(gentle upbeat music) >> Hello, everyone, welcome to theCUBE's coverage of International Women's Day. I'm John Furrier host here in Palo Alto, California. theCUBE's second year covering International Women's Day. It's been a great celebration of all the smart leaders in the world who are making a difference from all kinds of backgrounds, from technology to business and everything in between. Today we've got a great guest, Dominique Bastos, who's the senior Vice President of Cloud at Persistent Systems, formerly with AWS. That's where we first met at re:Invent. Dominique, great to have you on the program here for International Women's Day. Thanks for coming on. >> Thank you John, for having me back on theCUBE. This is an honor, especially given the theme. >> Well, I'm excited to have you on, I consider you one of those typecast personas where you've kind of done a lot of things. You're powerful, you've got great business acumen you're technical, and we're in a world where, you know the world's coming completely digital and 50% of the world is women, 51%, some say. So you got mostly male dominated industry and you have a dual engineering background and that's super impressive as well. Again, technical world, male dominated you're in there in the mix. What inspires you to get these engineering degrees? >> I think even it was more so shifted towards males. When I had the inspiration to go to engineering school I was accused as a young girl of being a tomboy and fiddling around with all my brother's toys versus focusing on my dolls and other kind of stereotypical toys that you would give a girl. I really had a curiosity for building, a curiosity for just breaking things apart and putting them back together. I was very lucky in that my I guess you call it primary school, maybe middle school, had a program for, it was like electronics, that was the class electronics. So building circuit boards and things like that. And I really enjoyed that aspect of building. I think it was more actually going into engineering school. Picking that as a discipline was a little bit, my mom's reaction to when I announced that I wanted to do engineering which was, "No, that's for boys." >> Really. >> And that really, you know, I think she, it came from a good place in trying to protect me from what she has experienced herself in terms of how women are received in those spaces. So I kind of shrugged it off and thought "Okay, well I'm definitely now going to do this." >> (laughs) If I was told not to, you're going to do it. >> I was told not to, that's all I needed to hear. And also, I think my passion was to design cars and I figured if I enroll in an industrial engineering program I could focus on ergonomic design and ultimately, you know have a career doing something that I'm passionate about. So yeah, so my inspiration was kind of a little bit of don't do this, a lot of curiosity. I'm also a very analytical person. I've been, and I don't know what the science is around left right brain to be honest, but been told that I'm a very much a logical person versus a feeler. So I don't know if that's good or bad. >> Straight shooter. What were your engineering degrees if you don't mind sharing? >> So I did industrial engineering and so I did a dual degree, industrial engineering and robotics. At the time it was like a manufacturing robotics program. It was very, very cool because we got to, I mean now looking back, the evolution of robotics is just insane. But you, you know, programmed a robotic arm to pick things up. I actually crashed the Civil Engineering School's Concrete Canoe Building Competition where you literally have to design a concrete canoe and do all the load testing and the strength testing of the materials and basically then, you know you go against other universities to race the canoe in a body of water. We did that at, in Alabama and in Georgia. So I was lucky to experience that two times. It was a lot of fun. >> But you knew, so you knew, deep down, you were technical you had a nerd vibe you were geeking out on math, tech, robotics. What happened next? I mean, what were some of the challenges you faced? How did you progress forward? Did you have any blockers and roadblocks in front of you and how did you handle those? >> Yeah, I mean I had, I had a very eye-opening experience with, in my freshman year of engineering school. I kind of went in gung-ho with zero hesitation, all the confidence in the world, 'cause I was always a very big nerd academically, I hate admitting this but myself and somebody else got most intellectual, voted by the students in high school. It's like, you don't want to be voted most intellectual when you're in high school. >> Now it's a big deal. (laughs) >> Yeah, you want to be voted like popular or anything like that? No, I was a nerd, but in engineering school, it's a, it was very humbling. That whole confidence that I had. I experienced prof, ooh, I don't want to name the school. Everybody can google it though, but, so anyway so I had experience with some professors that actually looked at me and said, "You're in the wrong program. This is difficult." I, and I think I've shared this before in other forums where, you know, my thermodynamic teacher basically told me "Cheerleading's down the hall," and it it was a very shocking thing to hear because it really made me wonder like, what am I up against here? Is this what it's going to be like going forward? And I decided not to pay attention to that. I think at the moment when you hear something like that you just, you absorb it and you also don't know how to react. And I decided immediately to just walk right past him and sit down front center in the class. In my head I was cursing him, of course, 'cause I mean, let's be real. And I was like, I'm going to show this bleep bleep. And proceeded to basically set the curve class crushed it and was back to be the teacher's assistant. So I think that was one. >> But you became his teacher assistant after, or another one? >> Yeah, I gave him a mini speech. I said, do not do this. You, you could, you could have broken me and if you would've done this to somebody who wasn't as steadfast in her goals or whatever, I was really focused like I'm doing this, I would've backed out potentially and said, you know this isn't something I want to experience on the daily. So I think that was actually a good experience because it gave me an opportunity to understand what I was up against but also double down in how I was going to deal with it. >> Nice to slay the misogynistic teachers who typecast people. Now you had a very technical career but also you had a great career at AWS on the business side you've handled 'em all of the big accounts, I won't say the names, but like we're talking about monster accounts, sales and now basically it's not really selling, you're managing a big account, it's like a big business. It's a business development thing. Technical to business transition, how do you handle that? Was that something you were natural for? Obviously you, you stared down the naysayers out of the gate in college and then in business, did that continue and how did you drive through that? >> So I think even when I was coming out of university I knew that I wanted to have a balance between the engineering program and business. A lot of my colleagues went on to do their PEs so continue to get their masters basically in engineering or their PhDs in engineering. I didn't really have an interest for that. I did international business and finance as my MBA because I wanted to explore the ability of taking what I had learned in engineering school and applying it to building businesses. I mean, at the time I didn't have it in my head that I would want to do startups but I definitely knew that I wanted to get a feel for what are they learning in business school that I missed out in engineering school. So I think that helped me when I transitioned, well when I applied, I was asked to come apply at AWS and I kind of went, no I'm going to, the DNA is going to be rejected. >> You thought, you thought you'd be rejected from AWS. >> I thought I'd be, yeah, because I have very much a startup founder kind of disruptive personality. And to me, when I first saw AWS at the stage early 2016 I saw it as a corporation. Even though from a techie standpoint, I was like, these people are insane. This is amazing what they're building. But I didn't know what the cultural vibe would feel like. I had been with GE at the beginning of my career for almost three years. So I kind of equated AWS Amazon to GE given the size because in between, I had done startups. So when I went to AWS I think initially, and I do have to kind of shout out, you know Todd Weatherby basically was the worldwide leader for ProServe and it was being built, he built it and I went into ProServe to help from that standpoint. >> John: ProServe, Professional services >> Professional services, right. To help these big enterprise customers. And specifically my first customer was an amazing experience in taking, basically the company revolves around strategic selling, right? It's not like you take a salesperson with a conventional schooling that salespeople would have and plug them into AWS in 2016. It was very much a consultative strategic approach. And for me, having a technical background and loving to solve problems for customers, working with the team, I would say, it was a dream team that I joined. And also the ability to come to the table with a technical background, knowing how to interact with senior executives to help them envision where they want to go, and then to bring a team along with you to make that happen. I mean, that was like magical for me. I loved that experience. >> So you like the culture, I mean, Andy Jassy, I've interviewed many times, always talked about builders and been a builder mentality. You mentioned that earlier at the top of this interview you've always building things, curious and you mentioned potentially your confidence might have been shaken. So you, you had the confidence. So being a builder, you know, being curious and having confidence seems to be what your superpower is. A lot of people talk about the confidence angle. How important is that and how important is that for encouraging more women to get into tech? Because I still hear that all the time. Not that they don't have confidence, but there's so many signals that potentially could shake confidence in industry >> Yeah, that's actually a really good point that you're making. A lot of signals that women get could shake their confidence and that needs to be, I mean, it's easy to say that it should be innate. I mean that's kind of like textbook, "Oh it has to come from within." Of course it does. But also, you know, we need to understand that in a population where 50% of the population is women but only 7% of the positions in tech, and I don't know the most current number in tech leadership, is women, and probably a smaller percentage in the C-suite. When you're looking at a woman who's wanting to go up the trajectory in a tech company and then there's a subconscious understanding that there's a limit to how far you'll go, your confidence, you know, in even subconsciously gets shaken a little bit because despite your best efforts, you're already seeing the cap. I would say that we need to coach girls to speak confidently to navigate conflict versus running away from it, to own your own success and be secure in what you bring to the table. And then I think a very important thing is to celebrate each other and the wins that we see for women in tech, in the industry. >> That's awesome. What's, the, in your opinion, the, you look at that, the challenges for this next generation women, and women in general, what are some of the challenges for them and that they need to overcome today? I mean, obviously the world's changed for the better. Still not there. I mean the numbers one in four women, Rachel Thornton came on, former CMO of AWS, she's at MessageBird now. They had a study where only one in four women go to the executive board level. And so there's still, still numbers are bad and then the numbers still got to get up, up big time. That's, and the industry's working on that, but it's changed. But today, what are some of the challenges for this current generation and the next generation of women and how can we and the industry meet, we being us, women in the industry, be strong role models for them? >> Well, I think the challenge is one of how many women are there in the pipeline and what are we doing to retain them and how are we offering up the opportunities to fill. As you know, as Rachel said and I haven't had an opportunity to see her, in how are we giving them this opportunity to take up those seats in the C-suite right, in these leadership roles. And I think this is a little bit exacerbated with the pandemic in that, you know when everything shut down when people were going back to deal with family and work at the same time, for better or for worse the brunt of it fell on probably, you know the maternal type caregiver within the family unit. You know, I've been, I raised my daughter alone and for me, even without the pandemic it was a struggle constantly to balance the risk that I was willing to take to show up for those positions versus investing even more of that time raising a child, right? Nevermind the unconscious bias or cultural kind of expectations that you get from the male counterparts where there's zero understanding of what a mom might go through at home to then show up to a meeting, you know fully fresh and ready to kind of spit out some wisdom. It's like, you know, your kid just freaking lost their whatever and you know, they, so you have to sort a bunch of things out. I think the challenge that women are still facing and will we have to keep working at it is making sure that there's a good pipeline. A good amount of young ladies of people taking interest in tech. And then as they're, you know, going through the funnel at stages in their career, we're providing the mentoring we're, there's representation, right? To what they're aspiring to. We're celebrating their interest in the field, right? And, and I think also we're doing things to retain them, because again, the pandemic affected everybody. I think women specifically and I don't know the statistics but I was reading something about this were the ones to tend to kind of pull it back and say well now I need to be home with, you know you name how many kids and pets and the aging parents, people that got sick to take on that position. In addition to the career aspirations that they might have. We need to make it easier basically. >> I think that's a great call out and I appreciate you bringing that up about family and being a single mom. And by the way, you're savage warrior to doing that. It's amazing. You got to, I know you have a daughter in computer science at Stanford, I want to get to that in a second. But that empathy and I mentioned Rachel Thornton, who's the CMO MessageBird and former CMO of AWS. Her thing right now to your point is mentoring and sponsorship is very key. And her company and the video that's on the site here people should look at that and reference that. They talk a lot about that empathy of people's situation whether it's a single mom, family life, men and women but mainly women because they're the ones who people aren't having a lot of empathy for in that situation, as you called it out. This is huge. And I think remote work has opened up this whole aperture of everyone has to have a view into how people are coming to the table at work. So, you know, props are bringing that up, and I recommend everyone look at check out Rachel Thornton. So how do you balance that, that home life and talk about your daughter's journey because sounds like she's nerding out at Stanford 'cause you know Stanford's called Nerd Nation, that's their motto, so you must be proud. >> I am so proud, I'm so proud. And I will say, I have to admit, because I did encounter so many obstacles and so many hurdles in my journey, it's almost like I forgot that I should set that aside and not worry about my daughter. My hope for her was for her to kind of be artistic and a painter or go into something more lighthearted and fun because I just wanted to think, I guess my mom had the same idea, right? She, always been very driven. She, I want to say that I got very lucky that she picked me to be her mom. Biologically I'm her mom, but I told her she was like a little star that fell from the sky and I, and ended up with me. I think for me, balancing being a single mom and a career where I'm leading and mentoring and making big decisions that affect people's lives as well. You have to take the best of everything you get from each of those roles. And I think that the best way is play to your strengths, right? So having been kind of a nerd and very organized person and all about, you know, systems for effectiveness, I mean, industrial engineering, parenting for me was, I'm going to make it sound super annoying and horrible, but (laughs) >> It's funny, you know, Dave Vellante and I when we started SiliconANGLE and theCUBE years ago, one of the things we were all like sports lovers. So we liked sports and we are like we looked at the people in tech as tech athletes and except there's no men and women teams, it's one team. It's all one thing. So, you know, I consider you a tech athlete you're hard charging strong and professional and smart and beautiful and brilliant, all those good things. >> Thank you. >> Now this game is changing and okay, and you've done startups, and you've done corporate jobs, now you're in a new role. What's the current tech landscape from a, you know I won't say athletic per standpoint but as people who are smart. You have all kinds of different skill sets. You have the startup warriors, you have the folks who like to be in the middle of the corporate world grow up through corporate, climb the corporate ladder. You have investors, you have, you know, creatives. What have you enjoyed most and where do you see all the action? >> I mean, I think what I've enjoyed the most has been being able to bring all of the things that I feel I'm strong at and bring it together to apply that to whatever the problem is at hand, right? So kind of like, you know if you look at a renaissance man who can kind of pop in anywhere and, oh, he's good at, you know sports and he's good at reading and, or she's good at this or, take all of those strengths and somehow bring them together to deal with the issue at hand, versus breaking up your mindset into this is textbook what I learned and this is how business should be done and I'm going to draw these hard lines between personal life and work life, or between how you do selling and how you do engineering. So I think my, the thing that I loved, really loved about AWS was a lot of leaders saw something in me that I potentially didn't see, which was, yeah you might be great at running that big account but we need help over here doing go to market for a new product launch and boom, there you go. Now I'm in a different org helping solve that problem and getting something launched. And I think if you don't box yourself in to I'm only good at this, or, you know put a label on yourself as being the rockstar in that. It leaves room for opportunities to present themselves but also it leaves room within your own mind to see yourself as somebody capable of doing anything. Right, I don't know if I answered the question accurately. >> No, that's good, no, that's awesome. I love the sharing, Yeah, great, great share there. Question is, what do you see, what do you currently during now you're building a business of Persistent for the cloud, obviously AWS and Persistent's a leader global system integrator around the world, thousands and thousands of customers from what we know and been reporting on theCUBE, what's next for you? Where do you see yourself going? Obviously you're going to knock this out of the park. Where do you see yourself as you kind of look at the continuing journey of your mission, personal, professional what's on your mind? Where do you see yourself going next? >> Well, I think, you know, again, going back to not boxing yourself in. This role is an amazing one where I have an opportunity to take all the pieces of my career in tech and apply them to building a business within a business. And that involves all the goodness of coaching and mentoring and strategizing. And I'm loving it. I'm loving the opportunity to work with such great leaders. Persistent itself is very, very good at providing opportunities, very diverse opportunities. We just had a huge Semicolon; Hackathon. Some of the winners were females. The turnout was amazing in the CTO's office. We have very strong women leading the charge for innovation. I think to answer your question about the future and where I may see myself going next, I think now that my job, well they say the job is never done. But now that Chloe's kind of settled into Stanford and kind of doing her own thing, I have always had a passion to continue leading in a way that brings me to, into the fold a lot more. So maybe, you know, maybe in a VC firm partner mode or another, you know CEO role in a startup, or my own startup. I mean, I never, I don't know right now I'm super happy but you never know, you know where your drive might go. And I also want to be able to very deliberately be in a role where I can continue to mentor and support up and coming women in tech. >> Well, you got the smarts but you got really the building mentality, the curiosity and the confidence really sets you up nicely. Dominique great story, great inspiration. You're a role model for many women, young girls out there and women in tech and in celebration. It's a great day and thank you for sharing that story and all the good nuggets there. Appreciate you coming on theCUBE, and it's been my pleasure. Thanks for coming on. >> Thank you, John. Thank you so much for having me. >> Okay, theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE here in Palo Alto getting all the content, check out the other interviews some amazing stories, lessons learned, and some, you know some funny stories and some serious stories. So have some fun and enjoy the rest of the videos here for International Women's Days, thanks for watching. (gentle inspirational music)

(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)

(intro music) >> "theCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> We're not going to- >> Hey everybody, welcome back to the Fira in Barcelona. My name is Dave Vellante, I'm here with Dave Nicholson, day four of MWC23. I mean, it's Dave, it's, it's still really busy. And you walking the floors, you got to stop and start. >> It's surprising. >> People are cheering. They must be winding down, giving out the awards. Really excited. Pier, look at you and Elias here. He's the vice president of Engineering Technology for Edge Computing Offers Strategy and Execution at Dell Technologies, and he's joined by Dan Cummins, who's a fellow and vice president of, in the Edge Business Unit at Dell Technologies. Guys, welcome. >> Thank you. >> Thank you. >> I love when I see the term fellow. You know, you don't, they don't just give those away. What do you got to do to be a fellow at Dell? >> Well, you know, fellows are senior technical leaders within Dell. And they're usually tasked to help Dell solve you know, a very large business challenge to get to a fellow. There's only, I think, 17 of them inside of Dell. So it is a small crowd. You know, previously, really what got me to fellow, is my continued contribution to transform Dell's mid-range business, you know, VNX two, and then Unity, and then Power Store, you know, and then before, and then after that, you know, they asked me to come and, and help, you know, drive the technology vision for how Dell wins at the Edge. >> Nice. Congratulations. Now, Pierluca, I'm looking at this kind of cool chart here which is Edge, Edge platform by Dell Technologies, kind of this cube, like cubes course, you know. >> AK project from here. >> Yeah. So, so tell us about the Edge platform. What, what's your point of view on all that at Dell? >> Yeah, absolutely. So basically in a, when we create the Edge, and before even then was bringing aboard, to create this vision of the platform, and now building the platform when we announced project from here, was to create solution for the Edge. Dell has been at the edge for 30 years. We sold a lot of compute. But the reality was people want outcome. And so, and the Edge is a new market, very exciting, but very siloed. And so people at the Edge have different personas. So quickly realize that we need to bring in Dell, people with expertise, quickly realize as well that doing all these solution was not enough. There was a lot of problem to solve because the Edge is outside of the data center. So you are outside of the wall of the data center. And what is going to happen is obviously you are in the land of no one. And so you have million of device, thousand of million of device. All of us at home, we have all connected thing. And so we understand that the, the capability of Dell was to bring in technology to secure, manage, deploy, with zero touch, zero trust, the Edge. And all the edge the we're speaking about right now, we are focused on everything that is outside of a normal data center. So, how we married the computer that we have for many years, the new gateways that we create, so having the best portfolio, number one, having the best solution, but now, transforming the way that people deploy the Edge, and secure the Edge through a software platform that we create. >> You mentioned Project Frontier. I like that Dell started to do these sort of project, Project Alpine was sort of the multi-cloud storage. I call it "The Super Cloud." The Project Frontier. It's almost like you develop, it's like mission based. Like, "Okay, that's our North Star." People hear Project Frontier, they know, you know, internally what you're talking about. Maybe use it for external communications too, but what have you learned since launching Project Frontier? What's different about the Edge? I mean you're talking about harsh environments, you're talking about new models of connectivity. So, what have you learned from Project Frontier? What, I'd love to hear the fellow perspective as well, and what you guys are are learning so far. >> Yeah, I mean start and then I left to them, but we learn a lot. The first thing we learn that we are on the right path. So that's good, because every conversation we have, there is nobody say to us, you know, "You are crazy. "This is not needed." Any conversation we have this week, start with the telco thing. But after five minutes it goes to, okay, how I can solve the Edge, how I can bring the compute near where the data are created, and how I can do that secure at scale, and with the right price. And then can speak about how we're doing that. >> Yeah, yeah. But before that, we have to really back up and understand what Dell is doing with Project Frontier, which is an Edge operations platform, to simplify your Edge use cases. Now, Pierluca and his team have a number of verticalized applications. You want to be able to securely deploy those, you know, at the Edge. But you need a software platform that's going to simplify both the life cycle management, and the security at the Edge, with the ability to be able to construct and deploy distributed applications. Customers are looking to derive value near the point of generation of data. We see a massive explosion of data. But in particular, what's different about the Edge, is the different computing locations, and the constraints that are on those locations. You know, for example, you know, in a far Edge environment, the people that service that equipment are not trained in the IT, or train, trained in it. And they're also trained in the safety and security protocols of that environment. So you necessarily can't apply the same IT techniques when you're managing infrastructure and deploying applications, or servicing in those locations. So Frontier was designed to solve for those constraints. You know, often we see competitors that are doing similar things, that are starting from an IT mindset, and trying to shift down to cover Edge use cases. What we've done with Frontier, is actually first understood the constraints that they have at the Edge. Both the operational constraints and technology constraints, the service constraints, and then came up with a, an architecture and technology platform that allows them to start from the Edge, and bleed into the- >> So I'm laughing because you guys made the same mistake. And you, I think you learned from that mistake, right? You used to take X86 boxes and throw 'em over the fence. Now, you're building purpose-built systems, right? Project Frontier I think is an example of the learnings. You know, you guys an IT company, right? Come on. But you're learning fast, and that's what I'm impressed about. >> Well Glenn, of course we're here at MWC, so it's all telecom, telecom, telecom, but really, that's a subset of Edge. >> Yes. >> Fair to say? >> Yes. >> Can you give us an example of something that is, that is, orthogonal to, to telecom, you know, maybe off to the side, that maybe overlaps a little bit, but give us an, give us an example of Edge, that isn't specifically telecom focused. >> Well, you got the, the Edge verticals. and Pierluca could probably speak very well to this. You know, you got manufacturing, you got retail, you got automotive, you got oil and gas. Every single one of them are going to make different choices in the software that they're going to use, the hyperscaler investments that they're going to use, and then write some sort of automation, you know, to deploy that, right? And the Edge is highly fragmented across all of these. So we certainly could deploy a private wireless 5G solution, orchestrate that deployment through Frontier. We can also orchestrate other use cases like connected worker, or overall equipment effectiveness in manufacturing. But Pierluca you have a, you have a number. >> Well, but from your, so, but just to be clear, from your perspective, the whole idea of, for example, private 5g, it's a feature- >> Yes. >> That might be included. It happened, it's a network topology, a network function that might be a feature of an Edge environment. >> Yes. But it's not the center of the discussion. >> So, it enables the outcome. >> Yeah. >> Okay. >> So this, this week is a clear example where we confirm and establish this. The use case, as I said, right? They, you say correctly, we learned very fast, right? We brought people in that they came from industry that was not IT industry. We brought people in with the things, and we, we are Dell. So we have the luxury to be able to interview hundreds of customers, that just now they try to connect the OT with the IT together. And so what we learn, is really, at the Edge is different personas. They person that decide what to do at the Edge, is not the normal IT administrator, is not the normal telco. >> Who is it? Is it an engineer, or is it... >> It's, for example, the store manager. >> Yeah. >> It's, for example, the, the person that is responsible for the manufacturing process. Those people are not technology people by any means. But they have a business goal in mind. Their goal is, "I want to raise my productivity by 30%," hence, I need to have a preventive maintenance solution. How we prescribe this preventive maintenance solution? He doesn't prescribe the preventive maintenance solution. He goes out, he has to, a consult or himself, to deploy that solution, and he choose different fee. Now, the example that I was doing from the houses, all of us, we have connected device. The fact that in my house, I have a solar system that produce energy, the only things I care that I can read, how much energy I produce on my phone, and how much energy I send to get paid back. That's the only thing. The fact that inside there is a compute that is called Dell or other things is not important to me. Same persona. Now, if I can solve the security challenge that the SI, or the user need to implement this technology because it goes everywhere. And I can manage this in extensively, and I can put the supply chain of Dell on top of that. And I can go every part in the world, no matter if I have in Papua New Guinea, or I have an oil ring in Texas, that's the winning strategy. That's why people, they are very interested to the, including Telco, the B2B business in telco is looking very, very hard to how they recoup the investment in 5g. One of the way, is to reach out with solution. And if I can control and deploy things, more than just SD one or other things, or private mobility, that's the key. >> So, so you have, so you said manufacturing, retail, automotive, oil and gas, you have solutions for each of those, or you're building those, or... >> Right now we have solution for manufacturing, with for example, PTC. That is the biggest company. It's actually based in Boston. >> Yeah. Yeah, it is. There's a company that the market's just coming right to them. >> We have a, very interesting. Another solution with Litmus, that is a startup that, that also does manufacturing aggregation. We have retail with Deep North. So we can do detecting in the store, how many people they pass, how many people they doing, all of that. And all theses solution that will be, when we will have Frontier in the market, will be also in Frontier. We are also expanding to energy, and we going vertical by vertical. But what is they really learn, right? You said, you know you are an IT company. What, to me, the Edge is a pre virtualization area. It's like when we had, you know, I'm, I've been in the company for 24 years coming from EMC. The reality was before there was virtualization, everybody was starting his silo. Nobody thought about, "Okay, I can run this thing together "with security and everything, "but I need to do it." Because otherwise in a manufacturing, or in a shop, I can end up with thousand of devices, just because someone tell to me, I'm a, I'm a store manager, I don't know better. I take this video surveillance application, I take these things, I take a, you know, smart building solution, suddenly I have five, six, seven different infrastructure to run this thing because someone say so. So we are here to democratize the Edge, to secure the Edge, and to expand. That's the idea. >> So, the Frontier platform is really the horizontal platform. And you'll build specific solutions for verticals. On top of that, you'll, then I, then the beauty is ISV's come in. >> Yes. >> 'Cause it's open, and the developers. >> We have a self certification program already for our solution, as well, for the current solution, but also for Frontier. >> What does that involve? Self-certification. You go through you, you go through some- >> It's basically a, a ISV can come. We have a access to a lab, they can test the thing. If they pass the first screen, then they can become part of our ecosystem very easily. >> Ah. >> So they don't need to spend days or months with us to try to architect the thing. >> So they get the premature of being certified. >> They get the Dell brand associated with it. Maybe there's some go-to-market benefits- >> Yes. >> As well. Cool. What else do we need to know? >> So, one thing I, well one thing I just want to stress, you know, when we say horizontal platform, really, the Edge is really a, a distributed edge computing problem, right? And you need to almost create a mesh of different computing locations. So for example, even though Dell has Edge optimized infrastructure, that we're going to deploy and lifecycle manage, customers may also have compute solutions, existing compute solutions in their data center, or at a co-location facility that are compute destinations. Project Frontier will connect to those private cloud stacks. They'll also collect to, connect to multiple public cloud stacks. And then, what they can do, is the solutions that we talked about, they construct that using an open based, you know, protocol, template, that describes that distributed application that produces that outcome. And then through orchestration, we can then orchestrate across all of these locations to produce that outcome. That's what the platform's doing. >> So it's a compute mesh, is what you just described? >> Yeah, it's, it's a, it's a software orchestration mesh. >> Okay. >> Right. And allows customers to take advantage of their existing investments. Also allows them to, to construct solutions based on the ISV of their choice. We're offering solutions like Pierluca had talked about, you know, in manufacturing with Litmus and PTC, but they could put another use case that's together based on another ISV. >> Is there a data mesh analog here? >> The data mesh analog would run on top of that. We don't offer that as part of Frontier today, but we do have teams working inside of Dell that are working on this technology. But again, if there's other data mesh technology or packages, that they want to deploy as a solution, if you will, on top of Frontier, Frontier's extensible in that way as well. >> The open nature of Frontier is there's a, doesn't, doesn't care. It's just a note on the mesh. >> Yeah. >> Right. Now, of course you'd rather, you'd ideally want it to be Dell technology, and you'll make the business case as to why it should be. >> They get additional benefits if it's Dell. Pierluca talked a lot about, you know, deploying infrastructure outside the walls of an IT data center. You know, this stuff can be tampered with. Somebody can move it to another room, somebody can open up. In the supply chain with, you know, resellers that are adding additional people, can open these devices up. We're actually deploying using an Edge technology called Secure Device Onboarding. And it solves a number of things for us. We, as a manufacturer can initialize the roots of trust in the Dell hardware, such that we can validate, you know, tamper detection throughout the supply chain, and securely transfer ownership. And that's different. That is not an IT technique. That's an edge technique. And that's just one example. >> That's interesting. I've talked to other people in IT about how they're using that technique. So it's, it's trickling over to that side of the business. >> I'm almost curious about the friction that you, that you encounter because the, you know, you paint a picture of a, of a brave new world, a brave new future. Ideally, in a healthy organization, they have, there's a CTO, or at least maybe a CIO, with a CTO mindset. They're seeking to leverage technology in the service of whatever the mission of the organization is. But they've got responsibilities to keep the lights on, as well as innovate. In that mix, what are you seeing as the inhibitors? What's, what's the push back against Frontier that you're seeing in most cases? Is it, what, what is it? >> Inside of Dell? >> No, not, I'm saying out, I'm saying with- >> Market friction. >> Market, market, market friction. What is the push back? >> I think, you know, as I explained, do yourself is one of the things that probably is the most inhibitor, because some people, they think that they are better already. They invest a lot in this, and they have the content. But those are again, silo solutions. So, if you go into some of the huge things that they already established, thousand of store and stuff like that, there is an opportunity there, because also they want to have a refresh cycle. So when we speak about softer, softer, softer, when you are at the Edge, the software needs to run on something that is there. So the combination that we offer about controlling the security of the hardware, plus the operating system, and provide an end-to-end platform, allow them to solve a lot of problems that today they doing by themselves. Now, I met a lot of customers, some of them, one actually here in Spain, I will not make the name, but it's a large automotive. They have the same challenge. They try to build, but the problem is this is just for them. And they want to use something that is a backup and provide with the Dell service, Dell capability of supply chain in all the world, and the diversity of the portfolio we have. These guys right now, they need to go out and find different types of compute, or try to adjust thing, or they need to have 20 people there to just prepare the device. We will take out all of this. So I think the, the majority of the pushback is about people that they already established infrastructure, and they want to use that. But really, there is an opportunity here. Because the, as I said, the IT/OT came together now, it's a reality. Three years ago when we had our initiative, they've pointed out, sarcastically. We, we- >> Just trying to be honest. (laughing) >> I can't let you get away with that. >> And we, we failed because it was too early. And we were too focused on, on the fact to going. Push ourself to the boundary of the IOT. This platform is open. You want to run EdgeX, you run EdgeX, you want OpenVINO, you want Microsoft IOT, you run Microsoft IOT. We not prescribe the top. We are locking down the bottom. >> What you described is the inertia of, of sunk dollars, or sunk euro into an infrastructure, and now they're hanging onto that. >> Yeah. >> But, I mean, you know, I, when we say horizontal, we think scale, we think low cost, at volume. That will, that will win every time. >> There is a simplicity at scale, right? There is a, all the thing. >> And the, and the economics just overwhelm that siloed solution. >> And >> That's inevitable. >> You know, if you want to apply security across the entire thing, if you don't have a best practice, and a click that you can do that, or bring down an application that you need, you need to touch each one of these silos. So, they don't know yet, but we going to be there helping them. So there is no pushback. Actually, this particular example I did, this guy said you know, there are a lot of people that come here. Nobody really described the things we went through. So we are on the right track. >> Guys, great conversation. We really appreciate you coming on "theCUBE." >> Thank you. >> Pleasure to have you both. >> Okay. >> Thank you. >> All right. And thank you for watching Dave Vellante for Dave Nicholson. We're live at the Fira. We're winding up day four. Keep it right there. Go to siliconangle.com. John Furrier's got all the news on "theCUBE.net." We'll be right back right after this break. "theCUBE," at MWC 23. (outro music)

(low-key music) >> The cube's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (uplifting electronic music) (crowd chattering in background) >> Welcome back to Spain, everybody. My name's Dave Vellante. I'm here with Dave Nicholson. We are live at the Fira in Barcelona, covering MWC23 day four. We've been talking about, you know, 5G all week. We're going to talk about it some more. Jillian Kaplan is here. She's the head of Global Telecom Thought Leadership at Dell Technologies, and we're pleased to have Meg Knauth, who's the Vice President for Digital Platform Engineering at T-Mobile. Ladies, welcome to theCUBE. Thanks for coming on. >> Thanks for having us. >> Yeah, thank you. >> All right, Meg, can you explain 5G and edge to folks that may not be familiar with it? Give us the 101 on 5G and edge. >> Sure, I'd be happy to. So, at T-Mobile, we want businesses to be able to focus on their business outcomes and not have to stress about network technology. So we're here to handle the networking behind the scenes for you to achieve your business goals. The main way to think about 5G is speed, reduced latency, and heightened security. And you can apply that to so many different business goals and objectives. You know, some of the use cases that get touted out the most are in the retail manufacturing sectors with sensors and with control of inventory and things of that nature. But it can be applied to pretty much any industry because who doesn't need more (chuckles) more speed and lower latency. >> Yeah. And reliability, right? >> Exactly. >> I mean, that's what you're going to have there. So it's not like it's necessarily going to- you know, you think about 5G and these private networks, right? I mean, it's not going to, oh, maybe it is going to eat into, there's a Venn there, I know, but it's not going to going to replace wireless, right? I mean, it's new use cases. >> Yeah. >> Maybe you could talk about that a little bit. >> Yeah, they definitely coexist, right? And Meg touched a little bit on like all the use cases that are coming to be, but as we look at 5G, it's really the- we call it like the Enterprise G, right? It's where the enterprise is going to be able to see changes in their business and the way that they do things. And for them, it's going to be about reducing costs and heightening ROI, and safety too, right? Like being able to automate manufacturing facilities where you don't have workers, like, you know, getting hit by various pieces of equipment and you can take them out of harm's way and put robots in their place. And having them really work in an autonomous situation is going to be super, super key. And 5G is just the, it's the backbone of all future technologies if you look at it. We have to have a network like that in order to build things like AI and ML, and we talk about VR and the Metaverse. You have to have a super reliable network that can handle the amount of devices that we're putting out today, right? So, extremely important. >> From T-Mobile's perspective, I mean we hear a lot about, oh, we spent a lot on CapEx, we know that. You know, trillion and a half over the next seven years, going into 5G infrastructure. We heard in the early keynotes at MWC, we heard the call to you know, tax the over the top vendors. We heard the OTT, Netflix shot back, they said, "Why don't you help us pay for the content that we're creating?" But, okay, so I get that, but telcos have a great business. Where's T-Mobile stand on future revenue opportunities? Are you looking to get more data and monetize that data? Are you looking to do things like partner with Dell to do, you know, 5G networks? Where are the opportunities for T-Mobile? >> I think it's more, as Jillian said, it's the opportunities for each business and it's unique to those businesses. So we're not in it just for ourselves. We're in it to help others achieve their business goals and to do more with all of the new capabilities that this network provides. >> Yeah, man, I like that answer because again, listening to some of the CEOs of the large telcos, it's like, hmm, what's in it for me as the customer or the business? I didn't hear enough of that. And at least in the early keynotes, I'm hearing it more, you know, as the show goes on. But I don't know, Dave, what do you think about what you've heard at the event? >> Well, I'm curious from T-Mobile's perspective, you know when a consumer thinks about 5G, we think of voice, text, and data. And if we think about the 5G network that you already have in place, I'm curious, if you can share this kind of information, what percentage of that's being utilized now? How much is available for the, you know, for the Enterprise G that we're talking about, and maybe, you know, in five years in the future, do you have like a projected mix of consumer use versus all of these back office, call them processes that a consumer's not aware of, but you know the factory floor being connected via 5G, that frontiers that emerges, where are we now and what are you looking towards? Does that make sense? Kind of the mixed question? >> Hand over the business plan! (all laugh) >> Yeah! Yeah, yeah, yeah. >> Yeah, I- >> I want numbers Meg, numbers! >> Wow. (Dave and Dave laugh) I'm probably actually not the right person to speak to that. But as you know, T-Mobile has the largest 5G network in North America, and we just say, bring it, right? Let's talk- >> So you got room, you got room for Jillian's stuff? >> Yeah, let's solve >> Well, we can build so many >> business problems together. >> private 5G networks, right? Like I would say like the opportunities are... There's not a limit, right? Because as we build out these private networks, right? We're not on a public network when we're talking about like connecting these massive factories or connecting like a retail store to you and your house to be able to basically continue to try on the clothes remotely, something like that. It's limitless and what we can build- >> So they're related, but they're not necessarily mutually exclusive in the sense that what you are doing in the factory example is going to interfere with my ability to get my data through T-mobile. >> No, no, I- >> These are separated. >> Yeah. Yeah. >> Okay. >> As we build out these private networks and these private facilities, and there are so many applications in the consumer space that haven't even been realized yet. Like, when we think about 4G, when 4G launched, there were no applications that needed 4G to run on our cell phones, right? But then the engineers got to work, right? And we ended up with Uber and Instagram stories and all these applications that require 4G to launch. And that's what's going to happen with 5G too, it's like, as the network continues to get built, in the consumer space as well as the enterprise space, there's going to be new applications realized on this is all the stuff that we can do with this amazing network and look how many more devices and look how much faster it is, and the lower latency and the higher bandwidth, and you know, what we can really build. And I think what we're seeing at this show compared to last year is this stuff actually in practice. There was a lot of talk last year, like about, oh, this is what we can build, but now we're building it. And I think that's really key to show that companies like T-Mobile can help the enterprise in this space with cooperation, right? Like, we're not just talking about it now, we're actually putting it into practice. >> So how does it work? If I put in a private network, what are you doing? You slice out a piece of the network and charge me for it and then I get that as part of my private network. How does it actually work for the customer? >> You want to take that one? >> So I was going to say, yeah, you can do a network slice. You can actually physically build a private network, right? It depends, there's so many different ways to engineer it. So I think you can do it either way, basically. >> We just, we don't want it to be scary, right? >> Yep. >> So it starts with having a conversation about the business challenges that you're facing and then backing it into the technology and letting the technology power those solutions. But we don't want it to be scary for people because there's so much buzz around 5G, around edge, and it can be overwhelming and you can feel like you need a PhD in engineering to have a conversation. And we just want to kind of simplify things and talk in your language, not in our language. We'll figure out the tech behind the scenes. Just tell us what problems we can solve together. >> And so many non-technical companies are having to transform, right? Like retail, like manufacturing, that haven't had to be tech companies before. But together with T-Mobile and Dell, we can help enable that and make it not scary like Meg said. >> Right, so you come into my factory, I say, okay, look around. I got all these people there, and they're making hoses and they're physically putting 'em together. And we go and we have to take a physical measurement as to, you know, is it right? And because if we don't do that, then we have to rework it. Okay, now that's a problem. Okay, can you help me digitize that business? I need a network to do that. I'm going to put in some robots to do that. This is, I mean, I'm making this up but this has got to be a common use case, right? >> Yeah. >> So how do you simplify that for the business owner? >> So we start with what we can provide, and then in some cases you need additional solution providers. You might need a robotics company, you might need a sensor company. But we have those contacts to bring that together for you so that you don't have to be the expert in all those things. >> And what do I do with all the data that I'm collecting? Because, you know, I'm not really a data expert. Maybe, you know, I'm good at putting hoses together, but what's the data layer look like here? (all laughing) >> It's a hose business! >> I know! >> Great business. >> Back to the hoses again. >> There's a lot of different things you can do with it, right? You can collect it in a database, you can send it up to a cloud, you can, you know, use an edge device. It depends how we build the network. >> Dave V.: Can you guys help me do that? Can you guys- >> Sure, yeah. >> Help me figure that out. Should I put it into cloud? Should I use this database or that data? What kind of skills do I need? >> And it depends on the size of the network, right? And the size of the business. Like, you know, there's very simple. You don't have to be a massive manufacturer in order to install this stuff. >> No, I'm asking small business questions. >> Yeah. >> Right, I might not have this giant IT team. I might not have somebody who knows how to do ETL and PBA. >> Exactly. And we can talk to you too about what data matters, right? And we can, together, talk about what data might be the most valuable to you. We can talk to you about how we use data. But again, simplifying it down and making it personal to your business. >> Your point about scary is interesting, because no one has mentioned that until you did in four days. Three? Four days. Somebody says, let's do a private 5G network. That sounds like you're offering, you know, it's like, "Hey, you know what we should do Dave? We'll build you a cruise ship." It's like, I don't need a cruise ship, I just want to go bass fishing. >> Right, right, right. >> But in fact, these things are scalable in the sense that it can be scaled down from the trillions of dollars of infrastructure investment. >> Yeah. >> Yeah. It needs to be focused on your outcome, right? And not on the tech. >> When I was at the Dell booth I saw this little private network, it was about this big. I'm like, how much is that? I want one of those. (all laugh) >> I'm not the right person to talk about that! >> The little black one? >> Yes. >> I wanted one of those, too! >> I saw it, it had a little case to carry it around. I'm like, that could fit in my business. >> Just take it with you. >> theCUBE could use that! (all laugh) >> Anything that could go in a pelican case, I want. >> It's true. Like, it's so incredibly important, like you said, to focus on outcomes, right? Not just tech for the sake of tech. What's the problem? Let's solve the problem together. And then you're getting the outcome you want. You'll know what data you need. If you know what the problem is, you're like, okay this is the data I need to know if this problem is solved or not. >> So it sounds like 2022 was the year of talking about it. 2023, I'm inferring is the year of seeing it. >> Yep. >> And 2024 is going to be the year of doing it? >> I think we're doing it now. >> We're doing it now. >> Yeah. >> Okay. >> Yeah, yeah. We're definitely doing it now. >> All right. >> I see a lot of this stuff being put into place and a lot more innovation and a lot more working together. And Meg mentioned working with other partners. No one's going to do this alone. You've got to like, you know, Dell especially, we're focused on open and making sure that, you know, we have the right software partners. We're bringing in smaller players, right? Like ISVs too, as well as like the big software guys. Incredibly, incredibly important. The sensor companies, whatever we need you've got to be able to solve your customer's issue, which in this case, we're looking to help the enterprise together to transform their space. And Dell knows a little bit about the enterprise, so. >> So if we are there in 2023, then I assume 2024 will be the year that each of your companies sets up a dedicated vertical to address the hose manufacturing market. (Meg laughing) >> Oh, the hose manufacturing market. >> Further segmentation is usually a hallmark of the maturity of an industry. >> I got a lead for you. >> Yeah, there you go. >> And that's one thing we've done at Dell, too. We've built like this use case directory to help the service providers understand what, not just say like, oh, you can help manufacturers. Yeah, but how, what are the use cases to do that? And we worked with a research firm to figure out, like, you know these are the most mature, these are the best ROIs. Like to really help hone in on exactly what we can deploy for 5G and edge solutions that make the most sense, not only for service providers, right, but also for the enterprises. >> Where do you guys want to see this partnership go? Give us the vision. >> To infinity and beyond. To 5G! (Meg laughing) To 5G and beyond. >> I love it. >> It's continuation. I love that we're partnering together. It's incredibly important to the future of the business. >> Good deal. >> To bring the strengths of both together. And like Jillian said, other partners in the ecosystem, it has to be approached from a partnership perspective, but focused on outcomes. >> Jillian: Yep. >> To 5G and beyond. I love it. >> To 5G and beyond. >> Folks, thanks for coming on theCUBE. >> Thanks for having us. >> Appreciate your insights. >> Thank you. >> All right. Dave Vellante for Dave Nicholson, keep it right there. You're watching theCUBE. Go to silliconANGLE.com. John Furrier is banging out all the news. theCUBE.net has all the videos. We're live at the Fira in Barcelona, MWC23. We'll be right back. (uplifting electronic music)

(intro music) >> Narrator: "theCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (gentle music) >> Hey everyone, welcome back to "theCUBE," the leader in live and emerging tech coverage. As you well know, we are live at MWC23 in Barcelona, Spain. Lisa Martin with Dave Nicholson. Day three of our coverage, as you know, 'cause you've been watching the first two days. A lot of conversations about ecosystem, a lot about disruption in the telco industry. We're going to be talking about Open RAN. You've heard some of those great conversations, the complexities, the opportunities. Two guests join Dave and me. Abdullah Abuzaid, Technical Product Manager at Dell, and Gil Hellmann, VP Telecom Solutions Engineering and Architecture at Wind River. Welcome to the program guys. >> Thank you. >> Nice to be here. >> Let's talk a little bit about Dell and Wind River. We'll each ask you both the same question, and talk to us about how you're working together to really address the complexities that organizations are having when they're considering moving from a closed environment to an open environment. >> Definitely. Thank you for hosting us. By end of the day, the relationship between Dell and Wind River is not a new. We've been collaborating in the open ecosystem for long a time enough. And that's one of the, our partnership is a result of this collaboration where we've been trying to make more efficient operation in the ecosystem. The open environment ecosystem, it has the plus and a concern. The plus of simplicity, choice of multiple vendors, and then the concern of complexity managing these vendors. Especially if we look at examples for the Open RAN ecosystem, dealing with multiple vendors, trying to align them. It bring a lot of operational complexity and TCO challenges for our customers, from this outcome where we build our partnership with Wind River in order to help our customer to simplify, or run deployment, operation, and lifecycle management and sustain it. >> And who are the customers, by the way? >> Mainly the CSP customers who are targeting Open RAN and Virtual RAN deployments. That digital transformation moving towards unified cloud environment, or a seamless cloud experience from Core to RAN, these are the customers we are working with them. >> You'll give us your perspective, your thoughts on the partnership, and the capabilities that you're enabling, the CSPs with that. >> Sure. It's actually started last year here in Barcelona, when we set together, and started to look at the, you know, the industry, the adoption of Open RAN, and the challenges. And Open RAN brings a lot of possibilities and benefit, but it does bring a lot of challenges of reintegrating what you desegregate. In the past, you purchase everything from one vendor, they provide the whole solution. Now you open it, you have different layers. So if you're looking at Open RAN, you have, I like to look at it as three major layers, the management, application, and the infrastructure. And we're starting to look what are the challenges. And the challenges of integration, of complexity, knowledge that operator has with cloud infrastructure. And this is where we basically, Dell and Winder River set together and say, "How can we ease this? "How we can make it simpler?" And we decided to partner and bring a joint infrastructure solution to market, that's not only integrated at a lab at the factory level, but it basically comes with complete lifecycle management from the day zero deployment, through the day two operation, everything done through location, through Dell supported, working out of the box. So basically taking this whole infrastructure layer integration pain out, de-risking everything, and then continuing from there to work with the ecosystem vendor to reintegrate, validate the application, on top of this infrastructure. >> So what is the, what is the Wind River secret sauce in this, in this mix, for folks who aren't familiar with what Wind River does? >> Yes, absolutely. So Wind River, for many, many don't know, we're in business since 1981. So over 40 years. We specialize high performance, high reliability infrastructure. We touch every aspect of your day and your life. From the airplane that you fly, the cars, the medical equipment. And if we go into the telco, most of the telco equipment that it's not virtualized, not throughout the fight today, using our operating system. So from all the leading equipment manufacturers and even the smaller one. And as the world started to go into desegregation in cloud, Wind River started to look at this and say, "Okay, everything is evolving. Instead of a device that included the application, the hardware, everything fused together, it's now being decomposed. So instead of providing the operating environment to develop and deploy the application to the device manufacturer, now we're providing it basically to build the cloud. So to oversimplify, I call it a cloud OS, okay. It's a lot more than OS, it's an operating environment. But we took basically our experience, the same experience that, you know, we used in all those years with the telco equipment manufacturer, and brought it into the cloud. So we're basically providing solution to build an on-premises scalable cloud from the core all the way to the far edge, that doesn't compromise reliability, doesn't compromise performance, and address all the telco needs. >> So I, Abdullah, maybe you can a answer this. >> Yeah. >> What is the, what does the go-to-market motion look like, considering that you have two separate companies that can address customers directly, separately. What does that, what does that look like if you're approaching a possible customer who is, who's knocking on the door? >> How does that work? >> Exactly. And this effort is a Dell turnkey sales service offering, or solution offering to our customers. Where Dell, in collaboration with Wind River, we proactively validate, integrate, and productize the solution as engineered system, knock door on our customer who are trying to transform to Open RAN or open ecosystem. We can help you to go through that seamless experience, by pre-validating with whatever workload you want to introduce, enable zero touch provisioning, and during the day one deployment, and ensure we have sustainable lifecycle management throughout the lifecycle of the product in, in operate, in operational network, as well as having a unified single call of support from Dell side. >> Okay. So I was just going to ask you about support. So I'm a CSP, I have the solution, I go to Dell for support. >> Exactly. >> Okay. So start with Dell, and level one, level two. And if there are complex issues related to the cloud core itself, then Wind River will be on our back supporting us. >> Talk a little bit about a cust, a CSP example that is, is using the technology, and some of the outcomes that they're able to achieve. I'd love to get both of your perspectives on that. >> Vodafone is a great example. We're here in Barcelona. Vodafone is the first ora network in Europe, and it's using our joint solution. >> What are some of the, the outcomes that it's helping them to achieve? >> Faster time to market. As you see, they already started to deploy the ORAN in commercial network, and very successful in the trials that they did last year. We're also not stopping there. We're evolving, working with them together to improve like stuff around energy efficiency. So continue to optimize. So the outcome, it's just simplifying it, and you know, ready to go. Using experience that we have, Wind River is powering the first basically virtualized RAN 5G network in the world. This is with Verizon. We're at the very large scale. We started this deployment in late '20 and '19, the first site. And then through 2020 to 2022, we basically rolled in large scale. We have a lot of experience learning from it, which what we brought into the table when we partnered with Dell. A lot of experience from how you deploy at scale. Many sites from a central location, updates, upgrade. So the whole day two operation, and this is coming to bearing the solution that basically Vodafone is deploying now, and which allowed them... If I, if I look at my engagement with Verizon, started years before we started. And it took quite some time until we got stuff running. And if you look at the Vodafone time schedule, was significantly compressed compared to the Verizon first deployment. And I can tell you that there are other service providers that were announced here by KDI, for example. It's another one moving even faster. So it's accelerating the whole movement to Ora. >> We've heard a lot of acceleration talk this week. I'd love to get your perspective, Abdullah, talking about, you know, you, you just mentioned two huge names in Telco, Vodafone and Verizon. >> Yep. >> Talk a little bit about Dell's commitment to helping telecommunications companies really advance, accelerate innovation so that all of us on the other end have this thing that just works wherever we are 24 by 7. >> Not exactly. And this, we go back to the challenges in Open ecosystem. Managing multiple vendors at the same time, is a challenge for our customers. And that's why we are trying to simplify their life cycle by have, by being a trusted partner, working with our customer through all the journey. We started with Dish in their 5G deployment. Also with Vodafone. We're finding the right partners working with them proactively before getting into, in front of the customer to, we've done our homework, we are ready to simplify the process for you to go for it. If you look at the RAN in particular, we are talking with the 5g. We have ran the simplification, but they still have on the other side, limited resources and skillset can support it. So, bringing a pro, ahead of time engineer system, with a zero touch of provisioning enablement, and sustainable life cycle management, it lead to the faster time to market deployment, TCO savings, improved margins for our customers, and faster business revenue for their end users. >> Solid outcomes. >> And, and what you just just described, justifies the pain associated with disaggregating and reintegrating, which is the way that Gill referenced it, which I think is great because you're not, you're not, you're not re-aggregating, (laughs) you're reintegrating, and you're creating something that's better. >> Exactly. >> Moving forward. Otherwise, why would you do it? >> Exactly. And if you look at it, the player in the ecosystem, you have the vendors, you have the service integrators, you have the automation enablers, but kind of they are talking in silos. Everyone, this is my raci, this is what I'm responsible for. I, I'm not able, I don't want to get into something else while we are going the extra mile by working proactively in that ecosystem to... Let's bring brains together, find out what's one plus one can bring three for our customers, so we make it end-to-end seamless experience, not only on the technical part, but also on the business aspect side of it. >> So, so the partnership, it's about reducing the pen. I will say eliminating it. So this is the, the core of it. And you mentioned getting better coverage for your phone. I do want to point out that the phones are great, but if you look at the premises of a 5G network, it's to enable a lot more things that will touch your life that are beyond the consumer and the phone. Stuff like connected vehicles. So for example, something as simple as collision avoidance, the ability for the car that goes in front of you to be able to see what's happening and broadcast this information to the car behind that have no ability to see it. And basically affect our life in a way that makes our driving safer. And for this, you need a ultra low, reliable low latency communication. You need a 5G network. >> I'm glad you brought that up, because you know, we think about, "Well we just have to be connected all the time." But those are some of the emerging technologies that are going to be potentially lifesaving, and, and really life transforming that you guys are helping to enable. So, really great stuff there, but so much promise coming down the road. What's next for Dell and Wind River? And, and when you're in conversations with prospective CSP's, what is the superpower that you deliver together? I'd love to get both of your perspectives. >> So, if you look at it, number one, customers look at it, last savings and their day-to-day operation. In 5G nature, we are talking the introduction of ORAN. This is still picking up. But there is a mutualization and densification of ORAN. And this is where we're talking on monetizing my deployment. Then the third phase, we're talking sustainability and advanced service introduction. Where I want to move not only ORAN, I want to bring the edge at the same side, I want to define the advanced use cases of edge, where it enables me with this pre-work being done to deliver more services and better SLA services. By end of the day, 5G as a girl mentioned earlier, is not about a good better phone coverage, or a better speed robot, but what customized SLA's I can deliver. So it enables me to deliver different business streams to my end users. >> Yeah. >> So yeah. I will say there are two pens. One, it's the technology side. So for an example, energy efficiency. It's a very big pin point. And sustainability. So we work a lot around this, and basically to advance this. So if you look at the integrated solution today, it's very highly optimized for resource consumption. But to be able to more dynamically be able to change your power profile without compromising the SLA. So this is one side. The other side, it's about all those applications that will come to the 5G network to make our life better. It's about integrating, validating, certifying those applications. So, it's not just easy to deploy an ORAN network, but it's easy to deploy those applications. >> I'd be curious to get your perspective on the question of ROI in this, in this space. Specifically with the sort of the macro headwinds (clears throat) the economies of the world are facing right now, if you accept that. What does the ROI timeline look like when you're talking about moving towards ORAN, adopting VRAN, an amazing, you know, a plethora of new services that can be delivered, but will these operators have the appetite to take that, make that investment and take on that risk based upon the ROI time horizon? Any thoughts on that? >> Yeah. So if you look at the early days or ORAN introduction in particular, most of the entrepreneurs of ORAN and Virtual RAN ran into the challenges of not only the complexity of open ecosystem, but the integration, is like the redos of the work. And that's where we are trying to address it via pre-engineered system or building an engineer system proactively before getting it to the customers. Per our result or outcomes we get, we are talking about 30 to 50% savings on the optics. We are talking 110 ROI for our customers, simply because we are reducing the redos, the time spent to discover and explore. Because we've done that rework ahead of time, we found the optimization issues. Just for example, any customer can buy the same components from any multiple vendors, but how I can bring them together and give, deliver for me the best performance that I can fully utilize, that's, that's where it brings the value for our customer, and accelerate the deployment and the operation of the network. >> Do you have anything to add before we close in the next 30 seconds? >> Yeah. Yeah. (laughs) >> Absolutely. I would say, we start to see the data coming from two years of operation at scale. And the data supports performance. It's the same or better than traditional system. And the cost of operation, it's as good or better than traditional. Unfortunately, I can't provide more specific data. But the point is, when something is unknown in the beginning, of course you're more afraid, you take more conservative approach. Now the data starts to flow. And from here, the intention needs to go even better. So more efficiency, so cost less than traditional system, both to operate as well as to build up. But it's definitely the data that we have today says, the, ORAN system is at part, at the minimum. >> So, definite ROI there. Guys, thank you so much for joining Dave and me talking about how you're helping organizations not just address the complexities of moving from close to open, but to your point, eliminating them. We appreciate your time and, and your insights. >> Thank you. >> All right. For our guests and for Dave Nicholson, I'm Lisa Martin. You're watching "theCUBE," the leader in live and emerging tech coverage. Live from MWC23. We'll be back after a short break. (outro music)

(inspirational music) (music ends) >> Okay, so now that we've heard Scott talk about smart data fabrics, it's time to see this in action. Right now we're joined by Jess Jowdy, who's the manager of Healthcare Field Engineering at InterSystems. She's going to give a demo of how smart data fabrics actually work, and she's going to show how embedding a wide range of analytics capabilities, including data exploration business intelligence, natural language processing and machine learning directly within the fabric makes it faster and easier for organizations to gain new insights and power intelligence predictive and prescriptive services and applications. Now, according to InterSystems, smart data fabrics are applicable across many industries from financial services to supply chain to healthcare and more. Jess today is going to be speaking through the lens of a healthcare focused demo. Don't worry, Joe Lichtenberg will get into some of the other use cases that you're probably interested in hearing about. That will be in our third segment, but for now let's turn it over to Jess. Jess, good to see you. >> Hi, yeah, thank you so much for having me. And so for this demo, we're really going to be bucketing these features of a smart data fabric into four different segments. We're going to be dealing with connections, collections, refinements, and analysis. And so we'll see that throughout the demo as we go. So without further ado, let's just go ahead and jump into this demo, and you'll see my screen pop up here. I actually like to start at the end of the demo. So I like to begin by illustrating what an end user's going to see, and don't mind the screen 'cause I gave you a little sneak peek of what's about to happen. But essentially what I'm going to be doing is using Postman to simulate a call from an external application. So we talked about being in the healthcare industry. This could be, for instance, a mobile application that a patient is using to view an aggregated summary of information across that patient's continuity of care or some other kind of application. So we might be pulling information in this case from an electronic medical record. We might be grabbing clinical history from that. We might be grabbing clinical notes from a medical transcription software, or adverse reaction warnings from a clinical risk grouping application, and so much more. So I'm really going to be simulating a patient logging in on their phone and retrieving this information through this Postman call. So what I'm going to do is I'm just going to hit send, I've already preloaded everything here, and I'm going to be looking for information where the last name of this patient is Simmons, and their medical record number or their patient identifier in the system is 32345. And so as you can see, I have this single JSON payload that showed up here of, just, relevant clinical information for my patient whose last name is Simmons, all within a single response. So fantastic, right? Typically though, when we see responses that look like this there is an assumption that this service is interacting with a single backend system, and that single backend system is in charge of packaging that information up and returning it back to this caller. But in a smart data fabric architecture, we're able to expand the scope to handle information across different, in this case, clinical applications. So how did this actually happen? Let's peel back another layer and really take a look at what happened in the background. What you're looking at here is our mission control center for our smart data fabric. On the left we have our APIs that allow users to interact with particular services. On the right we have our connections to our different data silos. And in the middle here, we have our data fabric coordinator which is going to be in charge of this refinement and analysis, those key pieces of our smart data fabric. So let's look back and think about the example we just showed. I received an inbound request for information for a patient whose last name is Simmons. My end user is requesting to connect to that service, and that's happening here at my patient data retrieval API location. Users can define any number of different services and APIs depending on their use cases. And to that end, we do also support full life cycle API management within this platform. When you're dealing with APIs, I always like to make a little shout out on this, that you really want to make sure you have enough, like a granular enough security model to handle and limit which APIs and which services a consumer can interact with. In this IRIS platform, which we're talking about today we have a very granular role-based security model that allows you to handle that, but it's really important in a smart data fabric to consider who's accessing your data and in what context. >> Can I just interrupt you for a second, Jess? >> Yeah, please. >> So you were showing on the left hand side of the demo a couple of APIs. I presume that can be a very long list. I mean, what do you see as typical? >> I mean you could have hundreds of these APIs depending on what services an organization is serving up for their consumers. So yeah, we've seen hundreds of these services listed here. >> So my question is, obviously security is critical in the healthcare industry, and API securities are like, really hot topic these days. How do you deal with that? >> Yeah, and I think API security is interesting 'cause it can happen at so many layers. So, there's interactions with the API itself. So can I even see this API and leverage it? And then within an API call, you then have to deal with all right, which end points or what kind of interactions within that API am I allowed to do? What data am I getting back? And with healthcare data, the whole idea of consent to see certain pieces of data is critical. So, the way that we handle that is, like I said, same thing at different layers. There is access to a particular API, which can happen within the IRIS product, and also we see it happening with an API management layer, which has become a really hot topic with a lot of organizations. And then when it comes to data security, that really happens under the hood within your smart data fabric. So, that role-based access control becomes very important in assigning, you know, roles and permissions to certain pieces of information. Getting that granular becomes the cornerstone of the security. >> And that's been designed in, it's not a bolt on as they like to say. >> Absolutely. >> Okay, can we get into collect now? >> Of course, we're going to move on to the collection piece at this point in time, which involves pulling information from each of my different data silos to create an overall aggregated record. So commonly, each data source requires a different method for establishing connections and collecting this information. So for instance, interactions with an EMR may require leveraging a standard healthcare messaging format like Fire. Interactions with a homegrown enterprise data warehouse for instance, may use SQL. For a cloud-based solutions managed by a vendor, they may only allow you to use web service calls to pull data. So it's really important that your data fabric platform that you're using has the flexibility to connect to all of these different systems and applications. And I'm about to log out, so I'm going to (chuckles) keep my session going here. So therefore it's incredibly important that your data fabric has the flexibility to connect to all these different kinds of applications and data sources, and all these different kinds of formats and over all of these different kinds of protocols. So let's think back on our example here. I had four different applications that I was requesting information for to create that payload that we saw initially. Those are listed here under this operations section. So these are going out and connecting to downstream systems to pull information into my smart data fabric. What's great about the IRIS platform is, it has an embedded interoperability platform. So there's all of these native adapters that can support these common connections that we see for different kinds of applications. So using REST, or SOAP, or SQL, or FTP, regardless of that protocol, there's an adapter to help you work with that. And we also think of the types of formats that we typically see data coming in as in healthcare we have HL7, we have Fire, we have CCDs, across the industry, JSON is, you know, really hitting a market strong now, and XML payloads, flat files. We need to be able to handle all of these different kinds of formats over these different kinds of protocols. So to illustrate that, if I click through these when I select a particular connection on the right side panel, I'm going to see the different settings that are associated with that particular connection that allows me to collect information back into my smart data fabric. In this scenario, my connection to my chart script application in this example, communicates over a SOAP connection. When I'm grabbing information from my clinical risk grouping application I'm using a SQL based connection. When I'm connecting to my EMR, I'm leveraging a standard healthcare messaging format known as Fire, which is a REST based protocol. And then when I'm working with my health record management system, I'm leveraging a standard HTTP adapter. So you can see how we can be flexible when dealing with these different kinds of applications and systems. And then it becomes important to be able to validate that you've established those connections correctly, and be able to do it in a reliable and quick way. Because if you think about it, you could have hundreds of these different kinds of applications built out and you want to make sure that you're maintaining and understanding those connections. So I can actually go ahead and test one of these applications and put in, for instance my patient's last name and their MRN, and make sure that I'm actually getting data back from that system. So it's a nice little sanity check as we're building out that data fabric to ensure that we're able to establish these connections appropriately. So turnkey adapters are fantastic, as you can see we're leveraging them all here, but sometimes these connections are going to require going one step further and building something really specific for an application. So why don't we go one step further here and talk about doing something custom or doing something innovative. And so it's important for users to have the ability to develop and go beyond what's an out-of-the box or black box approach to be able to develop things that are specific to their data fabric, or specific to their particular connection. In this scenario, the IRIS data platform gives users access to the entire underlying code base. So you not only get an opportunity to view how we're establishing these connections or how we're building out these processes, but you have the opportunity to inject your own kind of processing, your own kinds of pipelines into this. So as an example, you can leverage any number of different programming languages right within this pipeline. And so I went ahead and I injected Python. So Python is a very up and coming language, right? We see more and more developers turning towards Python to do their development. So it's important that your data fabric supports those kinds of developers and users that have standardized on these kinds of programming languages. This particular script here, as you can see actually calls out to our turnkey adapters. So we see a combination of out-of-the-box code that is provided in this data fabric platform from IRIS, combined with organization specific or user specific customizations that are included in this Python method. So it's a nice little combination of how do we bring the developer experience in and mix it with out-of-the-box capabilities that we can provide in a smart data fabric. >> Wow. >> Yeah, I'll pause. (laughs) >> It's a lot here. You know, actually- >> I can pause. >> If I could, if we just want to sort of play that back. So we went to the connect and the collect phase. >> Yes, we're going into refine. So it's a good place to stop. >> So before we get there, so we heard a lot about fine grain security, which is crucial. We heard a lot about different data types, multiple formats. You've got, you know, the ability to bring in different dev tools. We heard about Fire, which of course big in healthcare. And that's the standard, and then SQL for traditional kind of structured data, and then web services like HTTP you mentioned. And so you have a rich collection of capabilities within this single platform. >> Absolutely. And I think that's really important when you're dealing with a smart data fabric because what you're effectively doing is you're consolidating all of your processing, all of your collection, into a single platform. So that platform needs to be able to handle any number of different kinds of scenarios and technical challenges. So you've got to pack that platform with as many of these features as you can to consolidate that processing. >> All right, so now we're going into refinement. >> We're going into refinement. Exciting. (chuckles) So how do we actually do refinement? Where does refinement happen? And how does this whole thing end up being performant? Well the key to all of that is this SDF coordinator, or stands for Smart Data Fabric coordinator. And what this particular process is doing is essentially orchestrating all of these calls to all of these different downstream systems. It's aggregating, it's collecting that information, it's aggregating it, and it's refining it into that single payload that we saw get returned to the user. So really this coordinator is the main event when it comes to our data fabric. And in the IRIS platform we actually allow users to build these coordinators using web-based tool sets to make it intuitive. So we can take a sneak peek at what that looks like. And as you can see, it follows a flow chart like structure. So there's a start, there is an end, and then there are these different arrows that point to different activities throughout the business process. And so there's all these different actions that are being taken within our coordinator. You can see an action for each of the calls to each of our different data sources to go retrieve information. And then we also have the sync call at the end that is in charge of essentially making sure that all of those responses come back before we package them together and send them out. So this becomes really crucial when we're creating that data fabric. And you know, this is a very simple data fabric example where we're just grabbing data and we're consolidating it together. But you can have really complex orchestrators and coordinators that do any number of different things. So for instance, I could inject SQL logic into this or SQL code, I can have conditional logic, I can do looping, I can do error trapping and handling. So we're talking about a whole number of different features that can be included in this coordinator. So like I said, we have a really very simple process here that's just calling out, grabbing all those different data elements from all those different data sources and consolidating it. We'll look back at this coordinator in a second when we introduce, or we make this data fabric a bit smarter, and we start introducing that analytics piece to it. So this is in charge of the refinement. And so at this point in time we've looked at connections, collections, and refinements. And just to summarize what we've seen 'cause I always like to go back and take a look at everything that we've seen. We have our initial API connection, we have our connections to our individual data sources and we have our coordinators there in the middle that are in charge of collecting the data and refining it into a single payload. As you can imagine, there's a lot going on behind the scenes of a smart data fabric, right? There's all these different processes that are interacting. So it's really important that your smart data fabric platform has really good traceability, really good logging, 'cause you need to be able to know, you know, if there was an issue, where did that issue happen in which connected process, and how did it affect the other processes that are related to it? In IRIS, we have this concept called a visual trace. And what our clients use this for is basically to be able to step through the entire history of a request from when it initially came into the smart data fabric, to when data was sent back out from that smart data fabric. So I didn't record the time, but I bet if you recorded the time it was this time that we sent that request in and you can see my patient's name and their medical record number here, and you can see that that instigated four different calls to four different systems, and they're represented by these arrows going out. So we sent something to chart script, to our health record management system, to our clinical risk grouping application, into my EMR through their Fire server. So every request, every outbound application gets a request and we pull back all of those individual pieces of information from all of those different systems, and we bundle them together. And from my Fire lovers, here's our Fire bundle that we got back from our Fire server. So this is a really good way of being able to validate that I am appropriately grabbing the data from all these different applications and then ultimately consolidating it into one payload. Now we change this into a JSON format before we deliver it, but this is those data elements brought together. And this screen would also be used for being able to see things like error trapping, or errors that were thrown, alerts, warnings, developers might put log statements in just to validate that certain pieces of code are executing. So this really becomes the one stop shop for understanding what's happening behind the scenes with your data fabric. >> Sure, who did what when where, what did the machine do what went wrong, and where did that go wrong? Right at your fingertips. >> Right. And I'm a visual person so a bunch of log files to me is not the most helpful. While being able to see this happened at this time in this location, gives me that understanding I need to actually troubleshoot a problem. >> This business orchestration piece, can you say a little bit more about that? How people are using it? What's the business impact of the business orchestration? >> The business orchestration, especially in the smart data fabric, is really that crucial part of being able to create a smart data fabric. So think of your business orchestrator as doing the heavy lifting of any kind of processing that involves data, right? It's bringing data in, it's analyzing that information it's transforming that data, in a format that your consumer's not going to understand. It's doing any additional injection of custom logic. So really your coordinator or that orchestrator that sits in the middle is the brains behind your smart data fabric. >> And this is available today? It all works? >> It's all available today. Yeah, it all works. And we have a number of clients that are using this technology to support these kinds of use cases. >> Awesome demo. Anything else you want to show us? >> Well, we can keep going. I have a lot to say, but really this is our data fabric. The core competency of IRIS is making it smart, right? So I won't spend too much time on this, but essentially if we go back to our coordinator here, we can see here's that original, that pipeline that we saw where we're pulling data from all these different systems and we're collecting it and we're sending it out. But then we see two more at the end here, which involves getting a readmission prediction and then returning a prediction. So we can not only deliver data back as part of a smart data fabric, but we can also deliver insights back to users and consumers based on data that we've aggregated as part of a smart data fabric. So in this scenario, we're actually taking all that data that we just looked at, and we're running it through a machine learning model that exists within the smart data fabric pipeline, and producing a readmission score to determine if this particular patient is at risk for readmission within the next 30 days. Which is a typical problem that we see in the healthcare space. So what's really exciting about what we're doing in the IRIS world, is we're bringing analytics close to the data with integrated ML. So in this scenario we're actually creating the model, training the model, and then executing the model directly within the IRIS platform. So there's no shuffling of data, there's no external connections to make this happen. And it doesn't really require having a PhD in data science to understand how to do that. It leverages all really basic SQL-like syntax to be able to construct and execute these predictions. So, it's going one step further than the traditional data fabric example to introduce this ability to define actionable insights to our users based on the data that we've brought together. >> Well that readmission probability is huge, right? Because it directly affects the cost for the provider and the patient, you know. So if you can anticipate the probability of readmission and either do things at that moment, or, you know, as an outpatient perhaps, to minimize the probability then that's huge. That drops right to the bottom line. >> Absolutely. And that really brings us from that data fabric to that smart data fabric at the end of the day, which is what makes this so exciting. >> Awesome demo. >> Thank you! >> Jess, are you cool if people want to get in touch with you? Can they do that? >> Oh yes, absolutely. So you can find me on LinkedIn, Jessica Jowdy, and we'd love to hear from you. I always love talking about this topic so we'd be happy to engage on that. >> Great stuff. Thank you Jessica, appreciate it. >> Thank you so much. >> Okay, don't go away because in the next segment, we're going to dig into the use cases where data fabric is driving business value. Stay right there. (inspirational music) (music fades)

>> Okay, so now that we've heard Scott talk about smart data fabrics, it's time to see this in action. Right now we're joined by Jess Jowdy, who's the manager of Healthcare Field Engineering at InterSystems. She's going to give a demo of how smart data fabrics actually work, and she's going to show how embedding a wide range of analytics capabilities including data exploration, business intelligence natural language processing, and machine learning directly within the fabric, makes it faster and easier for organizations to gain new insights and power intelligence, predictive and prescriptive services and applications. Now, according to InterSystems, smart data fabrics are applicable across many industries from financial services to supply chain to healthcare and more. Jess today is going to be speaking through the lens of a healthcare focused demo. Don't worry, Joe Lichtenberg will get into some of the other use cases that you're probably interested in hearing about. That will be in our third segment, but for now let's turn it over to Jess. Jess, good to see you. >> Hi. Yeah, thank you so much for having me. And so for this demo we're really going to be bucketing these features of a smart data fabric into four different segments. We're going to be dealing with connections, collections, refinements and analysis. And so we'll see that throughout the demo as we go. So without further ado, let's just go ahead and jump into this demo and you'll see my screen pop up here. I actually like to start at the end of the demo. So I like to begin by illustrating what an end user's going to see and don't mind the screen 'cause I gave you a little sneak peek of what's about to happen. But essentially what I'm going to be doing is using Postman to simulate a call from an external application. So we talked about being in the healthcare industry. This could be for instance, a mobile application that a patient is using to view an aggregated summary of information across that patient's continuity of care or some other kind of application. So we might be pulling information in this case from an electronic medical record. We might be grabbing clinical history from that. We might be grabbing clinical notes from a medical transcription software or adverse reaction warnings from a clinical risk grouping application and so much more. So I'm really going to be assimilating a patient logging on in on their phone and retrieving this information through this Postman call. So what I'm going to do is I'm just going to hit send, I've already preloaded everything here and I'm going to be looking for information where the last name of this patient is Simmons and their medical record number their patient identifier in the system is 32345. And so as you can see I have this single JSON payload that showed up here of just relevant clinical information for my patient whose last name is Simmons all within a single response. So fantastic, right? Typically though when we see responses that look like this there is an assumption that this service is interacting with a single backend system and that single backend system is in charge of packaging that information up and returning it back to this caller. But in a smart data fabric architecture we're able to expand the scope to handle information across different, in this case, clinical applications. So how did this actually happen? Let's peel back another layer and really take a look at what happened in the background. What you're looking at here is our mission control center for our smart data fabric. On the left we have our APIs that allow users to interact with particular services. On the right we have our connections to our different data silos. And in the middle here we have our data fabric coordinator which is going to be in charge of this refinement and analysis those key pieces of our smart data fabric. So let's look back and think about the example we just showed. I received an inbound request for information for a patient whose last name is Simmons. My end user is requesting to connect to that service and that's happening here at my patient data retrieval API location. Users can define any number of different services and APIs depending on their use cases. And to that end we do also support full lifecycle API management within this platform. When you're dealing with APIs I always like to make a little shout out on this that you really want to make sure you have enough like a granular enough security model to handle and limit which APIs and which services a consumer can interact with. In this IRIS platform, which we're talking about today we have a very granular role-based security model that allows you to handle that, but it's really important in a smart data fabric to consider who's accessing your data and in what contact. >> Can I just interrupt you for a second? >> Yeah, please. >> So you were showing on the left hand side of the demo a couple of APIs. I presume that can be a very long list. I mean, what do you see as typical? >> I mean you can have hundreds of these APIs depending on what services an organization is serving up for their consumers. So yeah, we've seen hundreds of these services listed here. >> So my question is, obviously security is critical in the healthcare industry and API securities are really hot topic these days. How do you deal with that? >> Yeah, and I think API security is interesting 'cause it can happen at so many layers. So there's interactions with the API itself. So can I even see this API and leverage it? And then within an API call, you then have to deal with all right, which end points or what kind of interactions within that API am I allowed to do? What data am I getting back? And with healthcare data, the whole idea of consent to see certain pieces of data is critical. So the way that we handle that is, like I said, same thing at different layers. There is access to a particular API, which can happen within the IRIS product and also we see it happening with an API management layer, which has become a really hot topic with a lot of organizations. And then when it comes to data security, that really happens under the hood within your smart data fabric. So that role-based access control becomes very important in assigning, you know, roles and permissions to certain pieces of information. Getting that granular becomes the cornerstone of security. >> And that's been designed in, >> Absolutely, yes. it's not a bolt-on as they like to say. Okay, can we get into collect now? >> Of course, we're going to move on to the collection piece at this point in time, which involves pulling information from each of my different data silos to create an overall aggregated record. So commonly each data source requires a different method for establishing connections and collecting this information. So for instance, interactions with an EMR may require leveraging a standard healthcare messaging format like FIRE, interactions with a homegrown enterprise data warehouse for instance may use SQL for a cloud-based solutions managed by a vendor. They may only allow you to use web service calls to pull data. So it's really important that your data fabric platform that you're using has the flexibility to connect to all of these different systems and and applications. And I'm about to log out so I'm going to keep my session going here. So therefore it's incredibly important that your data fabric has the flexibility to connect to all these different kinds of applications and data sources and all these different kinds of formats and over all of these different kinds of protocols. So let's think back on our example here. I had four different applications that I was requesting information for to create that payload that we saw initially. Those are listed here under this operations section. So these are going out and connecting to downstream systems to pull information into my smart data fabric. What's great about the IRIS platform is it has an embedded interoperability platform. So there's all of these native adapters that can support these common connections that we see for different kinds of applications. So using REST or SOAP or SQL or FTP regardless of that protocol there's an adapter to help you work with that. And we also think of the types of formats that we typically see data coming in as, in healthcare we have H7, we have FIRE we have CCDs across the industry. JSON is, you know, really hitting a market strong now and XML, payloads, flat files. We need to be able to handle all of these different kinds of formats over these different kinds of protocols. So to illustrate that, if I click through these when I select a particular connection on the right side panel I'm going to see the different settings that are associated with that particular connection that allows me to collect information back into my smart data fabric. In this scenario, my connection to my chart script application in this example communicates over a SOAP connection. When I'm grabbing information from my clinical risk grouping application I'm using a SQL based connection. When I'm connecting to my EMR I'm leveraging a standard healthcare messaging format known as FIRE, which is a rest based protocol. And then when I'm working with my health record management system I'm leveraging a standard HTTP adapter. So you can see how we can be flexible when dealing with these different kinds of applications and systems. And then it becomes important to be able to validate that you've established those connections correctly and be able to do it in a reliable and quick way. Because if you think about it, you could have hundreds of these different kinds of applications built out and you want to make sure that you're maintaining and understanding those connections. So I can actually go ahead and test one of these applications and put in, for instance my patient's last name and their MRN and make sure that I'm actually getting data back from that system. So it's a nice little sanity check as we're building out that data fabric to ensure that we're able to establish these connections appropriately. So turnkey adapters are fantastic, as you can see we're leveraging them all here, but sometimes these connections are going to require going one step further and building something really specific for an application. So let's, why don't we go one step further here and talk about doing something custom or doing something innovative. And so it's important for users to have the ability to develop and go beyond what's an out of the box or black box approach to be able to develop things that are specific to their data fabric or specific to their particular connection. In this scenario, the IRIS data platform gives users access to the entire underlying code base. So you cannot, you not only get an opportunity to view how we're establishing these connections or how we're building out these processes but you have the opportunity to inject your own kind of processing your own kinds of pipelines into this. So as an example, you can leverage any number of different programming languages right within this pipeline. And so I went ahead and I injected Python. So Python is a very up and coming language, right? We see more and more developers turning towards Python to do their development. So it's important that your data fabric supports those kinds of developers and users that have standardized on these kinds of programming languages. This particular script here, as you can see actually calls out to our turnkey adapters. So we see a combination of out of the box code that is provided in this data fabric platform from IRIS combined with organization specific or user specific customizations that are included in this Python method. So it's a nice little combination of how do we bring the developer experience in and mix it with out of the box capabilities that we can provide in a smart data fabric. >> Wow. >> Yeah, I'll pause. >> It's a lot here. You know, actually, if I could >> I can pause. >> If I just want to sort of play that back. So we went through the connect and the collect phase. >> And the collect, yes, we're going into refine. So it's a good place to stop. >> Yeah, so before we get there, so we heard a lot about fine grain security, which is crucial. We heard a lot about different data types, multiple formats. You've got, you know the ability to bring in different dev tools. We heard about FIRE, which of course big in healthcare. >> Absolutely. >> And that's the standard and then SQL for traditional kind of structured data and then web services like HTTP you mentioned. And so you have a rich collection of capabilities within this single platform. >> Absolutely, and I think that's really important when you're dealing with a smart data fabric because what you're effectively doing is you're consolidating all of your processing, all of your collection into a single platform. So that platform needs to be able to handle any number of different kinds of scenarios and technical challenges. So you've got to pack that platform with as many of these features as you can to consolidate that processing. >> All right, so now we're going into refine. >> We're going into refinement, exciting. So how do we actually do refinement? Where does refinement happen and how does this whole thing end up being performant? Well the key to all of that is this SDF coordinator or stands for smart data fabric coordinator. And what this particular process is doing is essentially orchestrating all of these calls to all of these different downstream systems. It's aggregating, it's collecting that information it's aggregating it and it's refining it into that single payload that we saw get returned to the user. So really this coordinator is the main event when it comes to our data fabric. And in the IRIS platform we actually allow users to build these coordinators using web-based tool sets to make it intuitive. So we can take a sneak peek at what that looks like and as you can see it follows a flow chart like structure. So there's a start, there is an end and then there are these different arrows that point to different activities throughout the business process. And so there's all these different actions that are being taken within our coordinator. You can see an action for each of the calls to each of our different data sources to go retrieve information. And then we also have the sync call at the end that is in charge of essentially making sure that all of those responses come back before we package them together and send them out. So this becomes really crucial when we're creating that data fabric. And you know, this is a very simple data fabric example where we're just grabbing data and we're consolidating it together. But you can have really complex orchestrators and coordinators that do any number of different things. So for instance, I could inject SQL Logic into this or SQL code, I can have conditional logic, I can do looping, I can do error trapping and handling. So we're talking about a whole number of different features that can be included in this coordinator. So like I said, we have a really very simple process here that's just calling out, grabbing all those different data elements from all those different data sources and consolidating it. We'll look back at this coordinator in a second when we introduce or we make this data fabric a bit smarter and we start introducing that analytics piece to it. So this is in charge of the refinement. And so at this point in time we've looked at connections, collections, and refinements. And just to summarize what we've seen 'cause I always like to go back and take a look at everything that we've seen. We have our initial API connection we have our connections to our individual data sources and we have our coordinators there in the middle that are in charge of collecting the data and refining it into a single payload. As you can imagine, there's a lot going on behind the scenes of a smart data fabric, right? There's all these different processes that are interacting. So it's really important that your smart data fabric platform has really good traceability, really good logging 'cause you need to be able to know, you know, if there was an issue, where did that issue happen, in which connected process and how did it affect the other processes that are related to it. In IRIS, we have this concept called a visual trace. And what our clients use this for is basically to be able to step through the entire history of a request from when it initially came into the smart data fabric to when data was sent back out from that smart data fabric. So I didn't record the time but I bet if you recorded the time it was this time that we sent that request in. And you can see my patient's name and their medical record number here and you can see that that instigated four different calls to four different systems and they're represented by these arrows going out. So we sent something to chart script to our health record management system, to our clinical risk grouping application into my EMR through their FIRE server. So every request, every outbound application gets a request and we pull back all of those individual pieces of information from all of those different systems and we bundle them together. And for my FIRE lovers, here's our FIRE bundle that we got back from our FIRE server. So this is a really good way of being able to validate that I am appropriately grabbing the data from all these different applications and then ultimately consolidating it into one payload. Now we change this into a JSON format before we deliver it, but this is those data elements brought together. And this screen would also be used for being able to see things like error trapping or errors that were thrown alerts, warnings, developers might put log statements in just to validate that certain pieces of code are executing. So this really becomes the one stop shop for understanding what's happening behind the scenes with your data fabric. >> Etcher, who did what, when, where what did the machine do? What went wrong and where did that go wrong? >> Exactly. >> Right in your fingertips. >> Right, and I'm a visual person so a bunch of log files to me is not the most helpful. Well, being able to see this happened at this time in this location gives me that understanding I need to actually troubleshoot a problem. >> This business orchestration piece, can you say a little bit more about that? How people are using it? What's the business impact of the business orchestration? >> The business orchestration, especially in the smart data fabric is really that crucial part of being able to create a smart data fabric. So think of your business orchestrator as doing the heavy lifting of any kind of processing that involves data, right? It's bringing data in, it's analyzing that information, it's transforming that data, in a format that your consumer's not going to understand it's doing any additional injection of custom logic. So really your coordinator or that orchestrator that sits in the middle is the brains behind your smart data fabric. >> And this is available today? This all works? >> It's all available today. Yeah, it all works. And we have a number of clients that are using this technology to support these kinds of use cases. >> Awesome demo. Anything else you want to show us? >> Well we can keep going. 'Cause right now, I mean we can, oh, we're at 18 minutes. God help us. You can cut some of this. (laughs) I have a lot to say, but really this is our data fabric. The core competency of IRIS is making it smart, right? So I won't spend too much time on this but essentially if we go back to our coordinator here we can see here's that original that pipeline that we saw where we're pulling data from all these different systems and we're collecting it and we're sending it out. But then we see two more at the end here which involves getting a readmission prediction and then returning a prediction. So we can not only deliver data back as part of a smart data fabric but we can also deliver insights back to users and consumers based on data that we've aggregated as part of a smart data fabric. So in this scenario, we're actually taking all that data that we just looked at and we're running it through a machine learning model that exists within the smart data fabric pipeline and producing a readmission score to determine if this particular patient is at risk for readmission within the next 30 days. Which is a typical problem that we see in the healthcare space. So what's really exciting about what we're doing in the IRIS world is we're bringing analytics close to the data with integrated ML. So in this scenario we're actually creating the model, training the model, and then executing the model directly within the IRIS platform. So there's no shuffling of data, there's no external connections to make this happen. And it doesn't really require having a PhD in data science to understand how to do that. It leverages all really basic SQL like syntax to be able to construct and execute these predictions. So it's going one step further than the traditional data fabric example to introduce this ability to define actionable insights to our users based on the data that we've brought together. >> Well that readmission probability is huge. >> Yes. >> Right, because it directly affects the cost of for the provider and the patient, you know. So if you can anticipate the probability of readmission and either do things at that moment or you know, as an outpatient perhaps to minimize the probability then that's huge. That drops right to the bottom line. >> Absolutely, absolutely. And that really brings us from that data fabric to that smart data fabric at the end of the day which is what makes this so exciting. >> Awesome demo. >> Thank you. >> Fantastic people, are you cool? If people want to get in touch with you? >> Oh yes, absolutely. So you can find me on LinkedIn, Jessica Jowdy and we'd love to hear from you. I always love talking about this topic, so would be happy to engage on that. >> Great stuff, thank you Jess, appreciate it. >> Thank you so much. >> Okay, don't go away because in the next segment we're going to dig into the use cases where data fabric is driving business value. Stay right there.

>>Welcome to the Cube's special program series Women of the Cloud, brought to you by aws. I'm your host, Lisa Martin. Very pleased to welcome my next guest, Courtney Kissler joins me, the SVP of tech and CTO at Zli. Courtney, welcome to the program. >>Thank you. Thanks for having me. >>Our pleasure to have you Talk to me a little bit about your career path in tech and about your role that you're doing right now. >>Yeah, so I have spent most of my career in retail. So I spent 14 years at Nordstrom, which is where I learned a lot about what it takes to be, you know, a technology leader. And really that's where I started kind of my cloud transformation journey. And then after 14 years at Nordstrom, I ended up going to Starbucks as the VP of retail technology there. And really enjoyed working at a global scale, very different, learned a lot there as well. And also had cloud transformation there too. And then I went to Nike as the VP of Digital Platform Engineering and learned a lot there as well. Different scale and very different retail situation. And two years ago, almost two years, it'll be two years in January, I joined Zuli as SVP and cto. And what I love about Zulily is, I mean, we were really born digital first. And so cloud is a big part of our ecosystem and I love that we are innovators, we are data driven, we're about experimentation, and we leverage cloud in a variety of ways. >>I love that you have an amazing pedigree background of companies that you've worked for. I can imagine all the experiences that you've had and how they've shaped you into the leader that you are today. What are some, for people that are either in maybe starting a little bit farther back in their careers or early in tech, what are some of the recommendations that you have for those that really want to grow their career and invest? What do you, what do you tell them? >>Yeah, so I would say the biggest piece of advice I can give is be a lifelong learner. The thing about technology is the technology is gonna change and evolve, and the way you respond and react to that is what's critical. And so figuring out how to be somebody who could be a problem solver and learning all the time. I, I try really hard to surround myself with people who I can learn from and really grow and, and how might I continue to engage in the technology, you know, landscape, but really make sure that I have a way to continuously learn. >>That's so important to be able to, to have the confidence to raise your hand and say, I, I wanna try something new, or I don't understand something. You know, we, I, I was reading this some stats recently. I want women in tech and I saw that women won't apply for a job, say on LinkedIn unless they meet 100% of the job requirements. Whereas men will apply if they meet only 40%. And I think more women need to know and others that you don't have to meet all those job requirements. There's so much on the job learning. You have to have the appetite, you have to have good mentors, good sponsors, but raise your hand, right. Lean into the conversation. There's amazing things that can happen as a result. >>Absolutely. And that, I love that you touched on, you know, the leaning in and also like this is an industry term, it's not mine, but I love it. Called have a personal board of directors, have people who can help you navigate and network. I would say that's one of the biggest learnings that I had throughout my career was build a community and lean on that community. And often the encouragement that you get from that will also put you in a position to be okay with not having all of the boxes checked before you pursue your next opportunity. >>Right. I love that you talked about having a personal board of directors. I wish, I wish that's advice you probably do too. That, that we had 20 years ago when we started in our careers. But it's, it's such important advice. You know, technology makes it so easy for us to connect these days, whether, you know, you're on Facebook or Instagram or LinkedIn or Twitter, leverage that network, but really create that personal board of directors, I think is sage advice for anybody at any stage in their career. I don't think you have to be a newbie to be able to, to see the value in that. I think there can be value in it along the way. I'd love to hear some of your successes where you've solved problems related to cloud in your career. Tell me about some of those. Yeah, >>Yeah, so one just, you know, brief set of context. So I started my career in security infrastructure and operations. So I was learning how to support data centers. I mean, that was part of the landscape when I started my career. And then this thing called the cloud came in to like the environment. And all of us, I think who started as infrastructure, whether you are a CIS admin or those type of traditional roles, thought, what is this gonna do to me? Like as a, as someone who's been in infrastructure for a long time. And what I really appreciated about the leadership at Nordstrom is we said, let's embrace it. Let's figure out how to learn and let's figure out what this might look like for us. And at the time we weren't, we weren't in the cloud, but we said we are going to be, you know, and a lot of companies say this, you know, cloud first. Now it's easy to say, >>Right, >>What does it look like internally? And I think there's multiple dimensions. One is, are you really designing and architecting your applications and capabilities to take advantage of the cloud? I will share that there was a lot of debate internally, and I think a lot of organizations do this where they say, avoid vendor lock in, make sure you have flexibility and it's important to be intentional about the use of cloud. Also super important to leverage the capabilities because one of the things that, you know, I believe in is that cloud can create a way for you to free up your technologists mind share to focus on things that are more strategic. And over time, cloud has become commodity. It's something that you can adopt and leverage to avoid spending time on provisioning servers and doing things that are now automated and part of the cloud offering. >>Right. >>Yeah, >>Go ahead. Sorry. >>Oh, I was gonna say, and also I think skill. So it does take a different skillset. So I do think it's important for organizations to invest and also lean on your cloud partner. So one thing I really appreciate about AWS is that there's a lot of learning and lots of ways to get certified and understand how to be successful in a cloud environment. So I think it's important to also know that, that there is another skill set that needs to be developed in order to be successful. >>What are some of the, the innovations that excite you that are coming down the pipe with respect to cloud that you may adopt at Zil? >>Yeah, that's a great question. You know, we're constantly looking at, you know, what can we, what, what can we take advantage of? And I think what I get excited about is really the ongoing innovation when it comes to data driven insights and how do you incorporate the knowledge of your customers and the broader kind of, I'll call it retail landscape, into continuing to put relevant experiences in front of your customer. And I think doing that at scale is, I mean, you can achieve it in other ways. I think a great way to achieve it is leveraging cloud and the, the scale and performance and speed to, I'll call it like speed to data insights. Like you can get so much out of that and learn. And so for me, I think it's really anything that has to do with data. >>Every company has to be a data company these days. Whether you're in retail or automotive or or manufacturing, you have to become a data driven company. You have to be able to derive those insights you talked about, you know, in the retail space, I always think, oh, I'm such a demanding consumer because I, I've been trained thanks to the cloud that I could get whatever I want, whatever I'm looking for. And these companies will start to learn me in a non-car way, hopefully, and serve up relevant personalized content that, oh yeah, that's right. I need one of those. We ex we have that expectation in our personal lives and I think we bring it into our professional lives as well. And so every company needs to be able to, to be that data company, to deliver what the end user is more and more these days, expecting that the demands are gonna be met. >>Absolutely. And I, I really appreciated what you said too about there's that innovation and expectation of your customer. There's also some really amazing innovation that can happen for your internal developer community, leveraging the cloud. There's tooling, there's data driven insights as well. Like how long is it taking us to deploy software? Well, that's a learning moment. And often cloud can help you solve for speed to delivery, having high confidence in your ability to deliver, because many of the cloud tools allow you to, you know, do a canary deployment. I'm only gonna expose this to a percentage of my customers and then I'll bring it live to everybody else. There's ways to leverage cloud technology that also makes it innovative for the internal developer. And you might even say internal customers. >>That's a great point. I always think the, the internal customer experience and the external customer experience are linked strongly hand in hand. And, and one of the things that we're seeing more and more, I think a lot this year is how influential the developer role is becoming in the, in the decisions and the technologies and what to deliver to the end user customers. So that internal experience and external experience need to be hand in hand for them both to be successful. >>Agree. And you hear a lot of movement in the industry around like platforms and developer experience and DevOps is, is an area that I'm super passionate about. And really ultimately what it is, is how are we all delivering value as fast as possible to our customers with high quality? Cuz you know, if you're doing, if you're doing speed right, you're not compromising quality. And I think this again, is a recognition of where the industry has evolved to. You can use cloud as a platform to accelerate those capabilities. >>Yes, it needs to be that accelerant really for things to be successful. I'd love to get your thoughts on over the last few years, what are some of the biggest changes that you've seen in the workforce and innovation that come to mind? >>Wow. So I think, you know, what I've seen is really the shift in, you mentioned, you know, data, every company is data driven. So the expectation of technology is to also be data driven. And I often think sometimes that many technologists think of, of technology's too complicated. Like we don't, it's too hard to be data driven. And in reality we were kind of wired for being data driven. And so infusing that into how we think about everything we do and making sure that that's part of kind of the, the DNA of the organization. I'm also a big believer in observability. So like really having good knowledge of how are the things that you're building really performing? And that's another area where cloud can help. Where you can, you can really instrument the end to end journey and have transparency to that so that your, your teams are set up for success. They can understand the help, they can see it quickly, and they can respond quickly. >>You know, I always think the horizon for technology is, is infinite. The innovation, the capabilities we need to have good strong teams, diverse teams, teams that bring in different thoughts, different perspectives, different backgrounds. My last question for you is, is on diversity in terms of the tech workforce, what are some of the things that you are seeing and maybe some advice you would give for organizations to be able to really embody diversity, equity and inclusion? >>Yeah, so I'm a big believer in, in inclusion, like I think that it often doesn't get as much focus as it should. So it's, it's very similar to a customer funnel. If you attract a bunch of diverse talent but you can't retain them, then you're gonna continue to have a challenge. So my, my focus is often on am I creating an environment where diverse talent can thrive? Now, Zuli and our broader company Q rate, like we believe passionately in diversity, equity, and inclusion, and it shows up in everything we do. So I I also think actions matter. So it's one thing to say it's important, but it's it like leaders need to demonstrate the commitment. So we've done some pretty, i, I consider to be investments in how do we continue develop our internal talent and grow diverse leaders and leaders from any position. It doesn't mean that you move into management. If we have leaders that are in engineering roles and product management roles, it's like, how do we continue to invest and also create inclusive leadership opportunities where our leaders are learning what does it look like to operate as an inclusive leader? >>So important. But to your point, action, action is so important. Sounds like you guys are doing an amazing job at Zoo Lil, not only in terms of embracing cloud first being born in the cloud, but also from a DEI perspective. I, I love that. Courtney, thank you so much for sharing your journey with us, your recommendations and thoughts. I know the audience found a lot of value in it, as did I. >>Thanks again for having me. >>Oh, my pleasure. For Courtney Kissler, I'm Lisa Martin. You're watching the Cube Special Program series, women of the Cloud, brought to you by aws. Thanks for watching.

(upbeat music) >> Good afternoon, everyone. Welcome back to theCUBE Live in Detroit, Michigan. Lisa Martin here with John Furrier. We are covering KubeCon + CloudNativeCon '22. John, this is day two of our coverage wall-to-wall three days of coverage on theCUBE. We've been talking a lot about the developer and how the world is starting to really revolve around developer and DevOps portfolios. >> Yes, developers, startups, big companies, all transforming. This next segment, we want to hear from how Dell Technologies cloud natives, big time strategy there and looking forward to it. It's good. It's going to be a great segment. >> Yes, please welcome back one of our alumni to theCUBE. Brad Maltz is here, Senior Director of DevOps Portfolio and DevRel for Dell Technologies. Good to see you. >> Thank you guys for having me. >> So, Dell at KubeCon, what's going on? >> Yeah, that's literally the most common question I'm getting. So for us, it's a lot about our customer base is making that transformation into a DevOps world. And they have a ton of Dell and they're like, Hey, from a Dell perspective, how do you help us make that transformation into a DevOps operating model? So we're here to explain that. We're here to talk about infrastructure as code, our container Kubernetes story, our multi-cloud story. We're talking about all of it. >> Tell us about those stories and what the value is in it for companies to work with Dell as they transition. >> So when we look at it from a DevOps perspective for us, it's all about the culture, the operating model shift they're trying to make. And what that means to them is they have to figure out how do they automate all of the stacks they have to deal with. Whether it's going to be server, storage, data protection, network, and all the way up through the hypervisor and Kubernetes. That means they need to work with an ecosystem of tools. Things like Ansible, things like Terraform, all that stuff. Our job is to make our portfolio more consumable in the infrastructure as code space. That's one part of the discussion. The second part of the conversation is Kubernetes won. Kubernetes won the abstraction in this multi-cloud world and we as Dell are helping our customers consume Kubernetes. Whether it's by bringing solutions and more appliance oriented mentality to the market or whether it's actually enabling them with our container storage modules and CSI drivers. >> So it as supercloud as we call or multi-cloud as some people call it, you're starting to see the abstraction for interoperability, but essentially just distributed hybrid cloud. Edge as you guys have a big presence. So Dell's supplying not just the data center anymore. Cloud models are moving to hybrid on-premises, edge is growing. We saw some great use cases where military applications are using Kubernetes and all kinds of new things. So this real examples happening right now. This is going to impact Dell's customers and Dell as a supplier of compute and servers. And the gear that runs everything. Like at a telco, you can have a data center at an edge spot, like a box could be a data center. >> Telco is a great example cause we created the business, the Telco business unit. And in the Telco business unit, our goal was, hey, telco is a little different than enterprise edge. Enterprise edge, retail, manufacturing, healthcare. They have certain needs. Telco, much smaller group of customers that have a much different set of needs. And that's very similar is how do we scale at the edge? How do we control things programmatically? How do we do it in a secure way? And how do we do it so that our people internally don't have to deal with the underpinnings of all that infrastructure. Just make it easier for them. That's our goal through the edge discussions, through telco and all that. >> Yeah. We've been doing a big thing on why hardware matters. Hardware's back. We look at all the hyperscalers, the big competition is faster, faster, faster chips, faster the physics. This is part of the supply chain both hardware and software. Okay. So developers want more power. At the end of the day, this community here wants invisible infrastructure and they want it fast. >> Brad: Yes, that's exactly right. >> There's a lot under the hub. It's still servers. >> You still got firmware, you still got bio, you still got to management operating system, You still got to patch things, kernels, security issues, all of that from a server perspective. We haven't even talked about storage or networking or any of the other stuff. So there's a ton of buttons and dials under the covers. >> And that's totally going to be awesome. And the question comes in, okay, now take me to the cloud native because automation, infrastructures code, these are now the hotspots. Software supply chain, not hardware, software supply chain. So these are all things that are going to be intersecting. What's your view? >> In the multi-cloud view of the world, what we really have are our customers are saying, okay, we started on one cloud, Amazon or Azure or Google. And they're like, you know what? We had to go to a second cloud for whatever reason, many reasons. Now we have to manage two clouds. And by the way, we never got fully off-prem. So now we have all of our on-premises stuff plus multiple clouds. How do we deal with the complexity there? And the complexity there is everything from data problems of data mobility, data protection, replication, all that stuff. How do we deal with the actual application life cycle management across that? And that's where a lot of the tooling we're discussing comes in. That's where Kubernetes comes in and they want to do it in an agnostic way. 'Cause if they can't begin to transition to do it in a standardized layer, then the end of the day they're still going to be managing three totally different environments with three separate engineering teams. >> So is your target audience primarily existing Dell customers, legacy customers, or is it really wide open? >> It's actually been opening up. So we have kind of, the way I view it is we have three different segments that we're going to be going after. We have what I would say is the top 10% of the industry that's really able to skill up into this DevOps world very quickly. They're going to go after the GitOps, they're going to go after all those things. That's a combination of existing customers, but also the really, really large customers that can build their own clouds on-premises. We then have the other end of the spectrum. People that aren't making the shift. People that are like, you know what this DevOps transformation it's not going to help us there, but we still need server and storage and whatnot. And then I like to call it the squishy middle. 60, 70% of the market that's like, we can't scale up in time, we can't hire the people, they're not available 'cause that 10% just got them all, but we still have the same problems. And how do we operate in a world where we have that multi-cloud type of a problem, but we can't find the people. Now you got to figure out more of the no-code, low-code packaged solutions, packaged automation coming from companies like Dell and others. >> So there's customers that are either at the beginning of their journey are not convinced yet. What are some of the barriers that they're seeing that Dell can help them overcome? >> Number one thing, education. >> Lisa: Really? >> We're hearing that consistently here at KubeCon and just customer meetings all over the place. There is a segment of the industry that they're empowered to move into a DevOps model. They don't have the ability or resources. They're not able to say, I've been doing this forever in this way in storage. How do I do that in another thing? And they're scared. They want somebody to come in and kind of handhold them a little bit, but somebody they trust. Somebody they've been working with for a very long time. That's Dell's role. Hands-on labs, training materials, how-to videos, but do it in the comfortable way that they feel like, okay we got this. >> And the success with the customers has been that well-documented. The success with the company, again, continues to survive and thrive in all conditions. So Michael Dell knows what he's doing. Love following his strategy. Michael, if you're watching, I know he watches theCUBE video, congratulations. But now the hard question for Dell is this, the applications used to run on PCs, now they're running PCs under the covers and servers. The application space here at this community is enabled by Kubernetes, is creating a new application runtime like environment. I like, compared to the old app server days when things were like just application specific, development got easier. We're in that renaissance now where the app runtime is being enabled by Kubernetes. You guys been there, done that in the old school, now the new school. What's your view on this Kubernetes? What's Dell's view on? >> Yeah, so back to Kubernetes won in my head. It's just flat out won and part of the reason, and it beat out a lot of things. You remember Cloud Foundry, which there's still a thing, but Cloud Foundry went a little too far up into the application stack and constrained the application developers a bit too much. Kubernetes success is two things. It's because they're not constraining the developer, but they're also figuring out how to enable that IT operations mindset. And they become that happy medium that's out there. So now all of a sudden, application modernization conversations and cloud-native app development, there is a standard package. There's standard load balancing and security paradigm, standard registration mechanisms, all built into the Kubernetes layer, by the way, enabled by an ecosystem. And because they're actually going through that, what's happening now is we can finally move forward. We can take that next step and we can build around that ecosystem of Kubernetes. >> That is thematically something that we've been hearing, John, for the last day and a half is the maturation of Kubernetes People, what's next? We are ready for the next step. Talk about Dell as an enabler of that. >> Yeah, so a funny, another part of that paradigm is Kubernetes does not equal virtualization. And this is a hard one in this industry right now. A lot of people say, well, yeah, we did the VMware pivot and then the KVM and everything else and they're like, this is just another one of those pivots. I'm like, no it's not. Virtualization was the pivot of physical hardware became virtual hardware, but you still thought of it in CPU memory disc and you managed it in the same way. Kubernetes, it's a such a different way of thinking about operationalization and all that abstraction that what we're realizing is people need to take baby steps into Kubernetes right now. The maturity of it is great because there is an ecosystem around it, but the majority of the industry isn't even aware of the basics of Kubernetes right now. So our job, we look at it as the education part, but also can we deliver the solutions together with the OpenShift's of the world and the Tanzu's of the world and the Rancher's of the world. Can we deliver more of that full stack experience going into the next few years? That's where we believe we can help accelerate them. Education and that delivery mechanism. >> And the community support is going to be there too. You got to have the. >> 100%. >> The community, not just education, which you guys done before, but doing it with open source vibe. >> That's where DevRel comes in. So the DevRel half of my world now is all about Dell in the community. And to be part of community isn't just to say, Hey, I'm going to go sponsor something. That's not community to me. >> It doesn't hurt. >> It doesn't hurt, but we're going to do that. We're definitely going to help with that. What our notion is you got to participate, you got to contribute, you got to be there, you got to be part of the community. That's part of my developer relations team is to become part of it. >> You got to be part of it and belong. Belonging is earning. >> Brad: Yes. >> And that's the key. And the other thing we were talking about standards and Dell has won a lot of business 'cause the PC and the servers all had standards, standard components. Standards now in the community are being driven by developer consensus. >> Brad: Yes. >> So that is an interesting new paradigm. So if you make cloud native work where all the hardware and software that's powering the builders is invisible. The developers will tell you what they want. >> 100%. >> And that's why your Kubernetes, Cloud Foundry example is so on point. It's a little bit nuanced, but what happened there is, let's explain Kubernetes was loosely de facto enabling. They didn't try to take too much territory. They didn't over push. >> Brad: Exactly. >> They were very flexible, lightweight at first, but it was enabling. >> It was organic. >> And we called it on theCUBE, I'm not going to lie, we called that early on. So props to us. >> Brad: Good job. >> Pat on the back. >> Lisa: Pat your own back. >> We get it right a lot. But now there's impact though. But the Dell I think speaks to the theme here, which just we talked is that you got startups here. We had from Envoy, we saw the donator there. He started his own company. You got Dell, which has large enterprises running massive workloads with a lot of legacy and modernization. So you got a combination of both coming together. This is going to be a collision of innovation. >> Oh I look, that's exactly right. Part of what I've been getting is not just the end users, the infrastructure developers, and whatnot around here. Startups look, come to Dell, and they're like, why are you here? Like we build this and we don't talk to you. And we're like, why not? If we come to market and start delivering more of those Kubernetes oriented solutions and the Kubernetes stack experience, that's where you guys should be working with us. You're part of the ecosystem. >> Well, your job is to say to them, look it when you want to write your software for the edge and we have market share of the most hardware at the edge, 'cause we perform better on the edge. No one wants to write software on the slower platform. >> No. >> Name me one I want to write software that's just, this is something, but people don't understand that's why you're here. >> Brad: That's exactly right. >> The game is about performance. >> Brad: Yeah. >> Cloud can do it, you can do it with a machine. So it depends where in the distributed computing chain you're at. >> You bring up one topic that actually isn't a core discussion topic around DevOps, but I am seeing more HPC and a AI/ML conversations popping up in this DevOps cloud native space. 'Cause even the market of HPC, which is a very traditional market, commodity server driven in the past, they're starting to say, how do I take advantage of Kubernetes and all of the benefits that we've been talking about. >> What are some of the things that you've heard like in your sense is the key theme or the talk track of Kubernetes, its evolution? What's on the developer's minds the last day and a half at this conference? >> Oh, okay. That's a hard question, but a good one. So the way I look at it is probably it's the robustness of the features within Kubernetes, not the native features, but even partner included features. They just want to be able to handle security in a much more, I hate to say zero trust, but secure cloud native way. There's tools in the Kubernetes ecosystem that are so integrated into Kubernetes. They don't have to think sometimes as much about how do they do it themselves. They can go find through open source or off-the-shelf startup and say, I need that and I can spin it up in about five minutes and now I'm doing that without having to spend weeks or months and having to build that. And that's security is one example. You can go through the networking discussion, you can go through so many different areas. The fact is because of community and the ecosystem, that is the winning formula for Kubernetes to enable the development. That's all I'm hearing here is they're like, give me more, give me more startups, give me more of these technologies. >> And ease of use has been a big topic here. We've been talking before we came on camera about VMware has done great since it used the virtual machine example versus Kubernetes. That is millions of developers and operators on VMware. They have about 200,000 plus just in VMUG alone. So they are going to transform their careers. They're looking for a home. They're looking for a community for the next 10 years. I mean, VMware will still be around with Broadcom, but I'm speculating that it will be much more in maintenance mode. But to get someone's career in fourth gear, fifth gear, you got to go and get that next skill set, and that's the question. Where do all these operators, IT operators go to become enterprise operators? >> Brad: That's exactly right. >> That is a big topic. What's your reaction? >> Sp I'm actually a living proof of that. I grew up in the VMware ecosystem. And for me making that pivot, it took me many years. One of the ways I did that was I actually have run in Dell, our advanced development pivotal Dojos, if you remember Pivotal. >> Yes. >> And doing the Pair Programming in Agile. It took me that mental shift to say, okay, we were doing it that way and now there's a new way to do it through code with developers and using all the new buzzwords. And that pivot is different for somebody that's just starting now, and they don't have access to a Dojo that they can go handle like a whole bunch of pair programmers. How do they make that pivot? That's 100% what we have to do. >> Okay, so my question is this, this is a hard question for you, maybe you can answer or not or maybe you can. What's different now than the attempt in the past from Dell EMC to do work or align with the developers? I think, was it five, six years ago, it was an effort. Was it timing? What's different now from then? >> So that attempt was awesome. That team was great. I was very close to that team and that was from the EMC side originally is where they have built that out. And the notion of that was that we just have to go start contributing knowledge and technology into the community and start really taking the brand and trying to expand the brand to be relevant in that community. Nothing wrong. That was actually an amazing way they did it. I think through the merger there was definitely a little bit of, okay, well, maybe this isn't one of our top priorities right now and that's probably what happened through the actual merger. >> John: It's a little bit distraction. >> It was distraction. >> Timings wasn't as good now. >> You try merging a 67 billion merger. I mean it's just really hard to do. What happened here is I think we finally got past a lot of that with the merger and now we're in steady stage/growth mode, which is a notion that now we can go and do this again in the new world, taking our lessons learned from what we did before, and try to actually go and update that in these new power apps. >> And you could point to some specific timing issues. Like at that time this community wasn't as advanced along. Kubernetes wasn't as clear. Visibility to that value proposition. Although a lot of people were speculating what happened that way. >> Exactly. >> But now with multi-cloud, I think developers starting to see the reality that it ain't going to be one cloud. >> Well, multi-cloud is not one cloud, so 100%. >> Well, I mean there's multi-cloud today, but it's really not multi-cloud by the way it could be. The people have multiple clouds. I think that gives developers comfort that existing enterprise players. Remember Microsoft wasn't really in the cloud game six, seven years ago. Look where they are now. Significant progress, nipping at the heels of AWS. So all the enterprise players are back at the table. >> Brad: Yeah, definitely. We're here. >> And that's timing issue. >> We're here. >> Talk about, you're here, you are helping customers get to the basics of Kubernetes. You talked a lot about the importance of the education. >> Brad: Yes. >> That screams to me that Dell can be a facilitator of cultural change within organizations, whether it's a bank or a hospital or a retailer or whatnot. Another thing that I'm curious about, what you guys are doing, how you've evolved, Dell is a massive partner ecosystem. How is the partner ecosystem involved in helping customers build their DevOps portfolios and really start embracing, understanding, and learning about Kubernetes? >> So that's an ever changing world right now. And that's part of why we're here at KubeCon is to help expand that. We have a very, very strong partner community. Not even just channel, but like technology partner community. And our goal is to understand with our DevOps portfolio what needs to be the next step of that partner community. Do we have to go partner up with like the, I'll use examples, the Solo.io. Do we have to partner up with all the mesh companies, the HashiCorp, which we are, We have to understand where the layers that make sense and where don't. There are some that don't make sense because they're so often to an app developer land or they're so far above even Kubernetes sometimes that maybe they don't make sense in our partner community. >> How influential are, I know we got to go soon, but how influential are your customers in helping to make some of those decisions? It's all about the customer at the end of the day. >> They're the only one that's deciding for us. They have to come to us. We have to see the need. We have to understand the discussions through our sales mechanisms, our other mechanisms. We're using that data every single day, every hour to make those decisions. >> Awesome. Brad, it's been great to have you. Sorry we took more of your time than we planned, but it was so interesting. >> No, this is awesome. >> Dell at KubeCon, you've done a great job of explaining why that absolutely resonates, the relevance, and why customers should be looking at Dell as their partner for this. Thank you so much for your time and your insights. >> Thank you guys. >> All right. For John Furrier and our guest, I'm Lisa Martin. You're watching theCUBE live at KubeCon + CloudNativeCon '22 from Detroit, Michigan. Stick around, our next guest will be here in just a minute. (gentle music)

(upbeat music) >> Okay, welcome back everyone in live coverage here at theCUBE, Boston, Massachusetts, for AWS re:inforce 22 security conference for Amazon Web Services. Obviously reinvent the end of the years' the big celebration, "re:Mars" is the new show that we've covered as well. The res are here with theCUBE. I'm John Furrier, host with a great guest, Ameesh Divatia, co-founder, and CEO of a company called "Baffle." Ameesh, thanks for joining us on theCUBE today, congratulations. >> Thank you. It's good to be here. >> And we got the custom encrypted socks. >> Yup, limited edition >> 64 bitter 128. >> Base 64 encoding. >> Okay.(chuckles) >> Secret message in there. >> Okay.(chuckles) Secret message.(chuckles) We'll have to put a little meme on the internet, figure it out. Well, thanks for comin' on. You guys are goin' hot right now. You guys a hot startup, but you're in an area that's going to explode, we believe. >> Yeah. >> The SuperCloud is here, we've been covering that on theCUBE that people are building on top of the Amazon Hyperscalers. And without the capex, they're building platforms. The application tsunami has come and still coming, it's not stopping. Modern applications are faster, they're better, and they're driving a lot of change under the covers. >> Absolutely. Yeah. >> And you're seeing structural change happening in real time, in ops, the network. You guys got something going on in the encryption area. >> Yes >> Data. Talk about what you guys do. >> Yeah. So we believe very strongly that the next frontier in security is data. We've had multiple waves in security. The next one is data, because data is really where the threats will persist. If the data shows up in the wrong place, you get into a lot of trouble with compliance. So we believe in protecting the data all the way down at the field, or record level. That's what we do. >> And you guys doing all kinds of encryption, or other things? >> Yes. So we do data transformation, which encompasses three different things. It can be tokenization, which is format preserving. We do real encryption with counter mode, or we can do masked views. So tokenization, encryption, and masking, all with the same platform. >> So pretty wide ranging capabilities with respect to having that kind of safety. >> Yes. Because it all depends on how the data is used down the road. Data is created all the time. Data flows through pipelines all the time. You want to make sure that you protect the data, but don't lose the utility of the data. That's where we provide all that flexibility. >> So Kurt was on stage today on one of the keynotes. He's the VP of the platform at AWS. >> Yes. >> He was talking about encrypts, everything. He said it needs, we need to rethink encryption. Okay, okay, good job. We like that. But then he said, "We have encryption at rest." >> Yes. >> That's kind of been there, done that. >> Yes. >> And, in-flight? >> Yeah. That's been there. >> But what about in-use? >> So that's exactly what we plug. What happens right now is that data at rest is protected because of discs that are already self-encrypting, or you have transparent data encryption that comes native with the database. You have data in-flight that is protected because of SSL. But when the data is actually being processed, it's in the memory of the database or datastore, it is exposed. So the threat is, if the credentials of the database are compromised, as happened back then with Starwood, or if the cloud infrastructure is compromised with some sort of an insider threat like a Capital One, that data is exposed. That's precisely what we solve by making sure that the data is protected as soon as it's created. We use standard encryption algorithms, AES, and we either do format preserving, or true encryption with counter mode. And that data, it doesn't really matter where it ends up, >> Yeah. >> because it's always protected. >> Well, that's awesome. And I think this brings up the point that we want been covering on SiliconAngle in theCUBE, is that there's been structural change that's happened, >> Yes. >> called cloud computing, >> Yes. >> and then hybrid. Okay. Scale, role of data, higher level abstraction of services, developers are in charge, value creations, startups, and big companies. That success is causing now, a new structural change happening now. >> Yes. >> This is one of them. What areas do you see that are happening right now that are structurally changing, that's right in front of us? One is, more cloud native. So the success has become now the problem to solve - >> Yes. >> to get to the next level. >> Yeah. >> What are those, some of those? >> What we see is that instead of security being an afterthought, something that you use as a watchdog, you create ways of monitoring where data is being exposed, or data is being exfiltrated, you want to build security into the data pipeline itself. As soon as data is created, you identify what is sensitive data, and you encrypt it, or tokenize it as it flows into the pipeline using things like Kafka plugins, or what we are very clearly differentiating ourselves with is, proxy architectures so that it's completely transparent. You think you're writing to the datastore, but you're actually writing to the proxy, which in turn encrypts the data before its stored. >> Do you think that's an efficient way to do it, or is the only way to do it? >> It is a much more efficient way of doing it because of the fact that you don't need any app-dev resources. There are many other ways of doing it. In fact, the cloud vendors provide development kits where you can just go do it yourself. So that is actually something that we completely avoid. And what makes it really, really interesting is that once the data is encrypted in the data store, or database, we can do what is known as "Privacy Enhanced Computation." >> Mm. >> So we can actually process that data without decrypting it. >> Yeah. And so proxies then, with cloud computing, can be very fast, not a bottleneck that could be. >> In fact, the cloud makes it so. It's very hard to - >> You believe that? >> do these things in static infrastructure. In the cloud, there's infinite amount of processing available, and there's containerization. >> And you have good network. >> You have very good network, you have load balancers, you have ways of creating redundancy. >> Mm. So the cloud is actually enabling solutions like this. >> And the old way, proxies were seen as an architectural fail, in the old antiquated static web. >> And this is where startups don't have the baggage, right? We didn't have that baggage. (John laughs) We looked at the problem and said, of course we're going to use a proxy because this is the best way to do this in an efficient way. >> Well, you bring up something that's happening right now that I hear a lot of CSOs and CIOs and executives say, CXOs say all the time, "Our", I won't say the word, "Our stuff has gotten complicated." >> Yes. >> So now I have tool sprawl, >> Yeah. >> I have skill gaps, and on the rise, all these new managed services coming at me from the vendors who have never experienced my problem. And their reaction is, they don't get my problem, and they don't have the right solutions, it's more complexity. They solve the complexity by adding more complexity. >> Yes. I think we, again, the proxy approach is a very simple. >> That you're solving that with that approach. >> Exactly. It's very simple. And again, we don't get in the way. That's really the the biggest differentiator. The forcing function really here is compliance, right? Because compliance is forcing these CSOs to actually adopt these solutions. >> All right, so love the compliance angle, love the proxy as an ease of use, take the heavy lifting away, no operational problems, and deviations. Now let's talk about workloads. >> Yeah. >> 'Cause this is where the use is. So you got, or workloads being run large scale, lot a data moving around, computin' as well. What's the challenge there? >> I think it's the volume of the data. Traditional solutions that we're relying on legacy tokenizations, I think would replicate the entire storage because it would create a token wall, for example. You cannot do that at this scale. You have to do something that's a lot more efficient, which is where you have to do it with a cryptography approach. So the workloads are diverse, lots of large files in the workloads as well as structured workloads. What we have is a solution that actually goes across the board. We can do unstructured data with HTTP proxies, we can do structured data with SQL proxies. And that's how we are able to provide a complete solution for the pipeline. >> So, I mean, show about the on-premise versus the cloud workload dynamic right now. Hybrid is a steady state right now. >> Yeah. >> Multi-cloud is a consequence of having multiple vendors, not true multi-cloud but like, okay, they have Azure there, AWS here, I get that. But hybrid really is the steady state. >> Yes. >> Cloud operations. How are the workloads and the analytics the data being managed on-prem, and in the cloud, what's their relationship? What's the trend? What are you seeing happening there? >> I think the biggest trend we see is pipelining, right? The new ETL is streaming. You have these Kafka and Kinesis capabilities that are coming into the picture where data is being ingested all the time. It is not a one time migration. It's a stream. >> Yeah. >> So plugging into that stream is very important from an ingestion perspective. >> So it's not just a watchdog. >> No. >> It's the pipelining. >> It's built in. It's built-in, it's real time, that's where the streaming gets another diverse access to data. >> Exactly. >> Data lakes. You got data lakes, you have pipeline, you got streaming, you mentioned that. So talk about the old school OLTP, the old BI world. I think Power BI's like a $30 billion product. >> Yeah. >> And you got Tableau built on OLTP building cubes. Aren't we just building cubes in a new way, or, >> Well. >> is there any relevance to the old school? >> I think there, there is some relevance and in fact that's again, another place where the proxy architecture really helps, because it doesn't matter when your application was built. You can use Tableau, which nobody has any control over, and still process encrypted data. And so can with Power BI, any Sequel application can be used. And that's actually exactly what we like to. >> So we were, I was talking to your team, I knew you were coming on, and they gave me a sound bite that I'm going to read to the audience and I want to get your reaction to. >> Sure. >> 'Cause I love this. I fell out of my chair when I first read this. "Data is the new oil." In 2010 that was mentioned here on theCUBE, of course. "Data is the new oil, but we have to ensure that it does not become the next asbestos." Okay. That is really clever. So we all know about asbestos. I add to the Dave Vellante, "Lead paint too." Remember lead paint? (Ameesh laughs) You got to scrape it out and repaint the house. Asbestos obviously causes a lot of cancer. You know, joking aside, the point is, it's problematic. >> It's the asset. >> Explain why that sentence is relevant. >> Sure. It's the assets and liabilities argument, right? You have an asset which is data, but thanks to compliance regulations and Gartner says 75% of the world will be subject to privacy regulations by 2023. It's a liability. So if you don't store your data well, if you don't process your data responsibly, you are going to be liable. So while it might be the oil and you're going to get lots of value out of it, be careful about the, the flip side. >> And the point is, there could be the "Grim Reaper" waiting for you if you don't do it right, the consequences that are quantified would be being out of business. >> Yes. But here's something that we just discovered actually from our survey that we did. While 93% of respondents said that they have had lots of compliance related effects on their budgets. 75% actually thought that it makes them better. They can use the security postures as a competitive differentiator. That's very heartening to us. We don't like to sell the fear aspect of this. >> Yeah. We like to sell the fact that you look better compared to your neighbor, if you have better data hygiene, back to the. >> There's the fear of missing out, or as they say, "Keeping up with the Joneses", making sure that your yard looks better than the next one. I get the vanity of that, but you're solving real problems. And this is interesting. And I want to get your thoughts on this. I found, I read that you guys protect more than a 100 billion records across highly regulated industries. Financial services, healthcare, industrial IOT, retail, and government. Is that true? >> Absolutely. Because what we are doing is enabling SaaS vendors to actually allow their customers to control their data. So we've had the SaaS vendor who has been working with us for over three years now. They store confidential data from 30 different banks in the country. >> That's a lot of records. >> That's where the record, and. >> How many customers do you have? >> Well, I think. >> The next round of funding's (Ameesh laughs) probably they're linin' up to put money into you guys. >> Well, again, this is a very important problem, and there are, people's businesses are dependent on this. We're just happy to provide the best tool out there that can do this. >> Okay, so what's your business model behind? I love the success, by the way, I wanted to quote that stat to one verify it. What's the business model service, software? >> The business model is software. We don't want anybody to send us their confidential data. We embed our software into our customers environments. In case of SaaS, we are not even visible, we are completely embedded. We are doing other relationships like that right now. >> And they pay you how? >> They pay us based on the volume of the data that they're protecting. >> Got it. >> That in that case which is a large customers, large enterprise customers. >> Pay as you go. >> It is pay as you go, everything is annual licenses. Although, multi-year licenses are very common because once you adopt the solution, it is very sticky. And then for smaller customers, we do base our pricing also just on databases. >> Got it. >> The number of databases. >> And the technology just reviewed low-code, no-code implementation kind of thing, right? >> It is by definition, no code when it comes to proxy. >> Yeah. >> When it comes to API integration, it could be low code. Yeah, it's all cloud-friendly, cloud-native. >> No disruption to operations. >> Exactly. >> That's the culprit. >> Well, yeah. >> Well somethin' like non-disruptive operations.(laughs) >> No, actually I'll give an example of a migration, right? We can do live migrations. So while the databases are still alive, as you write your. >> Live secure migrations. >> Exactly. You're securing - >> That's the one that manifests. >> your data as it migrates. >> Awright, so how much funding have you guys raised so far? >> We raised 36 and a half, series A, and B now. We raised that late last year. >> Congratulations. >> Thank you. >> Who's the venture funders? >> True Ventures is our largest investor, followed by Celesta Capital, National Grid Partners is an investor, and so is Engineering Capital and Clear Vision Ventures. >> And the seed and it was from Engineering? >> Seed was from Engineering. >> Engineering Capital. >> And then True came in very early on. >> Okay. >> Greenspring is also an investor in us, so is Industrial Ventures. >> Well, privacy has a big concern, big application for you guys. Privacy, secure migrations. >> Very much so. So what we are believe very strongly in the security's personal, security is yours and my data. Privacy is what the data collector is responsible for. (John laughs) So the enterprise better be making sure that they've complied with privacy regulations because they don't tell you how to protect the data. They just fine you. >> Well, you're not, you're technically long, six year old start company. Six, seven years old. >> Yeah. >> Roughly. So yeah, startups can go on long like this, still startup, privately held, you're growing, got big records under management there, congratulations. What's next? >> I think scaling the business. We are seeing lots of applications for this particular solution. It's going beyond just regulated industries. Like I said, it's a differentiating factor now. >> Yeah >> So retail, and a lot of other IOT related industrial customers - >> Yeah. >> are also coming. >> Ameesh, talk about the show here. We're at re:inforce, actually we're live here on the ground, the show floor buzzing. What's your takeaway? What's the vibe this year? What if you had to share what your opinion the top story here at the show, what would be the two top things, or three things? >> I think it's two things. First of all, it feels like we are back. (both laugh) It's amazing to see people on the show floor. >> Yeah. >> People coming in and asking questions and getting to see the product. The second thing that I think is very gratifying is, people come in and say, "Oh, I've heard of you guys." So thanks to digital media, and digital marketing. >> They weren't baffled. They want baffled. >> Exactly. >> They use baffled. >> Looks like, our outreach has helped, >> Yeah. >> and has kept the continuity, which is a big deal. >> Yeah, and now you're a CUBE alumni, welcome to the fold. >> Thank you. >> Appreciate you coming on. And we're looking forward to profiling you some day in our startup showcase, and certainly, we'll see you in the Palo Alto studios. Love to have you come in for a deeper dive. >> Sounds great. Looking forward to it. >> Congratulations on all your success, and thanks for coming on theCUBE, here at re:inforce. >> Thank you, John. >> Okay, we're here in, on the ground live coverage, Boston, Massachusetts for AWS re:inforce 22. I'm John Furrier, your host of theCUBE with Dave Vellante, who's in an analyst session, right? He'll be right back with us on the next interview, coming up shortly. Thanks for watching. (gentle music)

(upbeat music) >> The stampede to cloud and massive VC investments has led to the emergence of a new generation of object store based data lakes. And with them two important trends, actually three important trends. First, a new category that combines data lakes and data warehouses aka the lakehouse is emerged as a leading contender to be the data platform of the future. And this novelty touts the ability to address data engineering, data science, and data warehouse workloads on a single shared data platform. The other major trend we've seen is query engines and broader data fabric virtualization platforms have embraced NextGen data lakes as platforms for SQL centric business intelligence workloads, reducing, or somebody even claim eliminating the need for separate data warehouses. Pretty bold. However, cloud data warehouses have added complimentary technologies to bridge the gaps with lakehouses. And the third is many, if not most customers that are embracing the so-called data fabric or data mesh architectures. They're looking at data lakes as a fundamental component of their strategies, and they're trying to evolve them to be more capable, hence the interest in lakehouse, but at the same time, they don't want to, or can't abandon their data warehouse estate. As such we see a battle royale is brewing between cloud data warehouses and cloud lakehouses. Is it possible to do it all with one cloud center analytical data platform? Well, we're going to find out. My name is Dave Vellante and welcome to the data platform's power panel on theCUBE. Our next episode in a series where we gather some of the industry's top analysts to talk about one of our favorite topics, data. In today's session, we'll discuss trends, emerging options, and the trade offs of various approaches and we'll name names. Joining us today are Sanjeev Mohan, who's the principal at SanjMo, Tony Baers, principal at dbInsight. And Doug Henschen is the vice president and principal analyst at Constellation Research. Guys, welcome back to theCUBE. Great to see you again. >> Thank guys. Thank you. >> Thank you. >> So it's early June and we're gearing up with two major conferences, there's several database conferences, but two in particular that were very interested in, Snowflake Summit and Databricks Data and AI Summit. Doug let's start off with you and then Tony and Sanjeev, if you could kindly weigh in. Where did this all start, Doug? The notion of lakehouse. And let's talk about what exactly we mean by lakehouse. Go ahead. >> Yeah, well you nailed it in your intro. One platform to address BI data science, data engineering, fewer platforms, less cost, less complexity, very compelling. You can credit Databricks for coining the term lakehouse back in 2020, but it's really a much older idea. You can go back to Cloudera introducing their Impala database in 2012. That was a database on top of Hadoop. And indeed in that last decade, by the middle of that last decade, there were several SQL on Hadoop products, open standards like Apache Drill. And at the same time, the database vendors were trying to respond to this interest in machine learning and the data science. So they were adding SQL extensions, the likes Hudi and Vertical we're adding SQL extensions to support the data science. But then later in that decade with the shift to cloud and object storage, you saw the vendor shift to this whole cloud, and object storage idea. So you have in the database camp Snowflake introduce Snowpark to try to address the data science needs. They introduced that in 2020 and last year they announced support for Python. You also had Oracle, SAP jumped on this lakehouse idea last year, supporting both the lake and warehouse single vendor, not necessarily quite single platform. Google very recently also jumped on the bandwagon. And then you also mentioned, the SQL engine camp, the Dremios, the Ahanas, the Starbursts, really doing two things, a fabric for distributed access to many data sources, but also very firmly planning that idea that you can just have the lake and we'll help you do the BI workloads on that. And then of course, the data lake camp with the Databricks and Clouderas providing a warehouse style deployments on top of their lake platforms. >> Okay, thanks, Doug. I'd be remiss those of you who me know that I typically write my own intros. This time my colleagues fed me a lot of that material. So thank you. You guys make it easy. But Tony, give us your thoughts on this intro. >> Right. Well, I very much agree with both of you, which may not make for the most exciting television in terms of that it has been an evolution just like Doug said. I mean, for instance, just to give an example when Teradata bought AfterData was initially seen as a hardware platform play. In the end, it was basically, it was all those after functions that made a lot of sort of big data analytics accessible to SQL. (clears throat) And so what I really see just in a more simpler definition or functional definition, the data lakehouse is really an attempt by the data lake folks to make the data lake friendlier territory to the SQL folks, and also to get into friendly territory, to all the data stewards, who are basically concerned about the sprawl and the lack of control in governance in the data lake. So it's really kind of a continuing of an ongoing trend that being said, there's no action without counter action. And of course, at the other end of the spectrum, we also see a lot of the data warehouses starting to edit things like in database machine learning. So they're certainly not surrendering without a fight. Again, as Doug was mentioning, this has been part of a continual blending of platforms that we've seen over the years that we first saw in the Hadoop years with SQL on Hadoop and data warehouses starting to reach out to cloud storage or should say the HDFS and then with the cloud then going cloud native and therefore trying to break the silos down even further. >> Now, thank you. And Sanjeev, data lakes, when we first heard about them, there were such a compelling name, and then we realized all the problems associated with them. So pick it up from there. What would you add to Doug and Tony? >> I would say, these are excellent points that Doug and Tony have brought to light. The concept of lakehouse was going on to your point, Dave, a long time ago, long before the tone was invented. For example, in Uber, Uber was trying to do a mix of Hadoop and Vertical because what they really needed were transactional capabilities that Hadoop did not have. So they weren't calling it the lakehouse, they were using multiple technologies, but now they're able to collapse it into a single data store that we call lakehouse. Data lakes, excellent at batch processing large volumes of data, but they don't have the real time capabilities such as change data capture, doing inserts and updates. So this is why lakehouse has become so important because they give us these transactional capabilities. >> Great. So I'm interested, the name is great, lakehouse. The concept is powerful, but I get concerned that it's a lot of marketing hype behind it. So I want to examine that a bit deeper. How mature is the concept of lakehouse? Are there practical examples that really exist in the real world that are driving business results for practitioners? Tony, maybe you could kick that off. >> Well, put it this way. I think what's interesting is that both data lakes and data warehouse that each had to extend themselves. To believe the Databricks hype it's that this was just a natural extension of the data lake. In point of fact, Databricks had to go outside its core technology of Spark to make the lakehouse possible. And it's a very similar type of thing on the part with data warehouse folks, in terms of that they've had to go beyond SQL, In the case of Databricks. There have been a number of incremental improvements to Delta lake, to basically make the table format more performative, for instance. But the other thing, I think the most dramatic change in all that is in their SQL engine and they had to essentially pretty much abandon Spark SQL because it really, in off itself Spark SQL is essentially stop gap solution. And if they wanted to really address that crowd, they had to totally reinvent SQL or at least their SQL engine. And so Databricks SQL is not Spark SQL, it is not Spark, it's basically SQL that it's adapted to run in a Spark environment, but the underlying engine is C++, it's not scale or anything like that. So Databricks had to take a major detour outside of its core platform to do this. So to answer your question, this is not mature because these are all basically kind of, even though the idea of blending platforms has been going on for well over a decade, I would say that the current iteration is still fairly immature. And in the cloud, I could see a further evolution of this because if you think through cloud native architecture where you're essentially abstracting compute from data, there is no reason why, if let's say you are dealing with say, the same basically data targets say cloud storage, cloud object storage that you might not apportion the task to different compute engines. And so therefore you could have, for instance, let's say you're Google, you could have BigQuery, perform basically the types of the analytics, the SQL analytics that would be associated with the data warehouse and you could have BigQuery ML that does some in database machine learning, but at the same time for another part of the query, which might involve, let's say some deep learning, just for example, you might go out to let's say the serverless spark service or the data proc. And there's no reason why Google could not blend all those into a coherent offering that's basically all triggered through microservices. And I just gave Google as an example, if you could generalize that with all the other cloud or all the other third party vendors. So I think we're still very early in the game in terms of maturity of data lakehouses. >> Thanks, Tony. So Sanjeev, is this all hype? What are your thoughts? >> It's not hype, but completely agree. It's not mature yet. Lakehouses have still a lot of work to do, so what I'm now starting to see is that the world is dividing into two camps. On one hand, there are people who don't want to deal with the operational aspects of vast amounts of data. They are the ones who are going for BigQuery, Redshift, Snowflake, Synapse, and so on because they want the platform to handle all the data modeling, access control, performance enhancements, but these are trade off. If you go with these platforms, then you are giving up on vendor neutrality. On the other side are those who have engineering skills. They want the independence. In other words, they don't want vendor lock in. They want to transform their data into any number of use cases, especially data science, machine learning use case. What they want is agility via open file formats using any compute engine. So why do I say lakehouses are not mature? Well, cloud data warehouses they provide you an excellent user experience. That is the main reason why Snowflake took off. If you have thousands of cables, it takes minutes to get them started, uploaded into your warehouse and start experimentation. Table formats are far more resonating with the community than file formats. But once the cost goes up of cloud data warehouse, then the organization start exploring lakehouses. But the problem is lakehouses still need to do a lot of work on metadata. Apache Hive was a fantastic first attempt at it. Even today Apache Hive is still very strong, but it's all technical metadata and it has so many different restrictions. That's why we see Databricks is investing into something called Unity Catalog. Hopefully we'll hear more about Unity Catalog at the end of the month. But there's a second problem. I just want to mention, and that is lack of standards. All these open source vendors, they're running, what I call ego projects. You see on LinkedIn, they're constantly battling with each other, but end user doesn't care. End user wants a problem to be solved. They want to use Trino, Dremio, Spark from EMR, Databricks, Ahana, DaaS, Frink, Athena. But the problem is that we don't have common standards. >> Right. Thanks. So Doug, I worry sometimes. I mean, I look at the space, we've debated for years, best of breed versus the full suite. You see AWS with whatever, 12 different plus data stores and different APIs and primitives. You got Oracle putting everything into its database. It's actually done some interesting things with MySQL HeatWave, so maybe there's proof points there, but Snowflake really good at data warehouse, simplifying data warehouse. Databricks, really good at making lakehouses actually more functional. Can one platform do it all? >> Well in a word, I can't be best at breed at all things. I think the upshot of and cogen analysis from Sanjeev there, the database, the vendors coming out of the database tradition, they excel at the SQL. They're extending it into data science, but when it comes to unstructured data, data science, ML AI often a compromise, the data lake crowd, the Databricks and such. They've struggled to completely displace the data warehouse when it really gets to the tough SLAs, they acknowledge that there's still a role for the warehouse. Maybe you can size down the warehouse and offload some of the BI workloads and maybe and some of these SQL engines, good for ad hoc, minimize data movement. But really when you get to the deep service level, a requirement, the high concurrency, the high query workloads, you end up creating something that's warehouse like. >> Where do you guys think this market is headed? What's going to take hold? Which projects are going to fade away? You got some things in Apache projects like Hudi and Iceberg, where do they fit Sanjeev? Do you have any thoughts on that? >> So thank you, Dave. So I feel that table formats are starting to mature. There is a lot of work that's being done. We will not have a single product or single platform. We'll have a mixture. So I see a lot of Apache Iceberg in the news. Apache Iceberg is really innovating. Their focus is on a table format, but then Delta and Apache Hudi are doing a lot of deep engineering work. For example, how do you handle high concurrency when there are multiple rights going on? Do you version your Parquet files or how do you do your upcerts basically? So different focus, at the end of the day, the end user will decide what is the right platform, but we are going to have multiple formats living with us for a long time. >> Doug is Iceberg in your view, something that's going to address some of those gaps in standards that Sanjeev was talking about earlier? >> Yeah, Delta lake, Hudi, Iceberg, they all address this need for consistency and scalability, Delta lake open technically, but open for access. I don't hear about Delta lakes in any worlds, but Databricks, hearing a lot of buzz about Apache Iceberg. End users want an open performance standard. And most recently Google embraced Iceberg for its recent a big lake, their stab at having supporting both lakes and warehouses on one conjoined platform. >> And Tony, of course, you remember the early days of the sort of big data movement you had MapR was the most closed. You had Horton works the most open. You had Cloudera in between. There was always this kind of contest as to who's the most open. Does that matter? Are we going to see a repeat of that here? >> I think it's spheres of influence, I think, and Doug very much was kind of referring to this. I would call it kind of like the MongoDB syndrome, which is that you have... and I'm talking about MongoDB before they changed their license, open source project, but very much associated with MongoDB, which basically, pretty much controlled most of the contributions made decisions. And I think Databricks has the same iron cloud hold on Delta lake, but still the market is pretty much associated Delta lake as the Databricks, open source project. I mean, Iceberg is probably further advanced than Hudi in terms of mind share. And so what I see that's breaking down to is essentially, basically the Databricks open source versus the everything else open source, the community open source. So I see it's a very similar type of breakdown that I see repeating itself here. >> So by the way, Mongo has a conference next week, another data platform is kind of not really relevant to this discussion totally. But in the sense it is because there's a lot of discussion on earnings calls these last couple of weeks about consumption and who's exposed, obviously people are concerned about Snowflake's consumption model. Mongo is maybe less exposed because Atlas is prominent in the portfolio, blah, blah, blah. But I wanted to bring up the little bit of controversy that we saw come out of the Snowflake earnings call, where the ever core analyst asked Frank Klutman about discretionary spend. And Frank basically said, look, we're not discretionary. We are deeply operationalized. Whereas he kind of poo-pooed the lakehouse or the data lake, et cetera, saying, oh yeah, data scientists will pull files out and play with them. That's really not our business. Do any of you have comments on that? Help us swing through that controversy. Who wants to take that one? >> Let's put it this way. The SQL folks are from Venus and the data scientists are from Mars. So it means it really comes down to it, sort that type of perception. The fact is, is that, traditionally with analytics, it was very SQL oriented and that basically the quants were kind of off in their corner, where they're using SaaS or where they're using Teradata. It's really a great leveler today, which is that, I mean basic Python it's become arguably one of the most popular programming languages, depending on what month you're looking at, at the title index. And of course, obviously SQL is, as I tell the MongoDB folks, SQL is not going away. You have a large skills base out there. And so basically I see this breaking down to essentially, you're going to have each group that's going to have its own natural preferences for its home turf. And the fact that basically, let's say the Python and scale of folks are using Databricks does not make them any less operational or machine critical than the SQL folks. >> Anybody else want to chime in on that one? >> Yeah, I totally agree with that. Python support in Snowflake is very nascent with all of Snowpark, all of the things outside of SQL, they're very much relying on partners too and make things possible and make data science possible. And it's very early days. I think the bottom line, what we're going to see is each of these camps is going to keep working on doing better at the thing that they don't do today, or they're new to, but they're not going to nail it. They're not going to be best of breed on both sides. So the SQL centric companies and shops are going to do more data science on their database centric platform. That data science driven companies might be doing more BI on their leagues with those vendors and the companies that have highly distributed data, they're going to add fabrics, and maybe offload more of their BI onto those engines, like Dremio and Starburst. >> So I've asked you this before, but I'll ask you Sanjeev. 'Cause Snowflake and Databricks are such great examples 'cause you have the data engineering crowd trying to go into data warehousing and you have the data warehousing guys trying to go into the lake territory. Snowflake has $5 billion in the balance sheet and I've asked you before, I ask you again, doesn't there has to be a semantic layer between these two worlds? Does Snowflake go out and do M&A and maybe buy ad scale or a data mirror? Or is that just sort of a bandaid? What are your thoughts on that Sanjeev? >> I think semantic layer is the metadata. The business metadata is extremely important. At the end of the day, the business folks, they'd rather go to the business metadata than have to figure out, for example, like let's say, I want to update somebody's email address and we have a lot of overhead with data residency laws and all that. I want my platform to give me the business metadata so I can write my business logic without having to worry about which database, which location. So having that semantic layer is extremely important. In fact, now we are taking it to the next level. Now we are saying that it's not just a semantic layer, it's all my KPIs, all my calculations. So how can I make those calculations independent of the compute engine, independent of the BI tool and make them fungible. So more disaggregation of the stack, but it gives us more best of breed products that the customers have to worry about. >> So I want to ask you about the stack, the modern data stack, if you will. And we always talk about injecting machine intelligence, AI into applications, making them more data driven. But when you look at the application development stack, it's separate, the database is tends to be separate from the data and analytics stack. Do those two worlds have to come together in the modern data world? And what does that look like organizationally? >> So organizationally even technically I think it is starting to happen. Microservices architecture was a first attempt to bring the application and the data world together, but they are fundamentally different things. For example, if an application crashes, that's horrible, but Kubernetes will self heal and it'll bring the application back up. But if a database crashes and corrupts your data, we have a huge problem. So that's why they have traditionally been two different stacks. They are starting to come together, especially with data ops, for instance, versioning of the way we write business logic. It used to be, a business logic was highly embedded into our database of choice, but now we are disaggregating that using GitHub, CICD the whole DevOps tool chain. So data is catching up to the way applications are. >> We also have databases, that trans analytical databases that's a little bit of what the story is with MongoDB next week with adding more analytical capabilities. But I think companies that talk about that are always careful to couch it as operational analytics, not the warehouse level workloads. So we're making progress, but I think there's always going to be, or there will long be a separate analytical data platform. >> Until data mesh takes over. (all laughing) Not opening a can of worms. >> Well, but wait, I know it's out of scope here, but wouldn't data mesh say, hey, do take your best of breed to Doug's earlier point. You can't be best of breed at everything, wouldn't data mesh advocate, data lakes do your data lake thing, data warehouse, do your data lake, then you're just a node on the mesh. (Tony laughs) Now you need separate data stores and you need separate teams. >> To my point. >> I think, I mean, put it this way. (laughs) Data mesh itself is a logical view of the world. The data mesh is not necessarily on the lake or on the warehouse. I think for me, the fear there is more in terms of, the silos of governance that could happen and the silo views of the world, how we redefine. And that's why and I want to go back to something what Sanjeev said, which is that it's going to be raising the importance of the semantic layer. Now does Snowflake that opens a couple of Pandora's boxes here, which is one, does Snowflake dare go into that space or do they risk basically alienating basically their partner ecosystem, which is a key part of their whole appeal, which is best of breed. They're kind of the same situation that Informatica was where in the early 2000s, when Informatica briefly flirted with analytic applications and realized that was not a good idea, need to redouble down on their core, which was data integration. The other thing though, that raises the importance of and this is where the best of breed comes in, is the data fabric. My contention is that and whether you use employee data mesh practice or not, if you do employee data mesh, you need data fabric. If you deploy data fabric, you don't necessarily need to practice data mesh. But data fabric at its core and admittedly it's a category that's still very poorly defined and evolving, but at its core, we're talking about a common meta data back plane, something that we used to talk about with master data management, this would be something that would be more what I would say basically, mutable, that would be more evolving, basically using, let's say, machine learning to kind of, so that we don't have to predefine rules or predefine what the world looks like. But so I think in the long run, what this really means is that whichever way we implement on whichever physical platform we implement, we need to all be speaking the same metadata language. And I think at the end of the day, regardless of whether it's a lake, warehouse or a lakehouse, we need common metadata. >> Doug, can I come back to something you pointed out? That those talking about bringing analytic and transaction databases together, you had talked about operationalizing those and the caution there. Educate me on MySQL HeatWave. I was surprised when Oracle put so much effort in that, and you may or may not be familiar with it, but a lot of folks have talked about that. Now it's got nowhere in the market, that no market share, but a lot of we've seen these benchmarks from Oracle. How real is that bringing together those two worlds and eliminating ETL? >> Yeah, I have to defer on that one. That's my colleague, Holger Mueller. He wrote the report on that. He's way deep on it and I'm not going to mock him. >> I wonder if that is something, how real that is or if it's just Oracle marketing, anybody have any thoughts on that? >> I'm pretty familiar with HeatWave. It's essentially Oracle doing what, I mean, there's kind of a parallel with what Google's doing with AlloyDB. It's an operational database that will have some embedded analytics. And it's also something which I expect to start seeing with MongoDB. And I think basically, Doug and Sanjeev were kind of referring to this before about basically kind of like the operational analytics, that are basically embedded within an operational database. The idea here is that the last thing you want to do with an operational database is slow it down. So you're not going to be doing very complex deep learning or anything like that, but you might be doing things like classification, you might be doing some predictives. In other words, we've just concluded a transaction with this customer, but was it less than what we were expecting? What does that mean in terms of, is this customer likely to turn? I think we're going to be seeing a lot of that. And I think that's what a lot of what MySQL HeatWave is all about. Whether Oracle has any presence in the market now it's still a pretty new announcement, but the other thing that kind of goes against Oracle, (laughs) that they had to battle against is that even though they own MySQL and run the open source project, everybody else, in terms of the actual commercial implementation it's associated with everybody else. And the popular perception has been that MySQL has been basically kind of like a sidelight for Oracle. And so it's on Oracles shoulders to prove that they're damn serious about it. >> There's no coincidence that MariaDB was launched the day that Oracle acquired Sun. Sanjeev, I wonder if we could come back to a topic that we discussed earlier, which is this notion of consumption, obviously Wall Street's very concerned about it. Snowflake dropped prices last week. I've always felt like, hey, the consumption model is the right model. I can dial it down in when I need to, of course, the street freaks out. What are your thoughts on just pricing, the consumption model? What's the right model for companies, for customers? >> Consumption model is here to stay. What I would like to see, and I think is an ideal situation and actually plays into the lakehouse concept is that, I have my data in some open format, maybe it's Parquet or CSV or JSON, Avro, and I can bring whatever engine is the best engine for my workloads, bring it on, pay for consumption, and then shut it down. And by the way, that could be Cloudera. We don't talk about Cloudera very much, but it could be one business unit wants to use Athena. Another business unit wants to use some other Trino let's say or Dremio. So every business unit is working on the same data set, see that's critical, but that data set is maybe in their VPC and they bring any compute engine, you pay for the use, shut it down. That then you're getting value and you're only paying for consumption. It's not like, I left a cluster running by mistake, so there have to be guardrails. The reason FinOps is so big is because it's very easy for me to run a Cartesian joint in the cloud and get a $10,000 bill. >> This looks like it's been a sort of a victim of its own success in some ways, they made it so easy to spin up single note instances, multi note instances. And back in the day when compute was scarce and costly, those database engines optimized every last bit so they could get as much workload as possible out of every instance. Today, it's really easy to spin up a new node, a new multi node cluster. So that freedom has meant many more nodes that aren't necessarily getting that utilization. So Snowflake has been doing a lot to add reporting, monitoring, dashboards around the utilization of all the nodes and multi node instances that have spun up. And meanwhile, we're seeing some of the traditional on-prem databases that are moving into the cloud, trying to offer that freedom. And I think they're going to have that same discovery that the cost surprises are going to follow as they make it easy to spin up new instances. >> Yeah, a lot of money went into this market over the last decade, separating compute from storage, moving to the cloud. I'm glad you mentioned Cloudera Sanjeev, 'cause they got it all started, the kind of big data movement. We don't talk about them that much. Sometimes I wonder if it's because when they merged Hortonworks and Cloudera, they dead ended both platforms, but then they did invest in a more modern platform. But what's the future of Cloudera? What are you seeing out there? >> Cloudera has a good product. I have to say the problem in our space is that there're way too many companies, there's way too much noise. We are expecting the end users to parse it out or we expecting analyst firms to boil it down. So I think marketing becomes a big problem. As far as technology is concerned, I think Cloudera did turn their selves around and Tony, I know you, you talked to them quite frequently. I think they have quite a comprehensive offering for a long time actually. They've created Kudu, so they got operational, they have Hadoop, they have an operational data warehouse, they're migrated to the cloud. They are in hybrid multi-cloud environment. Lot of cloud data warehouses are not hybrid. They're only in the cloud. >> Right. I think what Cloudera has done the most successful has been in the transition to the cloud and the fact that they're giving their customers more OnRamps to it, more hybrid OnRamps. So I give them a lot of credit there. They're also have been trying to position themselves as being the most price friendly in terms of that we will put more guardrails and governors on it. I mean, part of that could be spin. But on the other hand, they don't have the same vested interest in compute cycles as say, AWS would have with EMR. That being said, yes, Cloudera does it, I think its most powerful appeal so of that, it almost sounds in a way, I don't want to cast them as a legacy system. But the fact is they do have a huge landed legacy on-prem and still significant potential to land and expand that to the cloud. That being said, even though Cloudera is multifunction, I think it certainly has its strengths and weaknesses. And the fact this is that yes, Cloudera has an operational database or an operational data store with a kind of like the outgrowth of age base, but Cloudera is still based, primarily known for the deep analytics, the operational database nobody's going to buy Cloudera or Cloudera data platform strictly for the operational database. They may use it as an add-on, just in the same way that a lot of customers have used let's say Teradata basically to do some machine learning or let's say, Snowflake to parse through JSON. Again, it's not an indictment or anything like that, but the fact is obviously they do have their strengths and their weaknesses. I think their greatest opportunity is with their existing base because that base has a lot invested and vested. And the fact is they do have a hybrid path that a lot of the others lack. >> And of course being on the quarterly shock clock was not a good place to be under the microscope for Cloudera and now they at least can refactor the business accordingly. I'm glad you mentioned hybrid too. We saw Snowflake last month, did a deal with Dell whereby non-native Snowflake data could access on-prem object store from Dell. They announced a similar thing with pure storage. What do you guys make of that? Is that just... How significant will that be? Will customers actually do that? I think they're using either materialized views or extended tables. >> There are data rated and residency requirements. There are desires to have these platforms in your own data center. And finally they capitulated, I mean, Frank Klutman is famous for saying to be very focused and earlier, not many months ago, they called the going on-prem as a distraction, but clearly there's enough demand and certainly government contracts any company that has data residency requirements, it's a real need. So they finally addressed it. >> Yeah, I'll bet dollars to donuts, there was an EBC session and some big customer said, if you don't do this, we ain't doing business with you. And that was like, okay, we'll do it. >> So Dave, I have to say, earlier on you had brought this point, how Frank Klutman was poo-pooing data science workloads. On your show, about a year or so ago, he said, we are never going to on-prem. He burnt that bridge. (Tony laughs) That was on your show. >> I remember exactly the statement because it was interesting. He said, we're never going to do the halfway house. And I think what he meant is we're not going to bring the Snowflake architecture to run on-prem because it defeats the elasticity of the cloud. So this was kind of a capitulation in a way. But I think it still preserves his original intent sort of, I don't know. >> The point here is that every vendor will poo-poo whatever they don't have until they do have it. >> Yes. >> And then it'd be like, oh, we are all in, we've always been doing this. We have always supported this and now we are doing it better than others. >> Look, it was the same type of shock wave that we felt basically when AWS at the last moment at one of their reinvents, oh, by the way, we're going to introduce outposts. And the analyst group is typically pre briefed about a week or two ahead under NDA and that was not part of it. And when they dropped, they just casually dropped that in the analyst session. It's like, you could have heard the sound of lots of analysts changing their diapers at that point. >> (laughs) I remember that. And a props to Andy Jassy who once, many times actually told us, never say never when it comes to AWS. So guys, I know we got to run. We got some hard stops. Maybe you could each give us your final thoughts, Doug start us off and then-- >> Sure. Well, we've got the Snowflake Summit coming up. I'll be looking for customers that are really doing data science, that are really employing Python through Snowflake, through Snowpark. And then a couple weeks later, we've got Databricks with their Data and AI Summit in San Francisco. I'll be looking for customers that are really doing considerable BI workloads. Last year I did a market overview of this analytical data platform space, 14 vendors, eight of them claim to support lakehouse, both sides of the camp, Databricks customer had 32, their top customer that they could site was unnamed. It had 32 concurrent users doing 15,000 queries per hour. That's good but it's not up to the most demanding BI SQL workloads. And they acknowledged that and said, they need to keep working that. Snowflake asked for their biggest data science customer, they cited Kabura, 400 terabytes, 8,500 users, 400,000 data engineering jobs per day. I took the data engineering job to be probably SQL centric, ETL style transformation work. So I want to see the real use of the Python, how much Snowpark has grown as a way to support data science. >> Great. Tony. >> Actually of all things. And certainly, I'll also be looking for similar things in what Doug is saying, but I think sort of like, kind of out of left field, I'm interested to see what MongoDB is going to start to say about operational analytics, 'cause I mean, they're into this conquer the world strategy. We can be all things to all people. Okay, if that's the case, what's going to be a case with basically, putting in some inline analytics, what are you going to be doing with your query engine? So that's actually kind of an interesting thing we're looking for next week. >> Great. Sanjeev. >> So I'll be at MongoDB world, Snowflake and Databricks and very interested in seeing, but since Tony brought up MongoDB, I see that even the databases are shifting tremendously. They are addressing both the hashtag use case online, transactional and analytical. I'm also seeing that these databases started in, let's say in case of MySQL HeatWave, as relational or in MongoDB as document, but now they've added graph, they've added time series, they've added geospatial and they just keep adding more and more data structures and really making these databases multifunctional. So very interesting. >> It gets back to our discussion of best of breed, versus all in one. And it's likely Mongo's path or part of their strategy of course, is through developers. They're very developer focused. So we'll be looking for that. And guys, I'll be there as well. I'm hoping that we maybe have some extra time on theCUBE, so please stop by and we can maybe chat a little bit. Guys as always, fantastic. Thank you so much, Doug, Tony, Sanjeev, and let's do this again. >> It's been a pleasure. >> All right and thank you for watching. This is Dave Vellante for theCUBE and the excellent analyst. We'll see you next time. (upbeat music)

>>Okay, welcome back everyone. To the cubes coverage of ADB has re-invent 2021. I'm John fare with Dave Nicholson. My cohost we're here exploring all the future innovations. We've got a great guest we'll lead negam who's the EVP executive vice president chief research innovation officer cap, Gemini engineering will lead. Thanks for coming on the cube. Thank you. So I love the title, chief research, innovation engineering officer. >>I didn't make it up. They did. >>You got to love the cloud evolution right now because just more and more infrastructure as codes happening. You got this whole data abstraction layer developing where people are starting to see. Okay. I can have horizontally scalable governed data in a data lake. That's smart, someone intelligent and use machine learning. It seems to be the big trend here from AWS. More serverless, more goodness. So engineering kind of on the front lines here kind of making it happen. >>Yeah. So, uh, the question that our clients are asking us is how do these data center technologies moving over into cars, planes, trains, construction, equipment, industrial, right? And you know, maybe two decades ago it was called IOT. Uh, but we're not talking about just sensors, vertical lift aircraft, uh, software-defined cars, um, manufacturing facilities as a whole, you know, how are these data center technologies going to impact these companies? And it's not a architectural shift for say the Evie, the electric vehicle, many OEM, it's a financial transformation, right? Because if they can make their vehicle containerized, uh, if they can monitor the cars, behaviors, they can offer new types of experiences for their clients. So the questions we were asking ourselves is how do you get the cloud into the car? >>Yeah. And software driving, all that. So you've got software defined everything. Now you've got data-driven pun intended with the cars cloud everywhere. How does that look? What are the concerns, obviously, latency moving data around. They got outposts. Am I moving the cloud to the edge? How are you guys thinking? How are customers thinking through the architectural, I guess foundational playbook? Is there one? Yeah. >>I, you know, coming into this, I did ask my, my son, the question is hardware or software more important. And then he, you know, he's not, and he said, you know, we're coding our way out of hardware. It was very interesting insight software rules. That that is for sure. But when we're talking about physical products and these talking about trillions of dollars of investments going into green energy, uh, into autonomous driving into green aviation. So we're not, it's not just the matter of verse here. We're dealing real physical products. I think though the point for us as engineers or as an engineering businesses, how do you co-design hardware and software together? What are the questions you to ask about that machine learning model being moved over from AWS? For example, into the car, is the Silicon going to be able to support the inferencing rates that are required right. In real time and whatnot. So some of the things like that, >>Well, that's been a, it's been an age old battle between the idea that, uh, the flour that's nurtured in a walled garden is always going to be more beautiful than the one that grows out in the meadow. In other words, announcement, uh, at, in Adam's keynote, talking about advances in AWS Silicon. So what's your view on how important that is? You just sort of alluded to it as being important, the co-development of hardware and software together. >>Yeah. We're seeing product makers again, think, you know, anybody from a life sciences company building a digital therapeutics product, maybe a blood glucose monitor or, um, an automotive or even an aerospace, uh, going direct to Silicon asking questions around the performance of the Silicon and designing their experience around that. Right. So, uh, if they need a low latency, low power efficiency, green networks, they're taking those questions in-house or asking those questions in house. So, you know, AWS having a, sort of a portfolio of custom or bespoke Silicon now as part of the architectural discussion. Right? And so I look around here, I see a lot of developers who are going to have to get a little bit more versed in some of these questions around, you know, should I use an arm based chip? You know, do I use this Silicon partner? You know, what happens when I move it into the vehicle? And then I have over the air updates, how do I protect that code in an enclave in the car just to continue to use the so there's are a lot of architectural questions that I don't think software engineers typically ask when they're just dealing in the cloud. Uh, although at the end of the day over time, a lot of these will be abstracted from the developer to some degree, you know, that is just the nature of the game. >>It reminds me of the operating system theory of system software meeting hardware. And because you have software developers just want to code now, you're saying, well, now I'm responsible hardware. Well, not if it's programmer, was there a hard top two it's over, these are big questions and important ones I think is we're in a major inflection point, but it comes back down to, you mentioned aerospace space is the same problem. You can't send that break, fix engineer in space. Right. You've got software now. So you've got trust that security supply chain who's right. And who's doing the hardware now you've got the software supply chain. So a lot of interesting kind of, yeah. >>So you, you, you know, you check them off, back in into it, the supply chain problems with Silicon, and there are now alternatives to try and get around the bottlenecks using high-performance computers versus hundreds of ECS and a vehicle allows you kind of get away from the supply chain shortage. Uh there's you know, folks moving from one architecture to another, to avoid kind of getting locked in and then of course creating your own Silicon, or at least having more ownership over the Silicon. I think suffer defined systems, uh, are the way to go regardless of the industry. Uh, so you're going to make some decisions on performance, characteristics of the hardware, but ultimately you want a software defined system, so you can update it regularly. >>I was talking with doc some of the top hair executives. I talked to, um, the marketplace guys here, Deepak, uh, over at the, here at Amazon and containers comes up. You start to see a trend in containers where you see certified containers because containers are everywhere. You can put malware and containers. So, you know, think about like just hacking software. It's a surface area now. So you bring the software security model in there. So to see this kind of like certified containers, I can imagine certified infrastructure now because I mean, what's a processor, it's just a hardened top to a PC. Now you've got the cloud. If I have hardware, how do I know it's workable? How do I trust it? You know, how could it not be hacked? I don't want my car to be hacked and driven off the road. >>So, so, um, when you're dealing with a payment system or you're dealing with tick-tock different than when you're dealing with a car with life consequences. So we are very active in the software defined transformation of automotive. And it's easy to say, I'm just going to load it up with all this data center technology, but there's safety criticality issues that you have to take into considerations, but containers are well suited for that. Just requires some thought. I mean, my excitement, enthusiasm about this product engineering is if you just take any of these products and, and apply them into a product engineering context, there's so much invention and creativity can happen. Uh, but on the safety side, we're working through security enclaves using containers and hardware based roots of trust. So there's ways around, you know, malware and bad actors at the edge. Um, >>What's your, what's your take on explainable AI? Why got you might as well ask because this comes up a lot, explainable AI is hot in college right now, AI, that can be explained. It's kind of got some policy, uh, to it. What's your thoughts on this AI trend? Cause obviously it's everywhere. Um, I mean, what is explainable AI? Is that even real or how do you explain AI? Is that democratized? >>Yeah. Computer vision is a great example. I think to bring it to life I'm all of the audience probably knows this, but you could, you know, you can tell your kid that this is a cat once and they'll know every single cat out there is a cat, but if you, you, you need a thousands of images, uh, for a computer vision model to learn that this is a cat. And even, you know, you can probably give it an example, um, out of say a remote region of the world and it going to get confused. So to me, explainability is about adding some sort of certainty to the decision-making process. Um, and when there's a, some confusion, be able to understand why that happened. I think in, in automotive or any, even in quality assurance, being able to know that this product was definitively defective or this pedestrian definitively did cross the crosswalk or not. You know, it's very important because it could, you know, there are, there are consequences. So just being able to understand why the algorithm or the model said what it said, why did it make that judgment is super important, super important. >>So I've got to ask you now that we're here, re-invent from your engineering perspectives, you look at the landscape of AWS, the announcements. What, what, how do you think about it to other engineers out there trying to, uh, grok all the technology who really want to put innovation in place, whether it's creating new markets, new categories or innovating their existing business, how do you grab the class out and make it work for you? I mean, from an engineering standpoint, how do you look at AWS and say, how do I make this work better for me? >>Uh, so I mean, over the years, I, um, I think it's true. AWS has started to really look like a utility, you know, the days where it was called utility as a service. And, um, you know, I, I, I did attend a workshop on, I think it was called LightSail or something like that, but they are simplifying the way that you can consume this infrastructure to a degree that is somewhat phenomenal. Uh, and they're building any, yeah, they continue to expand the ecosystem. Um, so I mean, for me, it's, it's a utility. Uh, it's it's, it's, it's, it's, it's consumable. Uh, if you got an idea pick and roll your own. >>Okay. So back back to the, uh, the concept of AI and explainability, uh, one of my cars won't allow me to unlock certain functions because of the way that I drive. No one needs to explain to me why, because I know what I'm doing wrong, but I'm still frustrated by it. So that that's sort of leads to kind of the larger philosophical question to you about what you're seeing, where are we in this kind of leapfrog, constant pace of the technology exists, but people aren't culturally ready to accept it because it feels like right now to me that there isn't anything we can't do with cloud technology from a technical perspective, it can all be done. Swami's keynote today, talking about integrating all sorts of sources of data and actually leveraging them in the cloud. Um, technically possible yet 85% of it spend is still on prem. So, so what's your thought there? What are the, what are the inhibitors, what are the real inhibitors from a technology perspective versus the cultural ones? Uh, setting aside my lack of, uh, adherence to, uh, to driving lawful >>I industry by industry. I think in, um, you know, if you're trying to do a diagnostic on an MRI in an automated way, and there's going to be false positives, false negatives, and yes, we know that yeah, we know that there's going to be a physician participating in the final judgment call. Um, I think just getting a really good comfort level on the trustworthiness of these decision points, um, is really important. And so I don't blame folks for being reticent about, you know, trusting or, or asking some questions about, does this really work and are these autonomous systems as they become more and more precise, are they doing the right thing? Uh, I think there's research that has to be done on agency. You know, am I in patrol? What happened? Did I lose control? I think there's questions around handoffs, you know, and participation in decision-making. So I think just overall, just the broad area of trust and, uh, the relationship between the participants, the humans and the machines still. I think there's some work to do, to be honest with you. I think there's some work to do maybe in a manufacturing facility where everything's automated, you know, maybe it's a solved problem, but in an open road, when the vehicles driving, you know, in the middle afternoon, you know, you probably should ask some more questions. >>Well, I want to ask you what we got a couple of minutes left, real time data near real time, real time, always a big, hot topic. Seeing one more databases out there in the keynote today from Swami real-time are we there yet? How are we dealing with real-time data, software consuming the data? It comes to cars and things that are moving real time versus near real time. It could be life or death. I mean, this is big time. Where are we? >>So, um, I was trying to conduct a web conference. I won't tell the vendor because it has nothing to do with the vendor. Um, and I couldn't get a connection. I couldn't get a connection at reinvent. I just couldn't get it. I'm sorry guys. I can't get it. So I, you know, so we talk about real time talking about real-time operating systems and real time data collection at the edge. Yeah. We're there, we can collect the data and we can deploy a model in, you know, in the aircraft on the train to do predictive analytics. If we got to stream that data back home to the cloud, you know, we better figure out how to make sure we have a reliable and stable connection. 5g is a, you know, is, is, will be deployed, right? And it has ultra low latency, uh, and can achieve those types of, uh, requirements. But, uh, you know, it has to be in the right setting, right? That's to be the right setting and a facility, uh, very well controlled where you understand the density of the cell sites, small cells sound cells, and you really can deploy a, uh, a mobile robot, uh, wirelessly. Yes know, we can do that, but you know, kind of in, in, in other scenarios, we have a lot of ask that question about >>With the connections and making that false, huh? Well, he, thanks for coming on. Great insight, great conversation. Very deep, awesome work. Thanks for coming on and sharing your insights from cap Gemini. We're here in the cube, the worldwide leader in tech coverage live on the floor here at re-invent I'm John fare with Dave Nicholson. We write back.

>>I'm john free with the Q we are here. It's exciting news around the next evolution switching, Sony jean Donny, co founder and chief business officer Pensando and David Hughes chief product and technology officer Aruba HP. Welcome back. We just heard from Antonio neary and john Chambers about the HPV Ruba partnership with Pensando and the new switching platform. Tell me more about the exciting news you're announcing? >>Yeah, I'm really excited today to be introducing the CX 10,000 distributed services switch. It's a brand new class of switch way bringing together the best of Aruba switching technology adding to R C X portfolio combining with Pence Sandoz technology that technology embedded in the platform. The problem we're solving is that in a traditional data center, all of those services like fire walling and low balancing provided by centralized appliances. And while that might be okay for north south traffic traffic that's going in and out of the data center. It's not scalable and it's not cost effective to apply to every service in every port to every flow traversing their data center As we all know with microservices more and more of the traffickers east west over 70% today and growing and so what we're doing here with the C X 10,000 is giving enterprises away to take the smart nick technology that's been proven out by hyper scholars and introduce it into their data centers in a very cost effective and easy to deploy way we're embedding that capability in the top of rack switch so that we can apply Fireable services, low balancing services to every port To every flow, delivering 100 times a scale in terms of a CLS 10 times of performance, in terms of encryption at a third of the cost of those traditional network architectures. So it's a super exciting time, >>love the speed, love the energy there. But I gotta ask what makes this a new category of switch. >>Well if you take a look at the journey we have been on as we have evolved our data centers and the applications have evolved for our customers. Uh and the world is now a bold new world of multi cloud. Uh the architecture is in the data center which are leaves spine architectures have become the new norm. Software defined, networking is pervasively deployed by our customers but as this journey began five or seven or even about 10 years ago uh and has culminated into a much more mature set of building blocks. We have taken the problem from one space of automating networks in the data center to then introducing lots and lots of expensive appliances to bring about security for example, or the state full services, whether it's load balancing or whether it's encryption and visibility and telemetry types of services. Now the customers had to try, you know, trombone all the traffic in and out of these appliances driving up the cost uh and the complexity and when time comes to troubleshoot these environments, it's extremely complex because you're trying to rationalize fabrics coming from one place appliances coming from four or five different vendors, maintaining all the software elements that need to be kept track off. Uh and as more and more customers want to aspire towards zero trust security model. Uh we need to start to embrace a lot of the principles that have been implemented by the hyper scholars and the cloud vendors, which is doing away with the appliances doing away with agent technology on servers, but instead to bring that technology for east west uh into play as well as to ensure that if there are bad actors that are landing inside of the data centers that they do not have the ability to, you know, create attack surfaces with complete lateral movement. Today, that is possible. Uh if you look at 70% of all the attacks that have been happening here in the past few years, it's as a result of having a attack surface which is pretty large in the data centers. And that gets further complicated when you move towards a multi cloud environment where the perimeter of the data center is now moving into the edge. Whether that edges, whether fleet resides for our customers or whether that edge happens to be a co location edge where you're building your own rampant off ramps. So I think the compelling event essentially is driven by the whole notion of distribution of services and having them available from a security and from a services point of view and these are state full services as close to the workload as you possibly can get them. >>So you guys really hit on some key points, their cloud, native microservices East west, north south, um no perimeter edge. These are topics that we would talk about kind of individually over the years, it's happening now all at the same time, this is causing a lot of complexities and then the security challenges you just laid out are everywhere. This brings up a big conversation around solving this. How does this new architecture, this solution solve the complexity and the security challenges in the data center. >>If you look at the use cases that our customers are talking about. The first, the initial use case really is to bring about security and state full security for east west traffic right into the fabric of their data centers. So having the ability to deliver that while eliminating the complex appliances only to do the job which they do very well, which is not South protection of services. Uh that also allows us the ability then to start to deliver visibility and telemetry at the same time that we're delivering state full security firewall and micro segmentation services because what I cannot see, I cannot secure. Uh so those two elements are initial use cases out of the box for our customers as we deliver this platform to them and then as more and more use cases that are becoming evident to us through customer interactions come into play. For example, the co location edge that I would like. David to walk you through a bit more in terms of how we help solve for that use case. >>So for the cooler use case, I think we're moving from a world where people talk about data centers to now talking about centers of data and those centers of data. Yes, they can be in a core private data center, they could be in the cloud but more and more they're going to be distributed around the edge in co location environments. And what we need to be able to do is extend those services that were provided in the data center to be provided in those Kahlo's at the edge And again we want to do that without having to deploy a whole rack of appliances that may be cost more than a computer itself and so with the CX- 10,000 we can have that as a top of rack switch for that polo. And from that switch deploy all of the encryption and firewall ng services that that polo requires. And what's important is that we're doing it with the same policy framework under the same management system across the whole enterprise in the data center as well as in these co location environments and out into the cloud. >>So you guys mentioned visibility and a quick follow up on this question because you mentioned visibility can't see it, you can't protect it. But also there's a lot of workloads that people are trying to automate. These are two factors. Can you guys just double down on that? I want to just get that out there because I think this becomes a big thing. >>I think policy having the ability to have an intent based policy that is a foundational technology building block that we are brought together is a very important element. And then when you map it back to tools that Aruba is extending support for including this platform, become very valuable. So David, why don't you walk us >>through? You know, I think one of the advantages that we bring is that this is an extension of the Aruba C X switching portfolio. So yeah, it's a cloud native microservices, very modern switch architecture and we have a comprehensive management platform, the Aruba fabric controller. And so what we are doing is making sure that everything fits together nicely, that we're delivering a complete solution to our customers. But one important thing to mention here is that we are thinking about how customers can do this step by step. So no, we're not requiring them to rebuild their entire data center, They can do this one rack at a time. We can work with their existing spine and deploy one leaf at a time in a very measured way. And so we think it's a great way for enterprises to be able to consume this modern distributed platform. >>That's a great segment. The next question. I mean I totally see this as you guys are talking about the cloud native trend, driving a cloud operational model to every edge. The data center is just another edge. It's a center of data. Love that. I love that line. So I have to kind of ask the operational side of the question, how would an enterprise customers manage all this take us through the nuts and bolts of deploying and managing of his gum? A customer >>That's a very good question. If you take a look at the customer's deployment models and let's let's take the example of they want to now bring in this technology and build a part or highly secure part with it for east west and to make sure that they're protecting 100% of that east west traffic. I think that leveraging all the building blocks that we have innovated between us and Aruba. We want to make sure that the ecosystem that the customer has built, they want whether they have built it with companies like Splunk and service now or Guardianco, they want integration points will be made available to them. If you take a look at, take a step back and say for these environments as you aspire to go toward zero trade security. The issues of inserting security appliances into network flows and having the ability to map it to the knowledge of applications and their dependencies for policy becomes an important function to tackle. So once you accept that, Okay, I have state full security functions built into this top of rack device available for my applications and all workloads, whether they're container workloads, bare metal workload, virtualized workloads uh and I have complete visibility into those workloads without compromising on connectivity and I can control through enforcement of policy where I need it because now security is part of the fabric, it's not a bolt on. Then comes the job of integration with an ecosystem. So whether you're looking at seem and sold companies where we are delivering in close collaboration with Splunk, A Pensando app for Splunk there's also going to be the availability of an elastic module, A plug in module. Uh then turn attention to what's more automation and devops and civil playbooks for the C X 10-K will be made available day one so that where you do not have the ability to deploy the A. F. C. You can use your existing answerable toolkit and they're making those playbooks available to our customers. Uh They want integration with application discovery mapping companies like Guardianco, allowing them to discover who's talking to whom and push and enforce that policy through the C X 10-K will allow for more automated deployments of those policies and finally, compliance integration with vendors like too thin for continuous security compliance monitoring becomes extremely important as the screen depicts a lot of lot of visualization capabilities with companies like Elk which are in beta today and answerable and Splunk and Elk will all be targeted at first customer shipment. So again, telemetry visibility with the integration of the ecosystem. Uh, it becomes a very powerful combination for the customers as they look to operationalize this for day to day three and they, you know, day one, day two, day three automation. >>That's awesome. David, I'd like to let you weigh in on this whole question of operations because you're hitting all the marks here that are relevant cloud, native microservices, apps, explosion and data volume and velocity, hyper scale operational cloud operations, performance, price point security all in this one solution. This is big. Um, it's not like you mentioned earlier, it's not a rip and replace but you can roll it out how how do you see a customer best operational izing this new, >>You know, I think the answer is a little bit different for each customer but you are very careful at the beginning, we introduced this. It's an evolution of switching. It's not a revolution where we have to replace everything and I think that's really exciting is that it builds on the foundational architecture of leaf and spine. And what we're able to do is let that customer introduced these new capabilities one leaf at a time. So maybe when they're upgrading from 10 gigs to 25 gigs, it's a great time for them to introduce this capability into their data center um and then depending on their application, you know, it may be, as Sony said that they've got one particular application, a crown jewel application and so they want to build out that in one rack and provide, you know, very, very robust East west as well as north south um security around that application, but there's so many different ways that customers can deploy this technology and what's really exciting is now is we're beginning to work with our customers, learning about these new use cases and then feeding that back into our roadmap and we all >>know, as you get down lower in the network layer, security is distributed architecture. So everything is paramount like security, super relevant, great conversation, I gotta ask what's next with this technology. Yeah, >>well, you know the teams, the two engineering teams are working together and this is step one on, on a really exciting new path, I don't know, Sony, what would you say? >>I think there's a lot more to come here. This is just a starting point. We have an incredibly strong partnership and go to market partnership here with Uber team with this platform. It is just the beginning uh and it will lead our customers onto the multi cloud journey. Uh and last but not least, I would like to say that you know, in closing uh that are seldom opportunities where you look at disrupting the way things are happening while fitting into customers existing models. So this is, as I said with everything being software defined, you will continue to see as delivering at great velocity more and more software defined services, whether it's encryption, Lord balancing and other state full services over time. Making this technology easier to deploy by fitting into the existing ecosystem and continuing to provide them with the 100 extra scale, 10 X. The performance as well as the ability to do it at a third of the same, you know, at the third of the cost of what they would need to if they had to build this uh today with disparate devices, >>exciting news in the industry. You guys are the pros you've seen all the waves of innovation over the years. I guess my final final question would be, how would you summarize this point in time right now? This is pretty um exciting all this is all happening At the same time, customers are having opportunity to innovate the pandemic has shown a lot of scale and and the need for stability and security. This is a special moment. How would you guys weigh in on that? >>Yeah, I think about it every decade, there's a change in how data centers a belt. And so this is the change that's happening this decade. Moving to a distributed services, switch. The other big mega trend that I see is this move, as I said from data centers to stand as a data and the opportunity for customers to use this technology as they move out to the edge. Have distributed compute and tell us, what do you think Sony? >>I think I couldn't agree more. I think there are so many various technology transitions occurring now. The cloud being the biggest one. Uh the explosion of data and uh, you know, the customers making decisions of having a distributed model And if indeed two thirds, if not 75% of all data will be processed at the edge over the next few years. This architecture is prime for the enterprise to go leverage their best practices of today while they can gradually move that architecture is for the future, which is a multi cloud future >>centers of data, large scale cloud operations automation. The speed of innovation has never seen this before. Uh It's exciting time. Sunny, thank you for coming on. And David, thanks for chatting about this exciting new announcement. Thank you very much. >>Thank you. Thank you. >>This is the power of and hp. Ruba and Pensando partnership. I'm john forward the cube. Thanks for watching. Mhm

(calm techno music) >> Hey, welcome to this Cube Conversation. I'm Lisa Martin. I've got two guests here with me. Please Welcome Philipp Niemietz, the intermediate head of the department for the Laboratory of Machine Tools and Production Engineering or WZL. Philipp, welcome to the program. >> Thank you. >> And we have Russ Caldwell here as well, senior product manager at Dell Technologies. Russ, great to see you. >> Thanks for the invite. >> Absolutely. We're going to be talking about how the enhanced video capabilities of Dell EMC's streaming data platform are enabling manufacturing, anomaly detection, and quality control through the use of sensors, cameras, and x-ray cameras. We're going to go ahead, Philipp, and start with you. We're abbreviating the lab as you guys do as WZL. Talk to us about the lab. What types of problems are you solving? >> Yeah, thank you. In the laboratory for machine tools, we are looking at actually all the other problems that arise in production engineering in general. So that's from the actual manufacturing of work pieces and that's getting used in aerospace or automotive industries, and really dig into the specifics of how those metal parts are manufactured, how they are formed, what are the mechanics of this. So this is a very traditional area where we are coming from. We're also looking at like how to manage all those production systems, how to come up with decision-making processes that's moving those engineering environments forward. But in our department, we recently get... 10 years ago... This Industry 4.0 scenario is getting more and more pushed into authentic research. So more and more data is gathered. We have to deal with a lot of data coming from various sources, and how to actually include this in the research, how to derive new findings from this, or even maybe, even physical equations from all the data that we are gathering around this manufacturing technologies. And this is something that we're, from the research perspective, looking at. >> And talk to me about when you were founded. You're based in Germany, but when was the lab founded? >> The lab was founded 100 years ago, about 100 years ago. It's like a very long history. It is the largest institute for production engineering in Germany, or maybe even in Europe. >> Got it. Okay. Well, 100 years. Amazing innovation that I'm sure the lab has seen. Russ, let's go over to you. Talk to us about the Dell EMC streaming data platform or SDP is what referred to it. >> Yeah. Thanks Lisa. So it's interesting that Philipp brings up Industry 4.0 because this is a prime area where the streaming data platform comes into play. Industry 4.0 for manufacturing really kind of encompasses a few things. It's real-time data analysis. It's automation, machine learning. SDP pulls all that together. So it's a software solution from Dell EMC. And one of the ways we make it all happen is we've unified this concept of time in data. Historical data and real-time data are typically analyzed very, very differently. And so we're trying to support Industry 4.0 manufacturing use cases. That's really important, right? Looking at historical data and real-time data, so you can learn from the past, work you've done on the factory floor, and apply that in real-time analytics. And the platform is used to ingest store and analyze data of this real-time and historical data. It leverages a high availability and dynamic scaling with Kubernetes. So that makes it possible to have lot different projects on the platform. And it really offers a lot of methods to automate this high speed and high precision activities that Philipp's talking about here. There's a lot of examples where it comes into play. It's really exciting to work with Philipp and the team there in Germany. But what's great about it is it's a general purpose platform that supports things like construction where they're doing drones with video ingestion, tracking resources on the ground, and things like that. Predictive maintenance and safety for amusement parks, and many other use cases. But with Industry 4.0 and manufacturing, RWTH and Philipp's team has really kind of pushed the boundaries of what's possible to automate and analyze data for the manufacturing process. >> What a great background. So we understand about the lab. We understand about Dell EMC SDP. Philipp, let's go back to you. How was the lab using this technology? >> Yeah, good question. Maybe, going a little bit back to the details of the use case that we are presenting. We started maybe five, six years ago where all this Industry 4.0 was put into research where you wanted to get more data out of the process now. So we started to apply a little census to the machine, starting with the more traditional ones, like energy consumption and some control information that we get from the machine tool itself. But the sensor system are quite like not that complex. And we could deal with the amount of data fairly easy now using just a USB sticks and some local devices, just a storage. But as it's getting more sophisticated, we're getting more sensor data. We're applying new sensor systems with the tool where the extra process is taking place, throughout the year, like delicious information is hidden. So we're getting really close to the process, applying video data, bigger data streams, more sensor data, and even like are not something like an IoT scenarios. We usually have some data points per second, but we're talking here about census that have like maybe a million data points a second now. So every high frequencies that we have to deal with, and of course, then we had to come up with some system that actually have to do this, help to deal with this data. And yeah, use the classic big data stack that we then set up for ourselves in our research facility to deal with this amount of streaming data to then apply historical analysis. Like Russ just talked about on this classic Hadoop data stack where we used Kafka and Storm for ingestion, and then for streaming processing, and Spark for this traditional historical analysis. And actually, this is exactly where the streaming data platform came into play because we had a meeting with one of the techy account at the university. And we were like talking about this. We were having a chat about this problem. And he's like, "Oh, we have something going on in America, in USA with this a streaming data platform. It was still under a code name or something." And then actually, Russ and I got into contact then talking about the streaming data platform, and how we could actually use it, and get getting part. We were taking part in the alpha program, really working with the system with the developers. And it was really an amazing experience. >> Were you having scale problems with the original kind of traditional big data platform that you talked about with Hadoop, Apache, Kafka, Spark? Was that scale issues, performance issues? Is that why you looked to Dell EMC? >> Yeah. There were several issues, like one is the scaling option now. And when we were not always using all of the sensors, we are just using some of the sensors. We're thinking about account process to different manufacturing technologies, different machines that we have in our laboratory so that we can quickly add sensors. They are shut down sensors. Do not have to take care about setting up new workers or stuff so that the work balance is handled. But that's not the only thing. We also had a lot of issues with administrating this Hadoop stacks. It's quite error prone if you do it yourself, like we are still in the university even though we are very big level laboratory. We still have limited resources. So we spend a lot of time dealing with the dev ops of the system. And actually, this is something where on the streaming data platform actually helped us to reduce the time that we invested into this administration processes. We were able to take more time into the analytics, which is actually what we are interested in. And specifically, the point that Russ talked about this unified concept of time, we now can just apply one and that type of analysis on historical and streaming data, and do not have to separate domains that we have to deal with. Now we dealt with Kafka, and Storm on one side, and Spark on the other side. And now, we can just put it into one model and actually reduce the time now to maintain and handle and implement the code. >> The time reduction is critical for the overall laboratory, the workforce productivity of the folks that are using it. Russ, let's go back to you. Tell us about, first of all, how long has the Dell EMC SDP been around? And what are some of the key features that WZL is leveraging that you're also seeing benefit other industries? >> So the product actually officially launched in early 2020. So in the first quarter of 2020. But what Philipp was just talking about, his organization was actually in the alpha and the beta programs earlier than that in 2019. And that's actually where we had a cross-section of very different kinds of companies in all sorts of industries all over the world; in Japan, and Germany, in the US. And that's where we started to see this pattern of commonality of challenges, and how we could solve those. So one of those things we mentioned that unified concept of time is really powerful because with one line of code, you can actually jump to any point on the timeline of your data, whether it's the real-time data coming off of the sensors right now or something minutes, hours, years ago. And so it's really, really powerful for the developers. But we saw the common challenges that Philipp was just talking about everywhere. So the SDP, one of the great things about it is it's a single piece of software that will install, manage, secure, upgrade, and be supported of all the components that you just heard Philipp talking about. So all the pieces for the ingestion, the storage and the analytics are all in there. And that makes it easier to focus on the problem there. There was other common challenges that our customers were seeing as well. Things like this concept of derived streams, so that you can actually bring in raw streams of data, leave it in its raw form because many times, regulatory reasons, audit reasons, you want to not touch that data. But you can create parallel streams of that data that are called derived streams that are versions that you've altered for some consumption or reporting purposes without affecting the others. And that's powerful when you have multiple teams analyzing different data. And then finally, the thing that Philipp mentioned we saw everywhere, which was a unified way to interact with sensors all the same way because there's sensors for IoT sensors, telemetry log files, video, X-ray, infrared, all sorts of things. But being able to simplify that so that the developers and the data scientists can really build models to solve a business problem was really where we started to focus on how we wanted to bring to market the value of SDP. >> So you launched this, right? And you said early 2020, right before the pandemic and all of the chaos that has- >> Don't recommend that by the way. Don't recommend launching into a pandemic. But yes. >> I'm sure that a lot of lessons learned from silver linings, I'm sure. >> That's right. >> But obviously, big challenges there. I'm curious thought if you thought. One of the things that we've learned from the pandemic is that for so many industries, the access to real-time data is no longer just a nice to have. It is a critical differentiator for those that needed to pivot multiple times to survive in the early days to thrive to continue pivoting. I'm curious, what other industries you saw Russ that came to you saying, "All right, guys. We've got challenges here. Help us figure this out."? Give me a snapshot of some of the other industries that were sort of leading Edge last year. >> Sure. There was some surprising ones. I've mentioned it a little bit, but it's interesting you give me a chance to talk about them. 'cause what was also shocking about this was not only that the same problems that I just mentioned happened in multiple industries. It was actually the prevalence of certain kinds of data. So for example, the construction example I gave you where a company was using drones to ingest streaming video as well as Telemetry of all the equipment on the ground. Drones are in all sorts of industries. So it turns out that's a pattern. But even a lower level than just drone data is actually video data or any kind of media data. And so Philipp talked about they're using that kind of data as well in manufacturing. We're seeing video data in every industry combined with other sensor data. And that's what's really surprised us in the beta program. So working with Philipp, we actually altered our roadmap after we launched to realize that we needed to escalate even more features about video analysis and actually be able to take the process even closer to the Edge where the data's being generated. So the other industries, including construction, logistics, medicine, network traffic, all sorts of data, that is a continuous unbounded stream of data falls into the category of being able to be analyzed, stored, playback like a DVR with SDP. >> Playback like a DVR. I like that. Philipp, back over to you. Talk to us about what's next. Obviously, a tremendous amount of innovation in the first 100 years of WZL. Talk to me about what some of the lab's plans are for the future from a streaming data perspective, got a great foundation infrastructure there with Dell EMC. What's next? >> Like we are working together with a large industry consortium, and then we get a lot of information. Not information, but they really want to see that all this big data stuff that's coming into Industry 4.0. And Russ already talked about it. And then, I'm pretty satisfied in having all the data and the data centers that they have, but they want to push it to the Edge. So all the analytics, it's getting more and more to the Edge because they see that the more data you gather, the more data has to be transferred via the network. So we have to come up with ways on, of course, deploy all the model on the Edge, maybe do some analytics on the Edge. I don't know, something like federated learning to see. Maybe you don't even need to transfer the data to the data center. You can start learning approaches on the Edge and combine them with different data sources that are actually sharing the data, which is the specific point in like corporations that want to corporate using the different data sources, but have some privacy issues. So this is something that we are looking into. And also, working like low-code or no-code environments, like different framework that we use here just in our laboratory, but this is also something that we see in the industry. And more and more people have to interact with the data management systems. So they have to somehow get a lower access point than just some pile from script that they need to write. Maybe, they just need drag and drop environment where they can modify some ingestion or some transformation to the data. So they're not always the people and all the data engineers or the computer science experts have to deal with those kind of stuff, and other people can do as well. So this is something that we are looking into this in the next future. But, yeah. But there are a lot of different things, and there's not enough time to talk about all of them. >> So it sounds like an idea to democratize that data to allow more data citizens to leverage that, analyze it and extract value from it because we all know data is oil, it's gold, but only if you can actually get those analysis quickly and make decisions that really affect and drive the business. Russ, last question for you. Talk to us about what you see next coming in the industry. Obviously, launching this technology at a very interesting time, a lot of things have changed in the last year. You've learned a lot. You said you modified the technology based on the WZL implementation. But what are some of the things that you see coming next? >> So it's really interesting 'cause my colleague at Dell constantly reminds me that people develop solutions with the technology they have at the time, right? It's a really obvious statement, but it's really powerful to realize what customers of ours have been doing so far. It's been based on batch tools and storage tools that were available at the time, but weren't necessarily the best match for the problem that we're trying to solve. And the world is moving completely to a real-time view of their data. If you can understand that answer sooner, there's higher value for higher revenue, lower costs, safety, all sorts of reasons, right? To do that, everyone's realizing you can't really count on... Like Philipp, he can't count on moving all the data somewhere else to make that decision, that latency; or sometimes, rules around controlling what data can go. Really, we'll keep it from that. So being able to move code closer to the data is where we see things are really happening. This is actually why the streaming data platform has really focused heavily on Edge implementations. We have SDP Core for the core data center. We also have SDP Edge that runs on single node in three node configurations for a headless environments for all sorts of use cases where you need to move the code and make the decisions right when the data is generated at the sensors. The other things we see happening in the industry that are really important is everything's moving to a fully software-defined solution. This idea of being able to have software-defined stream ingestion, analytics and storage. You can deploy the solution you want in the form factor that you have available at your location is important, right? And so, fully software-defined solutions is really going to be where things are at, and which gives you this kind of cloud-like experience, but you can deploy it anywhere at the Edge, Core or cloud, right? And that's really, really powerful. Philipp picked up on the one that we see a lot of this idea of low-code, no-code whether it's things like node red in the IoT world, where you're being able to stitch together a sequence of functions to answer questions in real time or other more sophisticated tools. That ability to, like you said, democratize what people can do with the data in real time is going to be extremely valuable as things move forward. And then the biggest thing we see that we're really focused on is we need to make it as easy as possible to ingest any kind of data. The more data types that you can bring in, the more problems you can solve. And so bringing on as many on-ramps and connectivity into other solutions is really, really important. And for all that, SDP's team is really focused on trying to prioritize the customers like Philipp's team in the RWTH WZL labs there. But finding those common patterns everywhere so that we can actually kind of make it the norm to be analyzing streaming data, not just historical batch data. >> Right. That's outstanding. As you said, the world is moving to real-time analytics. Real-time data ingestion is absolutely critical on there. Just think of the problems that we don't even know about that we could solve. Guys, thank you for joining me today, talking about what WZL is doing with the Dell EMC streaming data platform, and all the innovations you've done so far, and what's coming in the future. We'll have to catch up in the next six months or so, and see what great progress you've made. Thank you for your time. >> Thanks, Lisa. >> Thank you. >> For my guests, I'm Lisa Martin. You're watching a Cube Conversation. (calm techno music)

(upbeat music) >> Welcome to theCUBE's coverage of HPE Discover 2021. I'm your host, Lisa Martin. I've got a couple of guests with me here from Infosys. Alumni Uma Lakshmipathy is back, Senior Vice President and Regional Head of EMEA at Infosys. Uma, it's great to see you welcome back to the program. >> Yeah. Hi Lisa. It's great to be back for Discover 2021. It's been a great opportunity to meet with a lot of stakeholders in HPE. >> Excellent. We're going to dig into that. And Saju Sankarankutty is here as well. The CTO, Cloud Advisory, VP-Hybrid Cloud Engineering, Platforms and Automation at Infosys. Saju, welcome to the program. >> Thank you, Lisa. It's a pleasure to be in the program. It is my first time, but I really enjoyed it as well. >> Well, welcome, welcome. So the next 15 minutes or so, we're going to unpack a survey that was just done. As we know, cloud has catalyzed a lot in the last year. One of those being cloud adoption. Talk to us about some of the things that you've seen as more and more enterprises are moving workloads to cloud. How is the hybrid cloud enabling businesses to grow, enabling them to actually have a competitive edge? >> Lisa, if you look at the pre-COVID scenario, there are many, many clients which actually made a significant move into cloud, but there were many few of the companies who didn't really take a mature cloud adoption. But those companies which actually did the adoption, we see that have taken a big step with the help of the, when the COVID hit them because they were able to be very resilient, but at the same time, they were able to, the cloud adoption really helped them to improve their business profits. When we did this cloud radar survey across all the geographies, we did it across the US, the Latin, the Asia Pacific, the EMEA markets, and when we looked at what our clients and enterprises were able to recover and get all of this whole cloud adoption, we got a number of 414 billions of profits that the enterprises can make by using this cloud adoption. And that's what we saw in this survey that we did with our clients. >> Yeah, that's huge enterprises. The survey found can add up to, you said 414 billion and net new profits annually through effective cloud adoption. Uma, sticking with you for a second, what does Infosys describe as effective cloud adoption? >> When we look at cloud adoption, we have enterprises who started shifting workloads, which are very comfortable for them. And then they started to take the more mature understanding of moving workloads, which are very critical to the business. So when we look at effective, it is a combination of both. The ones that were very easy to go to the cloud. The ones that made businesses able to bring in new applications, the new go-to markets to their segments, to their clients. But then, it is also about taking some of those legacy workloads and making a choice, the right choice to take it by transforming those applications and environments into the cloud adoption. And that's what we call as effective. It's just not the easy ones, but also those are complex and legacy riddled ones that effectively goes on to transform itself into a new way for their clients and for the experience of the users. >> So big changes coming big opportunities. Saju, we see we've talked about this for many times, more and more companies moving to multicloud arrangements for a variety of reasons. What have been some of the things that Infosys has experienced and what are some of your viewpoints on a multicloud? >> Thank you, Lisa. So if you look around, hybrid cloud has been the new normal and if you look at it, private cloud is becoming an essential component for hosting applications. When you look at it, it's more about applications which have low latency requirements, it has regulatory requirements, or it has a static demand of infrastructure. Now, what Infosys has done in this spaces is that we have developed a framework which we call it as a right cloud solution framework. And this is focused on implementing a hybrid multicloud leveraging and in-house developed tools and frameworks as well as platforms along with those strategic partner ecosystem. That is our biggest contribution onto the hybrid multicloud world. Now, the foundation of our framework is Infosys polycloud platform. It's a unified multicloud management platform. It can provision, it can orchestrate, it can also manage the cloud deployment across multiple of the environments. It can be a private, it can be a public, or it can be on the edge. Now, apart from all of these things, it also offers features and functionalities very similar to the hyperscalers. And either it can be in terms of the user experience or it can be in a commercial model or a technology stack or it can be reports or it can be persona based user experience and integration with multiple systems, it brings all of these functionalities seamlessly across the multiple hybrid ecosystem. That's the biggest contribution from Infosys in this space. >> Got it. Okay. Uma, as we see the, just clear growth of multicloud in every industry, talk to us about what the cloud radar survey uncovered with prospective? You've mentioned that big number, the correlation between cloud transformation and profitable growth for enterprises across any industry. >> So I did mention about that Lisa in the previous question as well. When we look at enterprises trying to take the cloud adoption, the big benefits for the enterprises do happen when they cross that layer of moving a significant part of their existing legacy in a very transformed new world. And that brings in the new way of working for the customers, for their end users and internally as well for the various stakeholders. And that I think is creating a cost structure for them, which is very, very optimal from where they were. But at the same time, it is enabling their ecosystem of users and customers to come and operate in a very seamless fashion. And that is the biggest advantage of boosting profits for them at the same time cutting costs within the internal stakeholders. So at one stage, you're optimizing your cost. At the other stage, you're bringing in an easiness for your clients to operate on, which is actually creating that enlarged profit boost. >> Uma, sticking with you for a second. If we unpack that growth, that business profit growth opportunity that the survey uncovered, are we talking about things like faster time to market, increasing scale? What are some of the things underneath that hood? >> So if you look at traditionally, cloud was considered the enabler for quick faster time to market, but now a cloud has become the central theme for resilience. If you look at the COVID pandemic, those enterprises which were already cloud enabled were able to resiliently and sustain their business and grow their businesses. So as the economy started opening up, if I can talk about an automotive client who is today enriching businesses out of China because they have the first economy that has opened up after the pandemic. So you see a lot of enablement for those enterprises which have already taken the cloud journey. And if you look at today, enterprises are in somewhere around 17 to 18% of cloud adoption. And if they can take that to the 40%, that's when they will see that kind of boosted profits and we can clearly see about 400 plus billion dollars of profits that enterprises can make. >> All right. Saju, let's talk to you for a second. If we look at some of the survey results, the acceleration that is expected to be seen by in the next year of enterprises moving so many more workloads to cloud. You talked about hybrid cloud, talk to me about how the experience of working with HPE and creating joint solution suites is going to help the customers facilitate and drive that transformation. >> Thank you, Lisa. So if you look at HPE, HPE comes with a fine set of technology and commercial constructs that complements our right cloud framework and they offer the solution, the whole sort of lot of solutions offer private cloud as a service, which is a major component of our right cloud framework. Either it is a container as a service with HPE's ezmeral data platform on HP hardware or VDA as a service based on a composable and conversed infrastructure or HPC cloud build on great systems and all of them commercially supported with an HPE GreenLake offering makes it very attractive for our customers. Now, these integrations have helped us in providing a very seamless metering and billing along with the chargeback solutions very much in line with what is being provided by hyperscalers. Apart from this, we also work very closely with HPE to create a very compelling sourcing strategy for driving hybrid cloud driven digital transformation while taking costs out and protecting the existing investments through various financial models for our customers helping them in terms of transforming their digital estate in the new cloud world. >> And Uma, I want to get your perspective as well, the HPE Infosys partnership. Talk to me about that being a win-win for your clients in every industry. >> So actually Lisa, it's a great question. And this probably is my third CUBE interview. And I've told this previously as well in my previous interviews as well. The relationship between Infosys and HPE is very, very strategic And it's very, very top down driven. And today, we've seen very high transformative opportunities that two organizations have come together and we won't call it win-win, but we call it win-win-win, which is essentially a win for HPE, win for Infosys, but even for the clients as well. So if you look at some of the engagements that we've jointly done, everything has been transformative. I can talk about energy client where we've done a huge virtual VDI engagement with them where we have been able to dig them very seamlessly when the COVID pandemic hit them. So then they're a significant part of their IT users, but being able to operate from their residences. I can talk about a great story about how we had enabled GreenLake for a wind energy company and how that GreenLake capability helped the customer to migrate the application seamlessly to a hybrid cloud. And there are so many examples of similar scale and size when we look at clients in the manufacturing space and the automobile sector where we really done a work very closely with HPE across all regions and all geographies to make this what I would call a win-win-win partnership. >> I like that, win-win-win. Who wouldn't want that? One more question, Uma for you. Talk to me about the next, as we talked about some of those survey results and I think folks can find that survey, the cloud radar survey on the infosys.com website. I found it on the homepage there. But looking at how much transformation is expected in the next 12 months or so, what are some of the things that we can expect from Infosys and HPE to help drive and catalyze that growth that you expect to see in the next 12 months? >> Yeah. And I was talking to you before this interview and you said that yes, we are to look at this. And I was feeling very happy that you had the opportunity to look at the site. And you said that, look, there's an opportunity to also make, to continuously provide feedback and we're very happy for clients to come in and look at it and do provide us the feedback. This is a constant learning for us. We are a big learning company. And when it comes to the next 12 months of agenda, I think the pipeline is very robust for both us and the HPE in terms of the way we want to take proactive transformational opportunities to our clients. Create a value differentiation on the hybrid cloud for them and clearly, this survey clearly came back to reflect back to us that our strategy that we've done together as partners is the right strategy because there is a significant headroom for growth in the cloud space for both Infosys and HPE. >> Excellent. Well, gentlemen, thank you for joining me today talking to me about what Infosys and HPE are doing together, unpacking some of the significant insights that the cloud radar survey has uncovered. We appreciate your time. >> Thank you, Lisa. Thank you. Thank you for giving us this opportunity. >> Absolutely. For Uma and Saju >> Thank you, Lisa. I'm Lisa Martin, you're watching theCUBEs coverage of HPE Discover 2021. (bright music)

>> Announcer: From around the Globe, it's theCUBE with coverage of Kube Con and Cloud Native Con Europe 2021 virtual brought to you by Red Hat, the cloud native computing foundation and ecosystem partners. >> Hello, and welcome back to theCUBE's coverage of Kube Con and Cloud Native Con 2021 virtual. I'm John Furrier, host of theCUBE, here with a great guest, I'm excited to talk to. His company, that he was part of founding CTO, was bought by Red Hat. Ali Golshan, Senior Director of Global Software Engineer at Red Hat, formerly CTO of StackRox. Ali thanks for coming on, I appreciate it. Thanks for joining us. >> Thanks for having me excited to be here. >> So big acquisition in January, where we covered it on SiliconANGLE, You guys, security company, venture backed amplify Sequoya and on and on. Big part of Red Hat story in their security as developers want to shift left as they say and as more and more modern applications are being developed. So congratulations. So real quick, just quick highlight of what you guys do as a company and inside Red Hat. >> Sure, so the company's premise was built around how do you bring security the entire application life cycle. So StackRox focuses on sort of three big areas that we talk about. One is, how do you secure the supply chain? The second part of it is, how do you secure infrastructure and foster management and then the third part is now, how do you protect the workload that run on top of that infrastructure. So this is the part that aligned really well with Red Hat which is, Red Hat had wanted to take a lot of what we do around infrastructure, foster management configuration management and developer tools integrated into a lot of the things they do and obviously the workload protection part was a very seamless part of integrating us into the OpenShift part because we were built around cloud native constructs and obviously Red Hat having some of the foremost experts around cloud native sort of created a really great asset. >> Yeah, you guys got a great story. Obviously cloud native applications are rocking and rolling. You guys were in early serverless emerges, Kubernetes and then security in what I call the real time developer workflow. Ones that are building really fast, pushing code. Now it's called day two operations. So cloud native did two operations kind of encapsulates this new environment. You guys were right in the sweet spot of that. So this became quite the big deal, Red Hat saw an opportunity to bring you in. What was the motivation when you guys did the deal Was it like, "wow" this is a good fit. How did you react? What was the vibe at the StackRox when this was all going down? >> Yeah, so I think there's really three areas you look for, anytime a company comes up and sort of starts knocking on your door. One is really, is the team going to be the right fit? Is the culture going to be the right environment for the people? For us, that was a big part of what we were taking into consideration. We found Red Hat's general culture, how they approach people and sort of the overall approach the community was very much aligned with what we were trying to do. The second part of it was really the product fit. So we had from very early on started to focus purely on the Kubernetes components and doing everything we could, we call it sort of our product approach built in versus bolted on and this is sort of a philosophy that Red Hat had adopted for a long time and it's a part of a lot of their developer tools, part of their shift left story as well as part of OpenShift. And then the third part of it was really the larger strategy of how do you go to market. So we were hitting that point where we were in triple digit customers and we were thinking about scalability and how to scale the company. And that was the part that also fit really well which was obviously, RedHat more and more hearing from their customers about the importance and the criticality of security. So that last part happened to be one part. We ended up spending a lot of time on it, ended up being sort of three out of three matches that made this acquisition happen. >> Well congratulations, always great to see startups in the right position. Good hustle, great product, great market. You guys did a great job, congratulations. >> Thank you. >> Now, the big news here at KubeCon as Linux foundation open-source, you guys are announcing that you're open-sourcing at StackRox, this is huge news, obviously, you now work for an open-source company and so that was probably a part of it. Take us through the news, this is the top story here for this segment tickets through open-source. Take us through the news. >> Yeah, so traditionally StackRox was a proprietary tool. We do have open-source tooling but the entire platform in itself was a proprietary tool. This has been a number of discussions that we've had with the Red Hat team from the very beginning. And it sort of aligns around a couple of core philosophies. One is obviously Red Hat at its core being an open-source company and being very much plugged into the community and working with users and developers and engineers to be able to sort of get feedback and build better products. But I think the other part of it is that, I think a lot of us from a historic standpoint have viewed security to be a proprietary thing as we've always viewed the sort of magic algorithms or black boxes or some magic under the hood that really moved the needle. And that happens not to be the case anymore also because StackRox's philosophy was really built around Kubernetes and Built-in, we feel like one of the really great messages around wide open-source of security product is to build that trust with the community being able to expose, here's how the product works, here's how it integrates here are the actions it takes here's the ramifications or repercussions of some of the decisions you may make in the product. Those all I feel make for very good stories of how you build connection, trust and communication with the community and actually get feedback on it. And obviously at its core, the company being very much focused on Kubernetes developer tools, service manage, these are all open-source toolings obviously. So, for us it was very important to sort of talk the talk and walk the walk and this is sort of an easy decision at the end of the day for us to take the platform open-source. And we're excited about it because I think most still want a productized supported commercial product. So while it's great to have some of the tip of the spear customers look at it and adopt the open-source and be able to drive it themselves. We're still hearing from a lot of the customers that what they do want is really that support and that continuous management, maintenance and improvement around the product. So we're actually pretty excited. We think it's only going to increase our velocity and momentum into the community. >> Well, I got some questions on how it's going to work but I do want to get your comment because I think this is a pretty big deal. I had a conversation about 10 years ago with Doug Cutting, who was the founder of Hadoop, And he was telling me a story about a company he worked for, you know all this coding, they went under and the IP was gone, the software was gone and it was a story to highlight that proprietary software sometimes can never see the light of day and it doesn't continue. Here, you guys are going to continue the story, continue the code. How does that feel? What's your expectations? How's that going to work? I'm assuming that's what you're going to open it up which means that anyone can download the code. Is that right? Take us through how to first of all, do you agree with that this is going to stay alive and how's it going to work? >> Yeah, I mean, I think as a founder one of the most fulfilling things to have is something you build that becomes sustainable and stands the test of time. And I think, especially in today's world open-source is a tool that is in demand and only in a market that's growing is really a great way to do that. Especially if you have a sort of an established user base and the customer base. And then to sort of back that on top of thousands of customers and users that come with Red Hat in itself, gives us a lot of confidence that that's going to continue and only grow further. So the decision wasn't a difficult one, although transparently, I feel like even if we had pushed back I think Red Hat was pretty determined about open-source and we get anyway, but it's to say that we actually were in agreement to be able to go down that path. I do think that there's a lot of details to be worked out because obviously there's sort of a lot of the nuances in how you build product and manage it and maintain it and then, how do you introduce community feedback and community collaboration as part of open-source projects is another big part of it. I think the part we're really excited about is, is that it's very important to have really good community engagement, maintenance and response. And for us, even though we actually discussed this particular strategy during StackRox, one of the hindering aspects of that was really the resources required to be able to manage and maintain such a massive open-source project. So having Red Hat behind us and having a lot of this experience was very relevant. I think, as a, as a startup to start proprietary and suddenly open it and try to change your entire business model or go to market strategy commercialization, changed the entire culture of the company can sometimes create a lot of headwind. And as a startup, like sort of I feel like every year just trying not to die until you create that escape velocity. So those were I think some of the risk items that Red Hat was able to remove for us and as a result made the decision that much easier. >> Yeah, and you got the mothership with Red Hat they've done it before, they've been doing it for generations. You guys, you're in the startup, things are going crazy. It's like whitewater rafting, it's like everything's happening so fast. And now you got the community behind you cause you're going to have the CNC if you get Kubecon. I mean, it's a pretty great community, the support is amazing. I think the only thing the engineers might want to worry about is go back into the code base and clean things up a bit, as you start to see the code I'm like, wait a minute, their names are on it. So, it's always always a fun time and all serious now this is a big story on the DevSecOps. And I want to get your thoughts on this because kubernetes is still emerging, and DevOps is awesome, we've been covering that in for all of the life of theCUBE for the 11 years now and the greatness of DevOps but now DevSecOps is critical and Kubernetes native security is what people are looking at. When you look at that trend only continuing, what's your focus? What do you see? Now that you're in Red Hat as the CTO, former CTO of StackRox and now part of the Red Hat it's going to get bigger and stronger Kubernetes native and shifting left-hand or DevSecOps. What's your focus? >> Yeah, so I would say our focus is really around two big buckets. One is, Kubernetes native, sort of a different way to think about it as we think about our roadmap planning and go-to-market strategy is it's mutually exclusive with being in infrastructure native, that's how we think about it and as a startup we really have to focus on an area and Kubernetes was a great place for us to focus on because it was becoming the dominant orchestration engine. Now that we have the resources and the power of Red Hat behind us, the way we're thinking about this is infrastructure native. So, thinking about cloud native infrastructure where you're using composable, reusable, constructs and objects, how do you build potential offerings or features or security components that don't rely on third party tools or components anymore? How do you leverage the existing infrastructure itself to be able to conduct some of these traditional use cases? And one example we use for this particular scenario is networking. Networking, the way firewalling in segmentation was typically done was, people would tweak IP tables or they would install, for example, a proxy or a container that would terminate MTLS or become inline and it would create all sorts of sort of operational and risk overhead for users and for customers. And one of the things we're really proud of as sort of the company that pioneered this notion of cloud native security is if you just leverage network policies in Kubernetes, you don't have to be inline you don't have to have additional privileges, you don't have to create additional risks or operational overhead for users. So we're taking those sort of core philosophies and extending them. The same way we did to Kubernetes all the way through service manager, we're doing the same sorts of things Istio being able to do a lot of the things people are traditionally doing through for example, proxies through layer six and seven, we want to do through Istio. And then the same way for example, we introduced a product called GoDBledger which was an open-source tool, which would basically look at a yaml on helm charts and give you best practices responses. And it's something you we want for example to your get repositories. We want to take those sort of principles, enabling developers, giving them feedback, allowing them not to break their existing workflows and leveraging components in existing infrastructure to be able to sort of push security into cloud native. And really the two pillars we look at are ensuring we can get users and customers up and running as quickly as possible and reduce as much as possible operational overhead for them over time. So we feel these two are really at the core of open-sourcing in building into the infrastructure, which has sort of given us momentum over the last six years and we feel pretty confident with Red Hat's help we can even expand that further. >> Yeah, I mean, you bring up a good point and it's certainly as you get more scale with Red Hat and then the customer base, not only in dealing with the threat detection around containers and cloud native applications, you got to kind of build into the life cycle and you've got to figure out, okay, it's not just Kubernetes anymore, it's something else. And you've got advanced cluster security with Red Hat they got OpenShift cloud platform, you're going to have managed services so this means you're going to have scale, right? So, how do you view that? Because now you're going to have, you guys at the center of the advanced cluster security paradigm for Red Hat. That's a big deal for them and they've got a lot of R and D and a lot of, I wouldn't say R and D, but they got emerging technologies developing around that. We covered that in depth. So when you start to get into advanced cluster, it's compliance too, it's not just threat detection. You got insights telemetry, data acquisition, so you have to kind of be part of that now. How do you guys feel about that? Are you up for the task? >> Yeah, I hope so it's early days but we feel pretty confident about it, we have a very good team. So as part of the advanced cluster security we work also very closely with the advanced cluster management team in Red Hat because it's not just about security, it's about, how do you operationalize it, how do you manage it and maintain it and to your point sort of run it longterm at scale. The compliance part of it is a very important part. I still feel like that's in its infancy and these are a lot of conversations we're having internally at Red Hat, which is, we all feel that compliance is going to sort of more from the standard benchmarks you have from CIS or particular compliance requirements like the power, of PCI or Nest into how do you create more flexible and composable policies through a unified language that allows you to be able to create more custom or more useful things specific to your business? So this is actually, an area we're doing a lot of collaboration with the advanced cluster management team which is in that, how do you sort of bring to light a really easy way for customers to be able to describe and sort of abstract policies and then at the same time be able to actually and enforce them. So we think that's really the next key point of what we have to accomplish to be able to sort of not only gain scale, but to be able to take this notion of, not only detection in response but be able to actually build in what we call declarative security into your infrastructure. And what that means is, is to be able to really dictate how you want your applications, your services, your infrastructure to be configured and run and then anything that is sort of conflicting with that is auto responded to and I think that's really the larger vision that with Red Hat, we're trying to accomplish. >> And that's a nice posture to have you build it in, get it built in, you have the declarative models then you kind of go from there and then let the automation kick in. You got insights coming in from Red Hat. So all these things are kind of evolving. It's still early days and I think it was a nice move by Red Hat, so congratulations. Final question for you is, as you prepare to go to the next generation KubeCon is also seeing a lot more end user participation, people, you know, cloud native is going mainstream, when I say mainstream, seeing beyond the hyperscalers in the early adopters, Kubernetes and other infrastructure control planes are coming in you start to see the platforms emerge. Nobody wants another security tool, they want platforms that enable applications handle tools. As it gets more complicated, what's going to be the easy button in security cloud native? What's the approach? What's your vision on what's next? >> Yeah so, I don't know if there is an easy button in security and I think part of it is that there's just such a fragmentation and use cases and sort of designs and infrastructure that doesn't exist, especially if you're dealing with such a complex stack. And not only just a complex stack but a potentially use cases that not only span runtime but they deal with you deployment annual development life cycle. So the way we think about it is more sort of this notion that has been around for a long time which is the shared responsibility model. Security is not security's job anymore. Especially, because security teams probably cannot really keep up with the learning curve. Like they have to understand containers then they have to understand Kubernetes and Istio and Envoy and cloud platforms and APIs. and there's just too much happening. So the way we think about it is if you deal with security a in a declarative version and if you can state things in a way where how infrastructure is ran is properly configured. So it's more about safety than security. Then what you can do is push a lot of these best practices back as part of your gift process. Involve developers, engineers, the right product security team that are responsible for day-to-day managing and maintaining this. And the example we think about is, is like CVEs. There are plenty of, for example, vulnerability tools but the CVEs are still an unsolved problem because, where are they, what is the impact? Are they actually running? Are they being exploited in the wild? And all these things have different ramifications as you span it across the life cycle. So for us, it's understanding context, understanding assets ensuring how the infrastructure has to handle that asset and then ensuring that the route for that response is sent to the right team, so they can address it properly. And I think that's really our larger vision is how can you automate this entire life cycle? So, the information is routed to the right teams, the right teams are appending it to the application and in the future, our goal is not to just pardon the workload or the compute environment, but use this information to action pardon application themselves and that creates that additional agility and scalability. >> Yeah it's in the lifecycle of that built in right from the beginning, more productivity, more security and then, letting everything take over on the automation side. Ali congratulations on the acquisition deal with Red Hat, buyout that was great for them and for you guys. Take a minute to just quickly answer final final question for the folks watching here. The big news is you're open-sourcing StackRox, so that's a big news here at KubeCon. What can people do to get involved? Well, just share a quick quick commercial for what people can do to get involved? What are you guys looking for? Take a pledge to the community? >> Yeah, I mean, what we're looking for is more involvement in direct feedback from our community, from our users, from our customers. So there's a number, obviously the StackRox platform itself being open-source, we have other open-source tools like the KubeLinter. What we're looking for is feedback from users as to what are the pain points that they're trying to solve for. And then give us feedback as to how we're not addressing those or how can we better design our systems? I mean, this is the sort of feedback we're looking for and naturally with more resources, we can be a lot faster in response. So send us feedback good or bad. We would love to hear it from our users and our customers and get a better sense of what they're looking for. >> Innovation out in the open love it, got to love open-source going next gen, Ali Golshan Senior Director of Global Software Engineering the new title at Red Hat former CTO and founder of StackRox which spread had acquired in January, 2021. Ali thanks for coming on congratulations. >> Thanks for having, >> Okay, so keeps coverage of Kube Con cloud native Con 2021. I'm John Furrie, your host. Thanks for watching. (soft music)