(upbeat music) >> Hey everyone. Welcome to theCUBE's coverage of the International Women's Showcase for 2022. I'm your host, Lisa Martin. I'm really excited because for the first time in my CUBE career of six years, I have a father daughter duo maybe the first time in CUBE's history. Frank and Emma Arrigo from AWS join me guys it's great to have you on the program. >> Great to be here thank you. >> So, Emma, let's go ahead and start with you. Talk to us about how you got to AWS and a little bit about your background. >> Yes, thanks Lisa. So I've joined AWS as a recent graduate from university. So I did my masters of data science and I was going through the grad, the grad job hunt applying for all these different places. And AWS appeared on my radar for an intern program. And Frank was there at the time and so I was like, "Should I do it?" But I still applied cause it was a great program. And so I went did that internship for three months over the summer of 2019-2020, and then I went back and finished my degree. And another grad role came up for in AWS in Tech U to be an associate solutions architect. And so I was approached to apply for that. And I got through to that program and joined the team almost a year ago in March, 2021 through Tech U and yeah, that's how I ended up at AWS. >> Excellent and so Frank, this is a pretty unique situation, father daughter duo at AWS let alone Amazon, let alone probably a lot of companies. Talk to me about it the parental lens. >> Yeah, look it is unique, there's a few family connections within AWS, but you know, definitely here in Australia, it's really rare, but I think the family connection is, you know Emma and we've got four kids, I've got four kids in total. So Emma has three brothers, you know, I've lived in tech, my entire career and so they've been part of it. You know, we've lived in the States, lived in Seattle for a couple of years. And so they'd come to the office and see what dad did. And so it wasn't a big surprise for them to understand what the role was and what we did, so, you know, they kind of grew up with it. And you know, when the opportunity came up for Emma, did the internship, I was excited for it because it was in a different area. It was working in a startup team doing some interesting work that really lined up with some of the interest Emma had. And so she kind of learned what it was like to be Amazonian through that internship and that was... I call that a long audition for a job. And she was then able to join Tech U program, which is a early career bootcamp, I like to think of it, which is the six month program to help our grads learn some of the fundamental skills because the value of a solutions architect or some of these other tech roles is you need experience. You need to have been in the game a while to be a trusted advisor to a customer. And it's hard to do that when you're a grad. So the bootcamp gives them the practical experience and then they get another six months on the job experience where they develop those skills and hone it and get ready to, you know, be a trusted advisor to the customers. >> Right, and that's such a great... I'm sure that's a tremendous opportunity to learn how to become that trusted advisor, especially from peers, such as yourself and I want to go back to you. Talk to me about your interest in IT, in data science. Was this something that you were always interested in primary school or in high school? Or was this something that kind of came on later on? >> Yeah, so my interest in tech kind of emerged as I went along in my education. So when I was younger, I really wanted to be an orthodontist for some reason. I don't know why. And then you just sort of in year eight and like early school sort of didn't really know what I wanted to do. Just sort of going through just trying to survive as a teenage girl at high school at an all girls school, didn't really have many, didn't really have career aspirations, I guess, and then one year I attended a information day at a university about engineering and that just really sparked my interest, I don't know why, but I was like, I've always been obsessed with like factories and those types of things and how things are made. And so that really just sparked my interest and I never really thought of it before. And so then that put STEM engineering on my radar and then I guess spoke with it about with the parents. And then they mentioned that tech would be a like IT, Information Technology would be really useful. And so then we approached the school to ask if I could do IT in year 11. So that's sort of our second last year of high school. And they said, "No, we couldn't do IT." I couldn't go to the boys' school to do IT. That girls don't do it or that not good at it. And I wasn't allowed, and they wouldn't let me do physics either. So I moved school in for the final two of high school to be able to do IT and physics to help, you know, get to the course I wanted to do. And so that was my journey into STEM. So it wasn't really on my radar, but then events like this and at university isn't it? Organizations sparked my interest. And then still when I entered university, I didn't know exactly what I wanted to major in nor where I wanted to work would never have thought it would be where, with my father, like I was aware of the world of IT and everything, but I wouldn't, if you'd asked me in first year, it wouldn't have been that I would probably, we would've said, I don't know an academic or something. I don't know. And then, but again, as the university went on and you attend networking events or club things, you sort of learn a bit more about the ecosystem. And then that's where yeah. Tech company sort of became where I was looking for jobs and roles for when I finished up. So that was kind of my journey to... >> So what I love though, that you and Frank, this is going to be a question for you, how Emma was told. "No, you can't study IT. No, you can't study physics. You can't go to the boys school and do that either." Talk to me about that, Frank, from your perspective as a parent of a daughter, and you said, I think she's got three brothers lucky, Emma, but talk to me about that from your perspective, in terms of going, my daughter has really has an in a strong interest in this and they're telling her no we're going to pivot and actually change schools to be able give her the opportunities that she wants to pursue. >> Yeah. Look, as a parent, we were shocked. You know, it was just an unexpected response, you know, in a lot of ways, the school that she was at was more of a finishing school than anything else, you know, preparing young ladies for marriage and, you know, career as a, I don't know, I will leave it at that. So we were really disappointed. And so very quickly we looked at other alternatives and other options and we pulled Emma out of school and we knew it was like the last two years are critical in Australia. We don't have a middle school and a senior school, it's all one, you know, combined thing. But those last two years are all about getting ready for university. And so we made a really tough call and we picked her up, dropped her into a totally new school. It was co-ed school. And then when we told her previous, her girl's school. I actually spoke to the vice principal and he said, "Oh, I can't believe you're sending her to a co-ed school. She's going to struggle 'cause boys are so much better in tech." And I was totally, I was lost for words, right? Because I felt back in my career and I had some amazing female managers, leaders, role models in my time that I worked for and I followed and they were always struggling because, you know, they were in the minority, but they were incredible, you know, technologists and leaders. And I just couldn't believe it. So as parents we made the tough call. We picked Emma up. We put her into another high school and she flourished, you know, Emma started a club, she got convolved with a whole bunch of other things. When she graduated, the teachers felt that she'd been there six years, right? The whole time of it. So she really made a mark, made an impact at this school and so much so that her younger brother then followed and went to that school and completed his high school there as well. But it, we just can't believe it. And we tell it everyone, this story, you know, we name the school, we won't name. We choose not to name them here, but we name the school because we just think it's really terrible guidance and terrible advice. Like we want people to follow their passion. I tell my kids and I tell the folks when I speak to, you know, early career folks, follow your passion first, guess what the job will appear. Right? You know, there'll be the... The work will come if you do something that you love. And then the second piece that I always say is, "Every future job is going to be a tech job." Technology is embedded in everything that we do. So the fact that you say, "A girl can't do technology," you're limiting yourselves, right? You don't want to think that, you want to think about the possibilities rather than the things you can't do. It's the things you can do. And the things that you haven't even thought about doing. So that's why, you know, it was so exciting to see that experience with Emma, and just seeing her grow through that and she became a bit of a STEM advocate at a high school as well. So, she saw the value of her role model that helped her. And she wants to be a.... Continue being a role model for others as well, which again, I think is admirable, right? It's about- >> Absolutely. >> Shining a light and leading and as a parent, irrespective that we work at the same company as a parent, that's what you want to see. You want to see your kids aim high and inspire others. That's what she does. >> Well, she's already been a role model too, I mean, to your younger brother, but one of the things that we say often, and theCUBE does a lot for women in technology events. And I'm fortunate to get to host a lot of those, we say, "You can't be what you can't see." So needing to have those role models who are visible. Now, it doesn't have to be female necessarily. and Frank you mentioned that you had female mentors and role models and in your illustrious career. But the important point is being able to elevate women into positions where others can see and can identify, "Oh, there's a role model. There's somebody that might be a mentor for me, or a sponsor down the road, it's critically important." And as of course, we look at the numbers in tech, women in technical roles are still quite low, but Emma, tell me a little bit about, you've been through the program. You talked about that. What are some of the things that you feel in like the last six months that you've been able to learn that had you not had this opportunity, maybe you wouldn't have. >> You know, I think that's a great point. So as a solutions architect, I get to be both technical. So hands on building an AWS, helping customers solve their problems, whether it be a data leak or I don't know, an image recognition tool to look for garbage dumped on the street or, and also thinking from the business perspective for the customers, so that's a fun part as the, of the role, but things I get to do. So currently I'm working on a demo for the conference in Sydney. So I'm building a traffic detection model using some computer vision and IOT so I get to bring my data science background to this build and also learn about new areas like IOT, Internet Of Things; Technologies. So that's been a really fun project and yeah, just having the ability to play around on AWS, we have... >> Right. Well, the exposure in the experiences is priceless. You can't put a price on that, but being able to get into the environment, learn it from a technical perspective, learn it from a practical perspective. And then of course get all the great things about getting to interact with customers and learning how different industries work, you mentioned you were in public sector. That just must be a field of dreams, I would imagine. >> I know. >> In some senses for you, right? >> Really have lucked out. I know it's, I'm like, "Wow, this my job is to play around with some new service, just because need to know about that for the customer meeting. Like I'm building a chatbot or helping build a chatbot for a customer, at the university. So yeah, things like that make it very, yeah. It's a pretty amazing role. >> It sounds, it sure sounds like it. And sounds like you're are excelling at it tremendously. Let me ask you Emma. For young girls who might be in a similar situation to where you were not that long ago with the school telling you, "No, you can't do IT." "No, you can't do physics." So you actually switched schools. What would you tell those young girls who might be in that situation about hearing the word, "No." And would you advise them to embrace a career in technology? >> Yeah, I would say that it really..... What makes me so sad is if my family didn't know about tech and had my... Supported me through that like if I would've just gone, "Oh, okay. I won't do it." You know what I mean? Like that just makes me really sad. How many people have missed out on studying what they wanted to study. So by having those types of experiences, so what I would say as advice is, "Back yourself, find supporters, whether it be your family or a teacher that you really sort of connect with, to be able to support you and through these decisions." And yeah, I think having those sponsors in a way, your advocates to help you make those choices and help support you through those choices. >> Yeah. I agree. And I have a feeling you're going to be one of those sponsors and mentors, if you aren't already Emma, I have a feeling that's just around the corner from you. So Frank, last question to you. What's the overall lesson here, if we look at statistics, I mentioned some of the stats about, you know, women in technical roles as usually less than 25% globally. But also we see data that shows that companies are more profitable and more performant when there's at least 30% of the executive suite it's women. So from your parental perspective, and from an Amazonian perspective, Frank, what's the lesson here? >> Well, look from an Amazonian perspective, we need to make sure that we have a team that represents our customers, right? And our customers aren't all boys. You know, they're not all blokes, as we say down here. So you've got to have a team that is made up of what represents your customers. So I think that's the Amazonian view. And so diverse perspectives, diverse experience, diverse backgrounds is what does that. The other from a parent, you know, I said it earlier. I think every future job is a tech job. And I think it's really important that as kids come through, you know, primary school, high school, whatever, they're prepared for that, they're already consumers of technology. You know, they need to be creators or, or participate in that environment. And I can give you an example, a few years ago, I worked for, at a large telco here. And we actually invested in a thing called code club, which was aimed at primary school kids, kids in grade four, five and six. So elementary school for my friends in America, it's kids in grade four, five and six. And they were learning how to use scratch. Scratch is this interactive tool like building lego to write programming and believe it or not, there were more girls interested and were part of code club. It was probably 60-40 was the ratio of young girls doing it compared to boys because it was creative, it was a creative outlet, they were building stuff and assembling and making these things that they loved to make. Right. But then what we saw was there'd be a drop off at high school, whether it's curriculum related or interests or distractions, I don't know what it is, but there things get lost along the way along high school. But I see it at the primary school stage at elementary school that the interest is there. So I think part of it is, there needs to be a bit of a switch up in education or other opportunities outside of school to really foster and nurture and develop this interest because it really does take all kinds to be successful in the role. And Emma talked about a chatbot that she's building and that's a conversational thing. I can't see geek boys having being able to impact and create a interesting conversation, right. Then there's other areas that seems to be skewed and biased based on a predominantly male view of the world. So we need the tech, the industry needs these diverse perspectives and these diverse views, because, you know, to your point, it's going to impact the bottom line. It's going to also deliver a better product and it's going to reflect society. It's going to reflect the customers that are using it because we're made up of every, every race and color, creed, gender. And we need a team that represents that. >> Exactly. I couldn't agree more. Well, it sounds like the Arrigo family are quite the supporters of this, but also we need more of both of you. We need more of the sponsors and the parents who are encouraging the kids and making the right decisions to help them get along that path. And we need more folks like Emma and more women that we can see, "Wow, look what she's doing in such a short time period. We want to be just like that." So you guys are, have both been fantastic. I thank you so much for joining me at the International. Women's Showcase, more power to your family. We need more folks like you guys, so great work. Keep it up. >> Thank you. >> Thanks Lisa. >> For Frank and Emma Arrigo, I'm Lisa Martin. You're watching theCUBE's coverage of International Women's Showcase 2022. (soothing music)

>> Announcer: Live from midtown Manhattan it's theCUBE covering Big Data New York City 2017. Brought to you by Silicon Angle Media and it's ecosystem sponsors. (upbeat techno music) >> Hello, everyone. Welcome back to theCUBE's exclusive coverage of Big Data NYC for all the access. It's our fifth year doing our own event in New York City. The hashtag is BigDataNYC. Also, in conjunction with Strata Hadoop, used to be called Hadoop World, then Strata Hadoop. Now, it's called Strata Data as they try to grope to where the future's going to be. A lot of hype over there. A lot of action. But here as where we do the intimate interviews and the stories. I'm John Furrier, co-host of theCUBE with Emma McGrattan who is the Senior Vice President of Engineering at Actian. Great to have you on. >> Thanks for having me. >> We love having everyone from Ireland cause the accidents great traction. So, I appreciate you coming on. Have a beer later at the pub. New York's got to lot of great Irish pubs. In all seriousness, we've had Actian on before. Mike Hoskins has been on. We had Jeff Veis on yesterday giving us the marketing angle of hybrid data that you guys are doing. What's under the hood? Because Actian has a lot of technology in their portfolio through how you guys had your growth strategy. But now as the world wants to bring it together you're seeing some real critical trends. >> Emma: Right. >> A lot of application development where data's important. Huge amount of security challenges. People are trying to build out and bring security out of IT. And then you've got all this data covering stuff. That's just on the top line. Then you got IOT. So, people are busy. Their plates are full, and data's the center of it. So, what are you guys doing to bring all of Actian together? >> Emma: That's a great question, perfect question for Actian. So, we have in Actian a number of products in the portfolio. And we believe that best fit product. So, if you're doing something like graph database, it doesn't make sense to put a Vector in Hadoop solution against that. And we've got the right fit technology for what we're doing. And for IOT we've got an embedded database that's as small as 30 megs. So, I've got PowerPoint files that are bigger than this database. You put it in a device, set it, it can run for 20 years. You never have to touch it. But all that data that's being generated typically you're generating it because you want, at some point, to be able to analyze it. And we've gone in the portfolio and Vector in Hadoop has the ability to take that data from the IOT sources and perform very high-speed analytics on that. So, the products that we have within the portfolio are focused around data integration, so pulling data into an environment where you're going to perform analysis or otherwise operationalize that data, data management. A lot of our customers are just doing CRM, ERP applications on our product platforms. And then the analytics is where I get really excited cause there's so much happening in the analytics world in terms of new types of applications being built, in terms of real time requirements, in terms of security and governance that you're talking about in reference in your question. And we've got a unique solution that can address all of those areas in our Vector in Hadoop products. So, it's interesting that we see the name Hadoop coming out of the show this week because we see that the focus on Hadoop kind of moving to the background and where the real focus is around the data and not so much-- >> And the business value. >> I hate to sound cliché about outcomes but we were joking on theCUBE yesterday and kind of can't coin the term, "Outcomes as a service." Which is kind of a goof on the whole, "It's about the outcomes." Which is a cliché in tech. But that really is the truth. At the end of the day, you've got a business goal. But the role of data now in real time is key. You're seeing people want real time. Not real time response with old data, they want the real data. So, people are starting to look at data as a really instrumental part of the development process. Similar with DevOps did with infrastructure as code, people want data to be like code. >> Emma: Exactly. >> And that is a hard >> Architectural challenge. So, if you go into your customer base what do you guys tell them? And I was going to the hybrid cloud as the marketing message. But I have challenged, I'm the CXO. I'm the CDO. I'm the CIO. I'm the CFO, COO, whatever the person making these huge, sweeping operational cost decisions. What's the architecture? Cause that's what people are working on right now. And how do you present that? >> Right. So, we recognize the fact that everybody's got a very distributed environment. And part of the message around hybrid data is that data can be generated pretty much any place. You may be generating data in the cloud with your own custom applications. You may be using salesforce.com or NetSuite or whatever. And you've got your on-premise sources of data generation. And what we provide in Actian is the ability to access all of that data in real time, and make it part of the applications that you're deploying that is going to be able to react in real time to changes. You don't want to be acting on yesterday's data because things have happened, things have moved on. So, the importance of real time is not lost on Actian. And all of these solutions that we bring together enable that real time analysis of what's happening in every part of the environment. So, it's hybrid in terms of the type of data that you're working with. It's hybrid in terms of it could be generated in the cloud, in any cloud or on-premise, and being able to pull all of that together an perform real time analysis is incredibly important to generate value from the data. >> Emma, I want to get your thoughts on a comment that I heard last night and then multiple times but the same pattern, they don't get it. "They" could be the venture capitalists as part of the startup. Or the customer has, "Oh, this is the way we do it." There's definitely things that are out there Silo's Legacy things that are-- Still not going away, and we know that. But how do you go into a customer saying look, there's a whole new way of doing things right now. It's not necessarily radical lift and shift or rip and replace. Whatever word you want to use. There's always a word that, you don't like rip and replace, we'll say lift and shift. It's the same thing, right? >> Right. >> You don't want to do a lot of incremental operational wholesale changes. >> Right. >> But you want to do incremental value now. How do you go in and say, "Look, this is the way you want to think about real time in your architecture." Because I don't necessarily want to change my operational mindset for the sake of Salesforce and all these different data sources. How do you guys have that conversation? >> So, Actian is unique in that we have a consumer base that goes back 20, 30 years. I personally will be at Actian 25 years in December. So, we've got customers that are running our I'd like to call them Legacy products, but they're products that powering their business every day of the week. And we've also got incredibly innovative product that we're on the bleeding edge. And what we've done in our recent release of Actian X is do combined bleeding edge technology with this more mature and proven technology. So, at Actian X you've got the OLTP database that was Ingres and now got rebranded because it's got new capabilities. And then we've taken the engine from Actian Vector product, and brought that into Actian X so that you can do in real time analysis of your OLTP data. And we act in real time to changes in the data. And it's interesting that you talk about real time because it means different things to different people. So, if you're talking to somebody doing risk analysis, real time is milliseconds. If you're talking to some customers, real time is yesterday's data and that's fine. And what we've done with Actian X is to provide that ability to determine for yourself what real time means to you and to provide a solution that enables you to respond in real time. Now, bringing analytics into what is a more traditional OLTP database, and kind of demonstrating for them some of the new capabilities it enables and opens up other opportunities as far as we can have conversations about maybe backing up that dataset to the cloud. Somebody that may have been risk averse and not looking at cloud all of a sudden is looking at cloud, looking at analytics, and then kind of opening up new opportunities for us. And new opportunities for them cause the data, as they say, is the new oil. >> That's great, great. And you guys have a good customer base to draw from. So, you've got to bring in the shiny new toy but make it work with existing. So, it sounds like you been like an extraction layer that you're building on tech that was very useful and is useful, by decoupling it with new software that adds value. Is it an extraction layer of sorts? >> We don't think of it as an extraction layer but certainly one could think of it that way because it's ... Well, yeah it's-- >> John: It's a product. You basically take the old product and bring new stuff to it. >> Exactly. >> Okay, so I got to ask you about the trend around IOT. Because IOT is one of those things right now that's super hype. And I think it's going to be even more hype. But security has been a big problem and I hear a lot honestly, certainly IOTs on the agenda. Industrial IOT is kind of the low-hanging fruit. They go to that first. But no one wants to be the next Equifax. So, there's a lot of security stuff that causes, plus there's other things going on they got to take care of. How do you guys talk about the security equation where you can come in and put in a reliable workable solution and still make the customer's feel like they're moving the ball down the field. >> So, that's one of the benefits that we have of being in the industry for as long as we have. We have very deep understanding as to what security requirements are. In terms of providing capabilities within the product to do things like control who can access what data and to what degree. Can they update it? Can they only read it? Providing the ability to encrypt the data. So, for many usecases the data is so sensitive that you'd always want to encrypt it when it's stored. You'd want any traffic coming in and out of the environment to be encrypted. Being able to audit everything that's happening in the environment, who's issuing what queries and from where and to set alarms or something if somebody attempts to access data that they shouldn't be attempting to access. So, taking all of those capabilities together, we're then able to look at things like GDPR. What are the requirements for securing the data? And we've got all the capabilities within the product. And we've got the credibility cause we've been doing this for 30 years, that we can secure these environments. We can conform to the various standards and mandates that are put in place for data security. So, we have a very strong story to tell-- >> John: What is your position >> John: On GDPR? Obviously, you've got a super important, I call it the Y2K that actually is real cause you have there compliance issues. There's a lot of, obviously, political things going on but this is a real problem, about to move fast as a solution. What are you guys offer there? >> Equifax was a prime example of why GDPR is incredibly important. So, for Actian, and you know, I talked about the capabilities we provide with regard to securing data, and secure access to that data. And when it comes to GDPR, a lot of it is around process. So, what we're doing is guiding our customers and making sure that they have secure processes in place. Putting all of the smarts into the technology, and then having somebody doing an offline backup on a CD that they leave on a seat on the train which has, in the past, been a source of data breeches, is an issue with process and not with technology. So, we're helping with that. And helping in educating-- >> John: Equifax had some >> BPN issues but also, I mean, I haven't reported on this yet also have confirmed that there were state actors involved, foreign actors penetrating in through their franchise relationships. So, in partnering in an open internet these days you need to understand who the partners are even if they're in the network. >> Absolutely. And that's why this whole idea of providing all of the capabilities required for data security including auditing, who's coming in. So, failed attempts to get into the system should be reported as problems. And that's a capability that we have within the database. >> So, you've been at Actian for 25 years, I did not know. That's cool. Good folks over there. I've been to the office a few times. I'm sure you got a good healthy customer base but for the folks that don't know Actian. What's the pitch from your standpoint? Not the marketing pitch hybrid data, I get that. I mean, what should they know about you guys. What is the problem that you saw? What do you bring to the table? From an engineering perspective, how do you differentiate? >> So, my primary focus is around high-speed analytics. And so, Actian enables the fastest SQL access to data, on Hadoop and off of Hadoop, proven through benchmarks. So, high-speed analytics is incredibly important. But for Actian, we're unique in having this 30 year history where we understand what it is to run 24/7, mission critical operational databases. So, Actian's known for products like Ingres, like Psql, and being able to analyze data that's operationalized but then also bringing in new data sources. Cause that's where things are really going. But people want to choose the best application whether it's in the cloud or on-premise, it doesn't matter. It's the best application for their need. And being able to pull all of that data together, and for operational purposes, and for analytics purposes is incredibly important. And Actian enables all of that. >> And that's where the hybrid is really clever and smart because you got the consumption side and the creation side, and data integration isn't a project, it's real. It just happens. >> Emma: Right. >> So, you want to enable that. I can see that would be a key benefit. Certainly as, whether these decentralized apps get more traction, you're going to start to see more immutable things transactions happening. Blockchain clearly points to that direction of the market where that's cool. Distributed computing has been around for awhile but now decentralized we know how to behave there. So, we're seeing some apps that will probably be rewritten for that. But again, if architected properly that should be a problem. >> Right, exactly. And we don't want anybody to have to rewrite apps. What we want to be able to do is to provide a platform where the data that you need is available. >> John: Yeah, they're called Dapps for decentralized apps. It's a whole new wave coming, it's not being talked about here at the show. We are on, obviously, at Silicon Angle and Wikibon are those trends as we're riding the big wave. Okay, Em, I want to ask you a final question. Kind of take your Actian hat off, put your Irish techie hat on, and let's get down and dirty on what the main problem in the industry is right now. If you look back and kind of go to the balcony if you will, look at the stage of the industry, obviously Hadoop is now in the background. It's an element of the bigger picture. We're seeing, we were commenting yesterday that these customers have these tool sheds of all these tools they've bought. They bought a hammer that wants to be a lawnmower, right? It's just like they have their tool platforms are being pitched at them. There's a lot of confusion. What's the main problem that the industry's trying to solve? If you look at it, if you can put the dots together. What is the big problem that needs to be solved, that the industry should be solving? >> So, I think data is every place, right? And there's not a whole lot of discipline around corralling that and putting security around it. Being able to deploy security policies across data regardless of where it's deployed or sourced. So, I think that's probably the biggest challenge is bringing compute to the data and pulling all of that together. And that's the challenge that we're addressing. >> And so, the unification, if you will, people use that word, all unifying data. What does that actually mean? You guys call it hybrid data which means you have some flexibility if you need it. >> Emma: Right. >> All right, cool. Emma, thanks so much for coming on theCUBE. Really appreciate it. Congratulations on your success. And again, you guys got to a good spot. You got a broad portfolio, you're bringing together with hybrid data. Best of luck. We'll keep in touch. Emma McGrattan here, the Senior Vice President of Engineering at Actian here on theCUBE. More live coverage here in New York City from theCUBE's coverage of Big Data NYC after this short break. (upbeat techno music)

>>And welcome to this special cube conversation. I'm John for a, your host of the cube. We're here in Palo Alto, California, and I'm here with a very special guest coming down from Seattle remotely into the cube studios is the leader at AWS Amazon web services, the vice president of database analytics and machine learning Swami. Great to see you cube alumni recently taking over the database business at AWS as a leader. Congratulations. And thanks for coming on the cube. >>Hey, my pleasure to be here, John, very excited to talk to you. >>Yeah. We've had many conversations on the cube and also in person and also online around all the major mega trends. You've had your hand in all the action, going back to your days when you were in school learning and, and writing papers. And 10 years ago, Amazon web services launched AWS dynamo, DB, fast, flexible, no SQL database that everyone loves today, which has inspired a generation of what I would call database distributing cloud scale, single digit millisecond performance at scale. And again, the key scale. And again, this is 10 years ago, so it seems like yesterday, but you guys are celebrating and your name was on the original paper with CTO Verner. Vogel's your celebrity. Congratulations. >>Thank you. Not sure about the celebrating part, but I'm very excited. At least I played a hand in building such an amazing technology that has enabled so many amazing customers along the way as well. So >>Trivia on the, on the paper as you were an intern at AWS, so you're getting your PhD. And then since, since rising through the ranks and involved in a lot of products over the years, and then leading the machine learning and AI, which is now changing the game at the industry level, but I got to ask you getting back to the story here. A lot of customers have built amazing things on top of dynamo DB, not to mention lots of other AWS and Amazon tech riding on it. Can you share some of the highlights that came out of the original paper? And so with some examples, because I think this is a point in time, 10 years ago, where you start to, so the KickUp of cloud scale, not just, just for developers and building startups, you're really starting to see the scale rise. >>Yeah, I actually, I mean, as you probably know, based on what he read to explain the Genesis of dynamo DB itself had to explain the Genesis of how Amazon got into building the original dynamo, right? And this was during the time when miner, I joined Ron esteem as an intern and, and Amazon was one of the pioneers in pushing the boundary of scale. And a year over year, our Q4 holiday season tends to be really, really bad for all the right reasons. We all want our holiday shopping done during that time. And you want to be able to scale your website, arters fulfillment centers, all of them at that time. And those are the times around 2005. And the answer is when people think our database, they think of a single database server that actually runs on a box and has a certain characteristics and does a scale and availability and whatnot. >>And it's usually relational. And then when we had a major disruption during Q4 that's when yeah, ask ourselves the question, why are we actually using a relational database for some of these things when they really didn't need the data model complexity of relational database. And normally I would say most companies where to actually ask an intern or a few engineers who are early in the career saying like, what the hell are you suggesting? Just go away. But Amazon being enabling Buddhists to build what they want. And they actually let us start reimagining what a database or our scale could look like. And that led to dynamo. And since she unstained mine, then we migrated from an traditional relational database stair this one for some of the amazon.com services. And then I moved on to actually start building some butts off our storage service and then our managed relational database service, I explicitly remember. >>And one of our customer advisory board, we're just the set off some of our leading customers who actually give us feedback on roadmap. Another son, Don, who's the CEO and chief geek of spunk bargain faker. And him actually looking at the Trinity me, I was starting in the corner and saying like you all, both tomorrow and why do I need to keep shotting my, my sequel database and reshooting assigned scaling. And this is the time when the state of the art in most databases were around. Like, you start sharding your relational database and constantly reshaping. And this is when most websites are starting to experience the kind of scale which we consider a normal month. During those times it was mostly, most companies used to have a single relational database backend and start scaling that way. And that conversation led entirely under duress, unaided read, lot of AWS leaders and myself saying like, Hey, what is a cloud database reimagined without the hampering SQL look like? And that led us to start building dynamo DB, but just a key value database at that time. Now we support document might've too, but that single digit millisecond latency at any scale imagine. So >>I think about that time at that time, 10 years ago, when you were having this conversation and I know the smug mug and I, he said, he's in totally geek and he's, he's good to point that out. You also have Netflix as customers too. I'd like to hear how that's evolved, but, but I think back at the time, if you look back then I got to ask you most people we've talked about this before. No one database rules, a world that's now standard people now don't see one database back then it was a one database kind of mindset back then. Yeah. And then you had that big data movement happening with Hadoop. You had the object store developing. So you're in you're you're circling around that area. What was it like then? I mean, take, take us through that because there was obvious visibility that, Hey, let's just store this. Now you see data lakes and that's all happening. But back then object store was kind of new. Yeah. >>Ah, it's a great question. Now, one of the things I realized early on, especially when I was working with binary, when you're saying amazon.com itself as an example, that the access patterns for various applications and Amazon, but let alone AWS customers tend to be very, very, very, some of them really just needed an object store. Some of them needed a relational database. Some of them really wanted a key value store within a fast latency. Some of them really needed a durable cash. And, but it so happens when you have a giant hammer. You use that for everything looks like a map, which is essentially the story at that time. And so everyone kept using the same database, irrespective of what the problem was because nobody else, I mean, thought about like, what else can we build that is better? So this let us do, literally I remember writing a paper with Bernard internally that is widely used in Amazon explaining what are all the menu of booklets that access. >>And then how do we go about actually solving for each of these things so that they can actually grow and innovate faster. And, and this was led to actually the Genesis of not only building IDs and so forth, but also dynamo and various other non-relational data. There's a still let alone not so storage access patterns and what not. So, and this was one of the big revelations he had just that there is not a single database that is going to meet the customer, needs us. The diversity of workloads in the internet is growing. And this was a key pivotal moment because with cloud now applications can scale very more instantly than before now. Building an application for Superbowl is very easier than before. That means that on, I mean, everybody is pushing the boundaries of what scale means, and they are expecting more from their obligations. That's when you need technologies like dynamo, DB, and that's exactly what dynamo already be set out to do. And since then, we are continuing to innovate on behalf of our customers and the purpose of the database story as well. And this concept has resonated well across the board. If you see that the database industry has also embraced this method, >>It's natural that you obviously evolved into the machine learning side of it because that's data is big part of that. And you see back then you, you bringing up kind of like flashes for me where it's like those, the data conversations back then and the data movement was just beginning. So the idea that you can have diversity in access methods of the kind of databases was a use case driven by the application, not so much database saying, this is how you have to work, that the script was flipped. It it's changed from infrastructure dictating to the applications, what to do. Now, the applications are going to the infrastructure and saying, give me what I want. I want to access something here in an office store, something here in no SQL that became the Genesis of infrastructure as code at a, at a global level. And so your paper kind of set the, the, the wave, the influence for this, no SQL did big data movement. It's created tons of value, maybe a third Mongo might've been influenced by this other people have been influenced. Can you share some stories of how people adopted the concept of dynamo DB and how that's changed in the industry and how has that helped the industry evolve? >>I mean, plus file data. Most share our experience of building and dynamo style data store. Very, it is a non-relational API and showing what are some of the experiences that the Venter in building such an paper and these set out early on itself, that it is should not be just a design paper, but it should be something that we shared our experiences. So even now, when I talked to my friends and colleagues and various other companies, one thing they always tell me is they appreciated the openness with which we were sharing. Some of the examples and learnings that we learned to not optimizing for percentile latencies, and what are some of the scalability challenges, how we solved and some of the techniques around things like sloppy Cora or various other stuff. We invented a lot of towns along the way too, but people really appreciated several of some of our findings and as talking about it. >>And since then I met so many other innovations are happening in the industry and the AWS, but also across the entire academia and industry in this space, the databases I've been going through what I call as a period of Renaissance, where one of the things, if you see our own arc, when Roger and I started on the database, front Disney started over the promo saying like, if you were to build a database where cloud is the new normal, this is again in 2008, we asked ourselves that question and what the belt that led us to start building things like dynamo, DB, RDS star. I know that alone, we reimagined data viruses with Redshift and several, and then several other databases like time stream for time series workloads started running Neptune for graph and whatnot. But at the moment we started actually asking that question and working backwards from customers. Then you will start being able to innovate accordingly. And this has worked really well. Then more than a hundred thousand AWS customers have chosen dynamo DB for mobile gaming tech IOT. Many of these are fast growing businesses, such as ledge, Darryl BNB, red fan, as soon as enterprises like Samsung Toyota, capital one and so far. So these are like really some meaningful clouds, let alone amazon.com. I run this. >>We have an internal customer is always good to have that entire inside customer. You know, I really find this a really profound use case because you're just talking, you know, in Amazonian terms, I'll just translate for the audience working backwards from the customer, which is the customer obsession you guys have. So here's, what's going on off the way I see it. You got dynamo, DB, paper, you and Verner, and the team Paul was a great as a great video on your blog posts that goes into the, to the talk he gave at around that time, which is fun to watch if you look back, but you have a radical enabler here, that's disrupting and changing S3 RDS, Aurora. These are game-changing concepts inside the, the landscape of AWS at the same time, you're working backwards from the customer. So the question I have for you as a leader and as a builder, how did you balance the working backwards from the customer while bringing something brand new and radical at that time to the market? >>Yeah, this is one of the S I mean hardest things to be, as leaders need to balance on. If you see many times, then we actually worked backwards from customers. The literal later translated this, literally do what customers are asking for, which is true nine out of 10 times, but there is one or a 10 times, you got to read between the lines on what they are asking. Because many times customers when are articulate that they need to go fast. If in the right way, they might say, Hey, I wish my heart storage goes faster, but they're not going to tell you they need a car, but you need to know and be able to translate and read between the lines we call it under the bucket of innovate on behalf of customers. And that is exactly the kind of a mantra we had when we were thinking about concepts like dynamo DB, because essentially at that time, almost everybody would, if I asked, they would just say, I wish a relational database could actually be able to scale from not just like a hundred gigabyte to one terabyte are, it can take up to like 2 million transactions, a second and so forth and still be cheap and made in reality as relational databases, the way they were engineered at that time, those are not going to meet the scale needs. >>So this is fair. We hunted read between the lines on what are some of the key Mustang needs from customers and then work backwards and then innovate on behalf of these workloads, be enabled by the sun oh four, which are some of the reasons that led to us launching some of the initial sets on dynamo on a single digit millisecond latency and seamless scale. At that time, databases didn't have the elasticity to go from like 10 requests, a second to like a hundred thousand or 1 million requests a second, and then scaled right back in an hour. So that was not possible. And we kind of enabled that. And that was an, a pretty big game changer that showed the elasticity of the cloud to a database. Well, >>Yeah, I think also just to, not to nerd out on this, but it enables a lot of other kind of cool scaled concepts, like queuing storage. It's all kind of together. This database piece of that you guys are solving. And again, props to you guys on the team. Congratulations. I have to ask, you know, more generally, how has your thinking changed since the paper? I'll see, you've got more experience under your belt. You don't yet have the gray hairs yet, but we'll see those soon come in, but you know, you're, you got a lot more experience. You're running teams, you're launching a lot of products. How has your thinking changed in the industry since the paper what's happening now? What's the big evolution. What are those new things now that are in the innovate on behalf of the customer? What's between the lines now, how do you see this happening? >>I mean, now since wanting dynamo via a victim, I had the opportunity to work on various problems in the big data space. There we've worked on some are fire things that you might be aware of in the analytics all the way from Redshift to quick side, too. Then I moved on to start some of our efforts, having built systems that enabled customer to store process and credit, and then analyze them. One of the realizations, I had this, the in around 2015 or 2016, I kinda had that machine learning was hitting a critical point where now it is ready for being scaled at option. Their cloud has basically enabled limitless compute and limitless storage, which are the factors that are holding back machine learning technology. Then I realized that now we have a unique opportunity to bring machine learning BI to everybody, not just folks with PhD in machine learning. >>And that's when I moved on from database and analytics areas, they started machine learning. We're just a descent area because machine learning is powered by data and then started building capabilities like SageMaker, which is our end to end ML platform to build, train and deploy them on models. And this, what does the leading enterprise platform by several gaggled users and then also a bunch of our AI services since then, I view the reason I'm giving all this historical context is one of the biggest realization I had early on itself. And 2016 as first machine learning is one of the most disruptive technologies. She will then country in our generation. This is right after cloud. I think these still are the most amazing combination that is going to revolutionize how we build applications and how we actually reason about that. Now, the second thing is that at the end of the day, when you look at the ANC and journey, it is not just about one database or one data Varroa. >>So one data lake product, or even 1:00 AM out platform. It is about the end to end journey where a customer is storing their order database. And then they are actually building a data lake that test customer history and order history. And they want to be able to personalize. And for their viewer experience are actually forecast what products to staff in their fulfillment center, but then all these things need to work and to handle. And that view is one of the big things that struck me for the past five years. And I've been on this journey in addition to building this Emma building blocks to connect the dots so that customers can go on this modern end to end data strategy as I call it, right. It goes beyond a single database technology or data technology, but putting now all of these end to end together so that customers don't end up spending six months connecting the dots, which has been the state of the down for the last couple of years. And we are bringing it down to matter of the Sundays. Now >>He's incredible Swami. Thank you so much for spending the time with us here in the, >>Yeah, my pleasure. Thanks again, Sean. Thanks for having me.

>>And welcome back to the cubes coverage of coop con cloud native con 2021. We're in person physical venom, John free hosted a Q a Dave Nicholson, my CO's and Emma Laney, who is our not so roving reporter unemployed, software engineer, unemployed comedian. Great to have you on the cube. >>Thank you for that list of credentials. >>You're doing great. I saw you're having some fun down there. We've got this new show or testing out called the grill. Here it is. Okay. Um, what's the focus, what's the story behind everything. >>Uh, the focus of the show is trying to have some fun with tech. You know, tech has a lot of self seriousness. Uh, there's a lot that's ripe to make fun of. We're also having fun. We're not trying to grill people in. We're not trying to roast them. Right? We're having people come through. They're sharing funny stories. We're having a contest to find the best man split nation of Kubernetes. Right now, I got to say, a woman is in the lead. Oh, she killed that contest, like called me, sweetie. And everything. It just proves that it's not about the man. You identify as it's about the condensation in your heart when it comes to mansplaining. >>Um, what is the best criteria that you, when you get a candidate for the mansplaining competition, what is the criteria? >>I mean, number one, we're looking for condensation. You get extra points for you, the phrase, well, actually we want a supercilious attitude. Uh, if you are partially into explaining it and then you stop yourself because you think you've used too technical of a term and then step it down, all of those gets you extra points in the mansplaining. >>Can I ask you, what's your biggest observation as you kind of look at this ecosystem? I mean, it's a big event, but it's, COVID postpone even in COVID people are wearing masks, not wearing masks. >>I mean, people are wearing masks for the most part. Uh, you know, I did love this, uh, red light, yellow light green light system. They came up with green, meaning please touch me. I've been inside for too long red meaning I still care about COVID yellow. You know, ask me, we'll figure it >>Out. All right. What's the funniest thing you've heard so far. >>The funniest thing I have to say, I asked someone what their favorite tech joke is. And he said it worked on my computer That really stirred up some memories. >>Oh man, we're in LA though. This is a great area. It's literally with the best comedians you could think of or work their way through the system. But with techno and everything is tech with gadgets and with like Kubernetes, I mean, it's, it's the material writes itself. I mean, >>Surely >>You must be having, >>Oh, I'm definitely having a ton of fun. Uh, I wouldn't say the material writes itself. I would say hire me to write material, but it is quite a fertile. >>Okay. What would you write for, uh, looking at the keynote today? Looking at the vibe here, obviously a lot of people show because they're remote, but visually it's a packed house here, but what's your first comedic view of the, as the fog lifts in this community? >>I have to say the thing that really stuck out to me from the keynote addresses was that people have not yet adjusted to being in person. There were some very, very delayed applause breaks where people realize they were not muted watching on a screen and you'd still go, oh, that's right. We should interact. Like God bless those speakers. It's uh, people have been inside for a long time. >>Um, part-time comedian too. I mean, co-hosting queue. Um, I don't, I, >>I don't find anything funny with technology. And I'm curious when you use the word supercilious, is that a, is that a comedic term? I, I, yes. >>I heard that before. It's the Latin form of super silly. Yeah. Which is my brand of comedy. >>So the mansplaining, I don't know if you need to like, woman's plane, some of this stuff to me, but I'll English >>Major Splain. Okay. Okay. Super silliest. >>It sounds super silly. So is it, is it, is it okay to have a ringer come in and attempt make an attempt at the mansplaining or >>Okay. A hundred >>Percent come in wearing it. >>I'm trying to make this a safe space for women at the conference. I'm the only woman you should be mansplaining to. I'm a martyr falling on the sword of mansplaining for all the great technical women at this conference. You slip that in >>And translate that. >>Of course, John, I don't know how to explain that to them more detailed. Um, what I love about the vibe is that this technical people they're snarky. If you get at their core, I mean, we were at the bar. Everyone was like totally leaning into like comedy and more fun because it's almost like they're bust out, come out of the closet and beat comedian. >>Oh, there is a broiling anger in the soul of every developer and every person who's worked on technology. And the question is going to be, can we get it on camera when they are not drunk, we're doing our >>Best to drink. These developers don't >>Think, oh, they do desperately. >>We saw a few partaking in the bar at the GTA merit and a lot going on. You had the, you know, they had warriors game going on. You have a lot of Dodgers were playing the giants. So pretty active bar scene for this crowd. >>Yeah, no, it was, uh, it was very fun. I personally was disappointed that the warriors are not actually staying in our hotel. You know, if this software thing doesn't work out, NBA wife is a possible second. >>And the Ritz Carlton was right behind us. You could be right there too. All right. So the grill is, uh, an experiment. We're having some fun with it, but the purpose is to just chill a bit. What's the, what would you say the goal of the show is for you? >>I'd say the goal is to get people to come out of their shells a little bit, to have some fun, to poke fun at some of the tendencies that we see in tech that we often don't bring up. You know, like I'm having so much fun with the man's pollination. Uh, I've lived it a bit. And my favorite is, uh, as I asked men to mansplain it to me, the panic in their eyes, that's my ultimate goal is just to make men afraid. >>And the panic is because they don't know if they're mansplaining all the time or actually purposely mansplaining is hard enough, but they do it naturally. Sorry. >>I have three daughters and I can't wait for them to see this stuff. I cannot >>Wait. That's going to be >>Great. Well, we have cooler gen Z. >>Well, we have t-shirts right. Let me see the t-shirts give everyone a quick, if you come on, this is day one of coupons. So if you do come on the show with the grill, I'm the t-shirt ferry. The grill is real. It's like the V the cubes version of the view, but >>Wow, just because I'm a woman, the, uh, the t-shirt is a big incentive. I'm sure a lot of people go to tech conferences don't get any free. T-shirts good. >>I got grilled by a net. Lilium, the cube at cube con con not cube >>Con. It's a medium rare grilling. >>I couldn't resist the view jokes. I know I'm in color. We'll keep our day jobs here in the comedian angle. We got to >>Believe that's true. Yes. When I look at the wavelengths of >>Light on that, I'm super stoked to have you try that. I think it's a great program, Greg. God. So you guys doing a great job, loved the vibe, love the energy, love the creativity, having some fun. See the poster one last time. And the idea is to have some fun, right? It's a tough time. We're all coming back from the pandemic, welcoming back from the pandemic. And this is just a fun way to kind of let the air out and have some fun. So thanks for everyone. Thank you so much for doing that. Thank you. All right. Cute coverage here. Coop gone. Cloud native con I'm John Perry, David Nicholson. Be back with more day, one coverage of three days after the short break.

>>Hello, everyone. Welcome to the a day in the life of a data science talk. Uh, my name is Terry Chang. I'm a data scientist for the ASML container platform team. And with me, I have in the chat room, they will be moderating the chat. I have Matt MCO as well as Doug Tackett, and we're going to dive straight into kind of what we can do with the asthma container platform and how we can support the role of a data scientist. >>So just >>A quick agenda. So I'm going to do some introductions and kind of set the context of what we're going to talk about. And then we're actually going to dive straight into the ASML container platforms. So we're going to walk straight into what a data scientist will do, kind of a pretty much a day in the life of the data scientists. And then we'll have some question and answer. So big data has been the talk within the last few years within the last decade or so. And with big data, there's a lot of ways to derive meaning. And then a lot of businesses are trying to utilize their applications and trying to optimize every decision with their, uh, application utilizing data. So previously we had a lot of focus on data analytics, but recently we've seen a lot of data being used for machine learning. So trying to take any data that they can and send it off to the data scientists to start doing some modeling and trying to do some prediction. >>So that's kind of where we're seeing modern businesses rooted in analytics and data science in itself is a team sport. We're seeing that it doesn't, we need more than data scientists to do all this modeling. We need data engineers to take the data, massage the data and do kind of some data manipulation in order to get it right for the data scientists. We have data analysts who are monitoring the models, and we even have the data scientists themselves who are building and iterating through multiple different models until they find a one that is satisfactory to the business needs. Then once they're done, they can send it off to the software engineers who will actually build it out into their application, whether it's a mobile app or a web app. And then we have the operations team kind of assigning the resources and also monitoring it as well. >>So we're really seeing data science as a team sport, and it does require a lot of different expertise and here's the kind of basic machine learning pipeline that we see in the industry now. So, uh, at the top we have this training environment and this is, uh, an entire loop. Uh, we'll have some registration, we'll have some inferencing and at the center of all, this is all the data prep, as well as your repositories, such as for your data, for any of your GitHub repository, things of that sort. So we're kind of seeing the machine learning industry, go follow this very basic pattern and at a high level I'll glance through this very quickly, but this is kind of what the, uh, machine learning pipeline will look like on the ASML container platform. So at the top left, we'll have our, our project depository, which is our, uh, persistent storage. >>We'll have some training clusters, we'll have a notebook, we'll have an inference deployment engine and a rest API, which is all sitting on top of the Kubernetes cluster. And the benefit of the container platform is that this is all abstracted away from the data scientist. So I will actually go straight into that. So just to preface, before we go into the data as small container platform, where we're going to look at is a machine learning example, problem that is, uh, trying to predict how long a specific taxi ride will take. So with a Jupiter notebook, the data scientists can take all of this data. They can do their data manipulation, train a model on a specific set of features, such as the location of a taxi ride, the duration of a taxi ride, and then model it to trying to figure out, you know, what, what kind of prediction we can get on a future taxi ride. >>So that's the example that we will talk through today. I'm going to hop out of my slides and jump into my web browser. So let me zoom in on this. So here I have a Jupiter environment and, um, this is all running on the container platform. All I need is actually this link and I can access my environment. So as a data scientist, I can grab this link from my it admin or my system administrator. And I could quickly start iterating and, and start coding. So on the left-hand side of the Jupiter, we actually have a file directory structure. So this is already synced up to my get repository, which I will show in a little bit on the container platform so quickly I can pull any files that are on my get hub repository. I can even push with a button here, but I can, uh, open up this Python notebook. >>And with all this, uh, unique features of the Jupiter environment, I can start coding. So each of these cells can run Python code and in specific the container at the ESMO container platform team, we've actually built our own in-house lime magic commands. So these are unique commands, um, that we can use to interact with the underlying infrastructure of the container platform. So the first line magic command that I want to mention is this command called percent attachments. When I run this command, I'll actually get the available training clusters that I can send training jobs to. So this specific notebook, uh, it's pretty much been created for me to quickly iterate and develop a model very quickly. I don't have to use all the resources. I don't have to allocate a full set of GPU boxes onto my little Jupiter environment. So with the training cluster, I can attach these individual data science notebooks to those training clusters and the data scientists can actually utilize those resources as a shared environment. >>So the, essentially the shared large eight GPU box can actually be shared. They don't have to be allocated to a single data scientist moving on. We have another line magic command, it's called percent percent Python training. This is how we're going to utilize that training cluster. So I will prepare the cell percent percent with the name of the training cluster. And this is going to tell this notebook to send this entire training cell, to be trained on those resources on that training cluster. So the data scientists can quickly iterate through a model. They can then format that model and all that code into a large cell and send it off to that training cluster. So because of that training cluster is actually located somewhere else. It has no context of what has been done locally in this notebook. So we're going to have to do and copy everything into one large cell. >>So as you see here, I'm going to be importing some libraries and I'm in a, you know, start defining some helper functions. I'm going to read in my dataset and with the typical data science modeling life cycle, we're going to have to take in the data. We're going to have to do some data pre-processing. So maybe the data scientists will do this. Maybe the data engineer will do this, but they have access to that data. So I'm here. I'm actually getting there to be reading in the data from the project repository. And I'll talk about this a little bit later with all of the clusters within the container platform, we have access to some project repository that has been set up using the underlying data fabric. So with this, I have, uh, some data preprocessing, I'm going to cleanse some of my data that I noticed that maybe something is missing or, uh, some data doesn't look funky. >>Maybe the data types aren't correct. This will all happen here in these cells. So once that is done, I can print out that the data is done cleaning. I can start training my model. So here we have to split our data, set into a test, train, uh, data split so that we have some data for actually training the model and some data to test the model. So I can split my data there. I could create my XG boost object to start doing my training and XG boost is kind of like a decision tree machine learning algorithm, and I'm going to fit my data into this, uh, XG boost algorithm. And then I'm going to do some prediction. And then in addition, I'm actually going to be tracking some of the metrics and printing them out. So these are common metrics that we, that data scientists want to see when they do their training of the algorithm. >>Just to see if some of the accuracy is being improved, if the loss is being improved or the mean absolute error. So things like that. So these are all things, data scientists want to see. And at the end of this training job, I'm going to be saving the model. So I'm going to be saving it back into the project repository in which we will have access to. And at the end, I will print out the end time so I can execute that cell. And I've already executed that cell. So you'll see all of these print statements happening here. So importing the libraries, the training was run reading and data, et cetera. All of this has been printed out from that training job. Um, and in order to access that, uh, kind of glance through that, we would get an output with a unique history URL. >>So when we send the training job to that training cluster, we'll the training cluster will send back a unique URL in which we'll use the last line magic command that I want to talk about called percent logs. So percent logs will actually, uh, parse out that response from the training cluster. And actually we can track in real time what is happening in that training job so quickly, we can see that the data scientist has a sandbox environment available to them. They have access to their get repository. They have access to a project repository in which they can read in some of their data and save the model. So very quick interactive environment for the data scientists to do all of their work. And it's all provisioned on the ASML container platform. And it's also abstracted away. So here, um, I want to mention that again, this URL is being surfaced through the container platform. >>The data scientist doesn't have to interact with that at all, but let's take, it's take a step back. Uh, this is the day to day in the life of the data scientists. Now, if we go backwards into the container platform and we're going to walk through how it was all set up for them. So here is my login page to the container platform. I'm going to log in as my user, and this is going to bring me to the, uh, view of the, uh, Emma lops tenant within the container platform. So this is where everything has been set up for me, the data scientist doesn't have to see this if they don't need to, but what I'll walk through now is kind of the topics that I mentioned previously that we would go back into. So first is the project repository. So this project deposited comes with each tenant that is created on the platform. >>So this is a more, nothing more than a shared collaborative workspace environment in which data scientist or any data scientist who is allocated to this tenant. They have this politics client that can visually see all their data of all, all of their code. And this is actually taking a piece of the underlying data fabric and using that for your project depository. So you can see here, I have some code I can create and see my scoring script. I can see the models that have been created within this tenant. So it's pretty much a powerful tool in which you can store your code store any of your data and have the ability to read and write from any of your Jupiter environments or any of your created clusters within this tenant. So a very cool ad here in which you can, uh, quickly interact with your data. >>The next thing I want to show is the source control. So here is where you would plug in all of your information for your source control. And if I edit this, you guys will actually see all the information that I've passed in to configure the source control. So on the backend, the container platform will take these credentials and connect the Jupiter notebooks you create within this tenant to that get repository. So this is the information that I've passed in. If GitHub is not of interest, we also have support for bit bucket here as well. So next I want to show you guys that we do have these notebook environments. So, um, the notebook environment was created here and you can see that I have a notebook called Teri notebook, and this is all running on the Kubernetes environment within the container platform. So either the data scientists can come here and create their notebook or their project admin can create the notebook. >>And all you'd have to do is come here to this notebook end points. And this, the container platform will actually map the container platform to a specific port in which you can just give this link to the data scientists. And this link will actually bring them to their own Jupiter environment and they can start doing all of their model just as I showed in that previous Jupiter environment. Next I want to show the training cluster. This is the training cluster that was created in which I can attach my notebook to start utilizing those training clusters. And then the last thing I want to show is the model, the deployment cluster. So once that model has been saved, we have a model registry in which we can register the model into the platform. And then the last step is to create a deployment clusters. So here on my screen, I have a deployment cluster called taxi deployment. >>And then all these serving end points have been configured for me. And most importantly, this endpoint model. So the deployment cluster is actually a wrap the, uh, train model with the flask wrapper and add a rest endpoint to it so quickly. I can operationalize my model by taking this end point and creating a curl command, or even a post request. So here I have my trusty postman tool in which I can format a post request. So I've taken that end point from the container platform. I've formatted my body, uh, right here. So these are some of the features that I want to send to that model. And I want to know how long this specific taxi ride at this location at this time of day would take. So I can go ahead and send that request. And then quickly I will get an output of the ride. >>Duration will take about 2,600 seconds. So pretty much we've walked through how a data scientists can quickly interact with their notebook. They can train their model. And then coming into the platform, we saw the project repository, we saw the source control. We can register the model within the platform, and then quickly we can operationalize that model with our deployment cluster, uh, and have our model up and running and available for inference. So that wraps up the demo. Uh, I'm gonna pass it back to Doug and Matt and see if they want to come off mute and see if there are any questions, Matt, Doug, you there. Okay. >>Yeah. Hey, Hey Terry, sorry. Sorry. Just had some trouble getting off mute there. Uh, no, that was a, that was an excellent presentation. And I think there are generally some questions that come up when I talk to customers around how integrated into the Kubernetes ecosystem is this capability and where does this sort of Ezreal starts? And the open source, uh, technologies like, um, cube flow as an example, uh, begin. >>Yeah, sure. Matt. So this is kind of one layer up. We have our Emma LOBs tenant and this is all running on a piece of a Kubernetes cluster. So if I log back out and go into the site admin view, this is where you would see all the Kubernetes clusters being created. And it's actually all abstracted away from the data scientists. They don't have to know Kubernetes. They just interact with the platform if they want to. But here in the site admin view, I had this Kubernetes dashboard and here on the left-hand side, I have all my Kubernetes sections. So if I just add some compute hosts, whether they're VMs or cloud compute hosts, like ETQ hosts, we can have these, uh, resources abstracted away from us to then create a Kubernetes cluster. So moving on down, I have created this Kubernetes cluster utilizing those resources. >>Um, so if I go ahead and edit this cluster, you'll actually see that have these hosts, which is just a click and a click and drop method. I can move different hosts to then configure my Kubernetes cluster. Once my Kubernetes cluster is configured, I can then create Kubernetes tenant or in this case, it's a namespace. So once I have this namespace available, I can then go into that tenant. And as my user, I don't actually see that it is running on Kubernetes. So in addition with our ML ops tenants, you have the ability to bootstrap cute flow. So queue flow is a open source machine learning framework that is run on Kubernetes, and we have the ability to link that up as well. So, uh, coming back to my Emma lops tenant, I can log in what I showed is the ASML container platform version of Emma flops. But you see here, we've also integrated QP flow. So, uh, very, uh, a nod to, uh, HPS contribution to, you know, utilizing open source. Um, it's actually all configured within our platform. So, um, hopefully, >>Yeah, actually, Tara, can you hear me? It's Doug. So there were a couple of other questions actually about key flare that came in. I wonder whether you could just comment on why we've chosen cube flow. Cause I know there was a question about ML flow in stead and what the differences between ML flow and coop flow. >>Yeah, sure. So the, just to reiterate, there are some questions about QP flow and I'm just, >>Yeah, so obviously one of, uh, one of the people watching saw the queue flow dashboard there, I guess. Um, and so couldn't help but get excited about it. But there was another question about whether, you know, ML flow versus cube flow and what the difference was between them. >>Yeah. So with flow, it's, it's an open source framework that Google has developed. It's a very powerful framework that comes with a lot of other unique tools and Kubernetes. So with Q flow, you really have the ability to launch other notebooks. You have the ability to utilize different Kubernetes operators like TensorFlow and PI torch. You can utilize a lot of the, some of the frameworks within Q4 to do training like Q4 pipelines, which visually allow you to see your training jobs, uh, within the queue flow. It also has a plethora of different serving mechanisms, such as Seldin, uh, for, you know, deploying your, your machine learning models. You have Ks serving, you have TF serving. So Q4 is very, it's a very powerful tool for data scientists to utilize if they want a full end to end open source and know how to use Kubernetes. So it's just a, another way to do your machine learning model development and right with ML flow, it's actually a different piece of the machine learning pipeline. So ML flow mainly focuses on model experimentation, comparing different models, uh, during the training and it off it can be used with Q4. >>The complimentary Terry I think is what you're saying. Sorry. I know we are dramatically running out of time now. So that was really fantastic demo. Thank you very much, indeed. >>Exactly. Thank you. So yeah, I think that wraps it up. Um, one last thing I want to mention is there is this slide that I want to show in case you have any other questions, uh, you can visit hp.com/asml, hp.com/container platform. If you have any questions and that wraps it up. So thank you guys.

>>welcome back. And thanks for joining us for our second session. External data, your new leading indicators. We'll be hearing from industry leaders as they share best practices and challenges in leveraging external data. This panel will be a true conversation on the part of the possible. All right, let's get to >>it >>today. We're excited to be joined by thought spots. Chief Data Strategy Officer Cindy Housing Deloitte's chief data officer Manteo, the founder and CEO of Eagle Alfa. And it Kilduff and Snowflakes, VP of data marketplace and customer product strategy. Matt Glickman. Cindy. Without further ado, the floor is yours. >>Thank you, Mallory. And I am thrilled to have this brilliant team joining us from around the world. And they really bring each a very unique perspective. So I'm going to start from further away. Emmett, Welcome. Where you joining us from? >>Thanks for having us, Cindy. I'm joining from Dublin, Ireland, >>great. And and tell us a little bit about Eagle Alfa. What do you dio >>from a company's perspective? Think of Eagle Alfa as an aggregator off all the external data sets on a word I'll use a few times. Today is a big advantage we could bring companies is we have a data concierge service. There's so much data we can help identify the right data sets depending on the specific needs of the company. >>Yeah. And so, Emma, you know, people think I was a little I kind of shocked the industry. Going from gardener to a tech startup. Um, you have had a brave journey as well, Going from financial services to starting this company, really pioneering it with I think the most data sets of any of thes is that right? >>Yes, it was. It was a big jump to go from Morgan Stanley. Uh, leave the comforts of that environment Thio, PowerPoint deck and myself raising funding eight years ago s So it was a big jump on. We were very early in our market. It's in the last few years where there's been real momentum and adoption by various types of verticals. The hedge funds were first, maybe then private equity, but corporate sar are following quite quickly from behind. That will be the biggest users, in our view, by by a significant distance. >>Yeah, great. Thank um, it So we're going to go a little farther a field now, but back to the U. S. So, Juan, where you joining us from? >>Hey, Cindy. Thanks for having me. I'm joining you from Houston, Texas. >>Great. Used to be my home. Yeah, probably see Rice University back there. And you have a distinct perspective serving both Deloitte customers externally, but also internally. Can you tell us about that? >>Yeah, absolutely. So I serve as the Lord consultants, chief data officer, and as a professional service firm, I have the responsibility for overseeing our overall data agenda, which includes both the way we use data and insights to run and operate our own business, but also in how we develop data and insights services that we then take to market and how we serve our dealers and clients. >>Great. Thank you, Juan. And last but not least, Matt Glickman. Kind of in my own backyard in New York. Right, Matt? >>Correct. Joining I haven't been into the city and many months, but yes, um, based in New York. >>Okay. Great. And so, Matt, you and Emmett also, you know, brave pioneers in this space, and I'm remembering a conversation you and I shared when you were still a J. P. Morgan, I believe. And you're Goldman Sachs. Sorry. Sorry. Goldman. Can you Can you share that with us? >>Sure. I made the move back in 2015. Um, when everyone thought, you know, my wife, my wife included that I was crazy. I don't know if I would call it Comfortable was emitted, but particularly had been there for a long time on git suffered in some ways. A lot of the pains we're talking about today, given the number of data, says that the amount of of new data sets that are always demand for having run analytics teams at Goldman, seeing the pain and realizing that this pain was not unique to Goldman Sachs, it was being replicated everywhere across the industry, um, in a mind boggling way and and the fortuitous, um, luck to have one of snowflakes. Founders come to pitch snowflake to Goldman a little bit early. Um, they became a customer later, but a little bit early in 2014. And, you know, I realized that this was clearly, you know, the answer from first principles on bond. If I ever was going to leave, this was a problem. I was acutely aware of. And I also was aware of how much the man that was in financial services for a better solution and how the cloud could really solve this problem in particular the ability to not have to move data in and out of these organizations. And this was something that I saw the future of. Thank you, Andi, that this was, you know, sort of the pain that people just expected to pay. Um, this price if you need a data, there was method you had thio. You had to use you either ftp data in and out. You had data that was being, you know, dropped off and, you know, maybe in in in a new ways and cloud buckets or a P i s You have to suck all this data down and reconstruct it. And God forbid the formats change. It was, you know, a nightmare. And then having issues with data, you had a what you were seeing internally. You look nothing like what the data vendors were seeing because they want a completely different system, maybe model completely differently. Um, but this was just the way things were. Everyone had firewalls. Everyone had their own data centers. There was no other way on git was super costly. And you know this. I won't even share the the details of you know, the errors that would occur in the pain that would come from that, Um what I realized it was confirmed. What I saw it snowflake at the time was once everyone moves to run their actual workloads in this in the cloud right where you're now beyond your firewall, you'll have all this scale. But on top of that, you'll be able to point at data from these vendors were not there the traditional data vendors. Or, you know, this new wave of alternative data vendors, for example, like the ones that eagle out for brings together And bring these all these data sets together with your own internal data without moving it. Yeah, this was a fundamental shift of what you know, it's in some ways, it was a side effect of everyone moving to the cloud for costs and scale and elasticity. But as a side effect of that is what we talked about, You know it snowflake summit, you know, yesterday was this notion of a data cloud that would connect data between regions between cloud vendors between customers in a way where you could now reference data. Just like your reference websites today, I don't download CNN dot com. I point at it, and it points me to something else. I'm always seeing the latest version, obviously, and we can, you know, all collaborate on what I'm seeing on that website. That's the same thing that now can happen with data. So And I saw this as what was possible, and I distinctly asked the question, you know, the CEO of the time Is this possible? And not only was it possible it was a fundamental construct that was built into the way that snowflake was delivered. And then, lastly, this is what we learned. And I think this is what you know. M It also has been touting is that it's all great if data is out there and even if you lower that bar of access where data doesn't have to move, how do I know? Right? If I'm back to sitting at Goldman Sachs, how do I know what data is available to me now in this this you know, connected data network eso we released our data marketplace, which was a very different kind of marketplace than these of the past. Where for us, it was really like a global catalog that would elect a consumer data consumer. Noah data was available, but also level the playing field. Now we're now, you know, Eagle, Alfa, or even, you know, a new alternative data vendor build something in their in their basement can now publish that data set so that the world could see and consume and be aligned to, you know, snowflakes, core business, and not where we wouldn't have to be competing or having to take, um, any kind of custody of that data. So adding that catalog to this now ubiquitous access, um really changed the game and, you know, and then now I seem like a genius for making this move. But back then, like I said, we've seen I seem like instant. I was insane. >>Well, given, given that snowflake was the hottest aipo like ever, you were a genius. Uh, doing this, you know, six years in advance. E think we all agree on that, But, you know, a lot of this is still visionary. Um, you know, some of the most leading companies are already doing this. But one What? What is your take our Are you best in class customers still moving the data? Or is this like they're at least thinking about data monetization? What are you seeing from your perspective? >>Yeah, I mean, I did you know, the overall appreciation and understanding of you know, one. I got to get my house in order around my data, um, has something that has been, you know, understood and acted upon. Andi, I do agree that there is a shift now that says, you know, data silos alone aren't necessarily gonna bring me, you know, new and unique insights on dso enriching that with external third party data is absolutely, you know, sort of the the ship that we're seeing our customers undergo. Um, what I find extremely interesting in this space and what some of the most mature clients are doing is, you know, really taking advantage of these data marketplaces. But building data partnerships right there from what mutually exclusive, where there is a win win scenario for for you know, that organization and that could be, you know, retail customers or life science customers like with pandemic, right the way we saw companies that weren't naturally sharing information are now building these data partnership right that are going are going into mutually benefit, you know, all organizations that are sort of part of that value to Andi. I think that's the sort of really important criteria. And how we're seeing our clients that are extremely successful at this is that partnership has benefits on both sides of that equation, right? Both the data provider and then the consumer of that. And there has to be, you know, some way to ensure that both parties are are are learning right, gaining you insights to support, you know, whatever their business organization going on. >>Yeah, great one. So those data partnerships getting across the full value chain of sharing data and analytics Emmett, you work on both sides of the equation here, helping companies. Let's say let's say data providers maybe, like, you know, cast with human mobility monetize that. But then also people that are new to it. Where you seeing the top use cases? Well, >>interestingly, I agree with one of the supply side. One of the interesting trends is we're seeing a lot more data coming from large Corporates. Whether they're listed are private equity backed, as opposed to maybe data startups that are earning money just through data monetization. I think that's a great trend. I think that means a lot of the best. Data said it data is yet to come, um, in terms off the tough economy and how that's changed. I think the category that's had the most momentum and your references is Geo location data. It's that was the category at our conference in December 2000 and 12 that was pipped as the category to watch in 2019. On it didn't become that at all. Um, there were some regulatory concerns for certain types of geo data, but with with covert 19, it's Bean absolutely critical for governments, ministries of finance, central banks, municipalities, Thio crunch that data to understand what's happening in a real time basis. But from a company perspective, it's obviously critical as well. In terms of planning when customers might be back in the High Street on DSO, fourth traditionally consumer transaction data of all the 26 categories in our taxonomy has been the most popular. But Geo is definitely catching up your slide. Talked about being a tough economy. Just one point to contradict that for certain pockets of our clients, e commerce companies are having a field day, obviously, on they are very data driven and tech literate on day are they are really good client base for us because they're incredibly hungry, firm or data to help drive various, uh, decision making. >>Yeah, So fair enough. Some sectors of the economy e commerce, electron, ICS, healthcare are doing great. Others travel, hospitality, Um, super challenging. So I like your quote. The best is yet to come, >>but >>that's data sets is yet to come. And I do think the cloud is enabling that because we could get rid of some of the messy manual data flows that Matt you talked about, but nonetheless, Still, one of the hardest things is the data map. Things combining internal and external >>when >>you might not even have good master data. Common keys on your internal data. So any advice for this? Anyone who wants to take that? >>Sure I can. I can I can start. That's okay. I do think you know, one of the first problems is just a cataloging of the information that's out there. Um, you know, at least within our organization. When I took on this role, we were, you know, a large buyer of third party data. But our organization as a whole didn't necessarily have full visibility into what was being bought and for what purpose. And so having a catalog that helps us internally navigate what data we have and how we're gonna use it was sort of step number one. Um, so I think that's absolutely important. Um, I would say if we could go from having that catalog, you know, created manually to more automated to me, that's sort of the next step in our evolution, because everyone is saying right, the ongoing, uh, you know, creation of new external data sets. It's only going to get richer on DSO. We wanna be able to take advantage of that, you know, at the at the pacing speed, that data is being created. So going from Emanuel catalog to anonymous >>data >>catalog, I think, is a key capability for us. But then you know, to your second point, Cindy is how doe I then connect that to our own internal data to drive greater greater insights and how we run our business or how we serve our customers. Andi, that one you know really is a It's a tricky is a tricky, uh, question because I think it just depends on what data we're looking toe leverage. You know, we have this concept just around. Not not all data is created equal. And when you think about governance and you think about the management of your master data, your internal nomenclature on how you define and run your business, you know that that entire ecosystem begins to get extremely massive and it gets very broad and very deep on DSO for us. You know, government and master data management is absolutely important. But we took a very sort of prioritized approach on which domains do we really need to get right that drive the greatest results for our organization on dso mapping those domains like client data or employee data to these external third party data sources across this catalog was really the the unlocked for us versus trying to create this, you know, massive connection between all the external data that we're, uh, leveraging as well as all of our own internal data eso for us. I think it was very. It was a very tailored, prioritized approach to connecting internal data to external data based on the domains that matter most to our business. >>So if the domains so customer important domain and maybe that's looking at things, um, you know, whether it's social media data or customer transactions, you prioritized first by that, Is that right? >>That's correct. That's correct. >>And so, then, Matt, I'm going to throw it back to you because snowflake is in a unique position. You actually get to see what are the most popular data sets is is that playing out what one described are you seeing that play out? >>I I'd say Watch this space. Like like you said. I mean this. We've you know, I think we start with the data club. We solve that that movement problem, which I think was really the barrier that you tended to not even have a chance to focus on this mapping problem. Um, this notion of concordance, I think this is where I see the big next momentum in this space is going to be a flurry of traditional and new startups who deliver this concordance or knowledge graph as a service where this is no longer a problem that I have to solve internal to my organization. The notion of mastering which is again when everyone has to do in every organization like they used to have to do with moving data into the organization goes away. And this becomes like, I find the best of breed for the different scopes of data that I have. And it's delivered to me as a, you know, as a cloud service that just takes my data. My internal data maps it to these 2nd and 3rd party data sets. Um, all delivered to me, you know, a service. >>Yeah, well, that would be brilliant concordance as a service or or clean clean master data as a service. Um, using augmented data prep would be brilliant. So let's hope we get there. Um, you know, so 2020 has been a wild ride for everyone. If I could ask each of you imagine what is the art of the possible or looking ahead to the next to your and that you are you already mentioned the best is yet to come. Can you want to drill down on that. What what part of the best is yet to come or what is your already two possible? >>Just just a brief comment on mapping. Just this week we published a white paper on mapping, which is available for for anyone on eagle alfa dot com. It's It's a massive challenge. It's very difficult to solve. Just with technology Onda people have tried to solve it and get a certain level of accuracy, but can't get to 100% which which, which, which makes it difficult to solve it. If if if there is a new service coming out against 100% I'm all ears and that there will be a massive step forward for the entire data industry, even if it comes in a few years time, let alone next year, I think going back to the comment on data Cindy. Yes, I think boards of companies are Mawr and Mawr. Viewing data as an asset as opposed to an expense are a cost center on bond. They are looking therefore to get their internal house in order, as one was saying, but also monetize the data they are sitting on lots of companies. They're sitting on potentially valuable data. It's not all valuable on a lot of cases. They think it's worth a lot more than it is being frank. But in some cases there is valuable data on bond. If monetized, it can drop to the bottom line on. So I think that bodes well right across the world. A lot of the best date is yet to come on. I think a lot of firms like Deloitte are very well positioned to help drive that adoption because they are the trusted advisor to a lot of these Corporates. Um, so that's one thing. I think, from a company perspective. It's still we're still at the first base. It's quite frustrating how slow a lot of companies are to move and adopt, and some of them are haven't hired CDO. Some of them don't have their internal house in order. I think that has to change next year. I think if we have this conference at this time next year, I would expect that would hopefully be close to the tipping point for Corporates to use external data. And the Malcolm Gladwell tipping point on the final point I make is I think, that will hopefully start to see multi department use as opposed to silos again. Parliaments and silos, hopefully will be more coordinated on the company's side. Data could be used by marketing by sales by r and D by strategy by finance holds external data. So it really, hopefully will be coordinated by this time next year. >>Yeah, Thank you. So, to your point, there recently was an article to about one of the airlines that their data actually has more value than the company itself now. So I know, I know. We're counting on, you know, integrators trusted advisers like Deloitte to help us get there. Uh, one what? What do you think? And if I can also drill down, you know, financial services was early toe all of this because they needed the early signals. And and we talk about, you know, is is external data now more valuable than internal? Because we need those early signals in just such a different economy. >>Yeah, I think you know, for me, it's it's the seamless integration of all these external data sources and and the signals that organizations need and how to bring those into, you know, the day to day operations of your organization, right? So how do you bring those into, You know, you're planning process. How do you bring that into your sales process on DSO? I think for me success or or where I see the that the use and adoption of this is it's got to get down to that level off of operations for organizations. For this to continue to move at the pace and deliver the value that you know, we're all describing. I think we're going to get there. But I think until organizations truly get down to that level of operations and how they're using this data, it'll sort of seem like a Bolton, right? So for me, I think it's all about Mawr, the seamless integration. And I think to what Matt mentioned just around services that could help connect external data with internal data. I'll take that one step beyond and say, How can we have the data connect itself? Eso I had references Thio, you know, automation and machine learning. Um, there's significant advances in terms of how we're seeing, you know, mapping to occur in a auto generated fashion. I think this specific space and again the connection between external and internal data is a prime example of where we need to disrupt that, you know, sort of traditional data pipeline on. Try to automate that as much as possible. And let's have the data, you know, connect itself because it then sort of supports. You know, the first concept which waas How do we make it more seamless and integrated into, you know, the business processes of the organization's >>Yeah, great ones. So you two are thinking those automated, more intelligent data pipelines will get us there faster. Matt, you already gave us one. Great, Uh, look ahead, Any more to add to >>it, I'll give you I'll give you two more. One is a bit controversial, but I'll throw that you anyway, um, going back to the point that one made about data partnerships What you were saying Cindy about, you know, the value. These companies, you know, tends to be somehow sometimes more about the data they have than the actual service they provide. I predict you're going to see a wave of mergers and acquisitions. Um, that it's solely about locking down access to data as opposed to having data open up. Um to the broader, you know, economy, if I can, whether that be a retailer or, you know, insurance company was thes prime data assets. Um, you know, they could try to monetize that themselves, But if someone could acquire them and get exclusive access that data, I think that's going to be a wave of, um, in a that is gonna be like, Well, we bought this for this amount of money because of their data assets s. So I think that's gonna be a big wave. And it'll be maybe under the guise of data partnerships. But it really be about, you know, get locking down exclusive access to valuable data as opposed to trying toe monetize it itself number one. And then lastly, you know. Now, did you have this kind of ubiquity of data in this interconnected data network? Well, we're starting to see, and I think going to see a big wave of is hyper personalization of applications where instead of having the application have the data itself Have me Matt at Snowflake. Bring my data graph to applications. Right? This decoupling of we always talk about how you get data out of these applications. It's sort of the reverse was saying Now I want to bring all of my data access that I have 1st, 2nd and 3rd party into my application. Instead of having to think about getting all the data out of these applications, I think about it how when you you know, using a workout app in the consumer space, right? I can connect my Spotify or connect my apple music into that app to personalize the experience and bring my music list to that. Imagine if I could do that, you know, in a in a CRM. Imagine I could do that in a risk management. Imagine I could do that in a marketing app where I can bring my entire data graph with me and personalize that experience for, you know, for given what I have. And I think again, you know, partners like thoughts. But I think in a unique position to help enable that capability, you know, for this next wave of of applications that really take advantage of this decoupling of data. But having data flow into the app tied to me as opposed to having the APP have to know about my data ahead of time, >>Yeah, yeah, So that is very forward thinking. So I'll end with a prediction and a best practice. I am predicting that the organizations that really leverage external data, new data sources, not just whether or what have you and modernize those data flows will outperform the organizations that don't. And as a best practice to getting there, I the CDOs that own this have at least visibility into everything they're purchasing can save millions of dollars in duplicate spend. So, Thio, get their three key takeaways. Identify the leading indicators and market signals The data you need Thio. Better identify that. Consolidate those purchases and please explore the data sets the range of data sets data providers that we have on the thought spot. Atlas Marketplace Mallory over to you. >>Wow. Thank you. That was incredible. Thank you. To all of our Panelists for being here and sharing that wisdom. We really appreciate it. For those of you at home, stay close by. Our third session is coming right up and we'll be joined by our partner AWS and get to see how you can leverage the full power of your data cloud complete with the demo. Make sure to tune in to see you >>then

>>from >>around the >>globe. It's the Cube with digital coverage of pager duty. Summit 2020. Brought to you by pager duty. Hey, welcome back to Brady. Jeffrey here with the Cube in Palo Alto studios today. And we're talking about an upcoming event. It's one of our favorites. This will be the fourth year that we've been doing it. And it's pager duty summit. And we're excited to have from the pager duty team. She's Caroline Gus, the VP of corporate marketing from pager duty. Caroline, Great to see you. >>Hi, Jeff. Great to see you again. >>Absolutely. So, you know, I was thinking before we turn on the cameras we've been doing pager duty for I think this will be like, say, our fourth year that first year was in the cool, um, cruise ship terminal pier. I gotta written appear 27 which was which was nice. And then the last two years, you've been in the, you know, historic Westin ST Francis in downtown San Francisco, which is a cool old venue, but oh, my goodness. You guys were busting at the seams last year. So this year, year to go virtual. There's a whole bunch of new things that that you could do in virtual that you couldn't do in physical space. At least when you're busting out of the seems so First off, Welcome and >>talk a little >>bit about planning for virtual versus planning for a physical event from, you know, head of marketing perspective. >>Absolutely. I mean, the first thing that's changed for us is the number of people that can come. It's five x the number of people that were able to join us, the Western last year. So we have, uh, we we expect to have 10,000 people registered on attending age duty summit. The second thing is thea share number of sessions that we can put on. Last year, I think we had around 25 sessions. This year we have between 40 and 50 on again. That's because we're not constrained by space and physical meeting rooms, so it's being a really exciting process for us. We've built a fantastic agenda on. It's very much personalized, you know, developers come to our event. They love our event for the opportunity to learn mixed with their peers, get best practices and hands on experience. So we have many more of those types of sessions when we have done previously, and that things like labs and Bird of Feather Sessions and Emma's. But we've also built a whole new track of content this year for executives. Page Julie has, um, many of the Fortune 500 on 4100 customers. We work very closely with CEO CTO, so we have built sessions that are really designed specifically for that audience on I think for us it's really opened up. The potential of this event made it so much broader and more appealing than we were able to do when we were, As you say, you know, somewhat confined by the location in downtown San Francisco. >>I think it's such an interesting point. Um, because before you were constrained, right, If you have X number of rooms over a couple of days, you know you've got to make hard decisions on breakouts and what could go in and what can't go in. And, you know, will there be enough demand for these for this session versus another session? Or from the perspective of an attendee, you know, do they have to make hard tradeoffs? I could only attend one session at one oclock on Tuesday and I got to make hard decisions. But this is, you said really opens up the opportunities. I think you said you doubled. You doubled your sessions on and you got five X a number of registrations. So I think, you know, way too many people think about what doesn't happen in digital vs talking about the things that you can do that are impossible in physical. >>Yeah, I think at the very beginning. Well, first of all, we held our Amir summit events in London in July. So that was great because we got Thio go through this experience once already. And what we learned was the rial removal of hurdles in this process. So, to your point about missing the session because you're attending another session, we were calling this sort of the Pelton version of events where you have live sessions. It's great to be there, live participate in the live Q and A, but equally you have an entire on demand library. So if you weren't able to go because there was something else at the same time, this is available on demand for you. So we are actually repeating live sessions on two consecutive day. So on the Monday we're on everything on the Tuesday I ask because show up again for life Q and A at the end of their sessions. But after that it's available forever on an on demand library. So for us, it was really removing hurdles in terms of the amount of content, the scheduling of the content on also the number of people that content in attend, no geographical boundaries anymore. It used to be that a customer of ours would think, Well, I'll send one or two people to the page duty summit. They could learn all the great innovation from page duty, and they'll bring it back to the team that's completely changed. You know, we have tens of 20 signing up on. All of them are able to get that experience firsthand. >>That's really interesting. I didn't didn't even think about, you know, kind of whole teams being able to attend down instead of just certain individuals because of budget constraints, or you can't send your whole team, you know, a way for a conference in a particular area. But the piece to that you're supporting that were over and over is that the net new registrants goes up so dramatically in terms of the names and and and who those individuals are because a lot of people just couldn't attend for for various reasons, whether it's cost, whether it's, uh, geography, whether it's they just can't take time off from from from leaving their primary job. So it's a really interesting opportunity to open up, um, the participation to such a much bigger like you said five x five X, and increase in the registration. That's pretty good number. >>That's right. Yeah. I mean, that crossed boundaries gone away. This event is free on DWhite. That's actually meant is, as I say, you know, larger teams from the same company are attending. Uh, In addition, we have a number of attendees who are not actually paid to duty customers right now to previously. This was very much a community event for, you know, our page duty users on now we actually have a large number of I asked, interested future customers that will be coming to the event. So that's really important for us. And also, I think, for our sponsor partners as well, because it's bordering out the audience for both of us. So let's >>talk about sponsors for a minute, because, um, one of the big things in virtual events that people are talking about quite often is. Okay, I can do the keynotes, and I could do the sessions. And now I have all these breakout sessions for, um, you know, training and certification and customer stories, etcetera. But when it comes to sponsors, right sponsors used, you know, go to events to set up a booth and hand out swag and wander badge. Right? And it really was feeding kind of a top level down funnel. That was really important. Well, now those have gone away. Physical events. So from the sponsor perspective, you know, what can they expect? What? What do you know the sponsor experience at pager duty Summit. Since I don't have a little tiny booth at the Westin ST Francis given out swag this year. >>Yeah. So one important thing is the agenda and how we're involving our sponsors in our agenda this time, something that we learned is we used to have very long keynotes. You know, the keynote could be an hour long on involved multiple components and people would stay in that room for a now er on did really stay and watch sessions all day. So we learned in the virtual format that we need to be shorter and more precise in our sessions on that opened up the opportunity to bring in more of our partners, our sponsorship partners. So zendesk Salesforce, Microsoft some examples. So they actually get to have their piece of both of our keynote sessions and of our technical product sessions. I'm really explain both the partnership with pager duty, but also they're called technology and the value that they provide customers. So I think that the presence of sponsors in content is much higher than it was before on we are still repeating the Expo format, so we actually do have on Expo Hall that any time there's breaking between sessions, you could go over to the Expo ball, and it actually runs throughout as well, and you can go in and you can talk to the teams. You can see product demos, so it's very much a virtual version of the Expo Hall where you went and you want around and you picked up a bit of swag, >>so you mentioned keynotes and and Jennifer and and the team has always had a fantastic keynotes. I mean, I just saw Jennifer being interviewed with Frank's Luqman and and Eric Juan from Zoom By by Curry, which was pretty amazing. I felt kind of jealous that I didn't get to do that. But, um, talk tell us a little bit about some of the speakers I know there'll be some some, you know, kind of big rally moment speakers as well as some that are more down to technical track or another track. Give us some highlights on on some of the people. I will be sharing the stage with Jennifer. >>Absolutely, I said. I think what's really unique about Page duty Summit is that we designed types of content for different types of attendees. So if you're a developer, your practitioner, we have something like this from Jones of Honeycombs, who's talking about who builds the tools that we all rely on today, and how do they collaborate to build them together in this virtual world? Or we have J. Paul Reed from Netflix talking about how to handle the stress of being involved in incidents, So that's really sessions for our core audience of developers who are part of our community and pager duty really helps them day to day with with that job. And then we have the more aspirational senior level speakers who could really learn from a ZA leader. So Bret Taylor, president and CEO of Salesforce, will be joining us on the main stage. You'll be talking about innovation and trust in today's world on. Then we have Derrick Johnson. He is president of N A A. C P, and he'll be talking about community engagement and particularly voter engagement, which is such an important topic for us right now. Aan den. We have leaders from within our customers who are really talking about the way they use pager duty thio drive change in their organization. So an example would be porches, bro. He runs digital for Fox on, and he's gonna be talking about digital acceleration. How large organization like Fox can really accelerate for this digital first world that we find ourselves living in right now, >>right? Well, you guys have such a developer focus because pager duty, the product of solution, has to integrate with so many other, um, infrastructure, you know, monitoring and, uh, and all of all those different systems because you guys were basically at the front line, you know, sending them the signals that go into those systems. So you have such a broad, you know, kind of ecosystem of technology partners. I don't know if people are familiar with all the integrations that you guys have built over the years, which is such a key piece of your go to market. >>That's right. I mean, we we like to say we're at the center of the digital ecosystem. We have 203 170 integrations on. That's important because we want anyone to be able to use page duty no matter what is in their technology stack technology stacks today are more complex than they've ever been before, particularly with businesses having to shift to this digital first model since we all began shelter in place, you know, we all are living through digital on working and learning through digital on DSO. The technology stacks that power that are more complicated than ever before. So by having 370 integrations, we really know that we conserve pretty much any set of services that your business. It's using. >>Yeah, we've all seen all the means right about who's who's pushing your digital transformation. You know, the CEO, the CEO or or covert. And we all know the answer to toe what's accelerated that whole process. So okay, but so before I let you go, I don't even think we've mentioned the date. So it's coming up Monday, September, September 21st through Thursday, September 24th not at the West End Online and again. What air? What are you hoping? You're kind of the key takeaways for the attendees after they come to the summit? >>Yeah, a couple of things. I mean, first of all, I think will be a sense of belonging. Three attendees, the uses, a pager duty. They are really the teams that are at the forefront of keeping our digital services working on. But what that means is responding to incidents we've actually seen. Ah, 38% increase in the volume of incidents on our platform since covert and shelter in place began. Wait 30 >>38% increase in incidents since mid March. >>That's correct. Since the beginning of on bear in mind incidents. Prior to that in the six months prior, they were pretty flat. There wasn't instant growth. But what we've also seen is a 20% improvement in the time that it takes to resolve an incident from five minutes down to four minutes. So what that really means is that the pager duty community is working really hard. They're improving their practices. Hopefully our platform, our platform is a key part of how, but these are some people under pressure, so I hope that people can come and they can experience a sense of belonging. They can learn from each other about experiences. How do you manage the stress of that situation on what are some of the great innovations that make your job easier in the year ahead? The second thing that we don't for that community is that we are offering certification for P. D. You page due to university for free this year. It's of course, with a value of $7500. Last year, you would attend page duty summit on you would sit through your sessions and you would learn and you would get certified. So this year it's offered for free. You take the course during summit. But you can also carry on if you miss anything for 30 days after. So we're really feeling that, you know, we're giving back there, offering a great program for certification and improved skills completely free to help our community in this in this time of pressure, >>right? Right. Well, it is a very passionate community, and, you know, we go to so many events and you can you can really tell it's palatable, you know, kind of what the where the tight communities are and where people are excited to see each other and where they help each other, not necessarily only at the event, but you know, throughout the year. And I think you know a huge shout out to Jennifer on the culture that she's built there because it is very warm. It's very inclusive, is very positive. And and that energy, you know, kind of goes throughout the whole company and ice the teaser. You know this in something that's built around a device that most of the kids today don't even know what a pager is, and just the whole concept of carrying a pager and being on call right and being responsible. It's a very different way to kind of look at the world when you're the one that has that thing on your hip and it's buzzing and someone's expecting, Ah, return call and you gotta fix something So you know, a huge shout out to keep a positive and you're smiling nice and big culture in a job where you're basically fixing broken things most of the time. >>Yeah, absolutely. I mean, there's, I think, a joke that we make you know these things only break on Friday night or your wedding anniversary or Thanksgiving. But one of the announcements we're most excited about this year is the level of automation on artificial intelligence that we're building into our platform that is really going to reduce the number of interruptions that developers get when they are uncle. >>Yeah, I look forward to more conversations because we're gonna be doing a bunch of Cube interviews like Normal and, uh, you know, applied artificial intelligence, I think, is where all the excitement is. It's not a generic thing. It's where you applied in a specific application to get great business outcomes. So I look forward to that conversation and hopefully we'll be able to talk again and good luck to you and the team in the last few weeks of preparation. >>Thanks so much, Jeff. I've enjoyed talking to you. Thanks for having me. >>Alright. You too. And we'll see you later. Alright. She is Caroline. I'm Jeff. You're watching the Cube. Thanks for watching. We'll see you next time.

everybody welcome to this digital coverage of the verdict of big data conference you're watching the cube and my name is Dave Galante it's my pleasure to invite in Ben white who's the senior database engineer at Domo been great to see you man thanks for coming on great to be here and here you know as I said you know earlier when we were off camera I really was hoping I could meet you face to face and in Boston this year but hey I'll take it and you know our community really wants to hear from experts like yourself but let's start with with domo is the company share with us what Domo does and what your role is there well if Parker can go straight to the official what Domo does is we provide we process data at bi to scale with we provide VI leverage a cloud scale in record time and so what that means is that you know we are a business operating system where we provide a number of analytical abilities to companies of all sizes but we do that at cloud scale and so I think that difference is quite a bit so a lot of your work if I understand it and just in terms of understanding with Domo does--is there's a lot of pressure in terms of being real-time it's not like you sometimes don't know what's coming at you so it's AD Hoch I wonder if you could sort of talk about that confirm that and maybe add a little color to it yeah absolutely absolutely that's probably the biggest challenge it is to being the operating Domo is that it is an ad hoc environment and certainly what that means is that you've got analysts and executives that are able to submit their own queries without very with very few limitations so from an engineering standpoint the challenge in that of course is that you don't have this predictable dashboard to plan for when it comes to performance planning and so it definitely presents some challenges for us that we've done some pretty unique things I think to address those right sounds like your background fits well with that I understand here if people have called you a database whisperer and an envelope pusher what does that mean to do a DBA in this in this day and age well the whisperer part is probably a lost art in the sense that it's not really sustainable right the idea that you know whatever it is I'm able to do with the database it has to be repeatable and so that's really what analytics comes in right and that's where pushing the envelope comes in in a little right away that's what vertical comes in with this open architecture and so as a person who has a reputation for saying I understand this is what our limitations should be but I think we can do more having a platform like vertical is such an open architecture kinda lets you push those limits by the bit I mean I've always felt like you know vertical when I first saw the Stonebreaker architecture and doctors some of the early founders I always felt like it was the Ferrari of databases certainly at the time and it sounds like you guys use it in that in that regard but talk a little bit more about how you use Vertica why in a ym ppy Vertica you know why why can't you do this with our DBMS educate us a little bit on some of the basics but for us it was part of what I mentioned when we start and we talked about the very nature of the demo platform where there's a an incredible amount of resiliency required and so Vertica the NPP platform of course allows us to build individual database clusters that can perform best for the workload that may be assigned to them so the the open the expandable the the the ability to grow vertically as your base grow those are all important factors when you're losing early on right without a real idea of how growth would be or what it would look like if you were kind of doing that something to the dark you looked at the vertical platforming you can see well as I grow I can kind of feel with this right I can do some some unique things with the platform in terms of this poking architecture that will allow me to not have to make all my decisions today right about Harlem so you're using Vertica I know at least in part you you working with AWS as well can you describe sort of your environment that you give anything on Prem is everything in the cloud what's your setup sure we have a hybrid cloud environment where we have a significant presence in public files in our own private cloud and so yeah having said that we certainly have a really an extensive presence I will say an AWS and so they're definitely the partner of our when it comes to providing the databases the server power that we need to operator but from the standpoint of engineering and architecting a database what was some of the challenges that you faced when you had to create that hybrid architecture what did you face and how did you overcome that well you know some of the there are some things we need faced in terms of wine and made it easy that Vertica and AWS have their own they play well together we'll say that and so vertical is designed to reprise I'm gonna AWS and so that part of it the care of itself not our own private cloud and being able to connect that because our public clouds has been a part of our own engineering ability and again I don't want to make a little light of it it's certainly not impossible and so we've some of the challenges though this pertains to the database really were in their early days that you mentioned when we talked a little bit earlier about marathas most recent Eon mode and I'm sure you'll get to that but when I think of our early challenges some of the early challenges were the architecture of enterprise mode when I talk about all of these this idea that we could have unique databases or database clusters of different sizes so this elasticity that's really if you know that the enterprise architecture that's not necessarily dandified architecture so we added this Munich things I think to overcome that right early to get around the rigidness though enterprise yeah I mean I hear you right Enterprise is complex and and you like when things are hardened and fossilized but in your ad hoc environment that's not what you needed so talking more about Aeon mode what what is e on mode for you and how do you apply it what are some of the challenges and opportunities there that you found um so the opportunities were certainly in its elastic architecture the ability to separate the storage immediately meant that for some of the unique data paths that we wanted to take right we could do that fairly quickly certainly we could expand databases right quickly but more importantly now you could reduce because previously in the past right when I mention the Enterprise Architect with the idea of growing a database in itself has its pain right as far as the time it takes to speed the data in that but to read to then think about taking that database back down no Innova though all of us under the eon right you had this elasticity where you could kind of start to think about auto scaling where you go up and down and maybe used to save some money or maybe you could improve performance or maybe in needham and at a time when the customers needed most in a real way right so it was definitely a game in that regard I always have to talk to the customers because I get to you know I hear from the vendor what they say and I think they sort of validate it so you know Vertica talks a lot about separating compute and storage they're not the only one from an architectural standpoint to do that but Vertica stresses that they're the only one that does that with a hybrid architecture they can do it off ram they can do it in the cloud from your experience well first of all is it true you may or may not know it is that advantageous to you and if so why well first of all it's certainly true earlier in some of the original beta ethnic for the arm prim GI mode stuff we I was able to participate in it and be aware of it so it's certainly a reality day I'm it's actually supported on pure spirit with flash played and it's time quite impressive you know for who who that who that will be for tough one a Spartacus question that they're probably still answering but I think obviously some enterprise users that probably have some hybrid cloud right they have some architecture they have some hardware that their sales want to make you so we certainly would probably fit into one of their you know their market segments that they would say we might be the wants to look at on pram er mo begin the the beauty of it is the elasticity right that the idea that you could have this and so a lot of times so I want to go back real quick to separating them and you know we start by separating it and I like to think of it maybe more as like decoupling because a new in a true way it's not necessary separated there's ultimately you bring the compute and the doors back together but to be able to typically couple it quickly replace knows bring in those that's certainly fits I think what we were trying to do in building this Emma I'll me let the ecosystem that could respond to a unknown or of a customer demand I see thank you for that clarification because you're right it's really not separating its decoupling in it that's important because you can scale them independently but you still need compute and you still need storage to run you your workloads but from a cost standpoint you're not to buy it in in chunks you can you can't buy granular segments for whatever your workload requires is that is that the correct understanding yeah and to be able to the ability to be able to reuse compute throw it in a scenario of AWS or even in the scenario your on-prem solution you've got this data that's safest here and ask for your in your storage but then the compute that you have you can reuse that right you could have a scenario that you have some query that needs more analytic more firepower more memory more what have you that you haven't so you can kind of move to the next important right that's maybe more important then and I grow them separately can I can I borrow it can I borrow that computer use for my perfect give it back type of thing and you can do that when you're so easily a couple different ooh all right and likewise if you have a down period where customers aren't using it you'd like to be able to not use that if you no longer require if you'd like to give it back go in it open the door to a lot of those things that allow performance and cross the spark to meet up we're going to ask you a question winsome pure a couple times are you using pure flash blade on-prem is that correct that is the solution that is supported that is supported by Vertica for the on print so at this point we were we have been discuss with them about some our own PLC's for that time before again we back to the idea of how do we see ourselves using it and so we've certainly discussed the feasibility of bringing it in and give it a job but that's not something we're Oh happily all right now then what is Domo for Domo tell us about that we really started this this idea even in the company where we say you know we should be using Domo in our everyday business the sales folks the marketing folks right everybody we're gonna use Domo it's a business platform for us in the engineering team it was kind of like well if we use Domo say for instance to be better at the database engineers now we've pointed Domo edits tell fried verdict is running Domo in the background for some degree and then we turn around and say hey Domo how can we better at running you and so it became this kind of cool thing we played with where we're now able to put some dumb methods together where we can actually do their eye we can monitor using our platform it's really good at processing large amounts of data and spitting out useful analytics right we take those analytics out make recommendation changes that the day so now you've got still more for Domo happening it allows us to sit at home and and work now even when we have to even before we had to well you know look look at us here right it couldn't mean in Boston physically we're now meeting remote you're you're on a hot spot because you got some weather and your satellite internet and in Atlanta and we're having a great conversation so so we're here with with Ben white who's the senior database engineer at Domo I want to ask you about some of the envelope-pushing that you've done around autonomous you hear that that word thrown around a lot means a lot of things to a lot of different people how do you look at autonomous and how does it fit with Eon and some of the other things that you're doing you know I'm a tall amidst the idea of economy is something that I don't even know that I'm I have already ready to define and so even in my discussion I often mention it as a road to it exactly where it is it's hard to pin down because there's always this idea how much trust do you give right to the system or how much how much is truly autonomous how much authority is being intervened by us the engineers so I do hate on using it but on this road towards autonomy when we look at what would how we're using Domo and even what that really means to vertical because in a lot of my examples and a lot of the things that we've engineered a demo work designs maybe over something I thought was a limitation day and so many times Oh as we've done that verdict is kind of met us like right after we've kind of engineered our architecture stuff than we thought it felt on our side Vertica has some released it kinda addresses it so the autonomy idea and the idea that we could analyzed metadata make recommendations and then execute those recommendations without intervention is that road to autonomy and once the databases start able to do that you can see in our ad-hoc environment how that would be pretty pretty useful where with literally millions of queries every hour trying to figure out what's the best you know probably for years I felt like I I T folks sometimes we really did not want that automation they wanted the knobs to turn but but I wonder if you comment I feel as though the level of complexity now with cloud with on-prem with you know hybrid multi clouds the scale the speed the real-time it just gets the pace is just too much for for humans and so it's almost like you know the industries is gonna have to capitulate to the Machine and then really trust the machine but I'm sitting I'm still sensing from you a little bit of hesitation there but light at the end of the tunnel I wonder if you could comment sure I think that in the light of the tunnel is even in recent months in recent we've really began incorporating more machine learning in artificial intelligence to the model right and back to where we're saying it so I do feel they were getting close for too finding conditions that we don't know about because right now our system is kind of a rule rules based system where we've said well these are the things that we should be looking for these are the things that we think are a problem to mature to the point where the database is recognized and anomalies and taken on at imagining saying these are problems you didn't know happen and that's kind of the next step right identifying the things you didn't know and that's where that's the path we're on now and that's probably more exciting even then kind of nailing down all the things you think you know and to figure out what we don't know yet so I want to close with I know you're a prominent member of the respected member of the Vertica a customer advisory board you know without divulging anything confidential to me what are the kinds of things that you want Vertica to do going forward I think some of the end a in database autonomy the ability to take some of the recommendations that we know we can derive from the metadata that already exists in the platform and start to execute some of the recommendation another thing we talk about and I'm gonna pretty open about talking to it is talking about it is the new version of the database designer I think it's something that I'm sure they're working on lightweight something that can give us that's database design without the overhead those are two things I think as they nail or particularly the database designer as they respect that they'll really have all the components in place to do in based economy and I think that's just some victory where they're headed yeah nice well Ben listen I really appreciate you coming on your a thought leader be very open open-minded verdict is you know really open community I mean they've always been quite transparent in terms of where they're going it's just awesome to have guys like you on the cube to share with our community so thank you so much and hopefully we can meet face to face currently absolutely will you stay safe in Boston I'm one of my favorite towns and so no doubt when this when the doors get back open I'll be from coming down or coming I'm gonna do work take care all right and thank you for watching everybody Villante with a cube we're here covering the virtual Vertica of big data conference you [Music]

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Welcome back to the Cube. We are in Las Vegas, Lisa Martin with John Wall's. I'm very excited that we're kind of color coordinated >>way. Didn't compare notes to begin with, but certainly the pink thing. It's worth it if >>you like. You complete me. >>Oh, thank you. Really, Joe, I don't hear that very often. My wife says that >>you tell that we're at the end of day one of the coverage of A W s three in bed. Good day, though. Yes, it has been very excited. We have a couple of guests joining us for our final segment on this. Please welcome. We have Bill Gerard CTO of Digital Transformation and Scale solutions at Intel Bill, welcome to our show. >>Thank you very much. Happy to be here >>And one of our friends. That's no stranger to the Cube. One of our former host, Bobby Allyn, the CEO of Cloud Generate. Bobby. >>Thank you. Thank you for having us. >>Guys, here we are. This there has not been a lull in the background noise all day. Not reinvent day one. But Bobby want to start with you. Talk to her audience about cloud genera. Who are you guys? What do you do? And what's different about what you're delivering? >>One of the first things is different about Claude Generous where we're located. So we're in Charlotte, which I call Silicon South. So we're kind of representing the East Coast, and we're a company that focuses, focuses on helping with workload, placement and transformation. So where you don't know whether something should go on from off grim. If you put it in Amazon, which service's should have consumed licensing models? Pricing models way help you make data driven decisions, right? So you're not just going based on opinion, you're going based on fact. >>And that's challenging because, you know, in the as, as John Ferrier would say, No Cloud Wanda Otto, which was compute network storage, it was the easy I shouldn't say easy, but the lift and shit applications that enterprises do are these workloads should go to the cloud. Now we have you know what's left over, and that's challenging for organization. Some of the legacy once can't move. How do you help from a Consul Tatum's down point that customers evaluate workloads? What data are they running? What the value that data has and if they are able to move some of those more challenging applications. >>So part of the framework for us, Lisa, is we want to make sure we understand what people are willing and able to change right, because sometimes it's not just about lower costs. Sometimes it's about agility, flexibility, deploying a different region. So what we often start with his wit is better look like you would assist us with life for your organization. And so then, based on that, we analyze the applications with an objective, data driven framework and then make sure the apse land where they're supposed to go. We're not selling any skewer product. We're selling advice to give you inside about what you should do, >>Bobby, I think. And maybe Bill to you could chime in here on this. If you give people a choice, What does this look like? What you know, What do you want? I don't want to do anything right. I want to stay put, right? But that obviously that's not an option, But you I'm sure you do get pushed back quite a bit from these almost the legacy mindset. And we've talked a lot about this whole transformation versus transition. Some people don't want to go, period. So how do you cajole them? Persuade them bring them along on this journey? Because it's gonna be a long trip. Yeah, I think you gotta pack a lunch. >>It's a good point. I think what we've seen, most of them have data experience that this is a tried and elements didn't get the results that they expected. This is where you know, the partnership that we have with call General. Really? You know that data driven, intelligent, based planning is super important, right? We want to really fundamentally health organizations move the right workloads, make sure they get the right results and not have to redo it. Right? And so part of that, you know, move when you're either past scars or not used to what you're doing. Give him the data and the information to be able to do that intelligently and make that as fast as they can. And you know, at the right, you know, experience in performance from a capability perspective. >>So so many businesses these days, if they're not legacy if they're not looking in the rear view mirror, what is the side mirror site? Objects are closer than they appear, even for Amazon. Right? For all of these companies, there are smaller organizations that might be born in a cloud compared to the legacy two words. And if they're not looking at, we have to transform from the top down digitally, truly transform. Their business may not be here in a year or two, so the choice and I think they need to pack a lunch and a hip flask for this because it's quite the journey. But I'm curious with the opportunity that cloud provides. When you have these consultation conversations, what are This? Could be so transformative not just to a business, but to a do an entire industry. Bill talked to us from your perspective about some of the things that you've seen and how this next generation of cloud with a I machine learning, for example, can can really transfer like what's the next industry that you think is prime to be really flipped upside down? >>Well, the good news is I think most of the industries in the segment that we talked to have realized they need to some level of transformation. So doing the business as usual really isn't an option to really grow and drive in the future. But I do think the next evolution really does center on what's happening in a I and analytics. Whether it's, you know, moving manufacturing from video based defect detection, supply chain integrity. You know what's happening from a retail was really the first in that evolution, but we see it in health care in Federal Data Center modernization, and it's really moving at a faster pace and adopting those cloud technologies wherever they needed, both in their data center in the public, cloud out of the edge. And we'll start to see a real shift from really consolidation in tow. Large hyper converts, data centers to distributed computing where everything again. And that's where we're excited about the work we're doing with the Amazon, the work we're doing with Eyes V partners to be at the capability where they need it, but I think it will be really the next. Evolution of service is everywhere. >>Never talk us through an example or use case of a customer that you're working with, a cloud genera with intel and and a W S. What does that trifecta look like for, say, a retailer or financial service is organization >>so that that looks like this? ELISA. When we when we talk about workload placement, we think that most companies look at that as a single question. It's at least a five fold question. Right there is the venue. There's the service. There's the configuration, the licensing model and the pricing model. You need to look at all five of those things. So even if you decided on a DBS is your strategic partner, we're not done yet. So we have a very large financialservices customer that I can't name publicly. But we've collaborated with them to analyze tens of thousands of workloads, some that go best off from some that go best on for him. And they need guidance and coaching on things like, Are you paying for redhead twice your pay for licensing on him? Are you also paying for that in the cloud? There are things that maybe should be running an RT s database as a service. Here's your opportunity to cut down on labor and shift some of the relationships tohave, toe re index and databases is not glamorous or differential to value for your business. Let's take advantage of what a TBS does well and make this better for your company. One of the things that I want to kind of introduce to piggyback on your question. We lean on people process technology as kind of the three, the three legged horse in the Enterprise. I want to change that people process product or people process problem. We're falling in love with the tech and getting lazy. Technology should be almost ubiquitous or under the covers to make a product better or to solve a problem for the customer. >>Well, maybe on that, I mean automation concern to come in and make a big play here because we're taking all these new tasks if you could automate them that you free your people, your developers to do their thing right. So you raise an interesting point on that about being lazy and relying on things. But yet you do want off put our offload some of these nasty not to free up that creativity and free up the people to do what they're supposed to be doing. It's a delicate balance, though, isn't it? It is. It is. This >>is where I think the data driven, you know, informed decisions important. We did a lot of research with Cloud Jenner and our customers, and there's really four key technical characteristics when evaluating workload. The 1st 1 of course, is the size of the data. Where is the created words They use Words that consumed the 2nd 1? Is the performance right? Either performance not only to other systems around it or the end user, but the performance of the infrastructure. What do you need out of the capability? The level of integration with other systems? And then, of course, security. We hear that time and again, right? Regulatory needs. What are we having from top secret data to company sensitive data? Really Getting that type of information to drive those workload placement decision becomes at the forefront of that on getting, you know, using cloud gender to help understand the number of interfaces in and out the sides of the data. The performance utilization of the system's really helps customers understand how to move the right workload. What's involved and then how to put that in the right eight of us instance, and use the right ideas capabilities, >>and you and you both have hit on something here because the complexity of this decision, because it's multi dimensional, you talked about the five points a little bit ago. Now you talked about four other factors. Sue, this is not a static environment, No, and to me that as you're making a decision, that point is what's very difficult for, I would assume for the people that you're interfacing with on the company level. Yes, because it's a moving target for them, right? They just it's it's dynamic and changing your data flows exponentially. Increasing capabilities are changing. How do you keep them from just breaking down? >>I don't want to jump in on that, because again, I'm going to repeat this again. That my thesis is often technology is the easy part. We need to have conversations about what we want to do. And so I had a conversation earlier today. Think of Amazon like a chef. They could make anything I want, but I need to decide what I want to eat. If I'm a vegan and he wants steak. That's not Amazons fault. If they can't cook something, that's a mismatch of a bad conversation. We need to communicate. So what I'm finding is a lot of executives are worried about this. There were Then you're going to give me the right the wrong answer to the right question. The reality is you may have the wrong question. First of all right, the question is usually further upstream, so the worry that you're gonna give me the wrong answer to the right question. But often you need to worry that you're getting your starting with the wrong question. You're gonna get the right answer asked the right question first. And then you got a chance to get to the final destination. But >>and then he in this multi cloud world that many organizations live in, mostly not My strategy could be by Emma A could be bi developer preference for different solutions. A lot of Serios air telling us we've inherited a lot of this multi cloud and technical debt. Exactly. So does not just compound the problem because to your point, I mean you think of one way we hear so many different stats about the number of clouds that on average enterprises using is like 5 to 9. That whole world. That's a reality for organizations. So in terms of how the business can be transformed by what you guys are doing together, it seems like there's a tremendous opportunity there. But to your point, Bobby, where do you start? How do you help them understand what? That right first question is at the executive level so that those four technical points that Bill talked about Tek thee you know, the executive staff is all on board with Yes, this is the question we're asking then will understand it. The technology is right. Sold >>it. It's got to start with, Really? What? The company's business imperatives, right? It can't start with an I t objective. It's it's Are we moving into new markets? Do we need thio deploy capabilities faster? Are we doing a digital customer experience? Transformation? Are we deploying new factories, new products into new regions, and so really the first areas? What's the core company strategy, imperatives of the business objectives? And >>then how >>does I t really help them achieve that? In some cases, it may be we have to shift and reduce our data center footprints way have to move capabilities to where we have a new region. Deployments, right? We've got to get him over to Europe. We don't have capabilities in Europe. We're going to Asia. I've got a mobile sales force now where I need to get that customer, meet the customer where they're doing, you know, in the retail store, and >>that >>really then leads quite simply, too. What are the capabilities that we have in house that we're using? >>How are >>they being utilized? And he's using them, and then how do we get them to where they need to be? Some cases accost, imperative. Some cases and agility, Time to market and another's and we're seeing this more often is really what are the new sets of technologies? A. I service is training in forgetting that we're not experience to do and set up, and we don't want to spend the time to go train our infrastructure teams on the technology. So we'll put our data scientists in there figuring out the right set of workloads, the right set of technology, that we can then transform and move our applications to utilize it really starts, I think with the business conversation, or what's the key inflection point that they're experiencing? >>And have you seen that change in the last few years that now it's where you know, cloud not cloud. What goes on Cloud was an I t conversation to your point, Bill. And then the CEO got involved in a little bit later. But now we're we're seeing and hearing the CEO has got to be involved from a business imperative perspective. >>Share some data, right? Uh, so, you know, a couple of years ago, everybody was pursuing cloud largely for cost. Agility started to become primary, and that's still very important. A lot of the internal enterprise data modernizations were essentially stalled a bit because they were trying to figure how much do we move to the the public cloud, right. We want to take advantage of those modern service is at that time, we did a lot of research with our partners. He was roughly 56% of enterprise workload for in their own data center. You know, the rest of them Republic Cloud. And then we saw really the work, the intelligent workload discussion that says we've had some false starts. Organizations now really consistently realize they need both, you know, their own infrastructure and public cloud, and we've actually seen on increase of infrastructure modernization. While they're moving more and more stuff to the cloud, they're actually growing there on centre. It's now roughly 59% on Prem today for that same business, and that's largely because they're using more. Cloud service is that they're also even using Maur on premise, and they're realizing it's a balance and not stalling one or starving one and then committing to the other the committing to both and really just growing the business where it needs to go. >>Strategic reasons. All right? >>Yes, well, there should be four strategic reasons. There aren't always back to your question about which question asked. One of the questions I often ask is, What do you think the benefits will be if you go to cloud? And part of what happens is is not a cloud capability? Problem is an expectation problem. You're not gonna put your GOP system in the cloud and dropped 30% costs in a month, and so that's where we need to have a conversation on, You know, let's iterating on what this is actually gonna look like. Let's evolve the organization. Let's change our thinking. And then the other part of this and this were clouded or an intel come in. Let's model with simulation looks like. So we're gonna take those legacy work clothes unless model containers. Let's model Micro Service is so before you have to invest in transformation to may not make sense. Let's see what the outcome's look like through simulation through a through M l and understand. Where does it make sense to apply? The resource is, you know, to double click on that solution that will help the business. >>I was gonna finish my last question, Bobby, with you saying, Why, Cloud General? But I think you just answered that. So last question for you, though, from from an expectation perspective, give me one of your favorite examples of customer whatever kind of industry there and that you've come in and helped them really level, set their expectations and kick that door wide open. >>That's tough, many >>to choose from. >>Yeah, let me let me try to tackle that one quickly. Store's computer databases. Those are all things that people look at I think what people are struggling with the most in terms of kind of expectations is what they're willing and able to change. So this is kind of what I leave on. Bill and I talked about this earlier today. A product is good, a plan is better. A partnership is best. Because with the enterprises of saying is, we're overwhelmed. Either fix it for me or get in there with me and do it right. Be in this together. So what we've learned is it's not about were close applications. It's all kind of the same. We need help. We're overwhelmed. I want a partner in telling Claude Juncker the get in this thing with me. Help me figure this out because I told you this cloud is at best a teenager. They just learned how to drive is very capable, but it needs some guard rails. >>I love that. Thanks you guys So much for explaining with Johnny what you guys are doing together and how you're really flipping the model for what customers need to be evaluated and what they need to be asking. We appreciate your time. >>Thank you for having us >>our pleasure. Thank you. for John Wall's I'm Lisa Martin. You've been watching the Cube at Reinvent 19 from Vegas. Wants to go tomorrow.

>>live from Boston, Massachusetts. It's the Q covering I. F s World Conference 2019. Brought to you by I. F. S. >>Welcome back to Boston, everybody. You're watching The Cube. The leader in live tech coverage is Day one coverage of the I. F s World Conference. Darren Russo's here is the CEO of F S Darren. Thanks for coming back in the Cube. Great TV again. So last year was your first year. He was kind of laid out your vision at the World Conference. How's progress? >>Yeah, Look, it's going incredibly well. We were really focused on how we go from being a pretty fragment of global business to being, you know, an integrated business where we were able to operate. You know, its scale globally in a very homogenous way, where the customer experience was the same, irrespective where they engaged with us. And, you know, we've made a tremendous amount of progress with it, So you know, the business is growing really strongly. Net revenues up 22% year on year. I lost its revenues up 40% year on year are clouds up in the triple digits, so you know it's tough to be critical of how it's going so far. >>That's great, Great. You're growing faster than your peers. I think the stat was you gave us three Ex factory except in the industry would be awesome. Is that means that your primary benchmark do you want? You want to gain share? You want to go faster than the big whales, I presume. I >>think two things One is customer satisfaction, we believe, is the key indicator of long term success. S O. You know, we're the number one ranked European efforts. Salmon gotten appearance sites. That's that is and always will be my number. One metric. Can we be way the number one from a customer satisfaction perspective? And then I believe the revenue stats will follow and you know that's where we are. So certainly, if you look at our our core peers, the big G R P vendors, all of them are flat on. Dhe were growing 20 ships since >>one of the things you mentioned in your Cube interview last year was one of the things that you wanted to focus on was I'll call regional alignment. Paul and I used to work for I D. G. I worked for I. D. C. You were editor in chief of Computer World. We work for a company, had more offices overseas and IBM, and it was really hard to herd the cats. And that was one of the things that you cited. Have you been able to get people generally poor or at the same time? And how has that affected your business? Yeah. Look, I >>think the big challenge before I arrived was that there wasn't really a strategy of global strategy for the business. My face had a way of working and there was a strong culture, but there wasn't really a strategy. And obviously it's difficult to be critical of people when they not following the strategy when there isn't one s o. You know, Step one was really making sure that we had a strategy on DDE that was really about being focused on the five industries that we focused on, focused on three solutions on dhe focused on the six segments of customer, which is half a 1,000,000,000 to 5 billion. So now, globally, you know, irrespective the office that you go to, um anywhere in the world, they're focused on those five industries they focused on those three solutions and they're focused on their customer segments. So it helps me. P. M >>I said during our preview video video this morning that I've been around this industry as long as I f s has, until last year had never even heard of it. Is that just me being clueless? There's something there >>that we were just saying before we started that we're the definitely the biggest software business you've never heard of. Um, and and and that's common, I think, you know, we were There are a couple of factors. One is that the business was very European centric. Andi didn't really engaged in a tremendous amount of marketing and media prison. So, you know, those are elements that, you know, I think we're doing a better job off now, But we have a long way to go. The challenge that we have is that where we compete, we win when we get in and were able to tell our story, and we're able to show the value we win. We just don't get into as many deals as we need to. And that's the challenge we have. >>Yeah, there was a lot of talk this morning about the importance of those five pillars of those five industries. If you're going to become the next S A P, you're gonna have to branch out beyond that. What is your thinking about diversify >>becoming the next? They say he is definitely not my ambition, You know, I think way remain focused on customer satisfaction. And, you know, I think that there's a there's a difference. Whatever it is leading them, it's not customer satisfaction. You worked >>there for four years. >>I worked there for four years. I know. I think the big thing for me is is that we've got to stay focused on their customer voice. They focused on what delivers value for our customers beyond just the rhetoric and hyperbole. You know, I think when you when you listen to a lot of the complexity that our customers are facing today, any customers are facing. Companies are facing increasingly disruptive times, and the tech industry is making life more difficult for them. The more best of breed solutions get both. The more fragments that potential the landscape is, the more complex it becomes for customers if they have to try and figure out. How do we integrate these things and derive value from this highly fragmented landscape? So you know, we're trying to solve that problem. How do we make it easier for customers to challenge in their industry? And that's where this whole for the challenges has check comes from. How do we help him to be disruptive in their industry? Have competitive advantage? >>That seems to be a sort of a fundamentally different thing about your approach, though. Is this focus on those vertical industry's most e r P companies did not do that. Is that something that is core to your values? >>Look, I >>think what we recognize is that as you move to the cloud, you have to drive to standard. That's just the reality of going to the cloud on what's happening for the horizontal E. R B vendors. So the locks of ASAP and Oracle is that they have one e r P solution that fits every industry. So if it's good for health insurance and it's good for a bank, then it's difficult to really get your head around the fact that it could be good for a defense manufacturer, but the functional requirements is simply vastly different on that means that you have to customize them. If you have to customize that, they can go to the cloud. So what we believe is that you have to have this vertical specialization, the five industries that we serve us all. A lot of commonality in the process is that they use. And that's why that vertical strategy is so key to our success. So you won't see us going into financial service is, or health care or retail worth that core application. We may in time in many years to come branch out. That will be a different solutions. >>So your tailor, that app for that module for that industry, Yes, just go deep, deep functionality. You're known for that, but at the same time you're also messaging. You want your customers to be able to tailor this for their environment. So square that circle for me. >>So I think when we talk about a choice and and I think tailoring is the wrong word, we talk about choice. We're talking about choice of deployments on Prem or in the cloud choice of customer choice of partner, rather who they're going to deploy with on Dhe, then The solution is really an industry solution that comes with that functional death. And we don't we don't advocate their customers customized that all. We really don't want them to customize it. What we explain to them in some detail is that the real value comes from adopting the solution for two standard and staying on a vanilla application. Because that vanilla application, you're going to be able to withstand future upgrades, the total cost of ownership gets lower. The processes that are embedded in that application or best of breed at the box. That's what they're intended to do, and that works when you have a vertical application. When you have a horizontal application and you're trying to have a do things that it shouldn't naturally be doing, that becomes company. >>Well, correct me if I'm wrong, but wasn't that essentially the message ASAP had when it went through? It's hyper growth in the late nineties. I mean, there was a Y two k thing there, too, but ah, lot of the message was around. Do it our way and and then you don't have to get stuck in a rut, >>So I think that when it came out with that generation of application. That certainly was what they had hoped would happen. But what happened in practice is that the system integrators came in and the whole business process reengineering explosion happened on Dhe. That's not how it how it manifested itself. So what you see is, you see, he's very large, monolithic ASAP applications that were customized over in some cases decades, not not. You know, if a customer is deploying for two standard, then they should be able to deploy in a period mission. In weeks, we spoke about our deployment with Racing Point. If one team and going live in 12 weeks, you know, we're a 700 million global business. We deployed a knife s in 24 weeks. You know, if a customer's deploying for two standard, it's measured in weeks. As soon as they start to talk about two years or three years or five years or seven years there, customizing the solution significantly. Yeah, I >>mean, it became just sort of a perpetual upgrade, maintenance and up for the time it had a business impact. But boy, you think a cloud today agility, you know, getting rid of waterfall approaches, Missus. Antithetical to today's Look >>what I don't point fingers here. I think that this just maturity come with experience. The line of business applications you'll see our EMS and your HR solutions have taught people that you can, if you think about this is look at sea. Are Emma's an example? You had Siebel before people would implement stable. They would customize Siebel that would take long implementations. They were highly bespoke applications and then sells. Force came along and just destroyed them, and they destroyed them. Because what people learned very quickly was that there was a really easy to consume, really easy to use application that functionally might be inferior. But the compromises that you'd make from a functionality perspective will weigh, outweighed by their time to value in ease of use. And and the learnings from CR mnh are in procurement. Those line of business applications have now being backed into in the e. R. P >>world. So in terms of capital allocation, you're owned by private equity, which is actually a public company. I'm interested in how you're allocating capital R and D, where you're where your emphasis is. You don't have to you have to do stock buy back, but, you know, describe the P relationship. >>So look, one of my learning's to see survive this is that not all private equity firms or equal they have different strategies are very fortunate to be with Ekiti, who are a growth investor. They're known as a growth investor on dhe, and they buy companies that are strong growth tech firms on dhe. They've been hugely supportive of us investing because they understand that the investment in technology is important. So, you know, just looking at some detail today we invest twice as much in R and D as we did three years ago, just to give you, you know, one data point. So there's a big focus on technology, and the thing is, is that we we have to invest in technology to drive those attributes that are discussed earlier. How do we How do we enable customers to adopt a solution? It's a standard so they can go alive quicker. How do we enable customers to be able to sit down in the front of the application like we do with the mobile phone and intuitively know how to use it? How do we reduce the total cost of ownership through automation. Those are capabilities that you know that they don't come for free. We have to invest in them. So big investments in technology. And >>I think the private equity guys, at least the modern ones, have realized Why should the V. C's have all the fun they realize? Hey, we can actually put some money in tow and the transforming we can have a bigger exit and actually make much better returns than sucking the company drive. Yeah, well, look, I think the other >>thing is is that you know, in public companies, you have the downside off. You know this this courtly metric Ondas quarterly cadence. Andi, you see very compromising decisions being made because you know, people can't afford to miss 1/4. There's no long term planning that's done on dhe. That's fundamentally not the case and the private equity world, you know, not unusual now for four p firms to hold companies for 5678 years on, and that allows you to take a very long term strategic view. If if if a shift from perpetual to subscription is the right thing to happen, they can do that without worrying that, you know, because of the definite earnings are revenue that you're going to get caned by the market next quarter. Andi. I think that that needs to, I think, better decision making for the long term. >>A lot of companies are struggling. >>If you have the right P for because you get bought by the firm of events, you want to go public. But the the you said something this morning that 50% of your customers each year or net knew, How are you pulling that off >>That 50% of our license revenue? Eso way we went about 300 odd new customers a year. Obviously, that's growing, as I said, you know, 40%. But you know, it's ah, I think, having done this for 25 years, there are companies that are or good at extracting revenue from their installed based. One of the analysts here has as a hashtag wallet Fracking is what do you think It's such a great So you know, they're good at Wallick fracking and and I think the customers that that our customers off those vendors know exactly who they are and you know I think that for us to that the fact that we're able to go out and win 50% of our license revenue from net new name customers, I think is a really strong indicator of the health of the business. It's much harder to do than just extracting revenue out of the install base. You know, we don't have a compliance practice. We've never charged a customer for you in direct access. You know, these are principles that we stand by, and it's easier to say that your customer centric on get 80% of your revenue, have your installed base because you're doing compliance rounds. But, you know, we put our money where our mouth is, and that's not that's not how we do it. >>Are these net new customers? Are they? Are they migrating from QuickBooks or they migrating from a Competitors >>know, because of the segment that we're in this half a 1,000,000,000 to 5 billion? I would say the majority of them are what I would call first generation the Rp solution. So you know you're talking about you know, the original generation of Microsoft's acquisitions, the divisions and the eggs actors and the Solomon's and so on on. And then, you know, it's a P R two and our three customers you're talking about customer sitting on, you know, the solutions that in for hoovered up the matrix B picks type customers, ace 400 customers. So they're you know, they're first generation your P solutions that simply don't have the flexibility to deal with the complexity and demands of modern business world. >>From 2009 about 2017 I f. S was pretty inquisitive and then just actually, I was gonna ask you >>when I started, you stopped >>it, right? But then, you know, today you announced an extra small acquisition, But how should we think about M and a >>look? The first year for me was really about trying to build a functional business. You know, we spoke about how fragmented this really hit to Jenna's business. Andi just occurred to me. You know, if we go out and we start to buy things, how do we integrate them into a business that's completely fragments? And you know, it had no identity or culture. So, you know, the last year has been focused on how do we build their common understanding of what it is that we're doing. We now have a very clear strategy. Five industries, three solutions, one segment. And you know, when you when you have that clarity of vision that it's really easy to guard and do him and I because you know what fits and what doesn't fit, you can understand exactly how you're gonna build value for customers on dhe. That's why the S t a deal is so good for us. Because we're now the undisputed leader in field service management, you know, 8000 our customers globally, which is way more than anybody else. Scott, Andi, you know, you should absolutely expect more from us. But it will be in the five industries, three technology segments and one customers. Isaac. >>Well, in the A p I enablement should obviously facility. >>Absolutely. I mean, I was just with a partner of ours now, and they have this amazing augmented reality solution. You know, it will be a combination of off going out there to build market, share a cz well, as finding you know, really innovative solutions that can help us advance the technology that we provide customers. >>You have a new slogan this year for the challengers, which seems to be aimed at companies that that imagine themselves as challenging the Giants, which is great. But if you're not a company that season sees themselves that way. Are the studies level home with I have s Look, >>I I think I was with a group of CEOs from one of the big analyst rooms, and they had the portfolio companies and their private equity firm and analysts that CEOs of the companies are having a conversation with him about digital transformation. And I I made a rather provocative statement which, you know, got unanimous agreement, which is that all of the CEOs there with either in an industry that was being disrupted and we're trying to figure out how they respond to that disruption or they would soon not every job and they all acknowledge that they absolutely fit into that category. In other words, all of them were being disrupted. All of them were facing a challenge. It was kind of like, you know, if it is happening to all of us at a more rapid pace than we have ever had before. So my view is, is that you know if if you're in the room and you're going, you know, if it's might not be for us because we're not a challenger. Yeah, The lights may not be on >>for Long s o double click on that. What role does I s play in terms of digital transformation? >>If I could just hold on there because the thing is, there are leaders in Mama, there challenges. And there are leaders. The leaders typically are gonna go with seif solution. They're gonna go with one of the legacy our peace. So I'm not suggesting that everybody necessarily is a challenger. There are leaders, you know, Nokia was a leader until they weren't because they were complacent. Andi, I think they you know, they didn't run on I office. So, you know, I think there are two segments. There are leaders and there are challenges, and we're there for the ones that are ready to disrupt. Sorry. >>Please clarify that. No. Good. So So get back to it. Sort of digital transformation and disruption. What do you see? Is the role of AARP generally, but specifically I f s. >>Look, I think we digital information. A lot of discussion about it on the stage this morning. I've just touched on it now. I think that it takes very different forms. What most industries are finding is that they're facing a lot of non traditional competition and they're having to innovate around their business models. They can't going to market in the same way as they did before. They're having to innovate because of this non traditional competition. Andi. Understanding your your customer's understanding, your your staff, understanding your supply chain understanding your financials are all critical parts of being able to respond to whatever their changes, and that's where the RP solution comes into it. I think there's an interesting challenge now, which is that as those applications have become more fragmented and you've got more based debris cloud applications Ah, lot of the value often E. R P was that you had this integrated set of applications that you had this one source of the truth andan. Fortunately for many customers today, they don't have that because they've got import all of these best of breed applications and they don't have one source of the truth that multiple invoices made it multiple versions of their customer in the databases. Andi we still stand for a single integrated the r p. So, you know, I think understanding those elements of your businesses key. I was with a customer of ours in Nebraska a short while ago, and they were talking about our existing office customer. They were talking about the steel import duties that were imposed through the trade war with China. And they were saying, Look, that they had been able to respond to that in a way that they had good visibility of the supply chain, who was improved, imposing the tariffs, how they were going to impact them when they were going to impact them. And because they had this integrated Siara AARP. They were able to pass those pricing changes onto their customers, and they survived this. What could have been a cataclysmic event for their business had they not had an integrated your pee? They not being able to have this visibility into the supply chain and the customer base. They may well have gone out of business just because of that one change >>to meet all day and all comes back to the data, putting their putting data at the core of their business. That integrated data pipeline is essentially what they get out of that last question. So thinking about the next 18 to 24 months, what are the milestones that observers should look for? One of the barometers that we should be watching. >>So look, in the next two years, it's it's really about us building incremental scale. We have, ah, four year plan, which I built when I came in. We're halfway through that plan. We've hit all of the metrics and exceeded most the metrics that we had on their plan. It's really continue to focus on the strategy. As I said, we focus on those five industries, continue to build market share, continue to focus on those three solution types and build market share and market dominance on those three solutions. Andi in that segment that I defined before, so no change from a strategy perspective. I think there's really value in the consistency that we bring on on their talk track and, you know, along the way we passed the $1,000,000,000 mark, which we will do, I think, in 2021 organically if we accelerate, some of the money will pass the 1,000,000,000 before, but you know business. The margins continue to expand. We focus on customer satisfaction and, you know, it's a It's a pretty straight, you know, traditional prey book that we have to execute on now. >>Well, congratulations. It's a great playbook, and you're growing very nicely. So love that. Look, we really an honor to the last couple of years. Learn a little bit about the company in your industry. So appreciate meeting you guys. Thank you. All right. And thank you for watching over right back with our next guest. Ready for this short break day Volonte with Paul Gill in. You're watching the Cube from I f s World Conference from Boston 2019 right back.

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019 brought to you by VM Wear and its ecosystem partners. >> Welcome back here on the Cube, we continue our coverage. We're live in San Francisco. Mosconi, North Day to wrapping up Day two of our three days of coverage here, Veum. World 2019 day Volante. John Wall's glad to have you with us here on the Cube. And we're now joined by Kevin Schatz. Camera. Who's the Vice president of service provider Strategy and solutions. A deli. Um, See, Kevin. Good to see this afternoon. Thank you. You as well. And, uh, yeah, Tarazi, Who is the S v p and chief technical officer at Dell Technologies in the heart. Good to see you. Thanks for taking the time to be with us. A couple of telco guys and we've had a lot of telco on and talking about it in terms of progress that you made. This was an area that you got into with a major commitment, some probably three years ago. Kind of bitch market for me then for where you were there on on day one to where you are now today and the progress you've made and maybe the service is that you're about to provide. Yeah, >> sure. So I think if we look over the last three years, our opportunity that we defined early on telecommunications space was the virtual ization, and software to find everything was leaving the data center. And we would see the software to find architecture extend all the way from radio through the core network through the cloud over a period of time. And it started with technologies like network function virtualization. So if we flash back three years ago, where our entire strategy was built on the premise that relationships with the network equipment providers like Nokia and Ericsson, where our primary path to market our primary opportunity, I think what we've realized is we've emerged in this space to a greater detail is that our expertise, our expertise and experience in building I T Networks and Building Cloud has led to the first wave of conversations in the telecommunications industry directly not through the network equipment providers, but that carriers want to engage directly with Delhi emcee for the lessons learned and how did to play. I tr detectors. And now, as we extend towards the edge that they want to engage directly with Del Technologies in terms of how we build cloud architectures. We've had a number of big announcements. Over the last several years. We've announced partnerships and engagements with NTT. We've announced partnerships and engagements with China Unicom. Just in the last three months, we've announced partnerships with our rounds around network EJ out of France and then most recently with 18 C on the automation of EJ infrastructure related to their airship project. I think from a benchmark perspective, it's just been a continued growth opportunity for us and recognition that the more we engaged in, the more we contribute as a productive member of what is a very complex and changing and transforming industry, the more success in relationships that will build, and the more it will translate into opportunity to sell to >> when you think about you have the the modernization of N F. E. For example, as a former technologist inside a large telco, Um, what were some of the challenges? Is it? It's taken a long time. Obviously, when you talk to some of the telcos, they say, Well, you know it affects our infrastructure, but we still get this application mass. I mean, maybe you could add some color and describe for our audience why it's been so challenging. >> Yeah, I think that's an excellent question. Um, going back to my days at Telco on data centers, even S d n and the software defined tools were just beginning to show up. So the biggest challenges where you were basically having toe work with predefined operating system. But he defined hardware. The hardware was not exposed for for GAM ability, the ability to take advantage of it. And then you had to interrogate multiple players of technology in a way where it took significant time, too, not only for software development, but for product development and user experience. Since then, many of those walls have come down, and some of them have come down very hard. When you look at what we're doing, Adele here and we lead for the open networking. Not only do you have the choice of operating system were also pushing hard. Don't new open operating systems for networking like Sonic with Microsoft and bade calm. And then we're taking industry leading steps to expose the silicon chips themselves for four GAM ability. These are all the components that are critical. When you talk about five G, for example, do you really have to have those capabilities? I also would say that the software evolution have made it to infrastructure. The Dev ops and the modern applications we talk about here is also available for infrastructure, which means you really can develop a capability in weeks instead of years and months. Five people can do in amazing parkas. All of this was not possible before, >> so we talked to Shekhar about this in the earlier segment challenges in the telco business. I mean, the one hand you got these quasi monopolies in some cases real monopolies that just chug along and do pretty well. But the same time you got the cost for a bit dramatically coming down, you've got the data growth doing this. You got over the top providers taking advantage of the those those networks, and so new infrastructure allows them to be more more agile. But there's a workforce component to that, and there's a skill set, and that's how they got to transform. I wonder if you could maybe talk about that a little bit. Kevin. >> Yeah, I think that's exactly right. I think when we work within this industry, it's not just a technology conversation. It's the ability to consume an operationalized technology. And I think that comes down to a number of different things, comes down to the processes that exist when it comes down to the skill sets that exists to be able to build these new processes around. And I think if we flash back several years ago, the model of how we build networks was that the team that operated it needed to understand networking. Right now, if you look at the team that needs to operate it, they need to understand networking. They need to understand, compute. They need to understand virtualization. They need to understand AP eyes. They need to be able to script and program. They need to understand some level of data science that they can close a loop in the operational models eventually with a I and machine learning technologies. So I think that the teams that are getting built look very different than the single soul capabilities that they've had in the past, right? These air smaller teams they're more agile teams that can develop and have their own more unique processes in each part of the network. Right? And even if we think about the organizational structures, we've always built vertical organizations. Right? When I had an appliance, that was an e p. C. I had an operations team that was focused on an e p. C. And I even broke that into an S gateway P Gateway and Emma, me et cetera. If we look at the world now, that s Gateway P Gateway. Mm E consists of a server consists of the networking that connects at server consists of a virtual ization layer. It consists of a stack of a software application, and all of those need to be automated, orchestrated program toe work as any PC does. So I think that the skill sets have just really expanded in terms of what's expected, >> and this is really important because the process is used to be pretty well known and hardened, so the infrastructure could be hard, and now it's of every every months, the more the market changes right. What kind of what kind of challenges is that bring to the telco provider? But also to the infrastructure provider. >> Yeah, I actually I have a really good way to describe what I think is happening. We heard it from a lot of our customers and not just tell cause but enterprises. I would say the last 5 to 10 years everybody's been dealing with Hybrid Cloud. The Move to Cloud Waas. The Big Challenge. While this remains a key challenge, a new challenge showed up, which is how to succeed in this new modern software development model. You know, are you able to do to move at that speed, which means you have full stack engineers? Can you develop the app beginning to end? It's not a nightie model anymore. Also, you no longer have an operations team. You really have to have saris who, able with software and also the customer service, changed to a softwood Devyn. So we're starting to hear from a lot of our customers. That's the next journey they really need help with. If you think of infrastructure, those challenges are even bigger, and this is where it's important to lean on technology partners who can help you with that, >> and you hit on five G a little bit ago. You have in your initial statement and we've kind of touched on the impact that it can have in terms of you understanding there. They're going to a transformative time, right? I mean, telcos are with new capabilities, and new opportunities in this whole edge is gonna be crazy. So you've got to you've got I would say some learning to do, but you have. You've got to get up to speed on what their new fundamentals are going to be, right? Yeah, I think that's >> true. I think where you know, we we've understood >> their fundamentals because it's the same transition that the IittIe world's gone through. And to a large degree, that cloud world has gone through. I think that the challenge we've we've been working to break through collectively as an industry is the paralysis at the rate of adoption of new technologies because they're so much change so quickly because we talk about virtualization. And then we're talking about kubernetes. We're talking about cloud native we're talking about Ah, bare Metal Service's. We continue to talk about Micro Micro Service's architectures. We see this progression of technology that's happening so fast in various segments of the industry. I think that the telecommunications industry has been somewhat paralyzed in terms of where do they jump in and which do they adopt and how fast they migrate between them. And which of them can be capable of being hardened to be telco grade and fit into their requirements. That they have for being able to offer regulated service >> is paralyzed because it's just too fast. It's too fast for a big amazed, a big decision to make for big. But but things are evolving too quickly. That's that's It's evolving >> too quickly. And they also sometimes have a concern that they get stuck on a dead end path, right, Because things change so quickly it's Do I jump here? Then here, then here, then here, Then here. Where do I follow a logical path and what we tend to find when we work with the telecommunications industry is that, yes, del technologies can define a strategy. Certainly VM wear and L E. M. C can define our individual strategies. Are operators can define their strategies. But there's just not one strategy for this industry. Reality is, is that when you get when you get together with an ecosystem of partners, and you work at a particular telecommunications company. That is a strategy, and you start from scratch when you go to the next right because they're their ability to consume technology. It's just so different the end game, maybe the same across the board. But the path to get there will look different, >> so every customer's different Get that. But clearly some patterns must be emerging. So my question is, where do you start your sitting down with What are you seeing in terms of common starting points and advice you'd give Thio? >> I think that to Maine has everybody starting with First of all, the physical infrastructure. Compute storage Networking is moving to X 86 model of some sort, which means many, many parts of their infrastructure today that is not based on X 86 needs to transition. So what? Seeing big art piece significant discussions of how you take compute and this new programmable networking and put it everywhere like in thousands of locations. So infrastructure wise, that is a known specific thing to be solved at early stages and given you know, that capability he's we've delivered toward enterprises. We have a lot of tools and capabilities to give them, and the 2nd 1 is that a lot of people are approaching this as a network issue. In reality, it's a cloud decision, not a network. You hurt Shaker, talk about it so the tools capabilities you need to build a cloud is completely different. This cloud may not be genetic cloud it needs to be. It needs to support the defense specific platforms under for they want Cloud, and they needed to support the specific capabilities. So that's the two. A year ago, nobody even could articulate. That was the challenge they were facing. But I would say that's what we are today. >> I would add to that that as we kind of think about the infrastructure and then that cloud decision that there's abstractions that exist between those right at the infrastructure layer, there is the need tohave, an automation system that has the ability to support multiple different cloud platforms that sit on top of it. And that's work that we're doing in the deli in seaside and then secondary to that at the cloud layer. It's the ability to support a multi virtualization environment. Virtual machines do exist and will continue to exist. Kubernetes and cloud native containerized applications do exist and will continue to exist. And the challenge becomes. How do I orchestrate an environment that allows those two exist simultaneously and be layered on top of a common building block of infrastructure? And I think that's really the power that the broader Del Technologies has is that we have all of these entities and capabilities in house. >> How long does this take? A telco toe transform is this decade. Is it? Is it Maur can Obviously certain parts can happen faster. But when when you sit down with with customers and they put together their plans, I mean, what what what's their time horizon? >> So I would argue that we define the first NFI standards and 2012. And if we look globally and even within the vast majority of the Indus story and carriers were somewhere in the 10 to 15% range, yeah, >> yeah, that too compelling. Uh, hey, is that enough? Maybe be a forcing function for making some of those decisions. Are the economics on moving toe X 86 are very compelling. It's 10 times the speed to deploy, and it's a massive order of magnitude and costs. Therefore, it's not something that you could wait on as you continue to build capacity. So that's is forcing the infrastructure decision. The second forcing function is that what five G's starting to look like is not network and wireless, independent from enterprise solutions, you really have to collapse. The single infrastructure you know to offer service is and why it lists embedded on That's another forcing function in terms of enterprises is starting to ask for those capabilities. >> You know, you mentioned X 86 couple times and when you think about the Telco Cloud generically what we're talking about here in the in the commercial cloud not to tell ghost no commercial but the mainstream cloud you're getting a lot of offload, you know, hardware offload alternative processing arm uh, GP use F p g a Z even, you know, custom, a six coming back. You've seen the same thing in the Telco club >> for sure, I think I think if if you look at what we've done over the last several years, we've seen this dramatic shift in almost a pendulum swing away from a six and proprietary hardware towards everything on X 86 I think what we've learned over the last several years at X 86 is a platform that has its value. But it's just not for every work with So we've seen things like network slicing and control, user plane separation and technologies that her first moving user playing very high Io applications back onto smart nicks and F PJs and eventually onto merchant silicon with programmable silicate in the network switches. But I think that even if you look at what's happening in in Public Cloud with things like GPU virtualization, they're still largely virtualized in the time domain, which means that they're used by a particular application for a period of time and then the next application scheduled it in the next application schedule. Is it that doesn't work for network workloads? So I think that what we're finding is we go to this Toko Cloud model, especially with offload in the virtual ization of Acceleration Technologies, is that it's an entire set of problems that just aren't solved in public cloud yet. >> Yeah, I would say, based on experience, the vast majority of network workloads have to be x 86 I definitely think arm cores and GPO offloads will play all at some point in the future. But they that's not the heavy duty that you need to offload those functions because most of these network applications were it. And for custom, a sick. That's very high performance that you know, it has high throughput. Security, built in ability to build service is directly into the silicon. So that kind of transition over time you'll feed. You see a lot of distributed applications, it and container formats all the way at the edge. But that transition to that kind of distributed model from what we are today is probably not possible. And I would argue you'll always have their mics off high performance, high throughput. I mean, think about it. If you're trying to activate 20,000 I ot devices instantly, you really need a high core density, you know, x 86 chip with significant memory. You really worry about the data plane and how much data you can put. So it's better >> we didn't even hit I ot dead. Wait, wait Another day, Another conversation. Hey, thanks for the time. We certainly appreciate it. Been a good show I for you all to write for, sir? Good. Good energy. Good vibes and good business. Thanks for the time We appreciate it. >> Thank you, guys. Thank >> you very much for your time. >> Watching the Cube live coverage Here it Veum World 2019 in San Francisco. Thank you.

>> lie from San Francisco celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019. Brought to you by VM Wear and its ecosystem partners. >> Welcome back to the cubes. Live coverage here in San Francisco, California, for VM World 2019. I'm Jeff Davis Davis, our 10th year, 10 years covering the M world. Quite a run. Got a great stories. More stories coming, Emma days. A lot of organic growth. A lot of typos in the startup scene. Our next two guests Mike Adams, CIA Director bm wear and Ziv Kalman. Oh, vich product line manager here. Welcome to the Cube. Great to see you. Yes, Curtsy to you guys. Got a lot of activity happening around bit fusion. A lot of news to share. Exciting. I mean, in the M. And a story has been high on VM. Where we talking back? Elsie earlier. Continue to fill in on the strategy. >> Yeah, absolutely. Give us the update. Yeah, I think the key thing for us is we really want to become a key player in the A. I am l space and say that those workloads should come on visa. And with this acquisition we think, provides a great framework for a lot of the hardware accelerator devices. The best of you known of those his GP use. But we think there's four coming market with PG A's and also custom a six. So we're super excited about that. >> For the folks that don't know much about the acquisition, what was the motivation? What was the company's core product? What was the interest? Yeah, the >> company had a product called Flex Direct, and that particular product was really focused on taking, ah, similar concept that a lot of V m writes No, which was, Hey, we knew that computes space. We were trying to take these isolated islands and pull them together. Same type of thing. Here you had these expensive devices that people were buying and they were isolated. And now if we could take a single server, it's got a bunch of GP use on it. Why don't we share it? You see all these papers that come out around machine learning at the very end. It says she's I'm amazed that thes GP user so underutilized even when we're actually using them. It's kind of like buying a car and then using the radio only right? Doesn't. It just doesn't make sense. I >> got this trend of alternative processors just sort of exploding all over the place. I mean, obviously in video, sort of people know what's going on there, but but you've got arm. Now you've got the edge coming in, you know, Intel. Still dominant in the server space. But even even storage devices today use different type, not in the not Intel processors in there. It's a combination of our mo are Sometimes you know, G. P uses you say F g a Z, even though they're sort of a narrow use case. You're seeing a six make a comeback. So you got all this additional processing power, you know, going. So that's a tailwind. Absolutely, guys, and it's sort of the intersection of those to maybe talk about some of the trends you see in that regard and how you're taking advantage of them. >> Yeah, it reminds me of many moons ago when we had new chips that were coming out. We said, Jesus, hardware, flurry here, right? And now we're in a really similar spot. Ziv and I see a lot of different types of devices and acceleration devices, whether it's computer network or storage. And in this particular case, right, we just see a hotbed of all these customers that air seeing the same problem, right? And we've got great partnerships with Intel you mentioned in video and and many others. And we just want to really leverage those for these devices because you look at V sphere and say, OK, your traditional workloads. We've done those very, very well. But as we get into containers, KUBERNETES, machine Learning and I, we want these newer cloud native and newer workloads to come our way. And taking advantage of these new capabilities really helps accelerate that in a big way. >> Could you >> explain Maur on the the sphere impact? Because, you know, first of all, of'em, where community you get the feedback right away on Twitter and a lot of things. But sometimes you gotta dig in and find out what people are thinking and where there might. I think that could be future up opportunities or because it meets skepticism. Well, the the sphere native having a eye on the sphere, that's just mind blowing to me. But I mean, I can see I can see a data processor kind of vibe going on here where data needs to be processed. That seems to be a trend. What is it going on with the sphere with this? Is there what's the what's to customers? No. >> Well, I think the first thing to clarify here is that, you know, some often there is this question. Why would Iran m Ellery I work? Look specifically envy. Sphere is a platform. But then customers do run Emily and workers and public clouds. And those layers are not that different than the spirits virtualization layer, and they're running it in virtual machines. So the whole idea would be fusion specifically, is it? Actually, we can make it even more efficient to run these workloads on top of the sphere because the underlying infrastructure that you two actually, you have to accelerate these workloads there. Today they are mostly GP use, obviously, but in the futures, Michael so mentioned you a six are coming in and effigies are coming in. We are going to make those as well. That's the plan using the B fusion framework. Be more efficient to use. A lot >> of people are skeptical around running machine learning on these are not skeptical because, I mean, it's great for any time you have the opportunity to automate something or used software to make something go away. That's not the difference. You're undifferentiated, so it makes sense. But I just can't figure out where, specifically, within these fears of being targeted to use >> where envy sphere as in, Well, >> if I'm operating the sphere on top operator, I got Debs kicking around the corner. I got a cloud Mom reclaiming. Where's this fit in? Where >> this fits into essentially any place for a visa is running. It doesn't matter if it would run on via MacLeod and for any other for cloud partnerships or on the the edge of our Vesey runs. This is a core capability of the sphere, so it doesn't matter. You know where physically or infrastructure is, we would be able to expose this technology. The idea is also that you mentioned the trends in the A six as they're coming into the enterprise. There's an architectural changes also coming in, and in the server perspective, it's just it's the servers are actually getting more dense there, in there, in there in the accelerator infrastructure that they have in them. So you're seeing four to a GP using a single server. Those are very powerful machines. You can just move oil, represent a single machine again. That brings us back to be fusion and descend. The segregated model affects territory used, which is very similar by the way to centralize stories use. >> You guys are on something really big here. I think that hardware assists off load anything. Hardware system, harbor off load is gonna be a more of a bigger trend. And we saw it happen big time and hyper converge just for storage and everything. But I think as you want to stack where kubernetes gonna flourish? Yeah. I mean, imagine all the service is that he turned on Turned off. I mean, that's not I mean, men even know when it gets turned on or off. >> Absolutely offload for awhile with things like a raise, right, trying to push processing off to a bigger ray that you've got there. And then one other thing you said that I think was really important is the audience, right? If you look at a i n m l, we have traditionally haven't talked to the data, scientists of the machine learning folks. And we need to get to the I t. Folks that air supporting those workloads saying similar to some other workloads that were new and saying these were gonna come your way. And so we need to be prepared and you need to be able to leverage. So >> what's the What's the pitch to those folks? What's that? What's what you guys saying to them? Because it is a benefit for Debs and Dev Ops is to have an ops right. You got the ops down. Okay, see that and this change happening. But a dev, What's the pitch? But how do you get their attention? What's the value proposition? >> The the Actually, that's the beauty of it. It's exactly the same bottle proposition that the sphere in Vienna, where the Vienna state provides the developers and the only thing is that now we are letting the the office people to actually provide this doing this infrastructure as well in the same efficient manner. So it's your transformation. Basically, it's giving the exact same value proposition. >> Talk about the multi cloud tie in here. We've heard a lot about multi cloud and I think multi cloud in part anyway, is being able to run any application and workload anywhere. And one of things about your technology is the ability to not have to rewrite the application to take advantage of acceleration. Does it fit into multi cloud? And if so, how? >> Yeah, when we made the bet Fusion acquisition, if you look at their story, they had the any any any story as well, just like we do. And so, you know, we made announcement this week within video and eight of us and VM, where it's definitely possible of the technology that we have to extend that even further. And so, you know, the only thing I know with users going forward is they're gonna have more than one cloud, and so we just need to prepare for that and make sure that it works. And it works well across the board and the common layer. When you look at our multi cloud strategy is vey sphere is going to be at each of those layers. So if it's ties in disease here, it should be pretty easy to make it work in each of those environments. >> What was that What was the announcement you made you share? The big >> one was being able to use in video in the context of cloud in AWS. So's GPU capabilities and bring it to the service as we do on Prem. And so that was a big piece. And then we also obviously, in making that announcement talking about Hey, you know, this is a critical area for us because not only are we doing this, but we're also saying that your bit fusion will help enhance this because we think in video and bit fusion work very well together as well. >> And is that a product of service? Ah, go to market initiative. >> In the case of the coordinated us, it would be offered as part of the service. So when you can consume the compute, you know you want a GPU, it'll be there for you to help run that workload in the cloud. >> And that's available. When >> that's an nvidia in AWS kind of question. When they are making that infrastructure available, it's essentially going to be a nun. In another instance, type that the ember cloud in AWS will offer okay, I >> mean, it's a tech preview. >> What if some of the things that people should know about because again, in the pattern I'm seeing here of'em world is as in love to stack with kubernetes being that abstraction layer that guessing eyes promoting heavily on rightfully so. We're big fans communes with that for the beginning is that you're gonna have this this purpose built, um, native capability so that when you guys got this native vibe going on native to hype the sphere native TSX native, what does that actually mean? Native like Cooper, naked native on I. But what does it native mean? Explain to the audience what that actually means. >> I'll start up. Sure. You could >> elaborate 30 minutes if you want. But what is that >> true native native? The idea >> for us was used kubernetes really two ways. You know, most of the time when we were talking about Cooper Naser Containers, it's running that on top of these Fair right? What happens if you could take the DNA of that and put it actually inside of east here? Right, so not only you could run these clusters and native pods, but you could also leverage some of the value and one of the things that Cubans does really well is it handles workloads really well. So if we take an example where we have 145 e ems and they make up your app, right, normally you'd have to go to each one of those and figure out OK, let's make some changes in tweaks. And now what I can do is I can treat all of those is one workload and I can move them. I could do really interesting things with that. And that's the power one of the powers that you have with Kubernetes. >> And that's where the differentiation. Then you don't think that there's a >> Yeah, exactly. I mean you are essentially getting There are a lot of benefits our customers, our values value that the customer is getting today from V Sphere, generically speaking, and our longtime customers are familiar with the value propositions. And what we are saying is that when you're getting something as a native capability is that essentially ties into all the other capabilities that you already were know very well and you will be able to get those. But with on top on, sometimes on top orbit in conjunction with what >> is that gonna enable? Now let's talk about the enablement. >> So let's go back all the way. If you go all the way back to be fusion, for example, if you enable it is a native technology, then if you're running containers or viens on the sphere natively they can consume to be fusion technology. If you have cool, it is. It can orchestrate natively, the PM's and containers that are using the confusion to collision. Excited. Oh, so this is the whole thing, >> more efficient platform standpoint, >> and it's easier to manage as well, because you don't have to install a bunch of stuff on top of each other because it's needed. It's part of the first. >> A lot of hassle go away that people might >> take it in and you're gonna have to guess tomorrow they're going to go deep into it with >> you. Great, we're excited. So we're hearing a lot, obviously, but kubernetes at this event and and but most of the audience, they're not developers. So how can you use the sort of bit Fusion mojo to attract developers for some of these new workloads, that air come into the marketplace? >> Yeah, I mean it's all about acquiring new audiences in a case of infusions. More the data scientists. In the case of the communities, it's more around the developer. But I think let's use the kubernetes examples as a good one and what we announced with Project Pacific. Basically, the way it looks, the technology looks to them. It'll look like the kubernetes, a p I with a little bit of east for goodness from the operator perspective, the people that we know the 20,000 that are here, it looks to them like the sphere was from kubernetes Goodness. So that's the right mix is you've got to get it. So it looks exactly the smells and feels just like what they're used to. And I think that's a that's a key aspect. And then for the data Scientists with fusion, we really need to say Okay, you know you want to run these workloads, but she's you're paying really a lot of money for these expensive, isolated devices, and you could get more value by kind of grouping them up and making sure that they're used kind of in aggregate, right? >> So there's more leverage on the data science side So if I'm say hiring someone I know I'm or more to work with with >> exactly, essentially, it's it's the same story. They don't need to change their applications, their framework. Their models use the same could interface, which is the GPU interface for for the GPS computer. >> So So let's talk about that. So data scientist, you know, they always complain that most their time is spent wrangling data That's their, you know, bugaboo. And then there's a collaboration between data scientists and developers, which probably doesn't happen enough. What are you seeing in terms of the trends from the data science role? And can you help solve some of those problems? >> Well, what we are about to solve is really access access to infrastructure for them. Easy access to the infrastructure in their software stack. And the way to get there is to make the data engineers that serve these data scientists and the application administrators that surges data scientist to get easy access to the infrastructure Dany to provide the software, and that's where the sphere eventually comes in. So it's not the Celia direct relationship with the end users. It's more enabling the entire organization that actually served these end users and let them use as much infrastructure as your partners. And >> that and that and user organization. The buffer >> guys last question share what the plans are. What's next? What's your goals for the next 6 to 12 months? I'll see. Get the acquisition under your belt. Native in these fear, a lot of other cool things. I mean that I could talk about >> customers and maybe you can talk about product from a customer perspective. You know, we want engage in proof of concepts. So we want to bring them in, let them test out the software. It already works with the beast here, so I'll be running with multiple proof of concepts across the globe. We >> use cases in the U. S. Case or what? >> Yeah, I mean, it's it's pretty simple at the moment. It seems to be most people that are using GP use around ml. We have a great demo down the floor that shows people trying to run inception, three year resident 50 And how can we actually help those v EMs that are running that? So that's gonna be my focus. The next six >> years you want get some use cases come over here, bring him up to Mike. >> And from that perspective, I mean, obviously, we acquired occasion in an early stage. The technology works well. It works well enough to be product eyes. However, Veum, wherein the sphere has very high enterprise software stone standards in terms of security and management and governance. All this capabilities so that's going to be are focused on the next, you know, even almost a year to make sure that we bring it up to a level where we can confidently provide it and sell. It is a product >> you gotta engineering hye bar there absolutely thanks to Russia coming on keeping the update, the end world coverage Breaking it down. 2019. It's the Cuba job for David. Thanks for watching Be back with more after this short break.

>> from the Silicon Angle Media Office in Boston, Massachusetts. It's the cue now Here's your host Day Volonte >> Hybrid. Welcome to this special edition of Cube Insights. This is the Cubes 10th year at VM World and leading up >> to V M World. >> We wanted to provide some data in some analysis to you all, and we're working with our partners at E. T. R Enterprise Technology Research. We first introduced you to them when IBM consummated the Red Hat acquisition and they provided some data. E T. R is affirmed. That does really detailed and fast ongoing data. They have, ah, large panel of end customers that they talked to about spending intentions, covering virtually every company in the Enterprise. It's it's great stuff. We reached out to them and came up with a number of questions that we wanted to address around Of'em World and VM where, so let me just start by showing you the questions that we ask them to help us with. And we did essentially what I call drill down survey. So we took their existing data sets. They just did a survey. They completed one in July on spending intentions for the second half of the year combined that, with all the time Siri's data that they had. So these are the questions that really are top of mind for I t decision makers in our community. First of all, what's the appetite for VM? We're spending the second half of 2019. We'll share some data on that. There's a second point is there's narrative out there that that containers are going to kill the M. Where, well, is that true? What is the day to say? How about Multi Cloud? It's the hot topic who was best positioned in multi cloud not only within the VM, where ecosystem but overall, obviously, the M, where has designs on multi cloud and is considered an early potential leader? How about NSX when VM wear but nice era? It changed the game on networking, changed their relationship with Cisco. How is Ennis Ex impacting spending on Cisco? Particularly, obviously a networking. The fifth question that we wanted to address is how is public cloud affecting the M where spend we know public cloud is growing faster than on Prem. What's the impact on the M wear? And then finally it was announced in the press that VM wear was going to acquire Pivotal. Why would that be all right? So let's get into it. The first thing that I want to address is the first question in spending intention. So this slide really shows the results of the second half survey. It's 600 >> and >> 93 respondents representing almost $300 billion in spending power. And so it's actually they were asked what you're spending intention intentions For the second half of 2019 you could see 41% of the respondents said they're going to spend Maur, and only 7% said they're gonna spend less. About 45% said >> they gonna hold firm >> small number 5%. So we're gonna add new and only a tiny infant testable. 2% said they were gonna replace the anywhere, so that's pretty good for an incumbent. And essentially it Sze holding serve and maybe doing a little bit. But even better than holding serve on. So So we saw. That is very positive. The next question that we want to address is the narrative of containers will kill the M, where we asked Pat Gelsinger about that on the Cube years ago, he said, Hey, we're gonna use this as a tail wind. We're gonna embrace containers. So the bottom line is there's very little evidence that containers are hurting the M where let alone killing the end. Where this is a portion of the survey, about 461 respondents on you can see that you know, the big big blip early on back in July 27. Dean. Big uptick in spending, and since then it's been relatively stable. But the important point here is the number of shared accounts that we went to essentially container customers and asked them about their VM wear. Spend. I say we eat. TR did. This is what they do on an ongoing basis, and you could see the number of shared accounts back in 17 was only eight. But as you go to the right hand side, the more recent surveys you're talking about 361 shared accounts of the data sample got much bigger. No evidence that the M where is being negatively impacted by containers kind of affirming the assertion of Pat Gelsinger. Let's talk about multi club. I have said that multi cloud to date has largely been a symptom of multi vendor It's cos acquiring Cloud Technologies for specific workloads. Its shadow i t. It's pockets of cloud activity versus a coherent strategy to manage across multiple clouds. True Hybrid Cloud. We're in the early stages, so the data here, in our view, shows that multi cloud really is jump ball. Um, Interestingly, however, Microsoft and Google is showing momentum. So with this slide shows is the cloud spending intentions. And we picked, you know, the top five players there, that air sort of angling around multi cloud ghoul with Antos. Clearly Microsoft coming from its large software estate of V M. Where, of course, which many believer are early favorite Red Hat with the IBM acquisition and Cisco. So what's interesting here is Google and Microsoft clearly have a lot of momentum kind of mind share in the market place, and not a lot of hard core spending going on and multi cloud. Everybody has multi clouds, but in terms of spending on specific products, does like Antos, for instance, from Google, designed for to support multi cloud. That's where in the early stages there, but you can see the sentiment that buyers have around multi cloud Google and Microsoft showing momentum. Interestingly, VM wear Red Hat and Cisco kind of, you know, bunched up as the big enterprise player. So that's why we call a jump. Oh, we see it is wide open. You know, Cisco might surprise some people, but it really doesn't surprise us. Cisco's coming at multi cloud from a position of networking strength of each of these players you know has their strength. Google with Antos Microsoft from its software state Veum, where clearly as the data center operating system red hat with open shift Now with IBM service is capability. And, of course, Sisko coming at it from networking and security. So so hard to conclude you know who wins out of this data but wanted to share that with you just in terms of what customers are thinking around multi cloud. Okay, big conversation in the community around networking generally specifically NSX. When VM wear beats us, go to the punch and acquired nice era. It stated that we want to do to networking in storage what we did for servers. Well, what did the end? Where do the servers they really co opted the marketplace changed the game and really became, you know, these central point of server management, and that's what they want to do with with networking. VM where is trying to de position Cisco as, ah, hardware vendor, Cisco is responding with its own software defined capabilities and is an interesting battle going on. What is the data show? This shows that network networking spend intentions for Cisco, the Red Line and the M Wear the Blue Line. You can see VM where NSX is sort of bouncing around but has very high mindshare. Where Cisco it's showing a holding firm, but a very gradual decline, I've said many times. Cisco very impressive company, 60 plus percent market share. They've held that for a long, long time, despite some of the successes that you've seen you by the likes of a risk juniper and F five et cetera. Cisco has held its dominant share, but nonetheless, it's clear that NSX is impacting Cisco's dominance. Certainly from a marketing standpoint, and you're seeing also, from a spending standpoint that NSX is really challenging Cisco. It'll be very interesting to see how that plays out over time. Okay, next question was okay. What about cloud. How is that affecting VM? Where we see the cloud numbers, we see the growth. What does that mean for VM wear? And you can see here this'll cloud customers of'em were spend about 718 respondents, and you can see the number of shared accounts in the sample is substantial. 3 94 3 79 for 69. It obviously changes by by the frequency that e t. R does these surveys and they do, you know, several times a year, as you can see, but, you know, large sample of shared accounts. And there's no question that Cloud customers continue to shift Maur. They're spending to the public cloud and potentially at the expense of the end, where you can see the gradual decline here and somewhat precipitous decline. VM. We're still very strong. Stock price is doing great, but there's a little question in our mind that long term VM where, despite cleaning up its cloud strategy with first the AWS Partnership and also now partnerships with Google and Microsoft, and of course, I'd be Emma's Well, they were first, but having public cloud partners nonetheless, we see that over time there's a riel tension there. That on Prem is not going to grab the market, share that growth that the cloud has. And that is a challenge for VM, where that we continue to watch finally pivotal. Why would a V M where acquire? Pivotal? Well, first of all, this is why Pivotal is not work. It doesn't have the momentum that it wants in the marketplace. You can see it's it's pretty steep decline over the last couple of years. On Dhe, it's precipitous. Ah, drop in stock price. Essentially, Del and the governance structure of Del Technologies, which course owns VM, wear a large portion of pivotal saying, Look, let's let's roll this back in. Let's give the stock price of boost. The stock went up 70 plus percent of the day that thou went down 800 points. And so this is why the M, where would buy Pivotal? You know, it's a forcing function, we believe, from from Del. It also makes sense, del in its family del technologies that has these software assets VM where is the mother ship of the Del software operation? So why not folded in personally? I think they should do it with some other software assets as well. Secureworks del Bumi, Arcee. All candidates to roll in potentially overtime to Vienna where at least portions of it, anyway. Okay, so let's summarize. What are the key takeaways? What's the appetite for Veum warrants in the second half of 2019? Pretty solid, we'd say. Well, containers kill VM where there's no evidence, certainly in the theater. But there are threats. Think about sass. How many SAS providers are actually running? VM where so, as SAS continues to grow in prominence of that is a potential blind spot for VM. Where that we're watching Who's best position in multi cloud? It's wide open. Microsoft look strong. Google clearly has some momentum. Cisco maybe surprises many, but I think it's not gonna be a winner. Take all we feel is, though there's a lot of opportunities, but number one is going to make the most money. And so it's a very important space that we're watching. House NSX impacting Cisco Spend. It's a battle, but NSX is clearly negatively pressuring, pressuring Cisco. How about Public Cloud? How is that affecting the M we're spend? We think it's slowly eating away at on print on Prem including the end, where I want to share with you a quote from one of the customers that E. T. R talked to its ahead of, ah, retail consumer organisation in North America. A long time I t practitioner says Veum wears everywhere that I've ever been. I've been a customer. Longtime VM were customer hair. She means it's the standard, but it's interesting situation to see what's their next step. How do they keep themselves relevant? I think they're always going to be a need for Veum where, especially because the ability to have the privacy of an extended network is key. However, with the cloud based environment and encrypted data, it's gonna be interesting to see how that all plays out how Veum wear deals with that approach. I think their next strategic steps are going to be crucial. I think that VM where has to be thinking long term. Okay, what do we do about Cloud? Remember VM, where early on tried to get into cloud and with its own public cloud option, became the cloud air. It failed. They got rid of it, cleaned up their cloud strategy. But why did VM where originally want to get into that business because they know that's world of growth is so yes, hybrid and multi cloud gives VM wear a lot of runway. The partnership with Amazon has a lot of momentum. I didn't share that data, but it's very clear that AWS uh Veum, where on AWS has strong momentum. And so that's certainly what the e t. Our data shows nonetheless, long term, you gotta ask what strategic moves will Michael Dell make to secure their position in the public cloud? Okay, lastly, whywould whywould vm will require pivotal. That's a duh. Okay, we gonna stated why So So that's the deal, thanks to our friends at E T. R. Really appreciate them sharing the data enterprise technology research If you wanted this, there's so many cuts on the data, it's it's unbelievable. You can cut it by large companies, small company industry applications and every company on the planet. You can compare companies together. It's really a powerful set of data, but also access tools that they have developed very, very nice, really modern version of survey panels. And so follow up with us. Follow up with them if you want more information and watch us at VM World will be covering these and many other issues that are tent year at VM World. All the key execs are gonna be on practitioners, customers, partners on, of course, analysts and the broader ecosystem technologists and John Ferrier stew Minuteman myself on the entire Cube team will be there to celebrate. So check it out, cube dot net and we'll see you next week. Thanks for watching.

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> Welcome back to Boston, Everybody. Money >> belong here with my co host, a student of John >> Kerry's. Also here today You watching the Cuban leader and on the ground tech coverage. This is day one of active fio 19 data driven content Conference hashtag data driven 19 red cara minuses. Here is the senior vice president of fantasy Sports A draftkings Greg. Thanks for coming on. What a cool title. >> Yeah, it's It's, you know, I was joking with my wife. Anytime you could be working fantasy sports, it's a great place to be. Everybody's a little bit jealous. >> So the formula is easy, right? Offer big giant prizes and everybody comes And that's all there is suing. Anybody can come >> in. I just have the dream job right now. >> So hugely competitive market. You guys, you become the >> leader. We were in the radio. Check out your websites. I mean, take us through the draft kings and your ascendancy. How you got here? >> So, you know, company started in 2012 initially around sort of the major big American sports on DH. Then really a CZ. We started scale that we saw there was a huge consumer interest in the product players that would come on. We're very, very, very sticky. Um, and we've just been kind of, you know, pushing, pushing on growing that using these. So the initial founders are three former analyst. So come on. It's always been sort of a very analytically driven company. So they looked at what we were dealing with, and it was we had L TVs that were way higher than our cracks. So let's keep marketing and growing and growing and growing and finding out ways to offer a better product. So, over 2015 we did a major marketing blitz, blew up the company Absolutely huge. Um, and since then we've been just constantly innovating, adding new sports, adding new features on DH, adding ways toe on the product. And then even more recently, just about a year ago, we expanded also into online sports betting over New Jersey has that's become a legal product across the U. S. So it's been a great time to be at the company a lot of fun. >> What what was your first sport was like Amazon started in books and then, you know, scaled out what was your first sport. So it's actually the first sport was >> baseball because of the time that they actually launched. So is the middle of April. Sporting calendar is a little bit thin. Right then, so is it was baseball to start, and then once football season started, that's really when things take on >> 2015 is when you started the marketing blitz and I remember just here in the ads and it was just intense, like a while. This company's going for it. So you sort of took >> all the chips I went >> all in and it worked. Yeah, I mean, it's part of the, you know, the lifeblood of the company. It's We're a company that ends up being taking risks, but we take calculated risks. So at any given point, you sort of say, like, Hey, what is the what is the range of outcomes over here? We're not playing for second place. We want to be a market leader, so you have to take risks in order, be a market leader. So let's take calculated risks. Let's make sure we're not being insane, but you know we did the math. We figured out what? This is A This is a worthwhile shot. We pushed him for it. Andi really took off from their love to bet on >> sure things. Yeah, well, Greg, we know the people that play the fantasy for it feel that data is what differentiates whether they're going to live in, you know, winner lose. Talk to us a little bit about the data journey inside your business And how that helped differentiate draftkings in the market. Yes. So we think Death draftkings >> is one of the most analytically based companies in the, you know, definitely in the market, but also into sort of like General Cos right now we use our analytics platform to inform pretty much everything we dio on. Go to your point. You're joking. You know, it seems like fantasy sports is easy throughout some giant prizes there, and everything will take care of itself. You know, running a fantasy sports car company. If you throw out a contest that's too big, you lose a ton of money. There's a lot of asymmetric risk in the business where if we're right, we make a little bit more. But if we're wrong. We lose a ton very, very, very fast. So our ability to be very, very sound analytically is what allows us to sort of pushed the envelope and grow, grow, grow but not, you know, lose our heads along the way. You know, some of the fun of that is really, you know, when we first ran, I think one of the most game changing contest we ran was actually back in October of 2014. It was the very first millionaire maker contest I could still remember. It was Week five of the 2020 14 NFL season where we said, Hey, this it's crazy. We need crazy things that happen in order for it to work. But if we're on a $20 contest to enter with $1,000,000 top prize and 2,000,000 of total prizes, it could go viral, go absolutely crazy. And if it loses, here's how it'll losing. Here's how much will hurt us. It's a worthwhile risk. Let's go for it. So that sort of energy of, you know, doing discipline analysis and constantly sort of them. Taking the risk on the back of it is what allowed us to build >> up the brand value that you would have got out of that was sort of worth that risk in part anyway. And you wouldn't have to hurt presumably. >> Exactly. We knew our downside. As long as you know your downside, you're normally in a pretty good spot to take those risks. >> So where do you >> see this All going mean? So the company has grown. You're at this kind of critical mass now, Like we said, highly competitive, you know, knock down. You know, if you take your eye off the ball. So how do you guys keep this going? >> So we have a huge challenge ahead of us over the next couple of years, as sports betting becomes legal across the US, we need to make sure that we are one of the top competitors in that market. Sports betting in the US, we expect to be an absolutely enormous market. It will probably be significantly larger than the fantasy sports market in terms of absolute revenue and even, you know, on order of magnitude more competitive. So we need to be executing each step along the way a CZ markets open up. We need to be able to get into getting two market very, very fast. And that means our tech team needs to be working feverishly to make sure that we can hit the requirements that each legislator and each regulator puts on market entry in their state. We didn't mean making sure we're constantly figuring out what are the product elements that are absolutely critical for our for our users. Is it Maura around the live betting experiences that around the different markets that you offer? It's around pricing. And how do we find these things, these different lovers and told them to make sure that we're putting out a great product for users. And if we do that and throw a great product after users were pretty sure we can make you want >> to be one stop shopping presumably, right? I mean, all sports, right? But But then you've got these niche sports betting. I mean eggs, invest. Example. I could think of this horse racing. You know where it is alive. It's gonna video. It's got commentators on the ground that you know the business really well. Is >> that Is that the strategy to go sort of horizontal and so be a one stop shop or you >> gonna sort of pick your spots? What is the day to tell you? >> You know, I think we're constantly talking about it. One of the things that allowed our fantasy sports business to grow so fast was going a little bit more horizontal. So we offered Gulf in Mass at a time period when the primary competitors and the space vandal did not. On DH, we built that product into one of our largest sports. It's, you know, right up there with MLB in terms of the actual size that that comes in a Z have gone also horizontal, we pulled in other places, like NASCAR. Mm, a great sports that people are interested in. It gets more users into our platform. And honestly, if uses are interested in a product, we don't want them to have to go elsewhere. We want to be able to have the offerings that any sort of, you know, critical mass type environment is going toe is gonna have >> Well, it's that experience, right? Well, I like to shop in Amazon. You do, too, because I >> trusted. And it's the same user experience. So, Greg, one of things >> I'm hearing from you is something that everybody tries for, but it's really challenging that speed. How do you react that fast and move the company into new markets and new offerings and keep innovating? You know, culturally technology wise, you know, How does Draftkings do that? You know, I think a za company, you know, from really every single person that we recruit in higher We've been actually execution Aly disciplined throughout the company's history. It's It's something that our founders did a great job of instilling in the culture right at the gates. I mean, we've tried to foster all the way along the way, which is all the best strategies of the world. They're going to fail if you can't execute well and every single person down the company knows that. And we try to, you know, enable each person to be as autonomous as possible in their ability to execute their their portion of the business that allows us to move really, really, really fast. You know, we disseminate that responsibility quickly, and each leader and sort of each person knows what they have to do to execute. There's a high degree of accountability behind that, you know, I'd like to say there's some. There's some magic recipe that's, um, secret sauce, but it's a lot of just great people doing great work everyday. Well, Greg, you know it's any your competitors that they look at, You know, Boston's been been doing pretty well in Draftkings era, you know, for the last few years. ES o Boston's been a great market for us. We've expanded Conover here on DH. The sports teams have been fantastic, although the Bruins it was a little bit sad about Game seven over there, but it happens. >> So his m o be the flagship news that no, I wouldn't say >> that MLB was first, primarily just of the time of the year when we launched. NFL is always going to go, are not always going to be, but for the for the foreseeable future is the dominant US sport on will remain the dominant US for >> no reason. I mean, kids there watch MLB anymore. Maybe the maybe the playoffs and the games. It was a game. I think I'm some Father's day was like almost five hours long, you know, gets called. You can come in and out. But you know what some of the trends. You see soccer. Is that growing NFL? Obviously huge. Do you see so niche sports like lax coming on. >> So, uh, you know, starting point NFL has been huge. We actually launched a new product Ah, little over a year ago called Showdown, which allowed you start to do fantasy for a single game as opposed to the combination of games that's taken off fantastically because that's tapping into more of the I'm going to sit down and watch this game, and I would love to have a fantasy team on that on this game. That's really expanded the audience like that. That >> was genius because, look, if you're >> out of the running, it doesn't matter because I'm weak. On top of that N b A and NHL on fire. The embassy put out a great product is an actual sport league. You know, the Finals were great. You hate to see the injuries, but it was a great final. Siri's very competitive. The NHL Finals has been very, very competitive. Golf is growing phenomenally as a sport, way farm or interesting golf than I ever anticipated when I first started with the company and it's one of the most exciting things. When the Masters comes each year, every screen has turned to it and we see a huge player. Player number is kind of coming into that one. Beyond that, you know NASCAR. What's been interesting? NASCAR's been having a tough couple years, but the Truck series for us? We launched it this year and the trucks have been great. I don't know if you've watched NASCAR Trucks. They're wildly entertaining. Uh, you know, Emma, you got the big fighter. So every sport sort of has its moments. It's a matter of like picking those moments and figuring out how to make >> the most of them. Do you see boxing at all making a comeback? >> So we have thought about how to get boxing into a into a fantasy. We don't have it at the moment. We're putting a lot of thought into it, so we are actually seeing through. We've seen, you know, we've been in the M M A space and we've seen the growth out from there where that sports doing great and you look at places like Bela Tor. The Professional Fighters league is other leagues, and then boxing is the next step. There's a lot of interest there. I don't think they have the right products yet to be able to kind of engage with that extra way. So that's one of things we're working on. Also, you need a marquee fighter. You always need a marquee fighter. Kind of helped bring in the interest over on that side. So, um, be interesting to see with Taki on sort of the downside of his career. At this point on DH, Mayweather hasn't been fighting much. Will be interesting to see. Who's that next meeting with Adam. But >> I grew up in an era >> of Marquis fighters. What? They would fight, you know, they literally fight 6 70 times a year, you know, and you had used huge names on DSO, and then mm comes along and he's really hurt, >> but it feels like it's tryingto so to resuscitate. Yeah. I mean, I think these things could >> be a little bit cyclical. Like you get one Marquis fighter out there like so my wife, this Filipino. So I'm a huge backing out fan now way watch every fight. Even when we were living in remote locations that forces watching at weird hours. He's a type of athlete that could bring popularity of the sport. So if there was a major U. S. Fighter that gains that degree of sort of, you know that that degree of fame people will be into it, I think >> Do do do your analytics sort of have a probe into the activity at the at the fan level at the sports level, not just the fantasy level or the betting level? Is that a sort of ah ah predictor for you? Yet we >> see a lot of correlations between how many people play our sport are fantasy game, and how many people actually follow the underlying sport. Way can also see trends in terms of If I'm from Boston, I probably pick more patriots in my fantasy lineups than, uh, normal on DH. You can actually see that as people play different sports that you know, the number one Q. Be drafted in in Boston is almost always gonna be Tom Brady. And once you leave that you start seeing Aaron Rodgers pop up. Let's really, really fast. So you see these little micro trends where it's like you are still a sports fan of your local team in your local environment, but it manifest itself in the fantasy. >> So what you think that is? Do you think it's fan affinity >> or do you think it's just the sort of lack of knowledge out inside? You're sort of a circle of trust. >> I think it's probably a combination. I mean, I could say is, you know, following the Celtics in the mid to thousands, I knew the depth of the Celtics pension, how they would use their rotation better than anybody else, Probably better than anybody else in the coaches would probably disagree. But it's like I knew that James Posey was a huge value play on Saturday nights. I knew. I kind of with I feel the Eddie House nights. Uh, so, you know, on your local team, you probably know those players at the not the top top echelon All Stars, but the guy's right beneath. You know them a little bit better and probably more comfortable using >> what's your favorite sport. >> So my favorite sport, from a fantasy perspective, is I play all the basket. I play all football, played basketball just during play offs, and I played baseball. But baseball I'm strictly a fantasy player. I don't really follow the sport to play. I'm just playing fantasy. Okay, >> That's great. So, what do you think? The conference. Here. >> You have you Have you had any timeto interact? I know you were swamped after coming off the stage. >> You know, it looks like a great turnout over here. There's a lot of enthusiasm amongst them from people. I was a little bit late to the late to show up this morning, so I got a bit Swanson eager to go and be able to catch up a bit more. >> Okay, Well, Greg, thanks so much for coming on. The Cuba's great to have your every pleasure meeting you. >> All right, people. Right there. Still, when I >> was back with our next guest, John for it is also in the house. You wanted The Cube from active field data driven 19. Right back