems. Um, without actually even needing I t right. So no code low code platforms are very adjacent to you to do serverless movement. Um, and that's where you know, that's what really excites me of this at this point, simply because, you know, we no longer need actual hardcore engineering as a trait Teoh use i t to move the needle forward. And that's what I love about the cloud native movement that it used to be hard. And it's getting simpler in a way also more complex in a way. What we're paying someone else Teoh to solve those issues. So I'm excited to see where you know, no code low code survivalism those the kinds of technologies will take us in the next decade. >>Absolutely wonderful. When you have technology that makes it more globally accessible There, obviously, you know, large generational shifts happening in the workforce. You Thank you so much for joining us, >>actually, Sue. >>All right. And I guess the final call to action really is We are looking for those guests out there, so, you know, practitioners, startups people that have a strong viewpoint. You can reach out to me. My emails just stew Stu at silicon angle dot com where you can hit me up on the twitters. I'm just at stew on there. Also. Eso thank you so much for joining us. Planning to do these in General Weekly cadence. You'll find the articles that go along with these on silicon angle dot com. Of course. All the video on the cube dot net I'm stew minimum in and love to hear more about your cloud Native insights >>Yeah, yeah, yeah, yeah, yeah

Published Date : Jun 26 2020

SUMMARY :

on And really happy to welcome back to the program to help me kick it off you piss And one of the reasons I wanted you to help me kick this off. of each other, work on, you know, adding business value, whatever that may be for any kind Well, so, you know, I think you said it really well. That's a lot of the engineering kind of was stuck inside of its own realm, or as what you see You know, the various tooling is that they have open source, you know, So it's kind of where you know, the battle is is raging on its And you know, if I'm, you know, a c i o If I'm But the point is, we can now use you know, these cloud resource is these cloud native services You know, we don't have, you know, a strong. So I'm excited to see where you know, no code low code survivalism those the obviously, you know, large generational shifts happening in the workforce. so, you know, practitioners, startups people that have a strong viewpoint.

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Ashesh Badani, Red Hat | Red Hat Summit 2020


 

from around the globe it's the cube with digital coverage of Red Hat summit 2020 brought to you by Red Hat hi I'm Stu min a man and this is the cubes coverage of Red Hat summit having digitally interviewing practitioners executives and thought leaders from around the world happy to welcome back to our program one of our cube alumni a chef des données is the senior vice president of cloud platforms with Red Hat ashesh thank you so much for joining us and great to see you yeah likewise thanks for having me on Stu good to see you again all right so a shesh since the last time we had you on the cube a few things have changed you know one of them is that IBM has now finished the the acquisition of bread hat and I've heard from you from a really long time you know OpenShift it's anywhere and everywhere but with your exhibition Red Hat it just means you know this only run on IBM mainframes and IBM cloud and all things blue correct well that's true for sure right so Stu you know we're talking for many many times as you know we've been committed to hybrid multi-cloud from the very GetGo right so open ships supported to run on bare metal on which was asian platforms will they come from us or BM where microsoft happy on private clouds like OpenStack as well as AWS Google cloud as well as on a sure now with the completion of the IBM acquisition Red Hat we obviously always partnered with IBM before but given if you will a little bit for a close relationship here you know IB has been very keen to make sure that they promote open ships and all their platforms right so as you can probably see open idea about up as well as open shift on Xeon mainframe it's so regardless of how you like open shape wherever you like open ship you will get it yeah oh so great client clarification it's not only on IBM but of course all of the IBM environment are supported as you said as well as ad abs Google Azure and the like yeah it's you know I remember years ago before IBM created their single condensed conference I think I attended the conference that would do you know Z and power and storage and people would be like you know what are they doing you know with that mainframe I'm like well you do know that can run Linux wait it can run Linux I'm like oh my god these been able to run Linux for a really long time so you want your latest container docker you know openshift stuff on there yeah that can sit on a mainframe I thought some very large global companies that that is absolutely a part of their overall story so so interesting you by the way you say that because we already have customers who've been a procuring openshift on mainframe right so if you made the investment frame it's running much typical applications for you looking to modernize on the applications and then services run on top you know open ship domain say now there's an available option which customers already taking advantage of so exactly right to your point we're seeing that yeah and it's just maybe it's good to kind of you know you've got a great view point as to customers deploying across all sorts of environment so you mentioned VMware environments the public cloud environment you know it was you know our premise a few years ago on the cube that you know kubernetes gets baked into all the platform and absolutely it's going to just be you know a layer underneath I actually think we won't be talking a lot about kubernetes if you fast forward a couple years just because you know it's in there it's I'm using it in all of my environment so what are you seeing from your customers where are we in that general doctrine and you know any specifics you can give us about you know kind of the breadth and the depth of what you're seeing yes so you're exactly right all right we're seeing that adoption continue on the path it's been not so we've got now over 1,700 customers of poor openshift running in all of these environments that you mention right so public-private you know a combination of the two running on traditional which ization environments as well as ensuring that they run in public cloud that scale in some cases managed by customers other cases you've managed by by us on their behalf in a public cloud so we're seeing all permutations if you will you know of that in play today we're also seeing a huge variety workloads right and to me that's actually really interesting it and past that all right so earliest days as you'd expect you know people don't play with micro services right so trying to build unity Marc services and run it right so part native what have you then you know as we were ensuring that we're supporting stateful application right now you're starting to see if you a legacy application move on right ensuring that you know we can run them support them at scale you know within the platform you know customers looking to modernize applications I will talk maybe in a few minutes also a little bit of kind of Lipton shift you know that that you know you've got to play as well but now also we're starting to see new workloads come on right so just you know most recently we announced you know some the work that we're doing with series of partners right from Nvidia to emerging a IML you know a I utter intelligence machine learning frameworks ice bees you know looking to bring those to market been ensuring that those are supported and can run with open ship right our partners with Nvidia ensuring open ship we support you know GPU based environment for specific workloads right whether it be performance sensitive or you know specific workloads they take advantage of July starting now to see a wide variety if you will of application types is also something that that were chimes all right so numbers of customers increasing types of workloads you know you know coming on grazing and then the diversity of underlying deployment environments you know whether they're running balls it's such an important piece and I'm so glad you talked about it there because you know my backgrounds infrastructure and we tend to look at things as to oh well you know I move from a VM to a container the cloud or all these other things but the only reason infrastructure exists is to run my applications it's my data and my application that are the most important things out there so a shesha I want to get in some of the news that you've got here your team working a lot of things I believe one of them talks about you know some of those those new ways that customers are building applications and how openshift fits into those yeah absolutely so look we've been you know on this journey as you know for several years now you know recently we announced the GA of open ship you know server smash it support sto right increasing interest as for turning micro services and I won't take advantage of those capabilities are coming in you know at this event we're now also announcing the GA of open ship serverless but we're starting to see obviously a lot of interest right you know we've seen likes of AWS spawn that up in the first instance but more and more customers interested in making sure that they can get you know portable way to run server list in any kubernetes environment like to take advantage of open source projects as building blocks if you will right so primitives in within kubernetes to allow for surveillance capability is loud for you know scale down to zero support and serving and eventing up and having portable functions you know run across those environments so that that's something that is important to us and we're starting to see support up in the marketplace yeah so I I'd love just you know we've obviously I'm sure you've got lots of breakouts in the open ship server list but you know I've been talking to your team for a number of years and people is like oh well you know just as cloud killed everything before you know serverless obviates the need for everything else that we were going to use before underlying openshift server list my understand Kay native either is the solution or a piece of the solution help us understand you know what service environments decides into what this means for both your infrastructure team as well as your kind of app dev team yeah yeah and a great great question right so Kay native is the basis of our solar solution you know that we're introducing on open chef to the marketplace yeah the best way for me to talk about this right is is no one size fits all right so you're going to have you know specific applications or service that will take advantage of several SK abilities there will be some others that will take advantage of you know running within open ship they'll be yet others you know we talked to the robot and the AI ml frameworks that will run with different characteristics also within the platform so now the platform is being built to help support a diversity multitude of different ways of interacting with it right so I think maybe Stu you're starting to allude to this a little bit right so now we're starting to focus on you know we've got a great set of building blocks you know on the right compute network storage you know a set of primitives that you know kubernetes laid out right thinking of the notions of clustering and being able to scale and we'll talk about what management is well off of those clusters up and then it changes to hey what are the capabilities now that I need to be able to make sure that I'm most effective most efficient regard to these workloads that have been done you're probably hearing me say workloads now several times right because we're increasingly focused on adoption adoption adoption right how can we ensure that when these 1700 plus hopefully you know hundreds if not thousands more of customers come on how they can get the most variety of applications onto this platform right so it can be a true abstraction over all the underlying you know physical resources that they have across every deployment that they've put up all right well I wish we could spend another hour talking about the serverless piece I definitely going to make sure I check out some of the breakout that covered the feast and we talked to you but I I know there's a lot more that the open shift updates have so what other announcements news do you have to cover for yeah so a couple of the things they said I wanna make sure I highlight here one is Ghibli called ACM advanced cluster management that when you're saying right so there's a fair amount of work that was happening with the IBM team working on Plus imagine capabilities we've been doing some of that work ourselves within Red Hat you know as part of IBM Red Hat coming together we've had several folks from IBM actually joined Red Hat and so we're now open sourcing and providing this cluster magical with it right so this is the notion of being able to run and manage these different clusters from openshift at scale across a multitude of ironmans be able to check on cluster help people to apply policy could consistently provide governance ensure that appropriate application they're running appropriate clusters and so on right a series of capabilities to really allow for you know multiple Buster's to be run at scale and managed effectively right so that's one set of go ahead stick yeah if I could when I hear about multi cluster management III I think of some of the solutions I've heard talked about in the industry so you know as you're arc from Microsoft hanzou from VMware when they talk about multi cluster management it is not only the kubernetes solutions that they are offering but also you know how do I at least monitor if not even allow a little bit of control across these environments but when you talk about cluster management is that all you know kind of the the openshift pieces or things like a KS d KS other you know options out there how do those fit into the overall you know management story yeah that's absolutely our goal right so you know we gotta get started somewhere right so we obviously want to make sure that we bring in to effect the solution to manage OpenShift cluster that scale and of course as we'd expect multiple other bus will exist from kubernetes you know like the ones you mentioned from the cloud provider as well as others from third parties and we want the solution to manage that as well but you obviously we're going to sort of take steps to get to through the end point of this journey so yes we will we will get there right we've got to get started somewhere yeah and if chesh any guidance when you look at people you know some of the solutions I mentioned out there when they start out it here's the vision so what what guidance would you give to customers about kind of where we are how fast they can expect these things mature and you know I know anything that Red Hat does is going to be fully open force and everything but you know what what's your guidance out there is what customers people yeah so what was an interesting point I think in this kubernetes journey right now right so when we if you will start off and stew you and I've been talking about this for at least five years not longer was this notion that you know we want to provide a platform that can be portable and successfully run in multiple deployment environments and we've done that over these years but all the while when we were doing that we're always thinking about what are the capabilities that are needed that are perhaps not developed upstream but will be over time but we can ensure that we can look ahead and bring that into the path up and for a really long time I think we we still do right we at Red Hat take a lot of stick for saying hey look you've pork the platform now barn I'll come back to that has always been look we're trying to help solve problems that we believe enterprise customers have we want to ensure that the available open source and we want upstream those capabilities always and back into the community all right but you know let's say making available a platform without our back role based access control what's going to be hard then for enterprise to to adopt that we've got to make sure we introduce that capability and then make sure that it's supported upstream as well and there's a series of capabilities and features like that that we work through and we always provide an abstraction with an open ship to make it more productive for developers administrators to use it and we always also support you know working with coop ctrl or command line interface from coop as well right and then we always hear back from folks saying well you know you've got your own abstraction you know that might make that seem like before collect no you can use both coops ETL you use you know OC commands right whichever one you know is better for you right you have at it right we're just trying to be more productive and now increasingly what we're seeing in the marketplace is this notion that you know we've got to make sure we work our way up from not just laying out a kubernetes distribution but thinking about the additional capabilities additional services that you can provide that'll be more valuable to customers I think Stu you're making the point earlier right increasingly the more popular and the more successful kubernetes becomes the less you will see in Europe which by the way is exactly the way it should be because that becomes then the basis of your underlying infrastructure you're confident that you've got a lock rock-solid bottom and now you as a customer you as a user of focusing all your energy and time on building the productive application and services yeah great great points there are chefs write the division people always talked about is if I'm leveraging cloud services um I shouldn't have to worry about what version they're running well when it comes to kubernetes ultimately we should be able to get there but you know I I know there's always a little bit of a Delta between the latest and newest version of kubernetes that comes out and what the manage services and not only manage services what what customers are doing their own environment right even my understanding even Google you know which is where kubernetes came out of if you're looking at g/kg gke is not on the latest what are we up 1.19 start from the community is shesh so um yeah where's what what's Red Hat's position on this how do you what version are you up to how do you think customers should think about managing across those environments because boy yeah I've got too many you know stars from you know interoperability history go back in 15 years and everything like you know oh my server BIOS doesn't work on that latest kernel.org version of what we're doing for linux um you know Red Hat is probably better prepared than any company in the industry to deal with that you know massive change happening from a code based standpoint I've heard you good presentation on you know the history of Linux and kubernetes and what's going forward so when it comes to the release of kubernetes where are you would open ship and how should people be thinking about you know upgrading from version yeah another excellent points to it's you've been following this pretty closely over the years so we're where we came at this was we actually learned quite a bit from our experience the company with OpenStack and so what would happen the OpenStack is you'd have customers that are on a certain version OpenStack and they kept saying hey look you know we want to consume clothes of drugs we want new features we will move faster and you know we'd obviously spend some time right from the release in community to actually shipping our distribution into customers and you know there's gonna be some more time for testing in QE to happen and some integration points that need to be certified before we make it available we often found that customer all right so they'd be let's say a small subset if you will within every customer or several customers who want to be close could you close the trunk majority actually wanted the stability especially as you know time wore on right they were Wonder sensibility and you can understand that right because now if you've got mission-critical applications running on it you don't necessarily want to go ahead and and you know put that at risk so the challenge that we addressed when we actually started shipping OpenShift for last summit right so so about a year ago was to say how can we provide you basically a way to help upgrade your clusters you know essentially remotely so you can upgrade if you will your clusters or at least be able to consume them at different speeds all right so what we introduced with open shop for was this ability to give you the on the over-the-air updates right so best we think about it is with regard to a phone all right so you know you have your phone you know new OS upgrades show up you get a notification you turn it on and you say hey look pull it down or you say it's their important time or you can go off and delay you know I do it a different point in time that same notion now exists within open show right which is to say we provide you three channels right so there's a stable channel where you're saying hey look you know maybe this cluster is production no no rush here you know I'll stay you know add or even even little further behind there's a fast Channel right for hey I want to be up latest and greatest or there's a third channel which allows for essentially features that are being in developed or still in early stage of development to be pushed out tree so now you can start you know consuming these upgrades based on hey I've got a dev team you know they want here with these quicker you know I've got these you know application that stable production right no rush it and then you can start managing that you know better yourself right so now if you will do that here will be that we're introducing into a kubernetes platform us the under kubernetes platform but adding additional value to be able to have that be managed much much in a much better fashion that observed the different needs of different if an organization allows for them to move at different speeds but at the same time gives you that same consistent platform with all this way running all right so a chef we started out the conversation talking about open shift anywhere and everywhere so you know in the cloud you talked about you know sitting on top of vmware vm farms very prevalent the data centers you know or bare bare metal i believe if i saw right one of the updates for open shift is how RedHat virtualization is you know working with open shift there and you know a lot of people out there kind of staring at what vmware did would be sore seven so maybe you can set it up a little bit of a compare contrast as to how you know red hats doing this rollout versus what you're seeing your partner vmware doing for how kubernetes fits into the virtualization fire yeah I feel like we're both approaching it from you know different perspective and land set that we come at it right so if I can the VMware perspective is likely hey look you know there's all of these installation in the vSphere you know in the marketplace you know how can we make sure that we help you know bring containers there and they've come up with a solution that you can argue is quite complicated in the way how they're achieving it our approach is different one right so we've always you know looked at this problem from the get-go with regard to containers is a new paradigm shift right it's not necessarily a revolution because most companies that we're looking at are working with existing application services but it's an evolution and in the way you know you're thinking about the world but this is definitely the long-term future and so how can we then think about you know introducing this environment this application platform into your environment and then be able to build or build a new application in it but also bring in existing applications to before and so with this release of open ship what we introducing is something a bit for calling open ship virtualization now which is if you got existing applications that sit in VMs how can we ensure that we bring those VMs into the platform but you know they've been certified their security boundaries around it or you know constraints or reforms have been put by your own internal organization around it and we can keep all of those but then still encapsulate that VM as a container have that be run natively within an environment and orchestrated by open ship you know kubernetes as the primary Orchestrator of those VMs just like it does with everything else that's cloud native orb or is running directly as container as well we think that's extremely powerful for us to really bring now the promise of urban Eddie's into a much wider market rights I talked about 79 customers you can argue that that 1700 is the early majority right or who else are the the almost scratching of the surface of the numbers that we believe will adopt this platform to get if you will the next if set of whatever five ten twenty thousand customers will have to make sure we meet them where they are now that you're introducing this notion of saying we can help migrate with a you know a series of tools that were also providing these VM based applications and then have them run within kubernetes in a consistent fashion it's going to extremely powerful really excited by it by those capabilities that predict that to our customers well I I think that puts a great exclamation point as to how we go from these early days off to you know the vast majority of environments yes once again congratulations to you and the team on the growth of momentum all the customer stories you know I've loved the opportunity to talk to many of the Red Hat customers about their digital transformation and how your cloud platform has been a piece of it so once again always a pleasure to catch up with you likewise thanks a lot Stuart good chatting with you and hope to see you in person soon absolutely.we at the cube of course hope to see you at events later in 2020 for the time being we of course fully digital always online check out the cube net for all of the archives as well as the event including all the digital ones that we are doing I'm sue minimun and as always thanks for watching the cube [Music]

Published Date : Apr 1 2020

SUMMARY :

in the industry so you know as you're

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UNLIST TILL 4/2 - Autonomous Log Monitoring


 

>> Sue: Hi everybody, thank you for joining us today for the virtual Vertica BDC 2020. Today's breakout session is entitled "Autonomous Monitoring Using Machine Learning". My name is Sue LeClaire, director of marketing at Vertica, and I'll be your host for this session. Joining me is Larry Lancaster, founder and CTO at Zebrium. Before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment in the question box below the slide and click submit. There will be a Q&A session at the end of the presentation and we'll answer as many questions as we're able to during that time. Any questions that we don't address, we'll do our best to answer them offline. Alternatively, you can also go and visit Vertica forums to post your questions after the session. Our engineering team is planning to join the forums to keep the conversation going. Also, just a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slides. And yes, this virtual session is being recorded and will be available for you to view on demand later this week. We'll send you a notification as soon as it's ready. So, let's get started. Larry, over to you. >> Larry: Hey, thanks so much. So hi, my name's Larry Lancaster and I'm here to talk to you today about something that I think who's time has come and that's autonomous monitoring. So, with that, let's get into it. So, machine data is my life. I know that's a sad life, but it's true. So I've spent most of my career kind of taking telemetry data from products, either in the field, we used to call it in the field or nowadays, that's been deployed, and bringing that data back, like log file stats, and then building stuff on top of it. So, tools to run the business or services to sell back to users and customers. And so, after doing that a few times, it kind of got to the point where I was really sort of sick of building the same kind of thing from scratch every time, so I figured, why not go start a company and do it so that we don't have to do it manually ever again. So, it's interesting to note, I've put a little sentence here saying, "companies where I got to use Vertica" So I've been actually kind of working with Vertica for a long time now, pretty much since they came out of alpha. And I've really been enjoying their technology ever since. So, our vision is basically that I want a system that will characterize incidents before I notice. So an incident is, you know, we used to call it a support case or a ticket in IT, or a support case in support. Nowadays, you may have a DevOps team, or a set of SREs who are monitoring a production sort of deployment. And so they'll call it an incident. So I'm looking for something that will notice and characterize an incident before I notice and have to go digging into log files and stats to figure out what happened. And so that's a pretty heady goal. And so I'm going to talk a little bit today about how we do that. So, if we look at logs in particular. Logs today, if you look at log monitoring. So monitoring is kind of that whole umbrella term that we use to talk about how we monitor systems in the field that we've shipped, or how we monitor production deployments in a more modern stack. And so basically there are log monitoring tools. But they have a number of drawbacks. For one thing, they're kind of slow in the sense that if something breaks and I need to go to a log file, actually chances are really good that if you have a new issue, if it's an unknown unknown problem, you're going to end up in a log file. So the problem then becomes basically you're searching around looking for what's the root cause of the incident, right? And so that's kind of time-consuming. So, they're also fragile and this is largely because log data is completely unstructured, right? So there's no formal grammar for a log file. So you have this situation where, if I write a parser today, and that parser is going to do something, it's going to execute some automation, it's going to open or update a ticket, it's going to maybe restart a service, or whatever it is that I want to happen. What'll happen is later upstream, someone who's writing the code that produces that log message, they might do something really useful for me, or for users. And they might go fix a spelling mistake in that log message. And then the next thing you know, all the automation breaks. So it's a very fragile source for automation. And finally, because of that, people will set alerts on, "Oh, well tell me how many thousands of errors are happening every hour." Or some horrible metric like that. And then that becomes the only visibility you have in the data. So because of all this, it's a very human-driven, slow, fragile process. So basically, we've set out to kind of up-level that a bit. So I touched on this already, right? The truth is if you do have an incident, you're going to end up in log files to do root cause. It's almost always the case. And so you have to wonder, if that's the case, why do most people use metrics only for monitoring? And the reason is related to the problems I just described. They're already structured, right? So for logs, you've got this mess of stuff, so you only want to dig in there when you absolutely have to. But ironically, it's where a lot of the information that you need actually is. So we have a model today, and this model used to work pretty well. And that model is called "index and search". And it basically means you treat log files like they're text documents. And so you index them and when there's some issue you have to drill into, then you go searching, right? So let's look at that model. So 20 years ago, we had sort of a shrink-wrap software delivery model. You had an incident. With that incident, maybe you had one customer and you had a monolithic application and a handful of log files. So it's perfectly natural, in fact, usually you could just v-item the log file, and search that way. Or if there's a lot of them, you could index them and search them that way. And that all worked very well because the developer or the support engineer had to be an expert in those few things, in those few log files, and understand what they meant. But today, everything has changed completely. So we live in a software as a service world. What that means is, for a given incident, first of all you're going to be affecting thousands of users. You're going to have, potentially, 100 services that are deployed in your environment. You're going to have 1,000 log streams to sift through. And yet, you're still kind of stuck in the situation where to go find out what's the matter, you're going to have to search through the log files. So this is kind of the unacceptable sort of position we're in today. So for us, the future will not be index and search. And that's simply because it cannot scale. And the reason I say that it can't scale is because it all kind of is bottlenecked by a person and their eyeball. So, you continue to drive up the amount of data that has to be sifted through, the complexity of the stack that has to be understood, and you still, at the end of the day, for MTTR purposes, you still have the same bottleneck, which is the eyeball. So this model, I believe, is fundamentally broken. And that's why, I believe in five years you're going to be in a situation where most monitoring of unknown unknown problems is going to be done autonomously. And those issues will be characterized autonomously because there's no other way it can happen. So now I'm going to talk a little bit about autonomous monitoring itself. So, autonomous monitoring basically means, if you can imagine in a monitoring platform and you watch the monitoring platform, maybe you watch the alerts coming from it or more importantly, you kind of watch the dashboards and try to see if something looks weird. So autonomous monitoring is the notion that the platform should do the watching for you and only let you know when something is going wrong and should kind of give you a window into what happened. So if you look at this example I have on screen, just to take it really slow and absorb the concept of autonomous monitoring. So here in this example, we've stopped the database. And as a result, down below you can see there were a bunch of fallout. This is an Atlassian Stack, so you can imagine you've got a Postgres database. And then you've got sort of Bitbucket, and Confluence, and Jira, and these various other components that need the database operating in order to function. So what this is doing is it's calling out, "Hey, the root cause is the database stopped and here's the symptoms." Now, you might be wondering, so what. I mean I could go write a script to do this sort of thing. Here's what's interesting about this very particular example, and I'll show a couple more examples that are a little more involved. But here's the interesting thing. So, in the software that came up with this incident and opened this incident and put this root cause and symptoms in there, there's no code that knows anything about timestamp formats, severities, Atlassian, Postgres, databases, Bitbucket, Confluence, there's no regexes that talk about starting, stopped, RDBMS, swallowed exception, and so on and so forth. So you might wonder how it's possible then, that something which is completely ignorant of the stack, could come up with this description, which is exactly what a human would have had to do, to figure out what happened. And I'm going to get into how we do that. But that's what autonomous monitoring is about. It's about getting into a set of telemetry from a stack with no prior information, and understanding when something breaks. And I could give you the punchline right now, which is there are fundamental ways that software behaves when it's breaking. And by looking at hundreds of data sets that people have generously allowed us to use containing incidents, we've been able to characterize that and now generalize it to apply it to any new data set and stack. So here's an interesting one right here. So there's a fella, David Gill, he's just a genius in the monitoring space. He's been working with us for the last couple of months. So he said, "You know what I'm going to do, is I'm going to run some chaos experiments." So for those of you who don't know what chaos engineering is, here's the idea. So basically, let's say I'm running a Kubernetes cluster and what I'll do is I'll use sort of a chaos injection test, something like litmus. And basically it will inject issues, it'll break things in my application randomly to see if my monitoring picks it up. And so this is what chaos engineering is built around. It's built around sort of generating lots of random problems and seeing how the stack responds. So in this particular case, David went in and he deleted, basically one of the tests that was presented through litmus did a delete of a pod delete. And so that's going to basically take out some containers that are part of the service layer. And so then you'll see all kinds of things break. And so what you're seeing here, which is interesting, this is why I like to use this example. Because it's actually kind of eye-opening. So the chaos tool itself generates logs. And of course, through Kubernetes, all the log files locations that are on the host, and the container logs are known. And those are all pulled back to us automatically. So one of the log files we have is actually the chaos tool that's doing the breaking, right? And so what the tool said here, when it went to determine what the root cause was, was it noticed that there was this process that had these messages happen, initializing deletion lists, selection a pod to kill, blah blah blah. It's saying that the root cause is the chaos test. And it's absolutely right, that is the root cause. But usually chaos tests don't get picked up themselves. You're supposed to be just kind of picking up the symptoms. But this is what happens when you're able to kind of tease out root cause from symptoms autonomously, is you end up getting a much more meaningful answer, right? So here's another example. So essentially, we collect the log files, but we also have a Prometheus scraper. So if you export Prometheus metrics, we'll scrape those and we'll collect those as well. And so we'll use those for our autonomous monitoring as well. So what you're seeing here is an issue where, I believe this is where we ran something out of disk space. So it opened an incident, but what's also interesting here is, you see that it pulled that metric to say that the spike in this metric was a symptom of this running out of space. So again, there's nothing that knows anything about file system usage, memory, CPU, any of that stuff. There's no actual hard-coded logic anywhere to explain any of this. And so the concept of autonomous monitoring is looking at a stack the way a human being would. If you can imagine how you would walk in and monitor something, how you would think about it. You'd go looking around for rare things. Things that are not normal. And you would look for indicators of breakage, and you would see, do those seem to be correlated in some dimension? That is how the system works. So as I mentioned a moment ago, metrics really do kind of complete the picture for us. We end up in a situation where we have a one-stop shop for incident root cause. So, how does that work? Well, we ingest and we structure the log files. So if we're getting the logs, we'll ingest them and we'll structure them, and I'm going to show a little bit what that structure looks like and how that goes into the database in a moment. And then of course we ingest and structure the Prometheus metrics. But here, structure really should have an asterisk next to it, because metrics are mostly structured already. They have names. If you have your own scraper, as opposed to going into the time series Prometheus database and pulling metrics from there, you can keep a lot more information about metadata about those metrics from the exporter's perspective. So we keep all of that too. Then we do our anomaly detection on both of those sets of data. And then we cross-correlate metrics and log anomalies. And then we create incidents. So this is at a high level, kind of what's happening without any sort of stack-specific logic built in. So we had some exciting recent validation. So Mayadata's a pretty big player in the Kubernetes space. Essentially, they do Kubernetes as a managed service. They have tens of thousands of customers that they manage their Kubernetes clusters for them. And then they're also involved, both in the OpenEBS project, as well as in the Litmius project I mentioned a moment ago. That's their tool for chaos engineering. So they're a pretty big player in the Kubernetes space. So essentially, they said, "Oh okay, let's see if this is real." So what they did was they set up our collectors, which took three minutes in Kubernetes. And then they went and they, using Litmus, they reproduced eight incidents that their actual, real-world customers had hit. And they were trying to remember the ones that were the hardest to figure out the root cause at the time. And we picked up and put a root cause indicator that was correct in 100% of these incidents with no training configuration or metadata required. So this is kind of what autonomous monitoring is all about. So now I'm going to talk a little bit about how it works. So, like I said, there's no information included or required about, so if you imagine a log file for example. Now, commonly, over to the left-hand side of every line, there will be some sort of a prefix. And what I mean by that is you'll see like a timestamp, or a severity, and maybe there's a PID, and maybe there's function name, and maybe there's some other stuff there. So basically that's kind of, it's common data elements for a large portion of the lines in a given log file. But you know, of course, the contents change. So basically today, like if you look at a typical log manager, they'll talk about connectors. And what connectors means is, for an application it'll generate a certain prefix format in a log. And that means what's the format of the timestamp, and what else is in the prefix. And this lets the tool pick it up. And so if you have an app that doesn't have a connector, you're out of luck. Well, what we do is we learn those prefixes dynamically with machine learning. You do not have to have a connector, right? And what that means is that if you come in with your own application, the system will just work for it from day one. You don't have to have connectors, you don't have to describe the prefix format. That's so yesterday, right? So really what we want to be doing is up-leveling what the system is doing to the point where it's kind of working like a human would. You look at a log line, you know what's a timestamp. You know what's a PID. You know what's a function name. You know where the prefix ends and where the variable parts begin. You know what's a parameter over there in the variable parts. And sometimes you may need to see a couple examples to know what was a variable, but you'll figure it out as quickly as possible, and that's exactly how the system goes about it. As a result, we kind of embrace free-text logs, right? So if you look at a typical stack, most of the logs generated in a typical stack are usually free-text. Even structured logging typically will have a message attribute, which then inside of it has the free-text message. For us, that's not a bad thing. That's okay. The purpose of a log is to inform people. And so there's no need to go rewrite the whole logging stack just because you want a machine to handle it. They'll figure it out for themselves, right? So, you give us the logs and we'll figure out the grammar, not only for the prefix but also for the variable message part. So I already went into this, but there's more that's usually required for configuring a log manager with alerts. You have to give it keywords. You have to give it application behaviors. You have to tell it some prior knowledge. And of course the problem with all of that is that the most important events that you'll ever see in a log file are the rarest. Those are the ones that are one out of a billion. And so you may not know what's going to be the right keyword in advance to pick up the next breakage, right? So we don't want that information from you. We'll figure that out for ourselves. As the data comes in, essentially we parse it and we categorize it, as I've mentioned. And when I say categorize, what I mean is, if you look at a certain given log file, you'll notice that some of the lines are kind of the same thing. So this one will say "X happened five times" and then maybe a few lines below it'll say "X happened six times" but that's basically the same event type. It's just a different instance of that event type. And it has a different value for one of the parameters, right? So when I say categorization, what I mean is figuring out those unique types and I'll show an example of that next. Anomaly detection, we do on top of that. So anomaly detection on metrics in a very sort of time series by time series manner with lots of tunables is a well-understood problem. So we also do this on the event types occurrences. So you can think of each event type occurring in time as sort of a point process. And then you can develop statistics and distributions on that, and you can do anomaly detection on those. Once we have all of that, we have extracted features, essentially, from metrics and from logs. We do pattern recognition on the correlations across different channels of information, so different event types, different log types, different hoses, different containers, and then of course across to the metrics. Based on all of this cross-correlation, we end up with a root cause identification. So that's essentially, at a high level, how it works. What's interesting, from the perspective of this call particularly, is that incident detection needs relationally structured data. It really does. You need to have all the instances of a certain event type that you've ever seen easily accessible. You need to have the values for a given sort of parameter easily, quickly available so you can figure out what's the distribution of this over time, how often does this event type happen. You can run analytical queries against that information so that you can quickly, in real-time, do anomaly detection against new data. So here's an example of that this looks like. And this kind of part of the work that we've done. At the top you see some examples of log lines, right? So that's kind of a snippet, it's three lines out of a log file. And you see one in the middle there that's kind of highlighted with colors, right? I mean, it's a little messy, but it's not atypical of the log file that you'll see pretty much anywhere. So there, you've got a timestamp, and a severity, and a function name. And then you've got some other information. And then finally, you have the variable part. And that's going to have sort of this checkpoint for memory scrubbers, probably something that's written in English, just so that the person who's reading the log file can understand. And then there's some parameters that are put in, right? So now, if you look at how we structure that, the way it looks is there's going to be three tables that correspond to the three event types that we see above. And so we're going to look at the one that corresponds to the one in the middle. So if we look at that table, there you'll see a table with columns, one for severity, for function name, for time zone, and so on. And date, and PID. And then you see over to the right with the colored columns there's the parameters that were pulled out from the variable part of that message. And so they're put in, they're typed and they're in integer columns. So this is the way structuring needs to work with logs to be able to do efficient and effective anomaly detection. And as far as I know, we're the first people to do this inline. All right, so let's talk now about Vertica and why we take those tables and put them in Vertica. So Vertica really is an MPP column store, but it's more than that, because nowadays when you say "column store", people sort of think, like, for example Cassandra's a column store, whatever, but it's not. Cassandra's not a column store in the sense that Vertica is. So Vertica was kind of built from the ground up to be... So it's the original column store. So back in the cStor project at Berkeley that Stonebraker was involved in, he said let's explore what kind of efficiencies we can get out of a real columnar database. And what he found was that, he and his grad students that started Vertica. What they found was that what they can do is they could build a database that gives orders of magnitude better query performance for the kinds of analytics I'm talking about here today. With orders of magnitude less data storage underneath. So building on top of machine data, as I mentioned, is hard, because it doesn't have any defined schemas. But we can use an RDBMS like Vertica once we've structured the data to do the analytics that we need to do. So I talked a little bit about this, but if you think about machine data in general, it's perfectly suited for a columnar store. Because, if you imagine laying out sort of all the attributes of an event type, right? So you can imagine that each occurrence is going to have- So there may be, say, three or four function names that are going to occur for all the instances of a given event type. And so if you were to sort all of those event instances by function name, what you would find is that you have sort of long, million long runs of the same function name over and over. So what you have, in general, in machine data, is lots and lots of slowly varying attributes, lots of low-cardinality data that it's almost completely compressed out when you use a real column store. So you end up with a massive footprint reduction on disk. And it also, that propagates through the analytical pipeline. Because Vertica does late materialization, which means it tries to carry that data through memory with that same efficiency, right? So the scale-out architecture, of course, is really suitable for petascale workloads. Also, I should point out, I was going to mention it in another slide or two, but we use the Vertica Eon architecture, and we have had no problems scaling that in the cloud. It's a beautiful sort of rewrite of the entire data layer of Vertica. The performance and flexibility of Eon is just unbelievable. And so I've really been enjoying using it. I was skeptical, you could get a real column store to run in the cloud effectively, but I was completely wrong. So finally, I should mention that if you look at column stores, to me, Vertica is the one that has the full SQL support, it has the ODBC drivers, it has the ACID compliance. Which means I don't need to worry about these things as an application developer. So I'm laying out the reasons that I like to use Vertica. So I touched on this already, but essentially what's amazing is that Vertica Eon is basically using S3 as an object store. And of course, there are other offerings, like the one that Vertica does with pure storage that doesn't use S3. But what I find amazing is how well the system performs using S3 as an object store, and how they manage to keep an actual consistent database. And they do. We've had issues where we've gone and shut down hosts, or hosts have been shut down on us, and we have to restart the database and we don't have any consistency issues. It's unbelievable, the work that they've done. Essentially, another thing that's great about the way it works is you can use the S3 as a shared object store. You can have query nodes kind of querying from that set of files largely independently of the nodes that are writing to them. So you avoid this sort of bottleneck issue where you've got contention over who's writing what, and who's reading what, and so on. So I've found the performance using separate subclusters for our UI and for the ingest has been amazing. Another couple of things that they have is they have a lot of in-database machine learning libraries. There's actually some cool stuff on their GitHub that we've used. One thing that we make a lot of use of is the sequence and time series analytics. For example, in our product, even though we do all of this stuff autonomously, you can also go create alerts for yourself. And one of the kinds of alerts you can do, you can say, "Okay, if this kind of event happens within so much time, and then this kind of an event happens, but not this one," Then you can be alerted. So you can have these kind of sequences that you define of events that would indicate a problem. And we use their sequence analytics for that. So it kind of gives you really good performance on some of these queries where you're wanting to pull out sequences of events from a fact table. And timeseries analytics is really useful if you want to do analytics on the metrics and you want to do gap filling interpolation on that. It's actually really fast in performance. And it's easy to use through SQL. So those are a couple of Vertica extensions that we use. So finally, I would like to encourage everybody, hey, come try us out. Should be up and running in a few minutes if you're using Kubernetes. If not, it's however long it takes you to run an installer. So you can just come to our website, pick it up and try out autonomous monitoring. And I want to thank everybody for your time. And we can open it up for Q and A.

Published Date : Mar 30 2020

SUMMARY :

Also, just a reminder that you can maximize your screen And one of the kinds of alerts you can do, you can say,

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UNLIST TILL 4/2 - Extending Vertica with the Latest Vertica Ecosystem and Open Source Initiatives


 

>> Sue: Hello everybody. Thank you for joining us today for the Virtual Vertica BDC 2020. Today's breakout session in entitled Extending Vertica with the Latest Vertica Ecosystem and Open Source Initiatives. My name is Sue LeClaire, Director of Marketing at Vertica and I'll be your host for this webinar. Joining me is Tom Wall, a member of the Vertica engineering team. But before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click submit. There will be a Q and A session at the end of the presentation. We'll answer as many questions as we're able to during that time. Any questions that we don't get to, we'll do our best to answer them offline. Alternatively, you can visit the Vertica forums to post you questions after the session. Our engineering team is planning to join the forums to keep the conversation going. Also a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slides. And yes, this virtual session is being recorded and will be available to view on demand later this week. We'll send you a notification as soon as it's ready. So let's get started. Tom, over to you. >> Tom: Hello everyone and thanks for joining us today for this talk. My name is Tom Wall and I am the leader of Vertica's ecosystem engineering team. We are the team that focuses on building out all the developer tools, third party integrations that enables the SoftMaker system that surrounds Vertica to thrive. So today, we'll be talking about some of our new open source initatives and how those can be really effective for you and make things easier for you to build and integrate Vertica with the rest of your technology stack. We've got several new libraries, integration projects and examples, all open source, to share, all being built out in the open on our GitHub page. Whether you use these open source projects or not, this is a very exciting new effort that will really help to grow the developer community and enable lots of exciting new use cases. So, every developer out there has probably had to deal with the problem like this. You have some business requirements, to maybe build some new Vertica-powered application. Maybe you have to build some new system to visualize some data that's that's managed by Vertica. The various circumstances, lots of choices will might be made for you that constrain your approach to solving a particular problem. These requirements can come from all different places. Maybe your solution has to work with a specific visualization tool, or web framework, because the business has already invested in the licensing and the tooling to use it. Maybe it has to be implemented in a specific programming language, since that's what all the developers on the team know how to write code with. While Vertica has many different integrations with lots of different programming language and systems, there's a lot of them out there, and we don't have integrations for all of them. So how do you make ends meet when you don't have all the tools you need? All you have to get creative, using tools like PyODBC, for example, to bridge between programming languages and frameworks to solve the problems you need to solve. Most languages do have an ODBC-based database interface. ODBC is our C-Library and most programming languages know how to call C code, somehow. So that's doable, but it often requires lots of configuration and troubleshooting to make all those moving parts work well together. So that's enough to get the job done but native integrations are usually a lot smoother and easier. So rather than, for example, in Python trying to fight with PyODBC, to configure things and get Unicode working, and to compile all the different pieces, the right way is to make it all work smoothly. It would be much better if you could just PIP install library and get to work. And with Vertica-Python, a new Python client library, you can actually do that. So that story, I assume, probably sounds pretty familiar to you. Sounds probably familiar to a lot of the audience here because we're all using Vertica. And our challenge, as Big Data practitioners is to make sense of all this stuff, despite those technical and non-technical hurdles. Vertica powers lots of different businesses and use cases across all kinds of different industries and verticals. While there's a lot different about us, we're all here together right now for this talk because we do have some things in common. We're all using Vertica, and we're probably also using Vertica with other systems and tools too, because it's important to use the right tool for the right job. That's a founding principle of Vertica and it's true today too. In this constantly changing technology landscape, we need lots of good tools and well established patterns, approaches, and advice on how to combine them so that we can be successful doing our jobs. Luckily for us, Vertica has been designed to be easy to build with and extended in this fashion. Databases as a whole had had this goal from the very beginning. They solve the hard problems of managing data so that you don't have to worry about it. Instead of worrying about those hard problems, you can focus on what matters most to you and your domain. So implementing that business logic, solving that problem, without having to worry about all of these intense, sometimes details about what it takes to manage a database at scale. With the declarative syntax of SQL, you tell Vertica what the answer is that you want. You don't tell Vertica how to get it. Vertica will figure out the right way to do it for you so that you don't have to worry about it. So this SQL abstraction is very nice because it's a well defined boundary where lots of developers know SQL, and it allows you to express what you need without having to worry about those details. So we can be the experts in data management while you worry about your problems. This goes beyond though, what's accessible through SQL to Vertica. We've got well defined extension and integration points across the product that allow you to customize this experience even further. So if you want to do things write your own SQL functions, or extend database softwares with UDXs, you can do so. If you have a custom data format that might be a proprietary format, or some source system that Vertica doesn't natively support, we have extension points that allow you to use those. To make it very easy to do passive, parallel, massive data movement, loading into Vertica but also to export Vertica to send data to other systems. And with these new features in time, we also could do the same kinds of things with Machine Learning models, importing and exporting to tools like TensorFlow. And it's these integration points that have enabled Vertica to build out this open architecture and a rich ecosystem of tools, both open source and closed source, of different varieties that solve all different problems that are common in this big data processing world. Whether it's open source, streaming systems like Kafka or Spark, or more traditional ETL tools on the loading side, but also, BI tools and visualizers and things like that to view and use the data that you keep in your database on the right side. And then of course, Vertica needs to be flexible enough to be able to run anywhere. So you can really take Vertica and use it the way you want it to solve the problems that you need to solve. So Vertica has always employed open standards, and integrated it with all kinds of different open source systems. What we're really excited to talk about now is that we are taking our new integration projects and making those open source too. In particular, we've got two new open source client libraries that allow you to build Vertica applications for Python and Go. These libraries act as a foundation for all kinds of interesting applications and tools. Upon those libraries, we've also built some integrations ourselves. And we're using these new libraries to power some new integrations with some third party products. Finally, we've got lots of new examples and reference implementations out on our GitHub page that can show you how to combine all these moving parts and exciting ways to solve new problems. And the code for all these things is available now on our GitHub page. And so you can use it however you like, and even help us make it better too. So the first such project that we have is called Vertica-Python. Vertica-Python began at our customer, Uber. And then in late 2018, we collaborated with them and we took it over and made Vertica-Python the first official open source client for Vertica You can use this to build your own Python applications, or you can use it via tools that were written in Python. Python has grown a lot in recent years and it's very common language to solve lots of different problems and use cases in the Big Data space from things like DevOps admission and Data Science or Machine Learning, or just homegrown applications. We use Python a lot internally for our own QA testing and automation needs. And with the Python 2 End Of Life, that happened at the end of 2019, it was important that we had a robust Python solution to help migrate our internal stuff off of Python 2. And also to provide a nice migration path for all of you our users that might be worried about the same problems with their own Python code. So Vertica-Python is used already for lots of different tools, including Vertica's admintools now starting with 9.3.1. It was also used by DataDog to build a Vertica-DataDog integration that allows you to monitor your Vertica infrastructure within DataDog. So here's a little example of how you might use the Python Client to do some some work. So here we open in connection, we run a query to find out what node we've connected to, and then we do a little DataLoad by running a COPY statement. And this is designed to have a familiar look and feel if you've ever used a Python Database Client before. So we implement the DB API 2.0 standard and it feels like a Python package. So that includes things like, it's part of the centralized package manager, so you can just PIP install this right now and go start using it. We also have our client for Go length. So this is called vertica-sql-go. And this is a very similar story, just in a different context or the different programming language. So vertica-sql-go, began as a collaboration with the Microsoft Focus SecOps Group who builds microfocus' security products some of which use vertica internally to provide some of those analytics. So you can use this to build your own apps in the Go programming language but you can also use it via tools that are written Go. So most notably, we have our Grafana integration, which we'll talk a little bit more about later, that leverages this new clients to provide Grafana visualizations for vertica data. And Go is another rising popularity programming language 'cause it offers an interesting balance of different programming design trade-offs. So it's got good performance, got a good current concurrency and memory safety. And we liked all those things and we're using it to power some internal monitoring stuff of our own. And here's an example of the code you can write with this client. So this is Go code that does a similar thing. It opens a connection, it runs a little test query, and then it iterates over those rows, processing them using Go data types. You get that native look and feel just like you do in Python, except this time in the Go language. And you can go get it the way you usually package things with Go by running that command there to acquire this package. And it's important to note here for the DC projects, we're really doing open source development. We're not just putting code out on our GitHub page. So if you go out there and look, you can see that you can ask questions, you can report bugs, you can submit poll requests yourselves and you can collaborate directly with our engineering team and the other vertica users out on our GitHub page. Because it's out on our GitHub page, it allows us to be a little bit faster with the way we ship and deliver functionality compared to the core vertica release cycle. So in 2019, for example, as we were building features to prepare for the Python 3 migration, we shipped 11 different releases with 40 customer reported issues, filed on GitHub. That was done over 78 different poll requests and with lots of community engagement as we do so. So lots of people are using this already, we see as our GitHub badge last showed with about 5000 downloads of this a day of people using it in their software. And again, we want to make this easy, not just to use but also to contribute and understand and collaborate with us. So all these projects are built using the Apache 2.0 license. The master branch is always available and stable with the latest creative functionality. And you can always build it and test it the way we do so that it's easy for you to understand how it works and to submit contributions or bug fixes or even features. It uses automated testing both for locally and with poll requests. And for vertica-python, it's fully automated with Travis CI. So we're really excited about doing this and we're really excited about where it can go in the future. 'Cause this offers some exciting opportunities for us to collaborate with you more directly than we have ever before. You can contribute improvements and help us guide the direction of these projects, but you can also work with each other to share knowledge and implementation details and various best practices. And so maybe you think, "Well, I don't use Python, "I don't use go so maybe it doesn't matter to me." But I would argue it really does matter. Because even if you don't use these tools and languages, there's lots of amazing vertica developers out there who do. And these clients do act as low level building blocks for all kinds of different interesting tools, both in these Python and Go worlds, but also well beyond that. Because these implementations and examples really generalize to lots of different use cases. And we're going to do a deeper dive now into some of these to understand exactly how that's the case and what you can do with these things. So let's take a deeper look at some of the details of what it takes to build one of these open source client libraries. So these database client interfaces, what are they exactly? Well, we all know SQL, but if you look at what SQL specifies, it really only talks about how to manipulate the data within the database. So once you're connected and in, you can run commands with SQL. But these database client interfaces address the rest of those needs. So what does the programmer need to do to actually process those SQL queries? So these interfaces are specific to a particular language or a technology stack. But the use cases and the architectures and design patterns are largely the same between different languages. They all have a need to do some networking and connect and authenticate and create a session. They all need to be able to run queries and load some data and deal with problems and errors. And then they also have a lot of metadata and Type Mapping because you want to use these clients the way you use those programming languages. Which might be different than the way that vertica's data types and vertica's semantics work. So some of this client interfaces are truly standards. And they are robust enough in terms of what they design and call for to support a truly pluggable driver model. Where you might write an application that codes directly against the standard interface, and you can then plug in a different database driver, like a JDBC driver, to have that application work with any database that has a JDBC driver. So most of these interfaces aren't as robust as a JDBC or ODBC but that's okay. 'Cause it's good as a standard is, every database is unique for a reason. And so you can't really expose all of those unique properties of a database through these standard interfaces. So vertica's unique in that it can scale to the petabytes and beyond. And you can run it anywhere in any environment, whether it's on-prem or on clouds. So surely there's something about vertica that's unique, and we want to be able to take advantage of that fact in our solutions. So even though these standards might not cover everything, there's often a need and common patterns that arise to solve these problems in similar ways. When there isn't enough of a standard to define those comments, semantics that different databases might have in common, what you often see is tools will invent plug in layers or glue code to compensate by defining application wide standard to cover some of these same semantics. Later on, we'll get into some of those details and show off what exactly that means. So if you connect to a vertica database, what's actually happening under the covers? You have an application, you have a need to run some queries, so what does that actually look like? Well, probably as you would imagine, your application is going to invoke some API calls and some client library or tool. This library takes those API calls and implements them, usually by issuing some networking protocol operations, communicating over the network to ask vertica to do the heavy lifting required for that particular API call. And so these API's usually do the same kinds of things although some of the details might differ between these different interfaces. But you do things like establish a connection, run a query, iterate over your rows, manage your transactions, that sort of thing. Here's an example from vertica-python, which just goes into some of the details of what actually happens during the Connect API call. And you can see all these details in our GitHub implementation of this. There's actually a lot of moving parts in what happens during a connection. So let's walk through some of that and see what actually goes on. I might have my API call like this where I say Connect and I give it a DNS name, which is my entire cluster. And I give you my connection details, my username and password. And I tell the Python Client to get me a session, give me a connection so I can start doing some work. Well, in order to implement this, what needs to happen? First, we need to do some TCP networking to establish our connection. So we need to understand what the request is, where you're going to connect to and why, by pressing the connection string. and vertica being a distributed system, we want to provide high availability, so we might need to do some DNS look-ups to resolve that DNS name which might be an entire cluster and not just a single machine. So that you don't have to change your connection string every time you add or remove nodes to the database. So we do some high availability and DNS lookup stuff. And then once we connect, we might do Load Balancing too, to balance the connections across the different initiator nodes in the cluster, or in a sub cluster, as needed. Once we land on the node we want to be at, we might do some TLS to secure our connections. And vertica supports the industry standard TLS protocols, so this looks pretty familiar for everyone who've used TLS anywhere before. So you're going to do a certificate exchange and the client might send the server certificate too, and then you going to verify that the server is who it says it is, so that you can know that you trust it. Once you've established that connection, and secured it, then you can start actually beginning to request a session within vertica. So you going to send over your user information like, "Here's my username, "here's the database I want to connect to." You might send some information about your application like a session label, so that you can differentiate on the database with monitoring queries, what the different connections are and what their purpose is. And then you might also send over some session settings to do things like auto commit, to change the state of your session for the duration of this connection. So that you don't have to remember to do that with every query that you have. Once you've asked vertica for a session, before vertica will give you one, it has to authenticate you. and vertica has lots of different authentication mechanisms. So there's a negotiation that happens there to decide how to authenticate you. Vertica decides based on who you are, where you're coming from on the network. And then you'll do an auth-specific exchange depending on what the auth mechanism calls for until you are authenticated. Finally, vertica trusts you and lets you in, so you going to establish a session in vertica, and you might do some note keeping on the client side just to know what happened. So you might log some information, you might record what the version of the database is, you might do some protocol feature negotiation. So if you connect to a version of the database that doesn't support all these protocols, you might decide to turn some functionality off and that sort of thing. But finally, after all that, you can return from this API call and then your connection is good to go. So that connection is just one example of many different APIs. And we're excited here because with vertica-python we're really opening up the vertica client wire protocol for the first time. And so if you're a low level vertica developer and you might have used Postgres before, you might know that some of vertica's client protocol is derived from Postgres. But they do differ in many significant ways. And this is the first time we've ever revealed those details about how it works and why. So not all Postgres protocol features work with vertica because vertica doesn't support all the features that Postgres does. Postgres, for example, has a large object interface that allows you to stream very wide data values over. Whereas vertica doesn't really have very wide data values, you have 30, you have long bar charts, but that's about as wide as you can get. Similarly, the vertica protocol supports lots of features not present in Postgres. So Load Balancing, for example, which we just went through an example of, Postgres is a single node system, it doesn't really make sense for Postgres to have Load Balancing. But Load Balancing is really important for vertica because it is a distributed system. Vertica-python serves as an open reference implementation of this protocol. With all kinds of new details and extension points that we haven't revealed before. So if you look at these boxes below, all these different things are new protocol features that we've implemented since August 2019, out in the open on our GitHub page for Python. Now, the vertica-sql-go implementation of these things is still in progress, but the core protocols are there for basic query operations. There's more to do there but we'll get there soon. So this is really cool 'cause not only do you have now a Python Client implementation, and you have a Go client implementation of this, but you can use this protocol reference to do lots of other things, too. The obvious thing you could do is build more clients for other languages. So if you have a need for a client in some other language that are vertica doesn't support yet, now you have everything available to solve that problem and to go about doing so if you need to. But beyond clients, it's also used for other things. So you might use it for mocking and testing things. So rather than connecting to a real vertica database, you can simulate some of that. You can also use it to do things like query routing and proxies. So Uber, for example, this log here in this link tells a great story of how they route different queries to different vertical clusters by intercepting these protocol messages, parsing the queries in them and deciding which clusters to send them to. So a lot of these things are just ideas today, but now that you have the source code, there's no limit in sight to what you can do with this thing. And so we're very interested in hearing your ideas and requests and we're happy to offer advice and collaborate on building some of these things together. So let's take a look now at some of the things we've already built that do these things. So here's a picture of vertica's Grafana connector with some data powered from an example that we have in this blog link here. So this has an internet of things use case to it, where we have lots of different sensors recording flight data, feeding into Kafka which then gets loaded into vertica. And then finally, it gets visualized nicely here with Grafana. And Grafana's visualizations make it really easy to analyze the data with your eyes and see when something something happens. So in these highlighted sections here, you notice a drop in some of the activity, that's probably a problem worth looking into. It might be a lot harder to see that just by staring at a large table yourself. So how does a picture like that get generated with a tool like Grafana? Well, Grafana specializes in visualizing time series data. And time can be really tricky for computers to do correctly. You got time zones, daylight savings, leap seconds, negative infinity timestamps, please don't ever use those. In every system, if it wasn't hard enough, just with those problems, what makes it harder is that every system does it slightly differently. So if you're querying some time data, how do we deal with these semantic differences as we cross these domain boundaries from Vertica to Grafana's back end architecture, which is implemented in Go on it's front end, which is implemented with JavaScript? Well, you read this from bottom up in terms of the processing. First, you select the timestamp and Vertica is timestamp has to be converted to a Go time object. And we have to reconcile the differences that there might be as we translate it. So Go time has a different time zone specifier format, and it also supports nanosecond precision, while Vertica only supports microsecond precision. So that's not too big of a deal when you're querying data because you just see some extra zeros, not fractional seconds. But on the way in, if we're loading data, we have to find a way to resolve those things. Once it's into the Go process, it has to be converted further to render in the JavaScript UI. So that there, the Go time object has to be converted to a JavaScript Angular JS Date object. And there too, we have to reconcile those differences. So a lot of these differences might just be presentation, and not so much the actual data changing, but you might want to choose to render the date into a more human readable format, like we've done in this example here. Here's another picture. This is another picture of some time series data, and this one shows you can actually write your own queries with Grafana to provide answers. So if you look closely here you can see there's actually some functions that might not look too familiar with you if you know vertica's functions. Vertica doesn't have a dollar underscore underscore time function or a time filter function. So what's actually happening there? How does this actually provide an answer if it's not really real vertica syntax? Well, it's not sufficient to just know how to manipulate data, it's also really important that you know how to operate with metadata. So information about how the data works in the data source, Vertica in this case. So Grafana needs to know how time works in detail for each data source beyond doing that basic I/O that we just saw in the previous example. So it needs to know, how do you connect to the data source to get some time data? How do you know what time data types and functions there are and how they behave? How do you generate a query that references a time literal? And finally, once you've figured out how to do all that, how do you find the time in the database? How do you do know which tables have time columns and then they might be worth rendering in this kind of UI. So Go's database standard doesn't actually really offer many metadata interfaces. Nevertheless, Grafana needs to know those answers. And so it has its own plugin layer that provides a standardizing layer whereby every data source can implement hints and metadata customization needed to have an extensible data source back end. So we have another open source project, the Vertica-Grafana data source, which is a plugin that uses Grafana's extension points with JavaScript and the front end plugins and also with Go in the back end plugins to provide vertica connectivity inside Grafana. So the way this works, is that the plugin frameworks defines those standardizing functions like time and time filter, and it's our plugin that's going to rewrite them in terms of vertica syntax. So in this example, time gets rewritten to a vertica cast. And time filter becomes a BETWEEN predicate. So that's one example of how you can use Grafana, but also how you might build any arbitrary visualization tool that works with data in Vertica. So let's now look at some other examples and reference architectures that we have out in our GitHub page. For some advanced integrations, there's clearly a need to go beyond these standards. So SQL and these surrounding standards, like JDBC, and ODBC, were really critical in the early days of Vertica, because they really enabled a lot of generic database tools. And those will always continue to play a really important role, but the Big Data technology space moves a lot faster than these old database data can keep up with. So there's all kinds of new advanced analytics and query pushdown logic that were never possible 10 or 20 years ago, that Vertica can do natively. There's also all kinds of data-oriented application workflows doing things like streaming data, or Parallel Loading or Machine Learning. And all of these things, we need to build software with, but we don't really have standards to go by. So what do we do there? Well, open source implementations make for easier integrations, and applications all over the place. So even if you're not using Grafana for example, other tools have similar challenges that you need to overcome. And it helps to have an example there to show you how to do it. Take Machine Learning, for example. There's been many excellent Machine Learning tools that have arisen over the years to make data science and the task of Machine Learning lot easier. And a lot of those have basic database connectivity, but they generally only treat the database as a source of data. So they do lots of data I/O to extract data from a database like Vertica for processing in some other engine. We all know that's not the most efficient way to do it. It's much better if you can leverage Vertica scale and bring the processing to the data. So a lot of these tools don't take full advantage of Vertica because there's not really a uniform way to go do so with these standards. So instead, we have a project called vertica-ml-python. And this serves as a reference architecture of how you can do scalable machine learning with Vertica. So this project establishes a familiar machine learning workflow that scales with vertica. So it feels similar to like a scickit-learn project except all the processing and aggregation and heavy lifting and data processing happens in vertica. So this makes for a much more lightweight, scalable approach than you might otherwise be used to. So with vertica-ml-python, you can probably use this yourself. But you could also see how it works. So if it doesn't meet all your needs, you could still see the code and customize it to build your own approach. We've also got lots of examples of our UDX framework. And so this is an older GitHub project. We've actually had this for a couple of years, but it is really useful and important so I wanted to plug it here. With our User Defined eXtensions framework or UDXs, this allows you to extend the operators that vertica executes when it does a database load or a database query. So with UDXs, you can write your own domain logic in a C++, Java or Python or R. And you can call them within the context of a SQL query. And vertica brings your logic to that data, and makes it fast and scalable and fault tolerant and correct for you. So you don't have to worry about all those hard problems. So our UDX examples, demonstrate how you can use our SDK to solve interesting problems. And some of these examples might be complete, total usable packages or libraries. So for example, we have a curl source that allows you to extract data from any curlable endpoint and load into vertica. We've got things like an ODBC connector that allows you to access data in an external database via an ODBC driver within the context of a vertica query, all kinds of parsers and string processors and things like that. We also have more exciting and interesting things where you might not really think of vertica being able to do that, like a heat map generator, which takes some XY coordinates and renders it on top of an image to show you the hotspots in it. So the image on the right was actually generated from one of our intern gaming sessions a few years back. So all these things are great examples that show you not just how you can solve problems, but also how you can use this SDK to solve neat things that maybe no one else has to solve, or maybe that are unique to your business and your needs. Another exciting benefit is with testing. So the test automation strategy that we have in vertica-python these clients, really generalizes well beyond the needs of a database client. Anyone that's ever built a vertica integration or an application, probably has a need to write some integration tests. And that could be hard to do with all the moving parts, in the big data solution. But with our code being open source, you can see in vertica-python, in particular, how we've structured our tests to facilitate smooth testing that's fast, deterministic and easy to use. So we've automated the download process, the installation deployment process, of a Vertica Community Edition. And with a single click, you can run through the tests locally and part of the PR workflow via Travis CI. We also do this for multiple different python environments. So for all python versions from 2.7 up to 3.8 for different Python interpreters, and for different Linux distros, we're running through all of them very quickly with ease, thanks to all this automation. So today, you can see how we do it in vertica-python, in the future, we might want to spin that out into its own stand-alone testbed starter projects so that if you're starting any new vertica integration, this might be a good starting point for you to get going quickly. So that brings us to some of the future work we want to do here in the open source space . Well, there's a lot of it. So in terms of the the client stuff, for Python, we are marching towards our 1.0 release, which is when we aim to be protocol complete to support all of vertica's unique protocols, including COPY LOCAL and some new protocols invented to support complex types, which is our new feature in vertica 10. We have some cursor enhancements to do things like better streaming and improved performance. Beyond that we want to take it where you want to bring it. So send us your requests in the Go client fronts, just about a year behind Python in terms of its protocol implementation, but the basic operations are there. But we still have more work to do to implement things like load balancing, some of the advanced auths and other things. But they're two, we want to work with you and we want to focus on what's important to you so that we can continue to grow and be more useful and more powerful over time. Finally, this question of, "Well, what about beyond database clients? "What else might we want to do with open source?" If you're building a very deep or a robust vertica integration, you probably need to do a lot more exciting things than just run SQL queries and process the answers. Especially if you're an OEM or you're a vendor that resells vertica packaged as a black box piece of a larger solution, you might to have managed the whole operational lifecycle of vertica. There's even fewer standards for doing all these different things compared to the SQL clients. So we started with the SQL clients 'cause that's a well established pattern, there's lots of downstream work that that can enable. But there's also clearly a need for lots of other open source protocols, architectures and examples to show you how to do these things and do have real standards. So we talked a little bit about how you could do UDXs or testing or Machine Learning, but there's all sorts of other use cases too. That's why we're excited to announce here our awesome vertica, which is a new collection of open source resources available on our GitHub page. So if you haven't heard of this awesome manifesto before, I highly recommend you check out this GitHub page on the right. We're not unique here but there's lots of awesome projects for all kinds of different tools and systems out there. And it's a great way to establish a community and share different resources, whether they're open source projects, blogs, examples, references, community resources, and all that. And this tool is an open source project. So it's an open source wiki. And you can contribute to it by submitting yourself to PR. So we've seeded it with some of our favorite tools and projects out there but there's plenty more out there and we hope to see more grow over time. So definitely check this out and help us make it better. So with that, I'm going to wrap up. I wanted to thank you all. Special thanks to Siting Ren and Roger Huebner, who are the project leads for the Python and Go clients respectively. And also, thanks to all the customers out there who've already been contributing stuff. This has already been going on for a long time and we hope to keep it going and keep it growing with your help. So if you want to talk to us, you can find us at this email address here. But of course, you can also find us on the Vertica forums, or you could talk to us on GitHub too. And there you can find links to all the different projects I talked about today. And so with that, I think we're going to wrap up and now we're going to hand it off for some Q&A.

Published Date : Mar 30 2020

SUMMARY :

Also a reminder that you can maximize your screen and frameworks to solve the problems you need to solve.

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UNLIST TILL 4/2 - Vertica @ Uber Scale


 

>> Sue: Hi, everybody. Thank you for joining us today, for the Virtual Vertica BDC 2020. This breakout session is entitled "Vertica @ Uber Scale" My name is Sue LeClaire, Director of Marketing at Vertica. And I'll be your host for this webinar. Joining me is Girish Baliga, Director I'm sorry, user, Uber Engineering Manager of Big Data at Uber. Before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment in the question box below the slides and click Submit. There will be a Q and A session, at the end of the presentation. We'll answer as many questions as we're able to during that time. Any questions that we don't address, we'll do our best to answer offline. Alternately, you can also Vertica forums to post your questions there after the session. Our engineering team is planning to join the forums to keep the conversation going. And as a reminder, you can maximize your screen by clicking the double arrow button, in the lower right corner of the slides. And yet, this virtual session is being recorded, and you'll be able to view on demand this week. We'll send you a notification as soon as it's ready. So let's get started. Girish over to you. >> Girish: Thanks a lot Sue. Good afternoon, everyone. Thanks a lot for joining this session. My name is Girish Baliga. And as Sue mentioned, I manage interactive and real time analytics teams at Uber. Vertica is one of the main platforms that we support, and Vertica powers a lot of core business use cases. In today's talk, I wanted to cover two main things. First, how Vertica is powering critical business use cases, across a variety of orgs in the company. And second, how we are able to do this at scale and with reliability, using some of the additional functionalities and systems that we have built into the Vertica ecosystem at Uber. And towards the end, I also have a little extra bonus for all of you. I will be sharing an easy way for you to take advantage of, many of the ideas and solutions that I'm going to present today, that you can apply to your own Vertica deployments in your companies. So stick around and put on your seat belts, and let's go start on the ride. At Uber, our mission is to ignite opportunity by setting the world in motion. So we are focused on solving mobility problems, and enabling people all over the world to solve their local problems, their local needs, their local issues, in a manner that's efficient, fast and reliable. As our CEO Dara has said, we want to become the mobile operating system of local cities and communities throughout the world. As of today, Uber is operational in over 10,000 cities around the world. So, across our various business lines, we have over 110 million monthly users, who use our rides, services, or eat services, and a whole bunch of other services that we provide to Uber. And just to give you a scale of our daily operations, we in the ride business, have over 20 million trips per day. And that each business is also catching up, particularly during the recent times that we've been having. And so, I hope these numbers give you a scale of the amount of data, that we process each and every day. And support our users in their analytical and business reporting needs. So who are these users at Uber? Let's take a quick look. So, Uber to describe it very briefly, is a lot like Amazon. We are largely an operation and logistics company. And employee work based reflects that. So over 70% of our employees work in teams, which come under the umbrella of Community Operations and Centers of Excellence. So these are all folks working in various cities and towns that we operate around the world, and run the Uber businesses, as somewhat local businesses responding to local needs, local market conditions, local regulation and so forth. And Vertica is one of the most important tools, that these folks use in their day to day business activities. So they use Vertica to get insights into how their businesses are going, to deeply into any issues that they want to triage , to generate reports, to plan for the future, a whole lot of use cases. The second big class of users, are in our marketplace team. So marketplace is the engineering team, that backs our ride shared business. And as part of this, running this business, a key problem that they have to solve, is how to determine what prices to set, for particular rides, so that we have a good match between supply and demand. So obviously the real time pricing decisions they're made by serving systems, with very detailed and well crafted machine learning models. However, the training data that goes into this models, the historical trends, the insights that go into building these models, a lot of these things are powered by the data that we store, and serve out of Vertica. Similarly, in each business, we have use cases spanning all the way from engineering and back-end systems, to support operations, incentives, growth, and a whole bunch of other domains. So the big class of applications that we support across a lot of these business lines, is dashboards and reporting. So we have a lot of dashboards, which are built by core data analysts teams and shared with a whole bunch of our operations and other teams. So these are dashboards and reports that run, periodically say once a week or once a day even, depending on the frequency of data that they need. And many of these are powered by the data, and the analytics support that we provide on our Vertica platform. Another big category of use cases is for growth marketing. So this is to understand historical trends, figure out what are various business lines, various customer segments, various geographical areas, doing in terms of growth, where it is necessary for us to reinvest or provide some additional incentives, or marketing support, and so forth. So the analysis that backs a lot of these decisions, is powered by queries running on Vertica. And finally, the heart and soul of Uber is data science. So data science is, how we provide best in class algorithms, pricing, and matching. And a lot of the analysis that goes into, figuring out how to build these systems, how to build the models, how to build the various coefficients and parameters that go into making real time decisions, are based on analysis that data scientists run on Vertica systems. So as you can see, Vertica usage spans a whole bunch of organizations and users, all across the different Uber teams and ecosystems. Just to give you some quick numbers, we have over 5000 weekly active, people who run queries at least once a week, to do some critical business role or problem to solve, that they have in their day to day operations. So next, let's see how Vertica fits into the Uber data ecosystem. So when users open up their apps, and request for a ride or order food delivery on each platform, the apps are talking to our serving systems. And the serving systems use online storage systems, to store the data as the trips and eat orders are getting processed in real time. So for this, we primarily use an in house built, key value storage system called Schemaless, and an open source system called Cassandra. We also have other systems like MySQL and Redis, which we use for storing various bits of data to support serving systems. So all of this operations generates a lot of data, that we then want to process and analyze, and use for our operational improvements. So, we have ingestion systems that periodically pull in data from our serving systems and land them in our data lake. So at Uber a data lake is powered by Hadoop, with files stored on HDFS clusters. So once the raw data lines on the data lake, we then have ETL jobs that process these raw datasets, and generate, modeled and customize datasets which we then use for further analysis. So once these model datasets are available, we load them into our data warehouse, which is entirely powered by Vertica. So then we have a business intelligence layer. So with internal tools, like QueryBuilder, which is a UI interface to write queries, and look at results. And it read over the front-end sites, and Dashbuilder, which is a dash, board building tool, and report management tool. So these are all various tools that we have built within Uber. And these can talk to Vertica and run SQL queries to power, whatever, dashboards and reports that they are supporting. So this is what the data ecosystem looks like at Uber. So why Vertica and what does it really do for us? So it powers insights, that we show on dashboards as folks use, and it also powers reports that we run periodically. But more importantly, we have some core, properties and core feature sets that Vertica provides, which allows us to support many of these use cases, very well and at scale. So let me take a brief tour of what these are. So as I mentioned, Vertica powers Uber's data warehouse. So what this means is that we load our core fact and dimension tables onto Vertica. The core fact tables are all the trips, all the each orders and all these other line items for various businesses from Uber, stored as partitioned tables. So think of having one partition per day, as well as dimension tables like cities, users, riders, career partners and so forth. So we have both these two kinds of datasets, which will load into Vertica. And we have full historical data, all the way since we launched these businesses to today. So that folks can do deeper longitudinal analysis, so they can look at patterns, like how the business has grown from month to month, year to year, the same month, over a year, over multiple years, and so forth. And, the really powerful thing about Vertica, is that most of these queries, you run the deep longitudinal queries, run very, very fast. And that's really why we love Vertica. Because we see query latency P90s. That is 90 percentile of all queries that we run on our platform, typically finish in under a minute. So that's very important for us because Vertica is used, primarily for interactive analytics use cases. And providing SQL query execution times under a minute, is critical for our users and business owners to get the most out of analytics and Big Data platforms. Vertica also provides a few advanced features that we use very heavily. So as you might imagine, at Uber, one of the most important set of use cases we have is around geospatial analytics. In particular, we have some critical internal dashboards, that rely very heavily on being able to restrict datasets by geographic areas, cities, source destination pairs, heat maps, and so forth. And Vertica has a rich array of functions that we use very heavily. We also have, support for custom projections in Vertica. And this really helps us, have very good performance for critical datasets. So for instance, in some of our core fact tables, we have done a lot of query and analysis to figure out, how users run their queries, what kind of columns they use, what combination of columns they use, and what joints they do for typical queries. And then we have laid out our custom projections to maximize performance on these particular dimensions. And the ability to do that through Vertica, is very valuable for us. So we've also had some very successful collaborations, with the Vertica engineering team. About a year and a half back, we had open-sourced a Python Client, that we had built in house to talk to Vertica. We were using this Python Client in our business intelligence layer that I'd shown on the previous slide. And we had open-sourced it after working closely with Eng team. And now Vertica formally supports the Python Client as an open-source project, which you can download to and integrate into your systems. Another more recent example of collaboration is the Vertica Eon mode on GCP. So as most of or at least some of you know, Vertica Eon mode is formally supported on AWS. And at Uber, we were also looking to see if we could run our data infrastructure on GCP. So Vertica team hustled on this, and provided us early preview version, which we've been testing out to see how performance, is impacted by running on the Cloud, and on GCP. And so far, I think things are going pretty well, but we should have some numbers about this very soon. So here I have a visualization of an internal dashboard, that is powered solely by data and queries running on Vertica. So this GIF has sequence have different visualizations supported by this tool. So for instance, here you see a heat map, downgrading heat map of source of traffic demand for ride shares. And then you will see a bunch of arrows here about source destination pairs and the trip lines. And then you can see how demand moves around. So, as the cycles through the various animations, you can basically see all the different kinds of insights, and query shapes that we send to Vertica, which powers this critical business dashboard for our operations teams. All right, so now how do we do all of this at scale? So, we started off with a single Vertica cluster, a few years back. So we had our data lake, the data would land into Vertica. So these are the core fact and dimension tables that I just spoke about. And then Vertica powers queries at our business intelligence layer, right? So this is a very simple, and effective architecture for most use cases. But at Uber scale, we ran into a few problems. So the first issue that we have is that, Uber is a pretty big company at this point, with a lot of users sending almost millions of queries every week. And at that scale, what we began to see was that a single cluster was not able to handle all the query traffic. So for those of you who have done an introductory course, on queueing theory, you will realize that basically, even though you could have all the query is processed through a single serving system. You will tend to see larger and larger queue wait times, as the number of queries pile up. And what this means in practice for end users, is that they are basically just seeing longer and longer query latencies. But even though the actual query execution time on Vertica itself, is probably less than a minute, their query sitting in the queue for a bunch of minutes, and that's the end user perceived latency. So this was a huge problem for us. The second problem we had was that the cluster becomes a single point of failure. Now Vertica can handle single node failures very gracefully, and it can probably also handle like two or three node failures depending on your cluster size and your application. But very soon, you will see that, when you basically have beyond a certain number of failures or nodes in maintenance, then your cluster will probably need to be restarted or you will start seeing some down times due to other issues. So another example of why you would have to have a downtime, is when you're upgrading software in your clusters. So, essentially we're a global company, and we have users all around the world, we really cannot afford to have downtime, even for one hour slot. So that turned out to be a big problem for us. And as I mentioned, we could have hardware issues. So we we might need to upgrade our machines, or we might need to replace storage or memory due to issues with the hardware in there, due to normal wear and tear, or due to abnormal issues. And so because of all of these things, having a single point of failure, having a single cluster was not really practical for us. So the next thing we did, was we set up multiple clusters, right? So we had a bunch of identities clusters, all of which have the same datasets. So then we would basically load data using ingestion pipelines from our data lake, onto each of these clusters. And then the business intelligence layer would be able to query any of these clusters. So this actually solved most of the issues that I pointed out in the previous slide. So we no longer had a single point of failure. Anytime we had to do version upgrades, we would just take off one cluster offline, upgrade the software on it. If we had node failures, we would probably just take out one cluster, if we had to, or we would just have some spare nodes, which would rotate into our production clusters and so forth. However, having multiple clusters, led to a new set of issues. So the first problem was that since we have multiple clusters, you would end up with inconsistent schema. So one of the things to understand about our platform, is that we are an infrastructure team. So we don't actually own or manage any of the data that is served on Vertica clusters. So we have dataset owners and publishers, who manage their own datasets. Now exposing multiple clusters to these dataset owners. Turns out, it's not a great idea, right? Because they are not really aware of, the importance of having consistency of schemas and datasets across different clusters. So over time, what we saw was that the schema for the same tables would basically get out of order, because they were all the updates are not consistently applied on all clusters. Or maybe they were just experimenting some new columns or some new tables in one cluster, but they forgot to delete it, whatever the case might be. We basically ended up in a situation where, we saw a lot of inconsistent schemas, even across some of our core tables in our different clusters. A second issue was, since we had ingestion pipelines that were ingesting data independently into all these clusters, these pipelines could fail independently as well. So what this meant is that if, for instance, the ingestion pipeline into cluster B failed, then the data there would be older than clusters A and C. So, when a query comes in from the BI layer, and if it happens to hit B, you would probably see different results, than you would if you went to a or C. And this was obviously not an ideal situation for our end users, because they would end up seeing slightly inconsistent, slightly different counts. But then that would lead to a bad situation for them where they would not able to fully trust the data that was, and the results and insights that were being returned by the SQL queries and Vertica systems. And then the third problem was, we had a lot of extra replication. So the 20/80 Rule, or maybe even the 90/10 Rule, applies to datasets on our clusters as well. So less than 10% of our datasets, for instance, in 90% of the queries, right? And so it doesn't really make sense for us to replicate all of our data on all the clusters. And so having this set up where we had to do that, was obviously very suboptimal for us. So then what we did, was we basically built some additional systems to solve these problems. So this brings us to our Vertica ecosystem that we have in production today. So on the ingestion side, we built a system called Vertica Data Manager, which basically manages all the ingestion into various clusters. So at this point, people who are managing datasets or dataset owners and publishers, they no longer have to be aware of individual clusters. They just set up their ingestion pipelines with an endpoint in Vertica Data Manager. And the Vertica Data Manager ensures that, all the schemas and data is consistent across all our clusters. And on the query side, we built a proxy layer. So what this ensures is that, when queries come in from the BI layer, the query was forwarded, smartly and with knowledge and data about which cluster up, which clusters are down, which clusters are available, which clusters are loaded, and so forth. So with these two layers of abstraction between our ingestion and our query, we were able to have a very consistent, almost single system view of our entire Vertica deployment. And the third bit, we had put in place, was the data manifest, which were the communication mechanism between ingestion and proxy. So the data manifest basically is a listing of, which tables are available on which clusters, which clusters are up to date, and so forth. So with this ecosystem in place, we were also able to solve the extra replication problem. So now we basically have some big clusters, where all the core tables, and all the tables, in fact, are served. So any query that hits 90%, less so tables, goes to the big clusters. And most of the queries which hit 10% heavily queried important tables, can also be served by many other small clusters, so much more efficient use of resources. So this basically is the view that we have today, of Vertica within Uber, so external to our team, folks, just have an endpoint, where they basically set up their ingestion jobs, and another endpoint where they can forward their Vertica SQL queries. And they are so to a proxy layer. So let's get a little more into details, about each of these layers. So, on the data management side, as I mentioned, we have two kinds of tables. So we have dimension tables. So these tables are updated every cycle, so the list of cities list of drivers, the list of users and so forth. So these change not so frequently, maybe once a day or so. And so we are able to, and since these datasets are not very big, we basically swap them out on every single cycle. Whereas the fact tables, so these are tables which have information about our trips or each orders and so forth. So these are partition. So we have one partition roughly per day, for the last couple of years, and then we have more of a hierarchical partitions set up for older data. So what we do is we load the partitions for the last three days on every cycle. The reason we do that, is because not all our data comes in at the same time. So we have updates for trips, going over the past two or three days, for instance, where people add ratings to their trips, or provide feedback for drivers and so forth. So we want to capture them all in the row corresponding to that particular trip. And so we upload partitions for the last few days to make sure we capture all those updates. And we also update older partitions, if for instance, records were deleted for retention purposes, or GDPR purposes, for instance, or other regulatory reasons. So we do this less frequently, but these are also updated if necessary. So there are endpoints which allow dataset owners to specify what partitions they want to update. 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So what we do is we load all the new data in temp tables, in all the clusters in phase one. And then when all the clusters give us access signals, then we basically promote them to primary and set them as the main serving tables for incoming queries. We also optimize the load, using Vertica Data Copy. So what this means is earlier, in a parallel pipelines scheme, we had to ingest data individually from HDFS clusters into each of the Vertica clusters. That took a lot of HDFS bandwidth. But using this nice feature that Vertica provides called Vertica Data Copy, we just load it data into one cluster and then much more efficiently copy it, to the other clusters. So this has significantly reduced our ingestion overheads, and speed it up our load process. And as I mentioned as the second phase of the commit, all data is promoted at the same time. Finally, we make sure that all the data is up to date, by doing some checks around the number of rows and various other key signals for freshness and correctness, which we compare with the data in the data lake. So in terms of schema changes, VDM automatically applies these consistently across all the clusters. So first, what we do is we stage these changes to make sure that these are correct. So this catches errors that are trying to do, an incompatible update, like changing a column type or something like that. So we make sure that schema changes are validated. And then we apply them to all clusters atomically again for consistency. And provide a overall consistent view of our data to all our users. So on the proxy side, we have transparent support for, replicated clusters to all our users. So the way we handle that is, as I mentioned, the cluster to table mapping is maintained in the manifest database. And when we have an incoming query, the proxy is able to see which cluster has all the tables in that query, and route the query to the appropriate cluster based on the manifest information. Also the proxy is aware of the health of individual clusters. So if for some reason a cluster is down for maintenance or upgrades, the proxy is aware of this information. And it does the monitoring based on query response and execution times as well. And it uses this information to route queries to healthy clusters, and do some load balancing to ensure that we award hotspots on various clusters. So the key takeaways that I have from the stock, are primarily these. So we started off with single cluster mode on Vertica, and we ran into a bunch of issues around scaling and availability due to cluster downtime. We had then set up a bunch of replicated clusters to handle the scaling and availability issues. Then we run into issues around schema consistency, data staleness, and data replication. So we built an entire ecosystem around Vertica, with abstraction layers around data management and ingestion, and proxy. And with this setup, we were able to enforce consistency and improve storage utilization. So, hopefully this gives you all a brief idea of how we have been able to scale Vertica usage at Uber, and power some of our most business critical and important use cases. So as I mentioned at the beginning, I have a interesting and simple extra update for you. So an easy way in which you all can take advantage of many of the features that we have built into our ecosystem, is to use the Vertica Eon mode. So the Vertica Eon mode, allows you to set up multiple clusters with consistent data updates, and set them up at various different sizes to handle different query loads. And it automatically handles many of these issues that I mentioned in our ecosystem. So do check it out. We've also been, trying it out on DCP, and initial results look very, very promising. So thank you all for joining me on this talk today. I hope you guys learned something new. And hopefully you took away something that you can also apply to your systems. We have a few more time for some questions. So I'll pause for now and take any questions.

Published Date : Mar 30 2020

SUMMARY :

Any questions that we don't address, So the first issue that we have is that,

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UNLIST TILL 4/2 - Model Management and Data Preparation


 

>> Sue: Hello, everybody, and thank you for joining us today for the virtual Vertica BDC 2020. Today's breakout session is entitled Machine Learning with Vertica, Data Preparation and Model Management. My name is Sue LeClaire, Director of Managing at Vertica and I'll be your host for this webinar. Joining me is Waqas Dhillon. He's part of the Vertica Product Management Team at Vertica. Before we begin, I want to encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click submit. There will be a Q and A session at the end of the presentation. We'll answer as many questions as we're able to during that time. Any questions that we don't address, we'll do our best to answer offline. Alternately, you can visit Vertica Forums to post your questions there after the session. Our engineering team is planning to join the forums to keep the conversation going. Also, a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slides, and yes, this virtual session is being recorded and will be available to view on demand later this week. We'll send you a notification as soon as it's ready. So, let's get started. Waqas, over to you. >> Waqas: Thank you, Sue. Hi, everyone. My name is Waqas Dhillon and I'm a Product Manager here at Vertica. So today, we're going to go through data preparation and model management in Vertica, and the session would essentially be starting with some introduction and going through some of the machine learning configurations and you're doing machine learning at scale. After that, we have two media sections here. The first one is on data preparation, and so we'd go through data preparation is, what are the Vertica functions for data exploration and data preparation, and then share an example with you. Similarly, in the second part of this talk we'll go through different export models using PMML and how that works with Vertica, and we'll share examples from that, as well. So yeah, let's dive right in. So, Vertica essentially is an open architecture with a rich ecosystem. So, you have a lot of options for data transformation and ingesting data from different tools, and then you also have options for connecting through ODBC, JDBC, and some other connectors to BI and visualization tools. There's a lot of them that Vertica connects to, and in the middle sits Vertica, which you can have on external tables or you can have in place analytics on R, on cloud, or on prem, so that choice is yours, but essentially what it does is it offers you a lot of options for performing your data and analytics on scale, and within that, data analytics machine learning is also a core component, and then you have a lot of options and functions for that. Now, machine learning in Vertica is actually built on top of the architecture that distributed data analytics offers, so it offers a lot of those capabilities and builds on top of them, so you eliminate the overhead data transfer when you're working with Vertica machine learning, you keep your data secure, storing and managing the models really easy and much more efficient. You can serve a lot of concurrent users all at the same time, and then it's really scalable and avoids maintenance cost of a separate system, so essentially a lot of benefits here, but one important thing to mention here is that all the algorithms that you see, whether they're analytics functions, advanced analytics functions, or machine learning functions, they are distributed not just across the cluster on different nodes. So, each node gets a distributed work load. On each node, too, there might be multiple tracks and multiple processors that are running with each of these functions. So, highly distributed solution and one of its kind in this space. So, when we talk about Vertica machine learning, it essentially covers all machine learning process and we see it as something starting with data ingestion and doing data analysis and understanding, going through the steps of data preparation, modeling, evaluation, and finally deployment, as well. So, when you're using with Vertica, you're using Vertica for machine learning, it takes care of all these steps and you can do all of that inside of the Vertica database, but when we look at the three main pillars that Vertica machine learning aims to build on, the first one is to have Vertica as a platform for high performance machine learning. We have a lot of functions for data exploration and preparation and we'll go through some of them here. We have distributed in-database algorithms for model training and prediction, we have scalable functions for model evaluation, and finally we have distributed scoring functions, as well. Doing all of the stuff in the database, that's a really good thing, but we don't want it isolated in this space. We understand that a lot of our customers, our users, they like to work with other tools and work with Vertica, as well. So, they might use Vertica for data prep, another two for model training, or use Vertica for model training and take those nodes out to other tools and do prediction there. So, integration is really important part of our overall offering. So, it's a pretty flexible system. We have been offering UdX in four languages, a lot of people find there over the past few years, but the new capability of importing PMML models for in-database scoring and exporting Vertica native-models, for external scoring it's something that we have recently added, and another talk would actually go through the TensorFlow integrations, a really exciting and important milestone that we have where you can bring TensorFlow models into Vertica for in-database scoring. For this talk, we'll focus on data exploration and preparation, importing PMML, and exporting PMML models, and finally, since Vertica is not just a cue engine, but also a data store, we have a lot of really good capability for model storage and management, as well. So, yeah. Let's dive into the first part on machine learning at scale. So, when we say machine learning at scale we're actually having a few really important considerations and they have their own implications. The first one is that we want to have speed, but also want it to come at a reasonable cost. So, it's really important for us to pick the right scaling architecture. Secondly, it's not easy to move big data around. It might be easy to do that on a smaller data set, on an Excel sheet, or something of the like, but once you're talking about big data and data analytics at really big scale, it's really not easy to move that data around from one tool to another, so what you'd want to do is bring models to the data instead of having to move this data to the tools, and the third thing here is that some sub-sampling it can actually compromise your accuracy, and a lot of tools that are out there they still force you to take smaller samples of your data because they can only handle so much data, but that can impact your accuracy and the need here is that you should be able to work with all of your data. We'll just go through each of these really quickly. So, the first factor here is scalability. Now, if you want to scale your architecture, you have two main options. The first is vertical scaling. Let's say you have a machine, a server, essentially, and you can keep on adding resources, like RAM and CPU and keep increasing the performance as well as the capacity of that system, but there's a limit to what you can do here, and the limit, you can hit that in terms of cost, as well as in terms of technology. Beyond a certain point, you will not be able to scale more. So, the right solution to follow here is actually horizontal scaling in which you can keep on adding more instances to have more computing power and more capacity. So, essentially what you get with this architecture is a super computer, which stitches together several nodes and the workload is distributed on each of those nodes for massive develop processing and really fast speeds, as well. The second aspect of having big data and the difficulty around moving it around is actually can be clarified with this example. So, what usually happens is, and this is a simplified version, you have a lot of applications and tools for which you might be collecting the data, and this data then goes into an analytics database. That database then in turn might be connected to some VI tools, dashboard and applications, and some ad-hoc queries being done on the database. Then, you want to do machine learning in this architecture. What usually happens is that you have your machine learning tools and the data that is coming in to the analytics database is actually being exported out of the machine learning tools. You're training your models there, and afterwards, when you have new incoming data, that data again goes out to the machine learning tools for prediction. With those results that you get from those tools usually ended up back in the distributed database because you want to put it on dashboard or you want to power up some applications with that. So, there's essentially a lot of data overhead that's involved here. There are cons with that, including data governance, data movement, and other complications that you need to resolve here. One of the possible solutions to overcome that difficulty is that you have machine learning as part of the distributed analytical database, as well, so you get the benefits of having it applied on all of the data that's inside of the database and not having to care about all of the data movement there, but if there are some use cases where it still makes sense to at least train the models outside, that's where you can do your data preparation outside of the database, and then take the data out, the prepared data, build your model, and then bring the model back to the analytics database. In this case, we'll talk about Vertica. So, the model would be archived, hosted by Vertica, and then you can keep on applying predictions on the new data that's incoming into the database. So, the third consideration here for machine learning on scale is sampling versus full data set. As I mentioned, a lot of tools they cannot handle big data and you are forced to sub-sample, but what happens here, as you can see in the figure on the left most, figure A, is that if you have a single data point, essentially any model can explain that, but if you have more data points, as in figure B, there would be a smaller number of models that could be able to explain that, and in figure C, even more data points, lesser number of models explained, but lesser also means here that these models would probably be more accurate, and the objective for building machine learning models is mostly to have prediction capability and generalization capability, essentially, on unseen data, so if you build a model that's accurate on one data point, it could not have very good generalization capabilities. The conventional wisdom with machine learning is that the more data points that you have for learning the better and more accurate models that you'll get out of your machine learning models. So, you need to pick a tool which can handle all of your data and does not force you to sub-sample that, and doing that, even a simpler model might be much better than a more complex model here. So, yeah. Let's go to data exploration and data preparation part. Vertica's a really powerful tool and it offers a lot of scalability in this space, and as I mentioned, will support the whole process. You can define the problem and you can gather your data and construct your data set inside Vertica, and then consider it a prepared training modeling deployment and managing the model, but this is a really critical step in the overall machine learning process. Some estimate it takes between 60 to 80% of the overall effort of a machine learning process. So, a lot of functions here. You can use part of Vertica, do data exploration, de-duplication, outlier detection, balancing, normalization, and potentially a lot more. You can actually go to our Vertica documentation and find them there. Within Vertica we divide them into two parts. Within data prep, one is exploration functions, the second is transformation functions. Within exploration, you have a rich set functions that you can use in DB, and then if you want to build your own you can use the UDX to do that. Similarly, for transformation there's a lot of functions around time series, pattern matching, outlier detection that you can use to transform that data, and it's just a snapshot of some of those functions that are available in Vertica right now. And again, the good thing about these functions is not just their presence in the database. The good thing is actually their ability to scale on really, really large data set and be able to compute those results for you on that data set in an acceptable amount of time, which makes your machine learning processes really critical. So, let's go to an example and see how we can use some of these functions. As I mentioned, there's a whole lot of them and we'll not be able to go through all of them, but just for our understanding we can go through some of them and see how they work. So, we have here a sample data set of network flows. It's a similar attack from some source nodes, and then there are some victim nodes on which these attacks are happening. So yeah, let's just look at the data here real quick. We'll load the data, we'll browse the data, compute some statistics around it, ask some questions, make plots, and then clean the data. The objective here is not to make a prediction, per se, which is what we mostly do in machine learning algorithms, but to just go through the data prep process and see how easy it is to do that with Vertica and what kind of options might be there to help you through that process. So, the first step is loading the data. Since in this case we know the structure of the data, so we create a table and create different column names and data types, but let's say you have a data set for which you do not already know the structure, there's a really cool feature in Vertica called flex tables and you can use that to initially import the data into the database and then go through all of the variables and then assign them variable types. You can also use that if your data is dynamic and it's changing, to board the data first and then create these definitions. So once we've done that, we load the data into the database. It's for one week of data out of the whole data set right now, but once you've done that we'd like to look at the flows just to look at the data, you know how it looks, and once we do select star from flows and just have a limit here, we see that there's already some data duplication, and by duplication I mean rows which have the exact same data for each of the columns. So, as part of the cleaning process, the first thing we'd want to do is probably to remove that duplication. So, we create a table with distinct flows and you can see here we have about a million flows here which are unique. So, moving on. The next step we want to do here, this is essentially time state data and these times are in days of the week, so we want to look at the trends of this data. So, the network traffic that's there, you can call it flows. So, based on hours of the day how does the traffic move and how does it differ from one day to another? So, it's part of an exploration process. There might be a lot of further exploration that you want to do, but we can start with this one and see how it goes, and you can see in the graph here that we have seven days of data, and the weekend traffic, which is in pink and purple here seems a little different from the rest of the days. Pretty close to each other, but yeah, definitely something we can look into and see if there's some real difference and if there's something we want to explore further here, but the thing is that this is just data for one week, as I mentioned. What if we load data for 70 days? You'd have a longer graph probably, but a lot of lines and would not really be able to make sense out of that data. It would be a really crowded plot for that, so we have to come up with a better way to be able to explore that and we'll come back to that in a little bit. So, what are some other things that we can do? We can get some statistics, we can take one sample flow and look at some of the values here. We see that the forward column here and ToS column here, they have zero values, and when we explore further we see that there's a lot of values here or records here for which these columns are essentially zero, so probably not really helpful for our use case. Then, we can look at the flow end. So, flow end is the end time when the last packet in a flow was sent and you can do a select min flow and max flow to see the data when it started and when it ended, and you can see it's about one week's of data for the first til eighth. Now, we also want to look at the data whether it's balanced or not because balanced data is really important for a lot of classification use cases that we want to try with this and you can see that source address, destination address, source port, and destination port, and you see it's highly in balanced data and so is versus destination address space, so probably something that we need to do, really powerful Vertica balancing functions that you can use within, and just sampling, over-sampling, or hybrid sampling here and that can be really useful here. Another thing we can look at is there's so many statistics of these columns, so off the unique flows table that we created we just use the summarize num call function in Vertica and it gives us a lot of really cool (mumbling) and percentile information on that. Now, if we look at the duration, which is the last record here, we can see that the mean is about 4.6 seconds, but when we look at the percentile information, we see that the median is about 0.27. So, there's a lot of short flows that have duration less than 0.27 seconds. Yes, there would be more and they'd probably bring the mean to the 4.6 value, but then the number of short flows is probably pretty high. We can ask some other questions from the data about the features. We can look at the protocols here and look at the count. So, we see that most of the traffic that we have is for TCP and UDP, which is sort of expected for a data set like this, and then we want to look at what are the most popular network services here? So again, simply queue here, select destination port count, add in the information here. We get the destination port and count for each. So, we can see that most of the traffic here is web traffic, HTTP and HTTPS, followed by domain name resolution. So, let's explore some more. We can look at the label distributions. We see that the labels that are given with that because this is essentially data for which we already know whether something was an anomaly or not, record was anomaly or not, and creating our algorithm based on it. So, we see that there's this background label, a lot of records there, and then anomaly spam seems to be really high. There are anomaly UDB scans and SSS scams, as well. So, another question we can ask is among the SMTP flows, how labels are distributed, and we can say that anomaly spam is highest, and then comes the background spam. So, can we say out of this that SMTP flows, they are spams, and maybe we can build a model that actually answers that question for us? That can be one machine learning model that you can build out of this data set. Again, we can also verify the destination port of flows that were labeled as spam. So, you can expect port 25 for SMTP service here, and we can see that SMTP with destination port 25, you have a lot of counts here, but there are some other destination ports for which the count is really low, and essentially, when we're doing and analysis at this scale, these data points might not really be needed. So, as part of the data prep slash data cleaning we might want to get rid of these records here. So now, what we can do is going back to the graph that I showed earlier, we can try and plot the daily trends by aggregating them. Again, we take the unique flow and convert into a flow count and to a manageable number that we can then feed into one of the algorithms. Now, PCA principle component analysis, it's a really powerful algorithm in Vertica, and what it essentially does is a lot of times when you have a high number of columns, which might be highly (mumbling) with each other, you can feed them into the PCA algorithm and it will get for you a list of principle components which would be linearly independent from each other. Now, each of these components would explain a certain extent of the variants of the overall data set that you have. So, you can see here component one explains about 73.9% of the variance, and component two explains about 16% of the variance. So, if you combine those two components alone, that would get you for around 90% of the variance. Now, you can use PCA for a lot of different purposes, but in this specific example, we want to see if we combine all the data points that we have together and we do that by day of the week, what sort of information can we get out of it? Is there any insight that this provides? Because once you have two data points, it's really easy to plot them. So, we just apply the PCA, we first (mumbling) it, and then reapply on our data set, and this is the graph we get as a result. Now, you can see component one is on the X axis here, component two on the y axis, and each of these points represents a day of the week. Now, with just two points it's easy to plot that and compare this to the graph that we saw earlier, which had a lot of lines and the more weeks that we added or the more days that we added, the more lines that we'd have versus this graph in which you can clearly tell that five days traffic starting from Monday til Friday, that's closely clustered together, so probably pretty similar to each other, and then Saturday traffic is pretty much apart from all of these days and it's also further away from Sunday. So, these two days of traffic is different from other days of traffic and we can always dive deeper into this and look at exactly what's happening here and see how this traffic is actually different, but with just a few functions and some pretty simple SQL queries, we were already able to get a pretty good insight from the data set that we had. Now, let's move on to our next part of this talk on importing and exporting PMML models to and from Vertica. So, current common practice is when you're putting your machine learning models into production, you'd have a dev or test environment, and in that you might be using a lot of different tools, Scikit and Spark, R, and once you want to deploy these models into production, you'd put them into containers and there would be a pool of containers in the production environment which would be talking to your database that could be your analytical database, and all of the new data that's incoming would be coming into the database itself. So, as I mentioned in one of the slides earlier, there is a lot of data transfer that's happening between that pool of containers hosting your machine learning training models versus the database which you'd be getting data for scoring and then sending the scores back to the database. So, why would you really need to transfer your models? The thing is that no machine learning platform provides everything. There might be some really cool algorithms that might compromise, but then Spark might have its own benefits in terms of some additional algorithms or some other stuff that you're looking at and that's the reason why a lot of these tools might be used in the same company at the same time, and then there might be some functional considerations, as well. You might want to isolate your data between data science team and your production environment, and you might want to score your pre-trained models on some S nodes here. You cannot host probably a big solution, so there is a whole lot of use cases where model movement or model transfer from one tool to another makes sense. Now, one of the common methods for transferring models from one tool to another is the PMML standard. It's an XML-based model exchange format, sort of a standard way to define statistical and data mining models, and helps you share models between the different applications that are PMML compliant. Really popular tool, and that's the tool of choice that we have for moving models to and from Vertica. Now, with this model management, this model movement capability, there's a lot of model management capabilities that Vertica offers. So, models are essentially first class citizens of Vertica. What that means is that each model is associated with a DB schema, so the user that initially creates a model, that's the owner of it, but he can transfer the ownership to other users, he can work with the ownership rights in any way that you would work with any other relation in a database would be. So, the same commands that you use for granting access to a model, changing its owner, changing its name, or dropping it, you can use similar commands for more of this one. There are a lot of functions for exploring the contents of models and that really helps in putting these models into production. The metadata of these models is also available for model management and governance, and finally, the import/export part enables you to apply all of these operations to the model that you have imported or you might want to export while they're in the database, and I think it would be nice to actually go through and example to showcase some of these capabilities in our model management, including the PMML model import and export. So, the workflow for export would be that we trained some data, we'll train a logistic regression model, and we'll save it as an in-DB Vertica model. Then, we'll explore the summary and attributes of the model, look at what's inside the model, what the training parameters are, concoctions and stuff, and then we can export the model as PMML and an external tool can import that model from PMML. And similarly, we'll go through and example for export. We'll have an external PMML model trained outside of Vertica, we'll import that PMML model and from there on, essentially, we'll treat it as an in-DB PMML model. We'll explore the summary and attribute of the model in much the same way as in in-DB model. We'll apply the model for in-DB scoring and get the prediction results, and finally, we'll bring some test data. We'll use that on test data for which the scoring needs to be done. So first, we want to create a connection with the database. In this case, we are using a Python Jupyter Notebook. We have the Vertica Python connector here that you can use, really powerful connector, allows you to do a lot of cool stuff to the database using the Jupyter front end, but essentially, you can use any other SQL front end tool or for that matter, any other Python ID which lets you connect to the database. So, exporting model. First, we'll create an logistic regression model here. Select logistic regression, we'll give it a model name, then put relation, which might be a table, time table, or review. There's response column and the predictor columns. So, we get a logistic regression model that we built. Now, we look at the models table and see that the model has been created. This is a table in Vertica that contains a list of all the models that are there in the database. So, we can see here that my model that we just created, it's created with Vertica models as a category, model type is logistic regression, and we have some other metadata around this model, as well. So now, we can look at some of the summary statistics of the model. We can look at the details. So, it gives us the predictor, coefficients, standard error, Z value, and P value. We can look at the regularization parameters. We didn't use any, so that would be a value of one, but if you had used, it would show it up here, the call string and also additional information regarding iteration count, rejected row count, and accepted row count. Now, we can also look at the list of attributes of the model. So, select get model attribute using parameter, model name is myModel. So, for this particular model that we just created, it would give us the name of all the attributes that are there. Similarly, you can look at the coefficients of the model in a column format. So, using parameter name myModel, and in this case we add attribute name equals details because we want all the details for that particular model and we get the predictor name, coefficient, standard error, Z value, and P value here. So now, what we can do is we can export this model. So, we used the select export models and we give it a path to where we want the model to be exported to. We give it the name of the model that needs to be exported because essentially might have a lot of models that you have created, and you give it the category here, which in our example is PMML, and you get a status message here that export model has been successful. So now, let's move onto the importing models example. In much the same way that we created a model in Vertica and exported it out, you might want to create a model outside of Vertica in another tool and then bring that to Vertica for scoring because Vertica contains all of the hard data and it might make sense to host that model in Vertica because scoring happens a lot more quickly than model training. So, in this particular case we do a select import models and we are importing a logistic regression model that was created in Spark. The category here again is PMML. So, we get the status message that the import was successful. Now, let's look at the attributes, look at the models table, and see that the model is really present there. Now previously when we ran this query because we had only myModel there, so that was the only entry you saw, but now once this model is imported you can see that as line item number two here, Spark logistic regression, it's a public schema. The category here however is different because it's not an individuated model, rather an imported model, so you get PMML here and then other metadata regarding the model, as well. Now, let's do some of the same operations that we did with the in-DB model so we can look at the summary of the imported PMML model. So, you can see the function name, data fields, predictors, and some additional information here. Moving on. Let's look at the attributes of the PMML model. Select your model attribute. Essentially the same query that we applied earlier, but the difference here is only the model name. So, you get the attribute names, attribute field, and number of rows. We can also look at the coefficient of the PMML model, name, exponent, and coefficient here. So yeah, pretty much similar to what you can do with an in-DB model. You can also perform all operations on an important model and one additional thing we'd want to do here is to use this important model for our prediction. So in this case, we'll data do a select predict PMML and give it some values using parameters model name, and logistic regression, and match by position, it's a really cool feature. This is true in this case. Sector, true. So, if you have model being imported from another platform in which, let's say you have 50 columns, now the names of the columns in that environment in which you're training the model might be slightly different than the names of the column that you have set up for Vertica, but as long as the order is the same, Vertica can actually match those columns by position and you don't need to have the exact same names for those columns. So in this case, we have set that to true and we see that predict PMML gives us a status of one. Now, using the important model, in this case we had a certain value that we had given it, but you can also use it on a table, as well. So in that case, you also get the prediction here and you can look at the (mumbling) metrics, see how well you did. Now, just sort of wrapping this up, it's really important to know the important distinction between using your models in any tool, any single node solution tool that you might already be using, like Python or R versus Vertica. What happens is, let's say you build a model in Python. It might be a single node solution. Now, after building that model, let's say you want to do prediction on really large amounts of data and you don't want to go through the overhead of keeping to move that data out of the database to do prediction every time you want to do it. So, what you can do is you can import that model into Vertica, but what Vertica does differently than Python is that the PMML model would actually be distributed across each mode in the cluster, so it would be applying on the data segments in each of those nodes and they might be different threads running for that prediction. So, the speed that you get here from all prediction would be much, much faster. Similarly, once you build a model for machine learning in Vertica, the objective mostly is that you want to use up all of your data and build a model that's accurate and is not just using a sample of the data, but using all the data that's available to it, essentially. So, you can build that model. The model building process would again go through the same technique. It would actually be distributed across all nodes in a cluster, and it would be using up all the threads and processes available to it within those nodes. So, really fast model training, but let's say you wanted to deploy it on an edge node and maybe do prediction closer to where the data was being generated, so you can export that model in a PMML format and all deploy it on the edge node. So, it's really helpful for a lot of use cases. And just some rising takeaways from our discussion today. So, Vertica's a really powerful tool for machine learning, for data preparation, model training, prediction, and deployment. You might want to use Vertica for all of these steps or some of these steps. Either way, Vertica supports both approaches. In the upcoming releases, we are planning to have more import and export capability through PMML models. Initially, we're supporting kmeans, linear, and logistic regression, but we keep on adding more algorithms and the plan is to actually move to supporting custom models. If you want to do that with the upcoming release, our TensorFlow indication is always there which you can use, but with PMML, this is the starting point for us and we keep on improving that. Vertica model can be exported in PMML format for scoring on other platforms, and similarly, models that get build in other tools can be imported for in-DB machine learning and in-DB scoring within Vertica. There are a lot of critical model management tools that are provided in Vertica and there are a lot of them on the roadmap, as well, which would keep on developing. Many ML functions and algorithms, they're already part of the in-DB library and we keep on adding to that, as well. So, thank you so much for joining the discussion today and if you have any questions we'd love to take them now. Back to you, Sue.

Published Date : Mar 30 2020

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UNLIST TILL 4/2 The Data-Driven Prognosis


 

>> Narrator: Hi, everyone, thanks for joining us today for the Virtual Vertica BDC 2020. Today's breakout session is entitled toward Zero Unplanned Downtime of Medical Imaging Systems using Big Data. My name is Sue LeClaire, Director of Marketing at Vertica, and I'll be your host for this webinar. Joining me is Mauro Barbieri, lead architect of analytics at Philips. Before we begin, I want to encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click Submit. There will be a Q&A session at the end of the presentation. And we'll answer as many questions as we're able to during that time. Any questions that we don't get to we'll do our best to answer them offline. Alternatively, you can also visit the vertical forums to post your question there after the session. Our engineering team is planning to join the forums to keep the conversation going. Also a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And yes, this virtual session is being recorded, and we'll be available to view on demand this week. We'll send you a notification as soon as it's ready. So let's get started. Mauro, over to you. >> Thank you, good day everyone. So medical imaging systems such as MRI scanners, interventional guided therapy machines, CT scanners, the XR system, they need to provide hospitals, optimal clinical performance but also predictable cost of ownership. So clinicians understand the need for maintenance of these devices, but they just want to be non intrusive and scheduled. And whenever there is a problem with the system, the hospital suspects Philips services to resolve it fast and and the first interaction with them. In this presentation you will see how we are using big data to increase the uptime of our medical imaging systems. I'm sure you have heard of the company Phillips. Phillips is a company that was founded in 129 years ago in actually 1891 in Eindhoven in Netherlands, and they started by manufacturing, light bulbs, and other electrical products. The two brothers Gerard and Anton, they took an investment from their father Frederik, and they set up to manufacture and sale light bulbs. And as you may know, a key technology for making light bulbs is, was glass and vacuum. So when you're good at making glass products and vacuum and light bulbs, then there is an easy step to start making radicals like they did but also X ray tubes. So Philips actually entered very early in the market of medical imaging and healthcare technology. And this is what our is our core as a company, and it's also our future. So, healthcare, I mean, we are in a situation now in which everybody recognize the importance of it. And and we see incredible trends in a transition from what we call Volume Based Healthcare to Value Base, where, where the clinical outcomes are driving improvements in the healthcare domain. Where it's not enough to respond to healthcare challenges, but we need to be involved in preventing and maintaining the population wellness and from a situation in which we episodically are in touch with healthcare we need to continuously monitor and continuously take care of populations. And from healthcare facilities and technology available to a few elected and reach countries we want to make health care accessible to everybody throughout the world. And this of course, has poses incredible challenges. And this is why we are transforming the Philips to become a healthcare technology leader. So from Philips has been a concern realizing and active in many sectors in many sectors and realizing what kind of technologies we've been focusing on healthcare. And we have been transitioning from creating and selling products to making solutions to addresses ethical challenges. And from selling boxes, to creating long term relationships with our customers. And so, if you have known the Philips brand from from Shavers from, from televisions to light bulbs, you probably now also recognize the involvement of Philips in the healthcare domain, in diagnostic imaging, in ultrasound, in image guided therapy and systems, in digital pathology, non invasive ventilation, as well as patient monitoring intensive care, telemedicine, but also radiology, cardiology and oncology informatics. Philips has become a powerhouse of healthcare technology. To give you an idea of this, these are the numbers for, from 2019 about almost 20 billion sales, 4% comparable sales growth with respect to the previous year and about 10% of the sales are reinvested in R&D. This is also shown in the number of patents rights, last year we filed more than 1000 patents in, in the healthcare domain. And the company is about 80,000 employees active globally in over 100 countries. So, let me focus now on the type of products that are in the scope of this presentation. This is a Philips Magnetic Resonance Imaging Scanner, also called Ingenia 3.0 Tesla is an incredible machine. Apart from being very beautiful as you can see, it's a it's a very powerful technology. It can make high resolution images of the human body without harmful radiation. And it's a, it's a, it's a complex machine. First of all, it's massive, it weights 4.6 thousand kilograms. And it has superconducting magnets cooled with liquid helium at -269 degrees Celsius. And it's actually full of software millions and millions of lines of code. And it's occupied three rooms. What you see in this picture, the examination room, but there is also a technical room which is full of of of equipment of custom hardware, and machinery that is needed to operate this complex device. This is another system, it's an interventional, guided therapy system where the X ray is used during interventions with the patient on the table. You see on the left, what we call C-arm, a robotic arm that moves and can take images of the patient while it's been operated, it's used for cardiology intervention, neurological intervention, cardiovascular intervention. There's a table that moves in very complex ways and it again it occupies two rooms, this room that we see here and but also a room full of cabinets and hardwood and computers. This is another another characteristic of this machine is that it has to operate it as it is used during medical interventions, and so it has to interact with all kind of other equipment. This is another system it's a, it's a, it's a Computer Tomography Scanner Icon which is a unique, it is unique due to its special detection technology. It has an image resolution up to 0.5 millimeters and making thousand by thousand pixel images. And it is also a complex machine. This is a picture of the inside of a compatible device not really an icon, but it has, again three rotating, which waits two and a half turn. So, it's a combination of X ray tube on top, high voltage generators to power the extra tube and in a ray of detectors to create the images. And this rotates at 220 right per minutes, making 50 frames per second to make 3D reconstruction of the of the body. So a lot of technology, complex technology and this technology is made for this situation. We make it for clinicians, who are busy saving people lives. And of course, they want optimal clinical performance. They want the best technology to treat the patients. But they also want predictable cost of ownership. They want predictable system operations. They want their clinical schedules not interrupted. So, they understand these machines are complex full of technology. And these machines may have, may require maintenance, may require software update, sometimes may even say they require some parts, horrible parts to be replaced, but they don't want to have it unplanned. They don't want to have unplanned downtime. They would hate send, having to send patients home and to have to reschedule visits. So they understand maintenance. They just want to have a schedule predictable and non intrusive. So already a number of years ago, we started a transition from what we call Reactive Maintenance services of these devices to proactive. So, let me show you what we mean with this. Normally, if a system has an issue system on the field, and traditional reactive workflow would be that, this the customer calls a call center, reports the problem. The company servicing the device would dispatch a field service engineer, the field service engineer would go on site, do troubleshooting, literally smell, listen to noise, watch for lights, for, for blinking LEDs or other unusual issues and would troubleshoot the issue, find the root cause and perhaps decide that the spare part needs to be replaced. He would order a spare part. The part would have to be delivered at the site. Either immediately or the engineer would would need to come back another day when the part is available, perform the repair. That means replacing the parts, do all the needed tests and validations. And finally release the system for clinical use. So as you can see, there is a lot of, there are a lot of steps, and also handover of information from one to between different people, between different organizations even. Would it be better to actually keep monitoring the installed base, keep observing the machine and actually based on the information collected, detect or predict even when an issue is is going to happen? And then instead of reacting to a customer calling, proactively approach the customer scheduling, preventive service, and therefore avoid the problem. So this is actually what we call Corrective Service. And this is what we're being transitioning to using Big Data and Big Data is just one ingredient. In fact, there are more things that are needed. The devices themselves need to be designed for reliability and predictability. If the device is a black box does not communicate to the outside world the status, if it does not transmit data, then of course, it is not possible to observe and therefore, predict issues. This of course requires a remote service infrastructure or an IoT infrastructure as it is called nowadays. The passivity to connect the medical device with a data center in enterprise infrastructure, collect the data and perform the remote troubleshooting and the predictions. Also the right processes and the right organization is to be in place, because an organization that is, you know, waiting for the customer to call and then has a number of few service engineers available and a certain amount of spare parts and stock is a different organization from an organization that actually is continuously observing the installed base and is scheduling actions to prevent issues. And in other pillar is knowledge management. So in order to realize predictive models and to have predictive service action, it's important to manage knowledge about failure modes, about maintenance procedures very well to have it standardized and digitalized and available. And last but not least, of course, the predictive models themselves. So we talked about transmitting data from the installed base on the medical device, to an enterprise infrastructure that would analyze the data and generate predictions that's predictive models are exactly the last ingredient that is needed. So this is not something that I'm, you know, I'm telling you for the first time is actually a strategic intent of Philips, where we aim for zero unplanned downtime. And we market it that way. We also is not a secret that we do it by using big data. And, of course, there could be other methods to to achieving the same goal. But we started using big data already now well, quite quite many years ago. And one of the reasons is that our medical devices already are wired to collect lots of data about the functioning. So they collect events, error logs that are sensor connecting sensor data. And to give you an idea, for example, just as an order of magnitudes of size of the data, the one MRI scanner can log more than 1 million events per day, hundreds of thousands of sensor readings and tens of thousands of many other data elements. And so this is truly big data. On the other hand, this data was was actually not designed for predictive maintenance, you have to think a medical device of this type of is, stays in the field for about 10 years. Some a little bit longer, some of it's shorter. So these devices have been designed 10 years ago, and not necessarily during the design, and not all components were designed, were designed with predictive maintenance in mind with IoT, and with the latest technology at that time, you know, progress, will not so forward looking at the time. So the actual the key challenge is taking the data which is already available, which is already logged by the medical devices, integrating it and creating predictive models. And if we dive a little bit more into the research challenges, this is one of the Challenges. How to integrate diverse data sources, especially how to automate the costly process of data provisioning and cleaning? But also, once you have the data, let's say, how to create these models that can predict failures and the degradation of performance of a single medical device? Once you have these models and alerts, another challenge is how to automatically recommend service actions based on the probabilistic information on these possible failures? And once you have the insights even if you can recommend action still recommending an action should be done with the goal of planning, maintenance, for generating value. That means balancing costs and benefits, preventing unplanned downtimes without of course scheduling and unnecessary interventions because every intervention, of course, is a disruption for the clinical schedule. And there are many more applications that can be built off such as the optimal management of spare parts supplies. So how do you approach this problem? Our approach was to collect into one database Vertica. A large amount of historical data, first of all historical data coming from the medical devices, so event logs, parameter value system configuration, sensor readings, all the data that we have at our disposal, that in the same database together with records of failures, maintenance records, service work orders, part replacement contracts, so basically the evidence of failures and once you have data from the medical devices, and data from the failures in the same database, it becomes possible to correlate event logs, errors, signal sensor readings with records of failures and records of part replacement and maintenance operations. And we did that also with a specific approach. So we, we create integrated teams, and every integrated team at three figures, not necessarily three people, they were actually multiple people. But there was at least one business owner from a service organization. And this business owner is the person who knows what is relevant, which use case are relevant to solve for a particular type of product or a particular market. What basically is generating value or is worthwhile tackling as an organization. And we have data scientists, data scientists are the one who actually can manipulate data. They can write the queries, they can write the models and robust statistics. They can create visualization and they are the ones who really manipulate the data. Last but not least, very important is subject matter experts. Subject Matter Experts are the people who know the failure modes, who know about the functioning of the medical devices, perhaps they're even designed, they come from the design side, or they come from the service innovation side or even from the field. People who have been servicing the machines in real life for many, many years. So, they are familiar with the failure models, but also familiar with the type of data that is logged and the processes and how actually the systems behave, if you if you if you if you allow me in, in the wild in the in the field. So the combination of these three secrets was a key. Because data scientist alone, just statisticians basically are people who can all do machine learning. And they're not very effective because the data is too complicated. That's why you more than too complex, so they will spend a huge amount of time just trying to figure out the data. Or perhaps they will spend the time in tackling things that are useless, because it's such an interesting knows much quicker which data points are useful, which phenomenon can be found in the data or probably not found. So the combination of subject matter experts and data scientists is very powerful and together gathered by a business owner, we could tackle the most useful use cases first. So, this teams set up to work and they developed three things mainly, first of all, they develop insights on the failure modes. So, by looking at the data, and analyzing information about what happened in the field, they find out exactly how things fail in a very pragmatic and quantitative way. Also, they of course, set up to develop the predictive model with associated alerts and service actions. And a predictive model is just not an alert is just not a flag. Just not a flag, only flag that turns on like a like a traffic light, you know, but there's much more than that. It's such an alert is to be interpreted and used by highly skilled and trained engineer, for example, in a in a call center, who needs to evaluate that error and plan a service action. Service action may involve the ordering a replacement of an expensive part, it may involve calling up the customer hospital and scheduling a period of downtime, downtime to replace a part. So it has an impact on the clinical practice, could have an impact. So, it is important that the alert is coupled with sufficient evidence and information for such a highly skilled trained engineer to plan the service session efficiently. So, it's it's, it's a lot of work in terms of preparing data, preparing visualizations, and making sure that old information is represented correctly and in a compact form. Additionally, These teams develop, get insight into the failure modes and so they can provide input to the R&D organization to improve the products. So, to summarize these graphically, we took a lot of historical data from, coming from the medical devices from the history but also data from relational databases, where the service, work orders, where the part replacement, the contact information, we integrated it, and we set up to the data analytics. From there we don't have value yet, only value starts appearing when we use the insights of data analytics the model on live data. When we process live data with the module we can generate alerts, and the alerts can be used to plan the maintenance and the maintenance therefore the plant maintenance replaces replacing downtime is creating value. To give an idea of the, of the type of I cannot show you the details of these modules, all of these predictive models. But to give you an idea, this is just a picture of some of the components of our medical device for which we have models for which we have, for which we call the failure modes, hard disk, clinical grade monitoring, monitors, X ray tubes, and so forth. This is for MRI machines, a lot of custom hardware and other types of amplifiers and electronics. The alerts are then displayed in a in a dashboard, what we call a Remote monitoring dashboard. We have a team of remote monitoring engineers that basically surveyors the install base, looks at this dashboard picks up these alerts. And an alert as I said before is not just one flag, it contains a lot of information about the failure and about the medical device. And the remote monitor engineer basically will pick up these alerts, they review them and they create cases for the markets organization to handle. So, they see an alert coming in they create a case. So that the particular call center in in some country can call the customer and schedule and make an appointment to schedule a service action or it can add it preventive action to the schedule of the field service engineer who's already supposed to go to visit the customer for example. This is a picture and high-level picture of the overall data person architecture. On the bottom we have install base install base is formed by all our medical devices that are connected to our Philips and more service network. Data is transmitted in a in a secure and in a secure way to our enterprise infrastructure. Where we have a so called Data Lake, which is basically an archive where we store the data as it comes from, from the customers, it is scrubbed and protected. From there, we have a processes ETL, Extract, Transform and Load that in parallel, analyze this information, parse all these files and all this data and extract the relevant parameters. All this, the reason is that the data coming from the medical device is very verbose, and in legacy formats, sometimes in binary formats in strange legacy structures. And therefore, we parse it and we structure it and we make it magically usable by data science teams. And the results are stored in a in a vertica cluster, in a data warehouse. In the same data warehouse, where we also store information from other enterprise systems from all kinds of databases from SQL, Microsoft SQL Server, Tera Data SAP from Salesforce obligations. So, the enterprise IT system also are connected to vertica the data is inserted into vertica. And then from vertica, the data is pulled by our predictive models, which are Python and Rscripts that run on our proprietary environment helps with insights. From this proprietary environment we generate the alerts which are then used by the remote monitoring application. It's not the only application this is the case of remote monitoring. We also have applications for particular remote service. So whenever we cannot prevent or predict we cannot predict an issue from happening or we cannot prevent an issue from happening and we need to react on a customer call, then we can still use the data to very quickly troubleshoot the system, find the root cause and advice or the best service session. Additionally, there are reliability dashboards because all this data can also be used to perform reliability studies and improve the design of the medical devices and is used by R&D. And the access is with all kinds of tools. So Vertica gives the flexibility to connect with JDBC to connect dashboards using Power BI to create dashboards and click view or just simply use RM Python directly to perform analytics. So little summary of the, of the size of the data for the for the moment we have integrated about 500 terabytes worth of data tables, about 30 trillion data points. More than eighty different data sources. For our complete connected install base, including our customer relation management system SAP, we also have connected, we have integrated data from from the factory for repair shops, this is very useful because having information from the factory allows to characterize components and devices when they are new, when they are still not used. So, we can model degradation, excuse me, predict failures much better. Also, we have many years of historical data and of course 24/7 live feeds. So, to get all this going, we we have chosen very simple designs from the very beginning this was developed in the back the first system in 2015. At that time, we went from scratch to production eight months and is also very stable system. To achieve that, we apply what we call Exhaustive Error Handling. When you process, most of people attending this conference probably know when you are dealing with Big Data, you have probably you face all kinds of corner cases you feel that will never happen. But just because of the sheer volume of the data, you find all kinds of strange things. And that's what you need to take care of, if you want to have a stable, stable platform, stable data pipeline. Also other characteristic is that, we need to handle live data, but also be able to, we need to be able to reprocess large historical datasets, because insights into the data are getting generated over time by the team that is using the data. And very often, they find not only defects, but also they have changed requests for new data to be extracted to distract in a different way to be aggregated in a different way. So basically, the platform is continuously crunching data. Also, components have built-in monitoring capabilities. Transparent transparency builds trust by showing how the platform behaves. People actually trust that they are having all the data which is available, or if they don't see the data or if something is not functioning they can see why and where the processing has stopped. A very important point is documentation of data sources every data point as a so called Data Provenance Fields. That is not only the medical device where it comes from, with all this identifier, but also from which file, from which moment in time, from which row, from which byte offset that data point comes. This allows to identify and not only that, but also when this data point was created, by whom, by whom meaning which version of the platform and of the ETL created a data point. This allows us to identify issues and also to fix only the subset of when an issue is identified and fixed. It's possible then to fix only subset of the data that is impacted by that issue. Again, this grid trusts in data to essential for this type of applications. We actually have different environments in our analytic solution. One that we call data science environment is more or less what I've shown so far, where it's deployed in our Philips private cloud, but also can be deployed in in in public cloud such as Amazon. It contains the years of historical data, it allows interactive data exploration, human queries, therefore, it is a highly viable load. It is used for the training of machine learning algorithms and this design has been such that we it is for allowing rapid prototyping and for large data volumes. In other environments is the so called Production Environment where we actually score the models with live data from generation of the alerts. So this environment does not require years of data just months, because a model to make a prediction does not need necessarily years of data, but maybe some model even a couple of weeks or a few months, three months, six months depending on the type of data on the failure which has been predicted. And this has highly optimized queries because the applications are stable. It only only change when we deploy new models or new versions of the models. And it is designed optimized for low latency, high throughput and reliability is no human intervention, no human queries. And of course, there are development staging environments. And one of the characteristics. Another characteristic of all this work is that what we call Data Driven Service Innovation. In all this work, we use the data in every step of the process. The First business case creation. So, basically, some people ask how did you manage to find the unlocked investment to create such a platform and to work on it for years, you know, how did you start? Basically, we started with a business case and the business case again for that we use data. Of course, you need to start somewhere you need to have some data, but basically, you can use data to make a quantitative analysis of the current situation and also make it as accurate as possible estimate quantitative of value creation, if you have that basically, is you can justify the investments and you can start building. Next to that data is used to decide where to focus your efforts. In this case, we decided to focus on the use cases that had the maximum estimated business impact, with business impact meaning here, customer value, as well as value for the company. So we want to reduce unplanned downtime, we want to give value to our customers. But it would be not sustainable, if for creating value, we would start replacing, you know, parts without any consideration for the cost of it. So it needs to be sustainable. Also, then we use data to analyze the failure modes to actually do digging into the data understanding of things fail, for visualization, and to do reliability analysis. And of course, then data is a key to do feature engineering for the development of the predictive models for training the models and for the validation with historical data. So data is all over the place. And last but not least, again, these models is architecture generates new data about the alerts and about the how good the alerts are, and how well they can predict failures, how much downtime is being saved, how money issues have been prevented. So this also data that needs to be analyzed and provides insights on the performance of this, of this models and can be used to improve the models found. And last but not least, once you have performance of the models you can use data to, to quantify as much as possible the value which is created. And it is when you go back to the first step, you made the business value you you create the first business case with estimates. Can you, can you actually show that you are creating value? And the more you can, have this fitness feedback loop closed and quantify the better it is for having more and more impact. Among the key elements that are needed for realizing this? So I want to mention one about data documentation is the practice that we started already six years ago is proven to be very valuable. We document always how data is extracted and how it is stored in, in data model documents. Data Model documents specify how data goes from one place to the other, in this case from device logs, for example, to a table in vertica. And it includes things such as the finish of duplicates, queries to check for duplicates, and of course, the logical design of the tables below the physical design of the table and the rationale. Next to it, there is a data dictionary that explains for each column in the data model from a subject matter expert perspective, what that means, such as its definition and meaning is if it's, if it's a measurement, the use of measure and the range. Or if it's a, some sort of, of label the spec values, or whether the value is raw or or calculated. This is essential for maximizing the value of data for allowing people to use data. Last but not least, also an ETL design document, it explains how the transformation has happened from the source to the destination including very important the failure and the strategy. For example, when you cannot parse part of a file, should you load only what you can parse or drop the entire file completely? So, import best effort or do all or nothing or how to populate records for which there is no value what are the default values and you know, how to have the data is normalized or transform and also to avoid duplicates. This again is very important to provide to the users of the data, if full picture of all the data itself. And this is not just, this the formal process the documents are reviewed and approved by all the stakeholders into the subject matter experts and also the data scientists from a function that we have started called Data Architect. So to, this is something I want to give about, oh, yeah and of course the the documents are available to the end users of the data. And we even have links with documents of the data warehouse. So if you are, if you get access to the database, and you're doing your research and you see a table or a view, you think, well, it could be that could be interesting. It looks like something I could use for my research. Well, the data itself has a link to the document. So from the database while you're exploring data, you can retrieve a link to the place where the document is available. This is just the quick summary of some of the of the results that I'm allowed to share at this moment. This is about image guided therapy, using our remote service infrastructure for remotely connected system with the right contracts. We can achieve we have we have reduced downtime by 14% more than one out of three of cases are resolved remotely without an engineer having to go outside. 82% is the first time right fixed rate that means that the issue is fixed either remotely or if a visit at the site is needed, that visit only one visit is needed. So at that moment, the engineer we decided the right part and fix this straightaway. And this result on average on 135 hours more operational availability per year. This therefore, the ability to treat more patients for the same costs. I'd like to conclude with citing some nice testimonials from some of our customers, showing that the value that we've created is really high impact and this concludes my presentation. Thanks for your attention so far. >> Thank you Morrow, very interesting. And we've got a number of questions that we that have come in. So let's get to them. The first one, how many devices has Philips connected worldwide? And how do you determine which related center data workloads get analyzed with protocols? >> Okay, so this is just two questions. So the first question how many devices are connected worldwide? Well, actually, I'm not allowed to tell you the precise number of connected devices worldwide, but what I can tell is that we are in the order of tens of thousands of devices. And of all types actually. And then, how would we determine which related sensor gets analyzed with vertica well? And a little bit how I set In the in the presentation is a combination of two approaches is a data driven approach and the knowledge driven approach. So a knowledge driven approach because we make maximum use of our knowledge of the failure modes, and the behavior of the medical devices and of their components to select what we think are promising data points and promising features. However, from that moment on data science kicks in, and it's actually data science is used to look at the actual data and come up with quantitative information of what is really happening. So, it could be that an expert is convinced that the particular range of value of a sensor are indicative of a particular failure. And it turns out that maybe it was too optimistic on the other way around that in practice, there are many other situations situation he was not aware of. That could happen. So thanks to the data, then we, you know, get a better understanding of the phenomenon and we get the better modeling. I bet I answered that, any question? >> Yeah, we have another question. Do you have plans to perform any analytics at the edge? >> Now that's a good question. So I can't disclose our plans on this right now, but at the edge devices are certainly one of the options we look at to help our customers towards Zero Unplanned Downtime. Not only that, but also to facilitate the integration of our solution with existing and future hospital IT infrastructure. I mean, we're talking about advanced security, privacy and guarantee that the data is always safe remains. patient data and clinical data remains does not go outside the parameters of the hospital of course, while we want to enhance our functionality provides more value with our services. Yeah, so edge definitely very interesting area of innovation. >> Another question, what are the most helpful vertica features that you rely on? >> I would say, the first that comes to mind, to me at this moment is ease of integration. Basically, with vertica, we will be able to load any data source in a very easy way. And also it really can be interfaced very easily with old type of ions as an application. And this, of course, is not unique to vertica. Nevertheless, the added value here is that this is coupled with an incredible speed, incredible speed for loading and for querying. So it's basically a very versatile tool to innovate fast for data science, because basically we do not end up another thing is multiple projections, advanced encoding and compression. So this allows us to perform the optimizations only when we need it and without having to touch applications or queries. So if we want to achieve high performance, we Basically spend a little effort on improving the projection. And now we can achieve very often dramatic increases in performance. Another feature is EO mode. This is great for for cloud for cloud deployment. >> Okay, another question. What is the number one lesson learned that you can share? >> I think that would my advice would be document control your entire data pipeline, end to end, create positive feedback loops. So I hear that what I hear often is that enterprises I mean Philips is one of them that are not digitally native. I mean, Philips is 129 years old as a company. So you can imagine the the legacy that we have, we will not, you know, we are not born with Web, like web companies are with with, you know, with everything online and everything digital. So enterprises that are not digitally native, sometimes they struggle to innovate in big data or into to do data driven innovation, because, you know, the data is not available or is in silos. Data is controlled by different parts of the organ of the organization with different processes. There is not as a super strong enterprise IT system, providing all the data, you know, for everybody with API's. So my advice is to, to for the very beginning, a creative creating as soon as possible, an end to end solution, from data creation to consumption. That creates value for all the stakeholders of the data pipeline. It is important that everyone in the data pipeline from the producer of the data to the to the consumers, basically in order to pipeline everybody gets a piece of value, piece of the cake. When the value is proven to all stakeholders, everyone would naturally contribute to keep the data pipeline running, and to keep the quality of the data high. That's the students there. >> Yeah, thank you. And in the area of machine learning, what types of innovations do you plan to adopt to help with your data pipeline? >> So, in the error of machine learning, we're looking at things like automatically detecting the deterioration of models to trigger improvement action, as well as connected with active learning. Again, focused on improving the accuracy of our predictive models. So active learning is when the additional human intervention labeling of difficult cases is triggered. So the machine learning classifier may not be able to, you know, classify correctly all the time and instead of just randomly picking up some cases for a human to review, you, you want the costly humans to only review the most valuable cases, from a machine learning point of view, the ones that would contribute the most in improving the classifier. Another error is is deep learning and was not working on it, I mean, but but also applications of more generic anomaly detection algorithms. So the challenge of anomaly detection is that we are not only interested in finding anomalies but also in the recommended proper service actions. Because without a proper service action, and alert generated because of an anomaly, the data loses most of its value. So, this is where I think we, you know. >> Go ahead. >> No, that's, that's it, thanks. >> Okay, all right. So that's all the time that we have today for questions. I want to thank the audience for attending Mauro's presentation and also for your questions. If you weren't able to, if we weren't able to answer your question today, I'd ask let we'll let you know that we'll respond via email. And again, our engineers will be at the vertica, on the vertica quorums awaiting your other questions. It would help us greatly if you could give us some feedback and rate the session before you sign off. Your rating will help us guide us as when we're looking at content to provide for the next vertica BTC. Also, note that a replay of today's event and a PDF copy of the slides will be available on demand, we'll let you know when that'll be by email hopefully later this week. And of course, we invite you to share the content with your colleagues. Again, thank you for your participation today. This includes this breakout session and hope you have a wonderful day. Thank you. >> Thank you

Published Date : Mar 30 2020

SUMMARY :

in the lower right corner of the slide. and perhaps decide that the spare part needs to be replaced. So let's get to them. and the behavior of the medical devices Do you have plans to perform any analytics at the edge? and guarantee that the data is always safe remains. on improving the projection. What is the number one lesson learned that you can share? from the producer of the data to the to the consumers, And in the area of machine learning, what types the deterioration of models to trigger improvement action, and a PDF copy of the slides will be available on demand,

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