>> from San Matteo. It's the Cube covering scaler. Innovation Day Brought to You by scaler. >> Welcome to this Special Cube Innovation Day. Here in San Mateo, California Scale is headquarters for a coast of the Cube. We're here with two great guests. Claudia Carpenter co founder Andy McAlister, Who's Dev evangelist? Uh, great to have you guys here a chat before we came on. Thanks for having us >> Great to be >> so scaler. It's all about the logs. The answer is in the logs. That's the title of the segment Them. I'll see the log files with a lot of exhaust in their data value extracting that, but it's got more operational impact. What's what's the Why is the answer in the locks? >> Because that's where the real information is. It's one thing to be able to tell that something is going around when your systems, but what is going wrong as engineers, what we tend to do is the old print. If it's like here's everything I can think of in this moment and leave it as breadcrumbs for myself to find later, then I need to go and look at those bread crumbs >> in a challenge. Of course, with this is that logs themselves are proliferating. There's lots of data. There's lots of services inside this logs, so you've gotta be able to find your answers as fast as possible. You can't afford Teo. Wait for something else. T lead you to them. You need to deep dive >> the way you guys have this saying it's the place to start. What does that mean? Why? Why is that the new approach? >> What We're trying to differentiate because there's this trend right now in the Dev Ops world towards metrics because they're much smaller to store it, pre digesting what's going on in your systems. And then you just play a lot of graphs and things like that. We agree with that. You do need to be able to see what's going on. You need to be able to set alerts. Metrics are good, but they only get you so far. A lot of people will go through. Look at metrics, dig through and then they stop, switch over and go to their logs. We like to start with the logs, build our metrics from them, and then we go direct to >> the source. I think a minute explain what you mean by metrics, because that has multiple meanings. Because the current way around metrics and you kind of talked about a new approach. Could you just take a minute? Explain what you meant by metrics and how logs are setting up the measures. The difference there. >> So to me, metrics is just counting things right? So at log files of these long textual representations of what's going on in my system and it's impossible to visually parce that I mean literally 10,000 lines. So you count. I've got five of this one in six of this one, and it's much smaller to store. I've got five of this one and six of this one, but that's also not very much information, so that's really the difference. >> But, you know, we have customers who use their metrics to help them indicate something might be wrong inside of here. The problem is, is that modern environments where we have instant gratification, needs and people you know, we'd be wait five seconds. Basically, it's a law sale online here. You need to know what's went wrong, not just where we went wrong or that something went wrong. So building for the logs to the metrics allows you to also have a perfect time back to that specific entrance ancient entrance that lets you be you out. What was wrong? >> He mention Claudia Death ops. And this is really kind of think of fun market because Dev Ops is now going mainstream and see the enterprise now started to adopt. It's still Jean Kim from Enterprise. Debs estimates only 3% of enterprise really there yet. So the action's on the cloud Native Public Cloud side where it's, you know, full blown, you know, cloud native more services. They're coming to see Cooper Netease things of that nature out there. And these services are being stood up and torn down while the rhythmically like. So with who the hell stores that data? That's the logs. The nature of log files and data is changing radically with Dev ops. I'm certainly this is going to be more complications but developers and figuring out what's what. How do you see that? What's your reaction to that trend? >> Yeah, so Dev Ops is a very exciting thing. At were Google. It was sort of like the new thing is the developers had to do their own operations, and that's where this comes from. Unfortunately, a lot of enterprise will just rename their ops people devil apps, and that's not the same thing. It's literally developers doing operations, Um, and right now that it's never been so exciting as as it is right now in the text axe, because you could get so much that's open source. Pre built glue this all these things together. But since you haven't written the code yourself, you've no way deal which going on. So it's kind of like Braille. You've got to go back and look and feel your way through it to figure out what's going on. And that's where logs come into play. >> The logs essentially, you know, lift up, get people eyesight into visibility of things that they care about. Absolute. So what's this red thing? Somebody read what is written? Rennes. >> One of the approaches. You'll hear things like golden signals. You'll hear youse, and you'll hear a red Corvette stands for rates, a rose and duration. And ready is a concept that says, How do you actually work with some of these complex technologies working with you're talking about and actually determined where your problems are. So if you think about it, rate is kind of how much traffic's going through a signal for this as a metric, it's accumulative number. So to back to Claudia's point, it's just number here. But if you're trapping goes up, you want to know what's going wrong here is self explanatory. Something broke, fix it, and then duration is how long things took. You talked about communities, Communities works hands in hands with this concept of micro services. Micro services are everywhere, and there were Khun B places that have thousands of little services, all serving the bigger need here. If one of them goes slow, you need to know what went slow as fast as possible. So rate duration and air is actually combined to give you the overall health of your system. While at the same point logs elect, you figure out what was causing >> the problem we'll take. I'm intrigued by what Claudia said. They're on this. You know, Braille concept is essentially a lot of people are flying blind date with what's going on, but you mentioned micro services. That's one area that's coming. Got state full data. Stateless data. They were given a P I economy. Certainly a state becomes important for these applications. You know, the developers don't may or may not know what's happening, so they need to have some intelligence. Also, security we've seen in the cloud. When you have a lot of people standing up instances whether it's on Amazon or other clouds, they don't actually have security on some of their things. So they got it. Figure out the trails of what the data looks like they need the log files to have understanding of. Did something happened? What happened? Why? What is the bottom line here? Claudia? What what people do to kind of get visibility So they're not flying blind as developers and organizations. >> Well, you gotta log everything you can within reason. They always have to take into account privacy and security. But logs much as you can and pull logs from every one of the components in your systems. The micro services that day was just talking about are so cool. And as engineers, we can't resist them way. Love, complexity >> and cool things. >> Things especially cool things and new things. >> New >> green things. Sorry, easily distracted. But there they are, harder to support. They can be a really difficult environment. So again it's back to bread crumbs, leaving that that trail and being able to go back and reconstruct what happened. >> Okay, what's the coolest thing about scaler since we thought about cool and relevant? You guys certainly in the relevant side thing. Check the box there. What's cool? What's cool about scaler telling us? >> That's great. Answer What isn't. But you know, honestly, when I came to work here, I no idea I was familiar with Log Management was really with long search and so forth. And the first time I actually saw the product, my jaw dropped. Okay, I now go to a trade show, for instance, and I'm showing people to use this. And I hit my return button to get my results. And you showed band with can be really bad and it stalls for 1/10 of a second, and I complain about it now. No, there is nothing quite as thrilling as getting your results as fast as you can think about them. Almost your thought processes the slow part of determining what's going on, and that is mind boggling. >> So the speed is the killer. >> The speed is like what killed me. But honestly, something that Chloe's been heavily involved in It takes you two minutes to get started. I mean, there's no long learning curve there. You get the product and you are there. You're ready to go >> close about ease of use and simplicity, because developers are fickle, but they're also loyal. Do you have a good product? They loved to get in that love the freebie. You know, the 30 day trial, They'll they'll kick the tires on anything. But the product isn't working. You hear about it when it does work. This mass traffic to people you know pound at the doorstep of the product. What's the compelling value proposition for the developer out there? Because they >> don't want to >> waste time. That's like the killer death to any product for development. Waste their time. They don't want to deal with it. >> So we live in the TL D our world right now. Frankly, if I have to read something, I usually move on on DH. That's the approach we take with scaler as well. Yes, we have some documentation, but I always feel like I have failed with the user interface design. If I require you to go read the documentation. So I try to take that into account with everything that we that we put out there making it really easy and fast it just jumping in, try stuff. >> How do you get to solve the complex complexity problem through attraction software? What's the secret sauce for the simplicity of this system? >> For me, it's a complete lack of patients. It's just like I wouldn't put up with that. I'm not gonna ask you to. Frankly, I view this sounds a little bit trite, but I've you Software's a relationship, and I view whoever is looking at it as a peer of mine, and I would be embarrassed if they couldn't figure it out if it wasn't obvious. But it is. We do have this sort of slope here of people who really know what's going on and people wanna optimize. This is your 80 20 split on people that don't know what just want to come in. I want both of them to be happy, so we need to blend those >> to talk about the value proposition of what you guys have because we've been covering you know log file mentioned Lock Management's Splunk events. We've gone, too. There's been no solution that I think may be going on 10 years old, that were once cutting edge. But the world changes so fast with Amazon Web services with Google Cloud with azure. Then get the international clouds out there as well. It's it's here. I mean, the scale is there, you got compute. You got the edge of the network right around the corner in the data problem's not going away. Log files going be needed. You have all this data exhausted, these value. >> If anything, there's always going to be more data that's out there. You're going to have more sources of that data coming in here. You're talking a little bit about you have the hybrid cloud. Where's part on prom? Part in the cloud. You could have multi clouds where across his boundaries. You're gonna have the wonderful coyote world where you have no idea when or where you're going to get an upload from too. This too and EJ environment. And you've got to worry about those and at the same time time, the logging, everything, the breadcrumbs. You have ephemeral events. They're not always there, and those are the ones that kill you. So the model is really simple and applaud Claudia for conning concept wise. But you're playing with concept of kiss, right? We'll hear its keep it simple and sophisticated at the same time. So I can teach you to do this demo in two minutes flat, and from there you can teach yourself everything else that this product's capable of doing it. That simple >> talk about who? The person out there that you want to use his product and why should they give scale or look what's in it for them. >> So for me, I think the perfect is to have Dev ops use it. It's developers. We really have designed a product less for ops and more for engineers. So one of the things that is different about scaler is you have somebody come in and set it up, parsed logs that ingestion of logs, which is different than splunk and sumo on DH. Then it's ready to use right out of the box. So for me, I think that our sweet spot, his engineers, because a lot of our formulations of things you do are more technical you're thinking about about you know what air the patterns here. I'm not going to say it's calculus, because then that wouldn't be simple. But it's along. Those >> engineers might be can also cloud Native is a really key party. People who were cloud native. We're actually looking at four in the cloud or cloud migration, >> right way C a lot. For instance, in the Croup. In any space from the Cloud Native Compute Foundation, we're seeing a tremendous instrument interest in Prometheus. We're seeing a lot of interest in usto with service mesh. The nice thing is that they are already all admitting logs themselves. And so, from our viewpoint, we bring them in. We put them together. So now you can look at each piece as it relates to the very other piece >> Claudia share with the folks who, watching this just some anecdotal use cases of what you guys have used internally, whether customers that give him a feel for how awesome scaler is and what's the what could they expect? >> Well, put me on the spot here. Um, >> I'll kick off. So we have a customer in Germany there need commerce shop, They have 1,000 engineers for here. When we started the product we replace because it was on a charge basis that was basically per user. They came back and they said, Oh my God, you don't understand our queries Air taking 15 minutes to get back By the time the quarry comes back, the engineer's forgotten why he asked the question for this. And so they loaded up. They rapidly discovered something unique. It's that they can discover things because anyone can use it. We now have 500 engineers that touch the log files every day, I will attest. Having written code myself, nobody reads log files for fun. But Scaler makes it easy to discover new things and new connections. And they actually look at what house >> discoveries of real value, proper >> discovery is a massive value proposition. Uh, where you figure out things that you don't know about back to that events thing that Claudia started about was, you can only measure the events that you can already considered. You can't measure things that didn't happen >> close. It quickly thought what the culture on David could chime in. What's the culture like here scaler? >> It is a unique culture and I know everyone probably says that about their startup, but we keep work life balance as a very important component. We're such nerds and unabashedly nerds. Wait, what we do. It's a joyful atmosphere to work in. Our founder, Steve Newman, is there in his flat, his flannel shirt, his socks cruising around. Um, and we are very much into our quality barcode. We have a lot of the principles of Google sort of combined into a start up. I mean to say it's a very honest environment, >> Sol. Heart problems make it a good environment. >> Yeah, and I value provide real values, are critical >> for me and have fun at the same point in time. The people here work hard, but they share what they're working on. They share information. They're not afraid to answer the what are you working on? Question. But we always managed to have fun. We are a pretty tight group that way. >> Well, thanks for sharing that insight. We have a lot of fun here in Innovation Day with the Q p. I'm John Furia. Thanks for watching

>> Narrator: Live from Stanford University, it's theCUBE covering the Women in Data Science Conference 2017. >> Hi welcome back to theCUBE, I'm Lisa Martin and we are live at Stanford University at the second annual Women in Data Science one day tech conference. We are joined by one of the speakers for the event today, Claudia Perlich, the Chief Scientist at Dstillery, Claudia, welcome to theCUBE. >> Claudia: Thank you so much for having me. It's exciting. >> It is exciting! It's great to have you here. You are quite the prolific author, you've won data mining competitions and awards, you speak at conferences all around the world. Talk to us what you're currently doing as the Chief Scientist for Dstillery. Who's Dstillery? What's the Chief Scientist's role and how are you really leveraging data and science to be a change agent for your clients. I joined Dstillery when it was still called Media6Degrees as a very small startup in the New York ad tech space. It was very exciting. I came out of the IBM Watson Research Lab and really found this a new challenging application area for my skills. What does a Chief Scientist do? It's a good question, I think it actually took the CEO about two years to finally give me a job description, (laughter) and the conclusion at that point was something like, okay there is technical contribution, so I sit down and actually code things and I build prototypes and I play around with data. I also am referred to as Intellectual Leadership, so I work a lot with the teams just kind of scoping problems, brainstorming was may work or dozen, and finally, that's what I'm here for today, is what they consider an Ambassador for the company, so being the face to talk about the more scientific aspects of what's happening now in ad tech, which brings me to what we actually do, right. One of the things that happened over the recent past in advertising is it became an incredible playground for data signs because the available data is incomparable to many other fields that I have seen. And so Dstillery was a pioneer in that space starting to look at initially social data things that people shared, but over the years it has really grown into getting a sense of the digital footprint of what people do. And our primary business model was to bring this to marketers to help them on a much more individualized basis identify who their customers current as well as futures are. Really get a very different understanding than these broad middle-aged soccer mom kind of categories to honor the individual tastes and preferences and actions that really truly reflect the variety of what people do. I'm many things as you mentioned, I publish mom, what's a mom, and I have a horse, so there are many different parts to me. I don't think any single one description fully captures that and we felt that advertising is a great space to explore how you can translate that and help both sides, the people that are being interacted with, as well as the brands that want to make sure that they reach the right individuals. >> Lisa: Very interesting. Well, as buyers journey as changed to mostly online, >> Exactly. >> You're right, it's an incredibly rich opportunity for companies to harness more of that behavioral information and probably see things that they wouldn't have predicted. We were talking to Walmart Labs earlier and one of the interesting insights that they shared was that, especially in Silicon Valley where people spend too much time in the car commuting-- (laughter) You have a long commute as well by train. >> Yes. >> And you'd think that people would want, I want my groceries to show up on my doorstep, I don't want to have to go into the store, and they actually found the opposite that people in such a cosmopolitan area as Silicon Valley actually want to go into the store and pick up-- >> Claudia: Yep. >> Their groceries, so it's very interesting how the data actually can sometimes really change. It's really the scientific method on a very different scale >> Claudia: Much smaller. >> But really using the behavior insights to change the shopping experience, but also to change the experience of companies that are looking to sell their products. >> I think that the last part of the puzzle is, the question is no longer what is the right video for the Super Bowl, I mean we have the Super Bowl coming up, right? >> Lisa: Right. Right. >> They did a study like when do people pay attention to the Super Bowl. You can actually tell, cuz you know what people don't do when they pay attention to the Super Bowl? >> Lisa: Mm,hmm. >> They're not playing around with their phones. They're actually not playing-- >> Lisa: Of course. >> Candy Crush and all these things, so what we see in the ad tech environment, we actually see that the demand for the digital ads go down when people really focus on what's going on on the big screen. But that was a diversion ... >> Lisa: It's very interesting (laughter) though cuz it's something that's very tangible and very ... It's a real world applications. Question for you about data science and your background. You mentioned that you worked with IBM Watson. Forbes has just said that Data Scientist is the best job to apply for in 2017. What is your vision? Talk to us about your team, how you've grown that up, how you're using big data and science to really optimize the products that you deliver to your customers. >> Data Science is really many, many different flavors and in some sense I became a Data Scientist long before the term really existed. Back then I was just a particular weird kind of geek. (laughter) You know all of a sudden it's-- >> Now it has a name. (laughter) >> Right and the reputation to be fun and so you see really many different application areas depending very different skillsets. What is originally the focus of our company has always been around, can we predict what people are going to do? That was always the primary focus and now you see that it's very nicely reflected at the event too. All of sudden communicating this becomes much bigger a part of the puzzle where people say, "Okay, I realize that you're really "good at predicting, but can you tell me why, "what is it these nuggets of inside-- >> Interpretation, right. >> "That you mentioned. Can you visualize what's going on?" And so we grew a team initially from a small group of really focused machine learning and predictive skills over to the broader can you communicate it. Can you explain to the customer archieve brands what happened here. Can you visualize data. That's kind of the broader shift and I think the most challenging part that I can tell in the broader picture of where there is a bit of a short coming in skillset, we have a lot of people who are really good today at analyzing data and coding, so that part has caught up. There are so many Data Science programs. What I still am looking for is how do you bring management and corporate culture to the place where they can truly take advantage of it. >> Lisa: Right. >> This kind of disconnect that we still have-- >> Lisa: Absolutely. >> How do we educate the management level to be comfortable evaluating what their data science group actually did. Whether they working on the right problems that really ultimately will have impact. I think that layer of education needs to receive a lot more emphasis compared to what we already see in terms of this increased skillset on just the sheer technical side of it. >> You mentioned that you teach-- >> Claudia: Mm,hmm. >> Before we went live here, that you teach at NYU, but you're also teaching Data Science to the business folks. I would love for you to expand a little bit more upon that and how are you helping to educate these people to understand the impact. Cuz that's really, really a change agent within the company. That's a cultural change, which is really challenging-- >> Claudia: Very much so. >> Lisa: What's their perception? What's their interest in understanding how this can really drive value? >> What you see, I've been teaching this course for almost six years now, and originally it was really kind of the hardcore coders who also happened to get a PhD on the side, who came to the course. Now you increasingly have a very broad collection of business minded people. I typically teach in the part-time, meaning they all have day jobs and they've realized in their day jobs, I need this. I need that. That skill. That knowledge. We're trying to get on the ground where without having to teach them python and ARM whatever the new toys are there. How can you identify opportunities? How do you know which of the many different flavors of Data Science, from prediction towards visualization to just analyzing historical data to maybe even causality. Which of these tools is appropriate for the task at hand and then being able to evaluate whether the level of support that a machine can only bring, is it even sufficient? Because often just because you can analyze data doesn't mean that the reliability of the model is truly sufficient to support then a downstream business project. Being able to really understand those trade offs without necessarily being able to sit down and code it yourself. That knowledge has become a lot more valuable and I really enjoy the brainstorming when we're just trying to scope a project when they come with problems from their day job and say, "Hey, we're trying to do that." And saying, "Are you really trying to do that?" "What are you actually able to execute? "What kind of decisions can you make?" This is almost like the brainstorming in my own company now brought out to much broader people working in hospitals, people working in banking, so I get exposed to all of these kinds of problems said and that makes it really exciting for me. >> Lisa: Interesting. When Dstillery is talking to customer or prospective customers, is this now something that you're finding is a board level conversation within businesses? >> Claudia: No, I never get bored of that, so there is a part of the business that is pretty well understood and executed. You come to us, you give us money, and we will execute a digital campaign, either on mobile phones, on video, and you tell me what it is that you want me to optimize for. Do you want people to click on your ad? Please don't say yes, that's the worst possible things you may ask me to do-- (laughter) But let's talk about what you're going to measure, whether you want people to show up in your store, whether you really care about signing up for a test drive, and then the system automatically will build all the models that then do all the real-time bidding. Advertising, I'm not sure how many people are aware, as your New York Times page loads, every single ad slot on that side is sold in a real-time auction. About 50 billion times a day, we receive a request whether we want to bid on the opportunity to show somebody an ad. >> Lisa: Wow. >> So that piece, I can't make 50 billion decisions a day. >> Lisa: Right. >> It is entirely automated. There's this fully automated machine learning that just serves that purpose. What makes it interesting for me now that ... Now this is kind of standard fare if you want to move over and is more interesting parts. Well, can you for instance predict which of the 15 different creatives I have for Jobani, should I show you? >> Lisa: Mm,hmm. >> The one with the woman running, or the one with the kid opening, so there is no nuances to it and exploring these new challenges or going into totally new areas talking about, for instance churn prediction, I know an awful lot about people, I can predict very many things and a lot of them go far beyond just how you interact with ads, it's almost the most boring part. We can see people researching diabetes. We can provide snapshots to farmer telling them here's really where we see a rise of activity on a certain topic and maybe this is something of interest to understand which population is driving those changes. These kinds of conversations really making it exciting for me to bring the knowledge of what I see back to many different constituents and see what kind of problems we can possibly support with that. >> Lisa: It's interesting too. It sounds like more, not just providing ad technology to customers-- >> Claudia: Yeah. >> You're really helping them understand where they should be looking to drive value for their businesses. >> Claudia: That's really been the focus increasingly and I enjoy that a lot. >> Lisa: I can imagine that, that's quite interesting. Want to ask you a little bit before we wrap up here about your talk today. I was looking at your, the title of your abstract is, "Beware what you ask for: The secret life of predictive models". (laughter) Talk to us about some of the lessons you learn when things have gone a little bit, huh, I didn't expect that. >> I'm a huge fan of predictive modeling. I love the capabilities and what this technology can do. This being said, it's a collection of aha moments where you're looking at this and this, this doesn't really smell right. To give you an example from ad tech, and I alluded to this, when people say, "Okay we want a high click through rate." Yes, that means I have to predict who will click on an ad. And then you realize that no matter what the campaign, no matter what the product, the model always chooses to show the ad on the flashlight app. Yeah, because that's when people fumble in the dark. The model's really, really good at predicting when people are likely to click on an ad, except that's really not what you intended-- >> Right. >> When you asked me to do that. >> Right. >> So it's almost the best and powerful that they move off into a sidetracked direction you didn't even know existed. Something similar happened with one of these competitions that I won. For Siemens Medical where you had to identify an FMI images of breast, which of these regions are most likely benign or which one have cancer. In both models we did really, really well, all was good. Until we realized that the patient ID was by far the most predictive feature. Now this really shouldn't happen. Your social security number shouldn't be able to predict-- >> Lisa: Right. >> Anything really. It wasn't the social security number, but when we started looking a little bit deeper, we realized what had happened is the data set was a sample from different sources, and one was a treatment center, and one was a screening center and they had certain ranges of patient IDs, so the model had learned where the machine stood, not what the image actually contained about the probability of having cancer. Whoever assembled the data set possibly didn't think about the downstream effect this can have on modeling-- >> Right. >> Which brings us back to the data science skill as really comprehensive starting all the way from the beginning of where the data is collected, all the way down to be extremely skeptical about your own work and really make sure that it truly reflects what you want it to do. You asked earlier like what makes really good Data Scientists. The intuition to feel when something is wrong and to be able to pinpoint and trace it back with the curiosity of really needing to understand everything about the whole process. >> Lisa: And also being not only being able to communicate it, but probably being willing to fail. >> Claudia: That is the number one really requirement. If you want to have a data-driven culture, you have to embrace failure, because otherwise you will fail. >> Lisa: How do you find the reception (laughter) to that fact by your business students. Is that something that they're used to hearing or does it sound like a foreign language to them? >> I think the majority of them are in junior enough positions that they-- >> Lisa: Okay. >> Truly embrace that and if at all, they have come across the fact that they weren't allowed to fail as often as they had wanted to. I think once you go into the higher levels of conversation and we see that a lot in the ad tech industry where you have incentive problems. We see a lot of fraud being targeted. At the end of the day, the ad agency doesn't want to confess to the client that yeah they just wasted five million dollars-- >> Lisa: Right. >> Of ad spend on bots, and even the CMO might not be feeling very comfortable confessing that to the CO-- >> Right. >> Claudia: Being willing to truly face up the truth that sometimes data forces you into your face, that can be quite difficult for a company or even an industry. >> Lisa: Yes, it can. It's quite revolutionary. As is this event, so Claudia Perlich we thank you so much for joining us-- >> My pleasure. >> Lisa: On theCUBE today and we know that you're going to be mentoring a lot of people that are here. We thank you for watching theCUBE. We are live at Stanford University from the Women in Data Science Conference. I am Lisa Martin and we'll be right back (upbeat music)

>>Okay. Okay. We're back at Caesar's Forum. The Snowflake summit 2022. The cubes. Continuous coverage this day to wall to wall coverage. We're so excited to have the analyst panel here, some of my colleagues that we've done a number. You've probably seen some power panels that we've done. David McGregor is here. He's the senior vice president and research director at Ventana Research. To his left is Tony Blair, principal at DB Inside and my in the co host seat. Sanjeev Mohan Sanremo. Guys, thanks so much for coming on. I'm glad we can. Thank you. You're very welcome. I wasn't able to attend the analyst action because I've been doing this all all day, every day. But let me start with you, Dave. What have you seen? That's kind of interested you. Pluses, minuses. Concerns. >>Well, how about if I focus on what I think valuable to the customers of snowflakes and our research shows that the majority of organisations, the majority of people, do not have access to analytics. And so a couple of things they've announced I think address those are helped to address those issues very directly. So Snow Park and support for Python and other languages is a way for organisations to embed analytics into different business processes. And so I think that will be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most most people in the organisation or not, analysts, they're doing some line of business function. Their HR managers, their marketing people, their salespeople, their finance people right there, not sitting there mucking around in the data. They're doing a job and they need analytics in that job. So, >>Tony, I thank you. I've heard a lot of data mesh talk this week. It's kind of funny. Can't >>seem to get away from it. You >>can't see. It seems to be gathering momentum, but But what have you seen? That's been interesting. >>What I have noticed. Unfortunately, you know, because the rooms are too small, you just can't get into the data mesh sessions, so there's a lot of interest in it. Um, it's still very I don't think there's very much understanding of it, but I think the idea that you can put all the data in one place which, you know, to me, stuff like it seems to be kind of sort of in a way, it sounds like almost like the Enterprise Data warehouse, you know, Clouded Cloud Native Edition, you know, bring it all in one place again. Um, I think it's providing, sort of, You know, it's I think, for these folks that think this might be kind of like a a linchpin for that. I think there are several other things that actually that really have made a bigger impression on me. Actually, at this event, one is is basically is, um we watch their move with Eunice store. Um, and it's kind of interesting coming, you know, coming from mongo db last week. And I see it's like these two companies seem to be going converging towards the same place at different speeds. I think it's not like it's going to get there faster than Mongo for a number of different reasons, but I see like a number of common threads here. I mean, one is that Mongo was was was a company. It's always been towards developers. They need you know, start cultivating data, people, >>these guys going the other way. >>Exactly. Bingo. And the thing is that but they I think where they're converging is the idea of operational analytics and trying to serve all constituencies. The other thing, which which also in terms of serving, you know, multiple constituencies is how snowflake is laid out Snow Park and what I'm finding like. There's an interesting I economy. On one hand, you have this very ingrained integration of Anaconda, which I think is pretty ingenious. On the other hand, you speak, let's say, like, let's say the data robot folks and say, You know something our folks wanna work data signs us. We want to work in our environment and use snowflake in the background. So I see those kind of some interesting sort of cross cutting trends. >>So, Sandy, I mean, Frank Sullivan, we'll talk about there's definitely benefits into going into the walled garden. Yeah, I don't think we dispute that, but we see them making moves and adding more and more open source capabilities like Apache iceberg. Is that a Is that a move to sort of counteract the narrative that the data breaks is put out there. Is that customer driven? What's your take on that? >>Uh, primarily I think it is to contract this whole notion that once you move data into snowflake, it's a proprietary format. So I think that's how it started. But it's hugely beneficial to the customers to the users, because now, if you have large amounts of data in parquet files, you can leave it on s three. But then you using the the Apache iceberg table format. In a snowflake, you get all the benefits of snowflakes. Optimizer. So, for example, you get the, you know, the micro partitioning. You get the meta data. So, uh, in a single query, you can join. You can do select from a snowflake table union and select from iceberg table, and you can do store procedures, user defined functions. So I think they what they've done is extremely interesting. Uh, iceberg by itself still does not have multi table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache iceberg in a raw format, they don't have it. But snowflake does, >>right? There's hence the delta. And maybe that maybe that closes over time. I want to ask you as you look around this I mean the ecosystems pretty vibrant. I mean, it reminds me of, like reinvent in 2013, you know? But then I'm struck by the complexity of the last big data era and a dupe and all the different tools. And is this different, or is it the sort of same wine new new bottle? You guys have any thoughts on that? >>I think it's different and I'll tell you why. I think it's different because it's based around sequel. So if back to Tony's point, these vendors are coming at this from different angles, right? You've got data warehouse vendors and you've got data lake vendors and they're all going to meet in the middle. So in your case, you're taught operational analytical. But the same thing is true with Data Lake and Data Warehouse and Snowflake no longer wants to be known as the Data Warehouse. There a data cloud and our research again. I like to base everything off of that. >>I love what our >>research shows that organisation Two thirds of organisations have sequel skills and one third have big data skills, so >>you >>know they're going to meet in the middle. But it sure is a lot easier to bring along those people who know sequel already to that midpoint than it is to bring big data people to remember. >>Mrr Odula, one of the founders of Cloudera, said to me one time, John Kerry and the Cube, that, uh, sequel is the killer app for a Yeah, >>the difference at this, you know, with with snowflake, is that you don't have to worry about taming the zoo. Animals really have thought out the ease of use, you know? I mean, they thought about I mean, from the get go, they thought of too thin to polls. One is ease of use, and the other is scale. And they've had. And that's basically, you know, I think very much differentiates it. I mean, who do have the scale, but it didn't have the ease of use. But don't I >>still need? Like, if I have, you know, governance from this vendor or, you know, data prep from, you know, don't I still have to have expertise? That's sort of distributed in those those worlds, right? I mean, go ahead. Yeah. >>So the way I see it is snowflake is adding more and more capabilities right into the database. So, for example, they've they've gone ahead and added security and privacy so you can now create policies and do even set level masking, dynamic masking. But most organisations have more than snowflake. So what we are starting to see all around here is that there's a whole series of data catalogue companies, a bunch of companies that are doing dynamic data masking security and governance data observe ability, which is not a space snowflake has gone into. So there's a whole ecosystem of companies that that is mushrooming, although, you know so they're using the native capabilities of snowflake, but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other, like relational databases, you can run these cross platform capabilities in that layer. So so that way, you know, snowflakes done a great job of enabling that ecosystem about >>the stream lit acquisition. Did you see anything here that indicated there making strong progress there? Are you excited about that? You're sceptical. Go ahead. >>And I think it's like the last mile. Essentially. In other words, it's like, Okay, you have folks that are basically that are very, very comfortable with tableau. But you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency, um, to San James Point. I think part of it, this kind of plays into it is what makes this different from the ado Pere is the fact that this all these capabilities, you know, a lot of vendors are taking it very seriously to make put this native obviously snowflake acquired stream. Let's so we can expect that's extremely capabilities are going to be native. >>And the other thing, too, about the Hadoop ecosystem is Claudia had to help fund all those different projects and got really, really spread thin. I want to ask you guys about this super cloud we use. Super Cloud is this sort of metaphor for the next wave of cloud. You've got infrastructure aws, azure, Google. It's not multi cloud, but you've got that infrastructure you're building a layer on top of it that hides the underlying complexities of the primitives and the a p I s. And you're adding new value in this case, the data cloud or super data cloud. And now we're seeing now is that snowflake putting forth the notion that they're adding a super path layer. You can now build applications that you can monetise, which to me is kind of exciting. It makes makes this platform even less discretionary. We had a lot of talk on Wall Street about discretionary spending, and that's not discretionary. If you're monetising it, um, what do you guys think about that? Is this something that's that's real? Is it just a figment of my imagination, or do you see a different way of coming any thoughts on that? >>So, in effect, they're trying to become a data operating system, right? And I think that's wonderful. It's ambitious. I think they'll experience some success with that. As I said, applications are important. That's a great way to deliver information. You can monetise them, so you know there's there's a good economic model around it. I think they will still struggle, however, with bringing everything together onto one platform. That's always the challenge. Can you become the platform that's hard, hard to predict? You know, I think this is This is pretty exciting, right? A lot of energy, a lot of large ecosystem. There is a network effect already. Can they succeed in being the only place where data exists? You know, I think that's going to be a challenge. >>I mean, the fact is, I mean, this is a classic best of breed versus the umbrella play. The thing is, this is nothing new. I mean, this is like the you know, the old days with enterprise applications were basically oracle and ASAP vacuumed up all these. You know, all these applications in their in their ecosystem, whereas with snowflake is. And if you look at the cloud, folks, the hyper scale is still building out their own portfolios as well. Some are, You know, some hyper skills are more partner friendly than others. What? What Snowflake is saying is that we're going to give all of you folks who basically are competing against the hyper skills in various areas like data catalogue and pipelines and all that sort of wonderful stuff will make you basically, you know, all equal citizens. You know the burden is on you to basically we will leave. We will lay out the A P. I s Well, we'll allow you to basically, you know, integrate natively to us so you can provide as good experience. But the but the onus is on your back. >>Should the ecosystem be concerned, as they were back to reinvent 2014 that Amazon was going to nibble away at them or or is it different? >>I find what they're doing is different. Uh, for example, data sharing. They were the first ones out the door were data sharing at a large scale. And then everybody has jumped in and said, Oh, we also do data sharing. All the hyper scholars came in. But now what snowflake has done is they've taken it to the next level. Now they're saying it's not just data sharing. It's up sharing and not only up sharing. You can stream the thing you can build, test deploy, and then monetise it. Make it discoverable through, you know, through your marketplace >>you can monetise it. >>Yes. Yeah, so So I I think what they're doing is they are taking it a step further than what hyper scale as they are doing. And because it's like what they said is becoming like the data operating system You log in and you have all of these different functionalities you can do in machine learning. Now you can do data quality. You can do data preparation and you can do Monetisation. Who do you >>think is snowflakes? Biggest competitor? What do you guys think? It's a hard question, isn't it? Because you're like because we all get the we separate computer from storage. We have a cloud data and you go, Okay, that's nice, >>but there's, like, a crack. I think >>there's uniqueness. I >>mean, put it this way. In the old days, it would have been you know, how you know the prime household names. I think today is the hyper scholars and the idea what I mean again, this comes down to the best of breed versus by, you know, get it all from one source. So where is your comfort level? Um, so I think they're kind. They're their co op a Titian the hyper scale. >>Okay, so it's not data bricks, because why they're smaller. >>Well, there is some okay now within the best of breed area. Yes, there is competition. The obvious is data bricks coming in from the data engineering angle. You know, basically the snowflake coming from, you know, from the from the data analyst angle. I think what? Another potential competitor. And I think Snowflake, basically, you know, admitted as such potentially is mongo >>DB. Yeah, >>Exactly. So I mean, yes, there are two different levels of sort >>of a on a longer term collision course. >>Exactly. Exactly. >>Sort of service now and in salesforce >>thing that was that we actually get when I say that a lot of people just laughed. I was like, No, you're kidding. There's no way. I said Excuse me, >>But then you see Mongo last week. We're adding some analytics capabilities and always been developers, as you say, and >>they trashed sequel. But yet they finally have started to write their first real sequel. >>We have M c M Q. Well, now we have a sequel. So what >>were those numbers, >>Dave? Two thirds. One third. >>So the hyper scale is but the hyper scale urz are you going to trust your hyper scale is to do your cross cloud. I mean, maybe Google may be I mean, Microsoft, perhaps aws not there yet. Right? I mean, how important is cross cloud, multi cloud Super cloud Whatever you want to call it What is your data? >>Shows? Cloud is important if I remember correctly. Our research shows that three quarters of organisations are operating in the cloud and 52% are operating across more than one cloud. So, uh, two thirds of the organisations are in the cloud are doing multi cloud, so that's pretty significant. And now they may be operating across clouds for different reasons. Maybe one application runs in one cloud provider. Another application runs another cloud provider. But I do think organisations want that leverage over the hyper scholars right they want they want to be able to tell the hyper scale. I'm gonna move my workloads over here if you don't give us a better rate. Uh, >>I mean, I I think you know, from a database standpoint, I think you're right. I mean, they are competing against some really well funded and you look at big Query barely, you know, solid platform Red shift, for all its faults, has really done an amazing job of moving forward. But to David's point, you know those to me in any way. Those hyper skills aren't going to solve that cross cloud cloud problem, right? >>Right. No, I'm certainly >>not as quickly. No. >>Or with as much zeal, >>right? Yeah, right across cloud. But we're gonna operate better on our >>Exactly. Yes. >>Yes. Even when we talk about multi cloud, the many, many definitions, like, you know, you can mean anything. So the way snowflake does multi cloud and the way mongo db two are very different. So a snowflake says we run on all the hyper scalar, but you have to replicate your data. What Mongo DB is claiming is that one cluster can have notes in multiple different clouds. That is right, you know, quite something. >>Yeah, right. I mean, again, you hit that. We got to go. But, uh, last question, um, snowflake undervalued, overvalued or just about right >>in the stock market or in customers. Yeah. Yeah, well, but, you know, I'm not sure that's the right question. >>That's the question I'm asking. You know, >>I'll say the question is undervalued or overvalued for customers, right? That's really what matters. Um, there's a different audience. Who cares about the investor side? Some of those are watching, but But I believe I believe that the from the customer's perspective, it's probably valued about right, because >>the reason I I ask it, is because it has so hyped. You had $100 billion value. It's the past service now is value, which is crazy for this student Now. It's obviously come back quite a bit below its IPO price. So But you guys are at the financial analyst meeting. Scarpelli laid out 2029 projections signed up for $10 billion.25 percent free time for 20% operating profit. I mean, they better be worth more than they are today. If they do >>that. If I If I see the momentum here this week, I think they are undervalued. But before this week, I probably would have thought there at the right evaluation, >>I would say they're probably more at the right valuation employed because the IPO valuation is just such a false valuation. So hyped >>guys, I could go on for another 45 minutes. Thanks so much. David. Tony Sanjeev. Always great to have you on. We'll have you back for sure. Having us. All right. Thank you. Keep it right there. Were wrapping up Day two and the Cube. Snowflake. Summit 2022. Right back. Mm. Mhm.

>>Okay. We're back here in the cube, focusing on the business promise of the cloud democratizing data, making it accessible and enabling everyone to get value from analytics, insights, and data. We're now moving into the eco systems segment the power of many versus the resources of one. And we're pleased to welcome. Barb Hills camp was the senior vice president partners and alliances at Ultrix and a special guest terror do week head of technology alliances at snowflake folks. Welcome. Good to see you. >>Thank you. Thanks for having me. Good to >>See Dave. Great to see you guys. So cloud migration, it's one of the hottest topics. It's the top one of the top initiatives of senior technology leaders. We have survey data with our partner ETR it's number two behind security and just ahead of analytics. So we're hovering around all the hot topics here. Barb, what are you seeing with respect to customer know cloud migration momentum and how does the Ultrix partner strategy fit? >>Yeah, sure. Partners are central, our company's strategy. They always have been, we recognize that our partners have deep customer relationships. And when you connect that with their domain expertise, they're really helping customers on their cloud and business transformation journey. We've been helping customers achieve their desired outcomes with our partner community for quite some time. And our partner base has been growing an average of 30% year over year, that partner, community and strategy now addresses several kinds of partners, spanning solution providers to global size and technology partners, such as snowflake and together, we help our customers realize that business promise of their journey to the cloud. Snowflake provides a scalable storage system altereds provides the business user friendly front end. So for example, it departments depend on snowflake to consolidate data across systems into one data cloud with Altryx business users can easily unlock that data in snowflake solving real business outcomes. Our GSI and solution provider partners are instrumental in providing that end to end benefit of a modern analytic stack in the cloud providing platform guidance, deployment, support, and other professional services. Okay, >>Great. Let's get a little bit more into the relationship between Altrix and in snowflake the partnership, maybe a little bit about the history, you know, what are the critical aspects that we should really focus on? Barb? Maybe you could start an Interra kindly way in as well. >>Yeah, so the relationship started in 2020 and all shirts made a big bag deep with snowflake co-innovating and optimizing cloud use cases together. We are supporting customers who are looking for that modern analytic stack to replace an old one or to implement their first analytic strategy. And our joint customers want to self-serve with data-driven analytics, leveraging all the benefits of the cloud, scalability, accessibility, governance, and optimizing our costs. Altrix proudly achieves highest elite tier and their partner program last year. And to do that, we completed a rigorous third party testing process, which also helped us make some recommended improvements to our joint stack. We wanted customers to have confidence. They would benefit from high quality and performance in their investment with us then to help customers get the most value out of the strength solution. We developed two great assets. One is the Altrix starter kit for snowflake, and we coauthored a joint best practices guide. >>The starter kit contains documentation, business workflows and videos, helping customers to get going more easily with an Alteryx and snowflake solution. And the best practices guide is more of a technical document, bringing together experiences and guidance on how Ultrix and snowflake can be deployed together. Internally. We also built a full enablement catalog resources, right? We wanted to provide our account executives more about the value of the snowflake relationship. How do we engage and some best practices. And now we have hundreds of joint customers such as Juniper and Sainsbury who are actively using our joint solution, solving big business problems, much faster. Cool. >>Tara, can you give us your perspective on the >>Yeah, definitely. Dave. So as Bart mentioned, we've got this standing very successful partnership going back, whereas with hundreds of happy joint customers. And when I look at the beginning, Ultrix has helped pioneer the concept of self-service analytics actually with use cases that we've worked on with, for, for data prep for BI users like Tableau and as Altrix has evolved to now becoming from data prep to now becoming a full end to end data science platform, it's really opened up a lot more opportunities for our partnership. Ultrix has invested heavily over the last two years in areas of deep integration for customers to fully be able to expand their investment, both technologies. And those investments include things like in database pushed down, right? So customers can, can leverage that elastic platform, that being the snowflake data cloud with Alteryx orchestrating the end to end machine learning workflows, Altryx also invested heavily in snow park, a feature we released last year around this concept of data programmability. So all users were regardless of their business analysts, regardless of their data, scientists can use their tools of choice in order to consume and get at data. And now with Altryx cloud, we think it's going to open up even more opportunities. It's going to be a big year for the partnership. >>Yeah. So, you know, Terike, we we've covered snowflake pretty extensively and you initially solve what I used to call the, I still call the snake swallowing the basketball problem and cloud data warehouse changed all that because you had virtually infinite resources. But so that's obviously one of the problems that you guys solved early on, but what are some of the common challenges or patterns or trends that you see with snowflake customers and where does Altryx come in? >>Sure. Dave there's there's handful that I can come up with today. The big challenges or trends for us, and Altrix really helps us across all of them. There are three particular ones I'm going to talk about the first one being self service analytics. If we think about it, every organization is trying to democratize data. Every organization wants to empower all their users, business users, you know, the, the technology users, but the business users, right? I think every, every organization has realized that if everyone has access to data and everyone can do something with data, it's going to make them competitively, give them a competitive advantage with all traits is something we share that vision of putting that power in the hands of everyday users, regardless of the skillsets. So with self-service analytics, with Ultrix designer, they've they started out with self-service analytics as the forefront, and we're just scratching the surface. >>I think there was an analyst report that shows that less than 20% of organizations are truly getting self-service analytics to their end users. Now with Altryx going to Ultrix cloud, we think that's going to be a huge opportunity for us. And then that opens up the second challenge, which is machine learning and AI, every organization is trying to get predictive analytics into every application that they have in order to be competitive in order to be competitive. And with Altryx creating this platform. So they can cater to both the everyday business user, the quote, unquote, citizen data scientists, and making it code friendly for data scientists, to be able to get at their notebooks and all the different tools that they want to use. They fully integrated in our snow park platform, which I talked about before, so that now we get an end to end solution catering to all, all lines of business. >>And then finally this concept of data marketplaces, right? We, we created snowflake from the ground up to be able to solve the data sharing problem, the big data problem, the data sharing problem. And Altryx, if we look at mobilizing your data, getting access to third-party data sets to enrich with your own data sets to enrich with, with your suppliers and with your partners, data sets, that's what all customers are trying to do in order to get a more comprehensive 360 view within their, their data applications. And so with Altryx is we're working on third-party data sets and marketplaces for quite some time. Now we're working on how do we integrate what Altrix is providing with, with the snowflake data marketplace so that we can enrich these workflows, these great rate workflows that Ultrix rating provides. Now we can add third party data into that workflow. So that opens up a ton of opportunities date. So those are three. I see easily that we're going to be able to solve a lot of customer challenges with. >>Excellent. Thank you for that. Terrick so let's stay on cloud a little bit. I mean, Altrix is undergoing a major transformation, big focus on the cloud. How does this cloud launch impact the partnership Terike from snowflakes perspective and then Barb, maybe, please add some color. >>Yeah, sure. Dave snowflake started as a cloud data platform. We saw our founders really saw the challenges that customers are having with becoming data-driven. And the biggest challenge was the complexity of having a managed infrastructure to even be able to, to get applications off the ground. And so we created something to be Claudia. We created to be a SAS managed service. So now that that Altrix is moving into the same model, right? A cloud platform, a SAS managed service, we're just, we're just removing more of the friction. So we're going to be able to start to package these end to end solutions that are SAS based that are fully managed. So customers can, can go faster. They don't have to worry about all of the underlying complexities of, of, of stitching things together. Right? So, so that's, what's exciting from my viewpoint >>And I'll follow up. So as you said, we're investing heavily in the cloud a year ago, we had to pray desktop products. And today we have four cloud products with cloud. We can provide our users with more flexibility. We want to make it easier for the users to leverage their snowflake data in the Alteryx platform, whether they're using our beloved on-premise solution or the new cloud products, we're committed to that continued investment in the cloud, enabling our joint partner solutions to meet customer requirements, wherever they store their data. And we're working with snowflake, we're doing just that. So as customers look for a modern analytic stack, they expect that data to be easily accessible, right within a fast, secure and scalable platform. And the launch of our cloud strategy is a huge leap forward in making Altrix more widely accessible to all users in all types of roles, our GSI and our solution provider partners have asked for these cloud capabilities at scale, and they're excited to better support our customers cloud and analytic ambitions. >>How about you go to market strategy? How would you describe your joint go to market strategy with snowflake? >>Sure. It's simple. We've got to work backwards from our customer's challenges, right? Driving transformation to solve problems, games agencies, or help them save money. So whether it's with snowflake or other GSI, other partner types, we've outlined a joint journey together from recruit solution development, activation enablement, and then strengthening our go to market strategies to optimize our results together. We launched an updated partner program and within that framework, we've created new benefits for our partners around opportunity registration, new role based enablement and training, basically extending everything we do internally for our own go-to-market teams to our partners. We're offering partner, marketing resources and funding to reach new customers together. And as a matter of fact, we recently launched a fantastic video with snowflake. I love this video that very simply describes the path to insights starting with your snowflake data. Right? We do joint customer webinars. We're working on joint hands-on labs and have a wonderful landing page with a lot of assets for our customers. Once we have an interested customer, we engage our respective account managers, collaborating through questions, proof of concepts really showcasing the desired outcome. And when you combine that with our partners technology or domain expertise, it's quite powerful, >>Tara, how do you see it? You'd go to market strategy. >>Yeah. Dave we've. So we initially started selling, we initially sold snowflake as technology, right? Looking at positioning the diff the architectural differentiators and the scale and concurrency. And we noticed as we got up into the larger enterprise customers, we were starting to see how do they solve their business problems using the technology, as well as them coming to us and saying, look, we want to also know how do you, how do you continue to map back to the specific prescriptive business problems we're having? And so we shifted to an industry focus last year, and this is an area where Ultrix has been mature for probably since their inception selling to the line of business, right? Having prescriptive use cases that are particular to an industry like financial services, like retail, like healthcare and life sciences. And so mark talked about these, these starter kits where it's prescriptive, you've got a demo and a way that customers can get off the ground and running, right? >>Because we want to be able to shrink that time to market, the time to value that customers can watch these applications. And we want to be able to, to, to tell them specifically how we can map back to their business initiatives. So I see a huge opportunity to align on these industry solutions. As BARR mentioned, we're already doing that where we've released a few around financial services working on healthcare and retail as well. So that is going to be a way for us to allow customers to go even faster and start to map to lines of business with Altryx >>Great. Thanks Derek, Bob, what can we expect if we're observing this relationship? What should we look for in the coming year? >>A lot specifically with snowflake, we'll continue to invest in the partnership. We're co innovators in this journey, including snow park extensibility efforts, which Derek will tell you more about shortly. We're also launching these great news strategic solution blueprints, and extending that at no charge to our partners with snowflake, we're already collaborating with their retail and CPG team for industry blueprints. We're working with their data marketplace team to highlight solutions, working with that data in their marketplace. More broadly, as I mentioned, we're relaunching the alternative partner program designed to really better support the unique partner types in our global ecosystem, introducing new benefits so that with every partner, achievement or investment with ultra we're providing our partners with earlier access to benefits, I could talk about our program for 30 minutes. I know we don't have time, but the key message here Alteryx is investing in our partner community across the business, recognizing the incredible value that they bring to our customers every day. >>Great Tarik. We'll give you the last word. What should we be looking for from, >>Yeah. Thanks. Thanks, Dave. As BARR mentioned, Ultrix has been the forefront of innovating with us. They've been integrating into making sure again, that customers get the full investment out of snowflake things like in database push down that I talked about before, but extensibility is really what we're excited about. The ability for Altrix to plug into this extensibility framework that we call snow park and to be able to extend out ways that the end users can consume snowflake through, through sequel, which has traditionally been the way that you consume snowflake as well as Java and Scala now Python. So we're excited about those, those capabilities. And then we're also excited about the ability to plug into the data marketplace to provide third party data sets, right? If they're PI day sets and in financial services, third party, data sets and retail. So now customers can build their data applications from end to end using ultrasound snowflake when the comprehensive 360 view of their customers, of their partners, of even their employees. Right. I think it's exciting to see what we're going to be able to do together with these upcoming innovations. >>Great stuff, Bob, Derek, thanks so much for coming on the program. Got to leave it right there in a moment. I'll be back with some closing thoughts in summary, don't go away.

>>Yeah, yeah, hello again and welcome to our last session of the day before we head to the meat. The experts roundtables how T Mobile is building a data driven organization with thought spot and whip prone. Today we'll hear how T Mobile is leaving Excel hell by enabling all employees with self service analytics so they can get instant answers on curated data. We're lucky to be closing off the day with these two speakers. Evo Benzema, manager of business intelligence services at T Mobile Netherlands, and Sanjeev Chowed Hurry, lead architect AT T Mobile, Netherlands, from Whip Chrome. Thank you both very much for being with us today, for today's session will cover how mobile telco markets have specific dynamics and what it waas that T Mobile was facing. We'll also go over the Fox spot and whip pro solution and how they address T mobile challenges. Lastly, but not least, of course, we'll cover Team Mobil's experience and learnings and takeaways that you can use in your business without further ado Evo, take us away. >>Thank you very much. Well, let's first talk a little bit about T Mobile, Netherlands. We are part off the larger deutsche Telekom Group that ISS operating in Europe and the US We are the second largest mobile phone company in the Netherlands, and we offer the full suite awful services that you expect mobile landline in A in an interactive TV. And of course, Broadbent. Um so this is what the Mobile is appreciation at at the moment, a little bit about myself. I'm already 11 years at T Mobile, which is we part being part of the furniture. In the meantime, I started out at the front line service desk employee, and that's essentially first time I came into a touch with data, and what I found is that I did not have any possibility of myself to track my performance. Eso I build something myself and here I saw that this need was there because really quickly, roughly 2020 off my employer colleagues were using us as well. This was a little bit where my efficient came from that people need to have access to data across the organization. Um, currently, after 11 years running the BR Services Department on, I'm driving this transformation now to create a data driven organization with a heavy customer focus. Our big goal. Our vision is that within two years, 8% of all our employees use data on a day to day basis to make their decisions and to improve their decision. So over, tuition Chief. Now, thank >>you. Uh, something about the proof. So we prize a global I T and business process consulting and delivery company. Uh, we have a comprehensive portfolio of services with presents, but in 61 countries and maybe 1000 plus customers. As we're speaking with Donald, keep customers Region Point of view. We primary look to help our customers in reinventing the business models with digital first approach. That's how we look at our our customers toe move to digitalization as much as possible as early as possible. Talking about myself. Oh, I have little over two decades of experience in the intelligence and tell cope landscape. Calico Industries. I have worked with most of the telcos totally of in us in India and in Europe is well now I have well known cream feed on brownfield implementation off their house on big it up platforms. At present, I'm actively working with seminal data transform initiative mentioned by evil, and we are actively participating in defining the logical and physical footprint for future architectures for criminal. I understand we are also, in addition, taking care off and two and ownership off off projects, deliveries on operations, back to you >>so a little bit over about the general telco market dynamics. It's very saturated market. Everybody has mobile phones already. It's the growth is mostly gone, and what you see is that we have a lot of trouble around customer brand loyalty. People switch around from provider to provider quite easily, and new customers are quite expensive. So our focus is always to make customer loyal and to keep them in the company. And this is where the opportunities are as well. If we increase the retention of customers or reduce what we say turned. This is where the big potential is for around to use of data, and we should not do this by only offering this to the C suite or the directors or the mark managers data. But this needs to be happening toe all employees so that they can use this to really help these customers and and services customers is situated. This that we can create his loyalty and then This is where data comes in as a big opportunity going forward. Yeah. So what are these challenges, though? What we're facing two uses the data. And this is, uh, these air massive over our big. At least let's put it like that is we have a lot of data. We create around four billion new record today in our current platforms. The problem is not everybody can use or access this data. You need quite some technical expertise to add it, or they are pre calculated into mawr aggregated dashboard. So if you have a specific question, uh, somebody on the it side on the buy side should have already prepared something so that you can get this answer. So we have a huge back lock off questions and data answers that currently we cannot answer on. People are limited because they need technical expertise to use this data. These are the challenges we're trying to solve going forward. >>Uh, so the challenge we see in the current landscape is T mobile as a civil mentioned number two telco in Europe and then actually in Netherlands. And then we have a lot of acquisitions coming in tow of the landscape. So overall complexity off technical stack increases year by year and acquisition by acquisition it put this way. So we at this time we're talking about Claudia Irureta in for Matic Uh, aws and many other a complex silo systems. We actually are integrated where we see multiple. In some cases, the data silos are also duplicated. So the challenge here is how do we look into this data? How do we present this data to business and still ensure that Ah, mhm Kelsey of the data is reliable. So in this project, what we looked at is we curated that around 10% off the data of us and made it ready for business to look at too hot spot. And this also basically help us not looking at the A larger part of the data all together in one shot. What's is going to step by step with manageable set of data, obviously manages the time also and get control on cost has. >>So what did we actually do and how we did? Did we do it? And what are we going to do going forward? Why did we chose to spot and what are we measuring to see if we're successful is is very simply, Some stuff I already alluded to is usual adoption. This needs to be a tool that is useable by everybody. Eso This is adoption. The user experience is a major key to to focus on at the beginning. Uh, but lastly, and this is just also cold hard. Fact is, it needs to save time. It needs to be faster. It needs to be smarter than the way we used to do it. So we focused first on setting up the environment with our most used and known data set within the company. The data set that is used already on the daily basis by a large group. We know what it's how it works. We know how it acts on this is what we decided to make available fire talksport this cut down the time around, uh, data modeling a lot because we had this already done so we could go right away into training users to start using this data, and this is already going on very successfully. We have now 40 heavily engaged users. We go went life less than a month ago, and we see very successful feedback on user experience. We had either yesterday, even a beautiful example off loading a new data set and and giving access to user that did not have a training for talk sport or did not know what thoughts, what Waas. And we didn't in our he was actively using this data set by building its own pin boards and asking questions already. And this shows a little bit the speed off delivery we can have with this without, um, much investments on data modeling, because that's part was already done. So our second stage is a little bit more ambitious, and this is making sure that all this information, all our information, is available for frontline uh, employees. So a customer service but also chills employees that they can have data specifically for them that make them their life easier. So this is performance KP ice. But it could also be the beautiful word that everybody always uses customer Terry, 60 fuse. But this is giving the power off, asking questions and getting answers quickly to everybody in the company. That's the big stage two after that, and this is going forward a little bit further in the future and we are not completely there yet, is we also want Thio. Really? After we set up the government's properly give the power to add your own data to our curated data sets that that's when you've talked about. And then with that, we really hope that Oh, our ambition and our plan is to bring this really to more than 800 users on a daily basis to for uses on a daily basis across our company. So this is not for only marketing or only technology or only one segment. This is really an application that we want to set in our into system that works for everybody. And this is our ambition that we will work through in these three, uh, steps. So what did we learn so far? And and Sanjeev, please out here as well, But one I already said, this is no which, which data set you start. This is something. Start with something. You know, start with something that has a wide appeal to more than one use case and make sure that you make this decision. Don't ask somebody else. You know what your company needs? The best you should be in the driver seat off this decision. And this is I would be saying really the big one because this will enable you to kickstart this really quickly going forward. Um, second, wellness and this is why we introduce are also here together is don't do this alone. Do this together with, uh I t do this together with security. Do this together with business to tackle all these little things that you don't think about yourself. Maybe security, governance, network connections and stuff like that. Make sure that you do this as a company and don't try to do this on your own, because there's also again it's removes. Is so much obstacles going forward? Um, lastly, I want to mention is make sure that you measure your success and this is people in the data domain sometimes forget to measure themselves. Way can make sure everybody else, but we forget ourselves. But really try to figure out what makes its successful for you. And we use adoption percentages, usual experience, surveys and and really calculations about time saved. We have some rough calculations that we can calculate changes thio monetary value, and this will save us millions in years. by just automating time that is now used on, uh, now to taken by people on manual work. So, do you have any to adhere? A swell You, Susan, You? >>Yeah. So I'll just pick on what you want to mention about. Partner goes live with I t and other functions. But that is a very keating, because from my point of view, you see if you can see that the data very nice and data quality is also very clear. If we have data preparing at the right level, ready to be consumed, and data quality is taken, care off this feel 30 less challenges. Uh, when the user comes and questioned the gator, those are the things which has traded Quiz it we should be sure about before we expose the data to the Children. When you're confident about your data, you are confident that the user will also get the right numbers they're looking for and the number they have. Their mind matches with what they see on the screen. And that's where you see there. >>Yeah, and that that that again helps that adoption, and that makes it so powerful. So I fully agree. >>Thank you. Eva and Sanjeev. This is the picture perfect example of how a thought spot can get up and running, even in a large, complex organization like T Mobile and Sanjay. Thank you for sharing your experience on how whip rose system integration expertise paved the way for Evo and team to realize value quickly. Alright, everyone's favorite part. Let's get to some questions. Evil will start with you. How have your skill? Data experts reacted to thought spot Is it Onley non technical people that seem to be using the tool or is it broader than that? You may be on. >>Yes, of course, that happens in the digital environment. Now this. This is an interesting question because I was a little bit afraid off the direction off our data experts and are technically skilled people that know how to work in our fight and sequel on all these things. But here I saw a lot of enthusiasm for the tool itself and and from two sides, either to use it themselves because they see it's a very easy way Thio get to data themselves, but also especially that they see this as a benefit, that it frees them up from? Well, let's say mundane questions they get every day. And and this is especially I got pleasantly surprised with their reaction on that. And I think maybe you can also say something. How? That on the i t site that was experienced. >>Well, uh, yeah, from park department of you, As you mentioned, it is changing the way business is looking at. The data, if you ask me, have taken out talkto data rather than looking at it. Uh, it is making the interactivity that that's a keyword. But I see that the gap between the technical and function folks is also diminishing, if I may say so over a period of time, because the technical folks now would be able to work with functional teams on the depth and coverage of the data, rather than making it available and looking at the technical side off it. So now they can have a a fair discussion with the functional teams on. Okay, these are refute. Other things you can look at because I know this data is available can make it usable for you, especially the time it takes for the I t. G. When graduate dashboard, Uh, that time can we utilize toe improve the quality and reliability of the data? That's yeah. See the value coming. So if you ask me to me, I see the technical people moving towards more of a technical functional role. Tools such as >>That's great. I love that saying now we can talk to data instead of just looking at it. Um Alright, Evo, I think that will finish up with one last question for you that I think you probably could speak. Thio. Given your experience, we've seen that some organizations worry about providing access to data for everyone. How do you make sure that everyone gets the same answer? >>Yes. The big data Girlfriends question thesis What I like so much about that the platform is completely online. Everything it happens online and everything is terrible. Which means, uh, in the good old days, people will do something on their laptop. Beirut at a logic to it, they were aggregated and then they put it in a power point and they will share it. But nobody knew how this happened because it all happened offline. With this approach, everything is transparent. I'm a big I love the word transparency in this. Everything is available for everybody. So you will not have a discussion anymore. About how did you get to this number or how did you get to this? So the question off getting two different answers to the same question is removed because everything happens. Transparency, online, transparent, online. And this is what I think, actually, make that question moot. Asl Long as you don't start exporting this to an offline environment to do your own thing, you are completely controlling, complete transparent. And this is why I love to share options, for example and on this is something I would really keep focusing on. Keep it online, keep it visible, keep it traceable. And there, actually, this problem then stops existing. >>Thank you, Evelyn. Cindy, That was awesome. And thank you to >>all of our presenters. I appreciate your time so much. I hope all of you at home enjoyed that as much as I did. I know a lot of you did. I was watching the chat. You know who you are. I don't think that I'm just a little bit in awe and completely inspired by where we are from a technological perspective, even outside of thoughts about it feels like we're finally at a time where we can capitalize on the promise that cloud and big data made to us so long ago. I loved getting to see Anna and James describe how you can maximize the investment both in time and money that you've already made by moving your data into a performance cloud data warehouse. It was cool to see that doubled down on with the session, with AWS seeing a direct query on Red Shift. And even with something that's has so much scale like TV shows and genres combining all of that being able to search right there Evo in Sanjiv Wow. I mean being able to combine all of those different analytics tools being able to free up these analysts who could do much more important and impactful work than just making dashboards and giving self service analytics to so many different employees. That's incredible. And then, of course, from our experts on the panel, I just think it's so fascinating to see how experts that came from industries like finance or consulting, where they saw the imperative that you needed to move to thes third party data sets enriching and organizations data. So thank you to everyone. It was fascinating. I appreciate everybody at home joining us to We're not quite done yet. Though. I'm happy to say that we after this have the product roadmap session and that we are also then going to move into hearing and being able to ask directly our speakers today and meet the expert session. So please join us for that. We'll see you there. Thank you so much again. It was really a pleasure having you.

>>from >>around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. >>Everyone welcome back to the cubes Coverage of AWS reinvent 2020 virtual. This is the Cube virtual. I'm John for your host this year. We're not in person. We're doing remote interviews because of the pandemic. The whole events virtual over three weeks for this week would be having a lot of coverage in and out of what's going on with the news. All that stuff here happening on the Cube Our next guest is a featured segment. Brown Venkatesh, VP of Engineering at Cloudera. Welcome back to the Cube Cube Alumni. Last time you were on was 2018 when we had physical events. Great to see you, >>like good to be here. Thank you. >>S O. You know, Cloudera obviously modernized up with Horton works. That comedy has been for a while, always pioneering this abstraction layer originally with a dupe. Now, with data, all those right calls were made. Data is hot is a big part of reinvent. That's a big part of the theme, you know, machine learning ai ai edge edge edge data lakes on steroids, higher level services in the cloud. This is the focus of reinvents. The big conversations Give us an update on cloud eras. Data platform. What's that? What's new? >>Absolutely. You are really speaking of languages. Read with the whole, uh, data lake architecture that you alluded to. It's uploaded. This mission has always been about, you know, we want to manage how the world's data that what this means for our customers is being ableto aggregate data from lots of different sources into central places that we call data lakes on. Then apply lots of different types of passing to it to direct business value that would cdp with Florida data platform. What we have essentially done is take those same three core tenants around data legs multifunctional takes on data stewardship of management to add on a bunch off cloud native capabilities to it. So this was fundamentally I'm talking about things like disaggregated storage and compute by being able to now not only take advantage of H d efs, but also had a pretty deep, fundamental level club storage. But this is the form factor that's really, really good for our customers. Toe or to operate that from a TCO perspective, if you're going to manage hundreds of terabytes of data like like a lot of a lot of customers do it. The second key piece that we've done with CDP has to do with us embracing containers and communities in a big way on primer heritages around which machines and clusters and things of that nature. But in the cloud context, especially in the context, off managed community services like Amazon CKs, this Lexus spin apart traditional workloads, Sequels, park machine learning and so on. In the context of these Cuban exiles containerized environments which lets customers spin these up in seconds. They're supposed to, you know, tens of minutes on as they're passing, needs grow and shrink. They can actually scale much, much faster up and down to, you know, to make sure that they have the right cost effective footprint for their compute e >>go ahead third piece. >>But the turkey piece of all of this right is to say, along with like cloud native orchestration and cloud NATO storage is that we've embraced this notion of making sure that you actually have a robust data discovery story around it. so increasingly the data sets that you create on top off a platform like CDP. There themselves have value in other use cases that you want to make sure that these data sets are properly replicated. They're probably secure the public government. So you can go and analyze where the data set came from. Capabilities of security and provenance are increasingly more important to our customers. So with CDP, we have a really good story around that data stewardship aspect, which is increasingly important as you as you get into the cloud. And you have these sophisticated sharing scenarios. The >>you know, Clotaire has always had and Horton works. Both companies had strong technical chops. It's well document. Certainly the queues been toe all the events and covered both companies since the inception of 10 years ago. A big data. But now we're in cloud. Big data, fast data, little data, all data. This is what the cloud brings. So I want to get your thoughts on the number one focus of problem solving around cloud. I gotta migrate. Or do I move to the cloud immediately and be born there? Now we know the hyper scale is born in the cloud companies like the Dropbox in the world. They were born in the cloud and all the benefits and goodness came with that. But I'm gonna be pivoting. I'm a company at a co vid with a growth strategy. Lift and shift. Okay, that was It's over. Now that's the low hanging fruit that's use cases kind of done. Been there, done that. Is it migration or born in the cloud? Take us through your thoughts on what does the company do right now? >>E thinks it's a really good question. If you think off, you know where our customers are in their own data journey, right? So increasingly. You know, a few years ago, I would say it was about operating infrastructure. That's where their head was at, right? Increasingly, I think for them it's about deriving value from the data assets that they already have on. This typically means in a combining data from different sources the structure data, some restructure data, transactional data, non transactional, data event oriented data messaging data. They wanna bring all of that and analyze that to make sure that they can actually identify ways toe monetize it in ways that they had not thought about when they actually stored the data originally, right? So I think it's this drive towards increasing monetization of data assets that's driving the new use cases on the platform. Traditionally, it used to be about, you know, sequel analysts who are, if you are like a data scientist using a party's park. So it was sort of this one function that you would focus on with the data. But increasingly, we're seeing these air about, you know, these air collaborative use cases where you wanna have a little bit of sequel, a little bit of machine learning, a little bit off, you know, potentially real time streaming or even things like Apache fling that you're gonna use to actually analyze the data eso when this kind of an environment. But we see that the data that's being generated on Prem is extremely relevant to the use case, but the speed at which they want to deploy the use case. They really want to make sure that they can take advantage of the clouds, agility and infinite capacity to go do that. So it's it's really the answer is it's complicated. It's not so much about you know I'm gonna move my data platform that I used to run the old way from here to there. But it's about I got this use case and I got to stand this up in six weeks, right in the middle of the pandemic on how do I go do that on the data that has to come from my existing line of business systems. I'm not gonna move those over, but I want to make sure that I can analyze the data from their in some cohesive Does that make sense? >>Totally makes sense. And I think just to kind of bring that back for the folks watching. And I remember when CDP was launching the thes data platforms, it really was to replace the data warehouse is the old antiquated way of doing things. But it was interesting. It wasn't just about competing at that old category. It was a new category. So, yeah, you had to have some tooling some sequel, you know, to wrangle data and have some prefabricated, you know, data fenced out somewhere in some warehouse. But the value was the new use cases of data where you never know. You don't know where it's going to come until it comes right, because if you make it addressable, that was the idea of the data platform and data Lakes and then having higher level services. So s so to me. That's, I think, one distinction kind of new category coexisting and disrupting an old category data warehousing. Always bought into that. You know, there's some technical things spark Do all these elements on mechanisms underneath. That's just evolution. But income in incomes cloud on. I want to get your thoughts on this because one of the things that's coming out of all my interviews is speed, speed, speed, deploying high, high, large scale at very large speed. This is the modern application thinking okay to make that work, you gotta have the data fabric underneath. This has always been kind of the dream scenario, So it's kind of playing out. So one Do you believe in that? And to what is the relationship between Cloudera and AWS? Because I think that kind of interestingly points to this one piece. >>Absolutely. So I think that yeah, from my perspective, this is what we call the shared data experience that's central to see PP like the idea is that, you know, data that is generated by the business in one use case is relevant and valid in another use case that is central to how we see companies leveraging data or the second order monetization that they're after, Right? So I think this is where getting out off a traditional data warehouse like data side of context, being able to analyze all of the data that you have, I think is really, really important for many of our customers. For example, many of them increasingly hold what they call this like data hackathons right where they're looking at can be answered. This new question from all the data that we have that is, that is a type of use case that's really hard to enable unless you have a very cohesive, very homogeneous view off all of your data. When it comes to the cloud partners, right, Increasingly, we see that the cloud native services, especially for the core storage, compute and security services are extremely robust that they give us, you know, the scale and that's really truly unparalled in terms of how much data we can address, how quickly we can actually get access to compute on demand when we need it. And we can do all of this with, like, a very, very mature security and governance fabric that you can fit into. So we see that, you know, technologies like s three, for example, have come a long way on along the journey with Amazon on this over the last 78 years. But we both learned how to operate our work clothes. When you're running a terabytes scale, right, you really have to pay attention to matters like scale out and consistency and parallelism and all of these things. These matters significantly right? And it's taken a certain maturity curve that you have to go through to get there. The last part of that is that because the TCO is so optimized with the customer to operate this without any ops on their side, they could just start consuming data, even if it's a terabyte of data. So this means that now we have to have the smarts in the processing engines to think about things like cashing, for example very, very differently because the way you cash data that Zinn hedge defense is very different from how you would do that in the context of his three are similarly, the way you think about consistency and metadata is very, very different at that layer. But we made sure that we can abstract these differences out at the platform layer so that as an as it is an application consumer, you really get the same experience, whether you're running these analytics on clam or whether you're running them in the cloud. And that's really central to how I see this space evolving is that we want to meet the customer where they are, rather than forcing them to change the way they work because off the platform that they're simple. >>So could you take them in to explain some of the integrations with AWS and some customer examples? Because, um, you know, first of all, cost is a big concern on everyone's mind because, you know, it's still lower costs and higher value with the cloud anyway. But it could get away from you. So you know, you're constantly petabytes of scale. There's a lot of data moving around. That's one thing to integration with higher level services. Can you give where does explain how Claudia integration with Amazon? What's the relation of customer wants to know. Hey, you guys, you know, partnering, explain the partnership. And what does it mean for me? >>Absolutely. So the way we look at the partnership hit that one person and ghetto. It's really a four layer cake because the lowest layer is the core infrastructure services. We talked about storage and computing on security, and I am so on and so forth. So that layer is a very robust integration that goes back a few years. The next layer up from that has to do with increasingly, you know, as our customers use analytic experiences from Florida on, they want to combine that with data that's actually in the AWS compute experiences like the red Ship, for example. That's what the analytics layer uploaded the data warehouse offering and how that interrupts would be other services in Amazon that could be relevant. This is common file formats that open source well form it really help us in this context to make sure that they have a very strong level of interest at the analytics there. The third layer up from that has to do with consumption. Like if you're gonna bring an analyst on board. You want to make sure that all of their sequel, like analyst experiences, notebooks, things of that nature that's really strong. And club out of the third layer on the highest layer is really around. Data sharing. That's as aws new and technologies like that become more prevalent. Now. Customers want to make sure that they can have these data states that they have in the different clouds, actually in a robbery. So we provide ways for them, toe browse and search data, regardless of whether that data is on AWS or on traffic. And so that's how the fourth layer in the stack, the vertical slice running through all of these, that we have a really strong business relationship with them both on the on the on the commercial market side as well as in AWS marketplace. Right? So we can actually by having cdp be a part of it of the US marketplace. This means that if you have an enterprise agreement with with Amazon, you can actually pay for CDP toe the credit sexuality purchased. This is a very, very tight relationship that's designed again for these large scale speeds and feeds. Can the customer >>so just to get this right. So if I love the four layer cake icings the success of CDP love that birthday candles can be on top to when you're successful. But you're saying that you're going to mark with Amazon two ways marketplace listing and then also jointly with their enterprise field programs. That right? You say because they have this program you can bundle into the blanket pos or Pio processes That right can explain that again. >>S so if you think this'll states, if you're talking about are significant. So we want to make sure that, you know, we're really aligned with them in terms off our cloud migration strategy in terms of how the customer actually execute to what is a fairly you know, it's a complex deployment to deploy a large multiple functions did and existed takes time, right, So we're gonna make sure that we navigate this together jointly with the U. S. To make sure that from a best practices standpoint, for example, were very well aligned from a cost standpoint, you know what we're telling the customer architecturally is very rather nine. That's that's where I think really the heart of the engineering relationship between the two companies without. >>So if you want Cloudera on Amazon, you just go in. You can click to buy. Or if you got to deal with Amazon in terms of global marketplace deal, which they have been rolling out, I could buy there too, Right? All right, well, run. Thanks for the update and insight. Um, love the four layer cake love gets. See the modernization of the data platform from Cloudera. And congratulations on all the hard work you guys been doing with AWS. >>Thank you so much. Appreciate. >>Okay, good to see you. Okay, I'm John for your hearing. The Cube for Cube virtual for eight of us. Reinvent 2020 virtual. Thanks for watching.

(upbeat music) >> From the CUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hi everyone, this is Dave Vellante from the CUBE. And we're getting ready for the Snowflake Data cloud summit four geographies, eight tracks more than 40 sessions for this global event. Starts on November 17th, where we're tracking the rise of the Data cloud. You're going to hear a lot about that, now, by now, you know, the story of Snowflake or you know, what maybe you don't but a new type of cloud native database was introduced in the middle part of last decade. And a new set of analytics workloads has emerged that is powering a transformation within organizations. And it's doing this by putting data at the core of businesses and organizations. You know, for years we marched to the cadence of Moore's law. That was the innovation engine of our industry, but now that's changed it's data plus machine intelligence plus cloud. That's the new innovation cocktail for the technology industry and industries overall. And at the Data cloud summit we'll hear from Snowflake executives, founders, technologists, customers, and ecosystems partners. And of course, you going to hear from interviews on the CUBE. So, let's dig in a little bit more and help me are two Snowflake experts. Felipe Hoffa is a data cloud advocate and Kent Graziano is a chief technical evangelist post at Snowflake. Gents, great to see you. Thanks for coming on. >> Yeah, thanks for having us on, this is great. >> Thank you. >> So guys first, I got to congratulate you on getting to this point. You've achieved beyond escape velocity and obviously one of the most important IPOs of the year, but you got a lot of work to do. I know that what, what are the substantive aspects behind the Data cloud? >> I mean, it's a new concept right? We've been talking about infrastructure clouds and SaaS applications living in application clouds and Data cloud is the ability to really share all that data that we've been collected. You know, we've spent what how many a decade or more with big data now but have we been able to use it effectively? And that's really where the Data cloud is coming in and Snowflake and making that a more seamless, friendly, easy experience to get access to the data. I've been in data warehousing for nearly 30 years now. And our dream has always been to be able to augment an organization's analytics with data from outside their organization. And that's just been a massive pain in the neck with having to move files around and replicate the data and maybe losing track of where it came from or where it went. And the Data cloud is really giving our customers the ability to do that in a much more governed way, a much more seamless way and really make it push button to give anyone access to the data they need and have the performance to do the analytics in near real time. It's total game changer is as you already know and just it's crazy what we're able to do today compared it to what we could do when I started out in my career. >> Well, I'm going to come back to that 'cause I want to tap your historical perspective, but Felipe let me ask you, So, why did you join Snowflake? You're you're the newbie here? What attracted you? >> Exactly? I'm the newbie, I used to work at Google until August. I was there for 10 years. I was a developer advocate there also for data you might have heard about the BigQuery. I was doing a lot of that. And then as time went by Snowflake started showing up more and more in my feeds within my customers in my community. And it came the time, well, I felt that like, you know, when wherever you're working, once in a while you think I should leave this place I should try something new, I should move my career forward. While at Google, I thought that so many times, as anyone would do, and it was only when Snowflake showed up, like where Snowflake is going now, why Snowflake is being received by all the customers that I saw this opportunity. And I decided that moving to Snowflake would be a step forward for me. And so far I'm pretty happy, like the timing has been incredible, but more than the timing and everything, it's really, really a great place for data. What I love first is data, sharing data, analyzing data and how Snowflake is doing it's for me to mean phenomenal. >> So, Kent, I want to come back to you and I say tap maybe your historical perspective here. And you said it's always been a dream that you could do these other things bringing in external data. I would say this, that I don't want to push a little bit on this because I have often said that the EDW marketplace really never lived up to its promises of 360 degree views of the customer real time or near real time analytics. And, and it really has been as you kind of described are a real challenge for a lot of organizations. When Hadoop came in we got excited that it was going to actually finally live up to that vision and, and duped it a lot and don't get me wrong, I mean, the whole concept of bring that compute to data and lowering the cost and so forth. But it certainly didn't minimize complexity. And, and it seems like, feels like Snowflake is on the cusp of actually delivering on that promise that we've been talking about for 30 years. I wonder, if you could share your perspective is it, are we going to get there this time? >> Yeah. And as far as I can tell working with all of our customers some of them are there. I mean, they thought through those struggles that you were talking about that I saw throughout my career and now with getting on Snowflake they're delivering customer 360 they're integrating weblogs and IOT data with structured data from their ERP systems or CRM systems, their supply chain systems. And it really is coming to fruition. I mean, the industry leaders, you know, Bill Inman and Claudia Imhoff, they've had this vision the whole time but the technology just wasn't able to support it. And the cloud, as we said about the internet, changed everything. And then Ben wine teary, and they're in their vision and building the system, taking the best concepts from the Hadoop world and the data Lake world and the enterprise data warehouse world and putting it all together into this, this architecture that's now Snowflake and the Data cloud solve it. I mean, it's the classic benefit of hindsight is 2020 after years in the industry, they'd seen these problems and said like, how can we solve them? Does the Cloud let us solve these problems? And the answer was yes, but it did require writing everything from scratch and starting over with, because the architecture of the Cloud just allows you to do things that you just couldn't do before. >> Yeah. I'm glad you brought up you know, some of the originators of the data warehouse because it really wasn't their fault. They were trying to solve a problem. It was the marketers that took it and really kind of made promises that they couldn't keep. But, the reality is when you talk to customers in the so old EDW days and this is the other thing I want to tap you guys' brains on. It was very challenging. I mean, one customer one time referred to it as a snake, swallowing a basketball. And what he meant by that is every time there's a change Sarbanes Oxley comes and we have to ingest all this new data. It's like, Oh, it's to say everything slows down to a grinding halt. Every time Intel came out with a new microprocessor, they would go out and grab a new server as fast as they possibly could. He called it chasing the chips and it was this endless cycle of pain. And so, you know, the originators of the data whereas they didn't have the compute power they didn't have the Cloud. And so, and of course they didn't have the 30, 40 years of pain to draw upon. But I wonder if you could, could maybe talk a little bit about the kinds of things that can be done now that we haven't been able to do here to form. >> Well, yeah. I remember early on having a conversation with Bill about this idea of near real time data warehousing and saying, is this real, is this something really people need? And at the time he was a couple of decades ago, he said now to them they just want to load their data sooner than once a month. That was the goal. And that was going to be near real time for them. And, but now I'm seeing it with our customers. It's like, now we can do it, you know, with things like the Kafka technology and snow pipe in Snowflake that people are able to get that refresh way faster and have near real time analytics access to that data in a much more timely manner. And so it really is coming true. And the, the compute power that's there, as you said, we've now got this compute power in the Cloud that we never dreamed of. I mean, you would think of only certain, very large, massive global companies or governments could afford super computers. And that's what it would have taken. And now we've got nearly the power of a super computer in our mobile device that we all carry around with us. So being able to harness all that now in the Cloud is really opening up opportunities to do things with data and access data in a way that, again really, we just kind of dreamed of before as like we can democratize data when we get to this point. And I think that's where we are. We're at that inflection point where now it's possible to do it. So the challenge on organizations is going to be how do we do it effectively? How do we do it with agility? And how do we do it in a governed manner? You mentioned Sarbanes Oxley, GDPR, CCPA, all of those are out there. And so we have all of that as well. And so that's where we're going to get into it, right into the governance and being able to do that in a very quick, flexible, extensible manner and Snowflakes really letting people do it now. >> Well, yeah. And you know, again, we've been talking about Hadoop and I, again, for all my fond thoughts of that era, and it's not like Hadoop is gone but it was a lot of excitement around it, but governance was a huge problem. And it was kind of a bolt on. Now, Felipe I going to ask you, like, when you think about a company like Google, your former employer, you know, data is at the core of their business. And so many companies the data is not at the core of their business. Something else is, it's a process or a manufacturing facility or whatever it is. And the data is sort of on the outskirts. You know, we often talk about in, in stove pipes. And so we're now seeing organizations really put data at the core of their, it becomes central to their DNA. I'm curious as to your thoughts on that. And also, if you've got a lot of experience with developers, is there a developer angle here in this new data world? >> For sure, I mean, I love seeing everything like throughout my career at Google and my two months here and talking to so many companies, you never thought before like these are database companies but they are the ones that keep rowing. The ones that keep moving to the next stage of their development is because they are focusing on data. They are adapting the processes, they are learning from it. Me, I focus a lot on developers. So, I met when I started this career as an advocate of first, I was a software engineer and my work so far, has we worked, I really loved talking to the engineers on the other companies. Like, maybe I'm not the one solving the business problem, but at the end of the day, when these companies have a business problem that they want to grow, they want to have data. There are other engineers that are scientists like me that want to work for the company and bring the best technology to solve the problems. And Yeah, there's so much where data can help, yes, as we evolved the system for the company, and also for us, for understanding the systems things like of survivability, and recently there was a big company a big launch on survivability (indistinct) whether they are running all of their data warehousing needs. And all of that needs on snowflake, just because running these massive systems and being able to see how they're working generates a lot of data. And then how do you manage it? How do you analyze it? Or Snowflake is really there to help cover the two areas. >> It's interesting my business partner, John farrier cohost of the CUBE, he said, gosh I would say middle of the last decade, maybe even around the time 2013, when Snowflake was just coming out, he said, he predicted the data would be the new development kit. And it's really at the center of a lot of the data life cycle the what I call the data pipelines. I know people use that term differently but I'm very excited about the Data cloud summit and what we're going to learn there. And I get to interview a lot of really cool people. So, I appreciate you guys coming up, but, Kent who should attend the Data cloud summit, I mean, what should they expect to learn? >> Well, as you said earlier, Dave, there's so many tracks and there's really kind of something for everyone. So, we've got a track on unlocking the value of the Data cloud, which is really going to speak to the business leaders, you know, as to what that vision is, what can we do from an organizational perspective with the Data cloud to get that value from the data to move our businesses forward. But we've also done for the technicians migrating to snowflake. Sessions on how to do the migration, modernizing your data Lake, data science, how to do analytics with the, and data science in Snowflake and in the Data cloud, and even down to building apps. So the developers and building data products. So, you know, we've got stuff for developers, we've got stuff for data scientists. We've got stuff for the data architects like myself and the data engineers on how to build all of this out. And then there's going to be some industry solution spotlights as well. So we can talk about different verticals folks in FinTech and healthcare, there's going to be stuff for them. And then for our data superheroes we have a hallway track where we're going to get talks from the folks that are in our data superheroes which is really our community advocacy program. So these are folks who are out there in the trenches using Snowflake delivering value at their organizations. And they're going to talk down and dirty. How did they make this stuff happen? So it's going to be to some hope, really something for everyone, fireside chats with our executives. Of course something I'm really looking forward to myself. So was fun to hear from Frank and Christian and Benoit about what's the next big thing, what are we doing now? Where are we going with all of this? And then there is going to be a some awards we'll be giving out our data driver awards for our most innovative customers. So this is going to be a lot, a lot for everybody to consume and enjoy and learn about this, this new space of, of the Data cloud. >> Well, thank you for that Kent. And I'll second that, at least there's going to be a lot for everybody. If you're an existing Snowflake customer there's going to be plenty of two or one content, we can get in to the how to use and the best practice, if you're really not that familiar with Snowflake, or you're not a customer, there's a lot of one-on-one content going on. So, Felipe, I'd love to hear from you what people can expect at the Data cloud summit. >> Totally, so I would like to plus one to everyone that can say we have a phenomenal schedule that they, the executive will be there. I really wanted to especially highlight the session I'm preparing with Trevor Noah. I'm sure you might have heard of him. And we are having him at the Data cloud summit and we are going to have a session. We are going to talk about data. We are preparing a session. That's all about how people that love data that people that want to make that actionable. How can they bring storytelling and make it more, have more impact as he has well learn to do through his life? >> That's awesome, So, we have Trevor Noah, we're not just going to totally geek out here. we're going to have some great entertainment as well. So, I want you to go to snowflake.com and click on Data cloud summit 2020 there's four geos. It starts on November 17th and then runs through the week and in the following week in Japan. So, so check that out. We'll see you there. This is Dave Vellante for the CUBE. Thanks for watching. (upbeat music)

>> (Instructor)From the cube studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a cube conversation. >> Hi everyone. This is Dave Volante, the cube, and we're getting ready for the snowflake data cloud summit four geographies eight tracks, more than 40 sessions for this global event starts on November 17th, where we're tracking the rise of the data cloud. You're going to hear a lot about that now by now, you know the story of Snowflake or you know, what maybe you don't, but a new type of cloud native database was introduced in the middle part of last decade. And a new set of analytics workloads has emerged that is powering a transformation within organizations. And it's doing this by putting data at the core of businesses and organizations. You know for years, we marched to the cadence of Moore's law. That was the innovation engine of our industry, but now that's changed it's data plus machine intelligence plus cloud. That's the new innovation cocktail for the technology industry and industries overall. And at the data cloud summit, we'll hear from snowflake executives, founders, technologists, customers, and ecosystems partners. And of course, you're going to hear from interviews on the cube. So let's dig in a little bit more and to help me, are two snowflake experts, Filipe Hoffa is a data cloud advocate and Kent Graziano is a chief technical evangelists post at Snowflake. Gents great to see you. Thanks for coming on. >> Yeah thanks for having us on this is great. >> Thank you. >> So guys, first, I got to congratulate you on getting to this point. You've achieved beyond escape velocity, and obviously one of the most important IPOs of the year, but you got a lot of work to do I know that Filipe, let me start with you data cloud. What's a data cloud and what are we going to learn about it at the data cloud summit? >> Oh, that's an excellent question. And let me tell you a little bit about our story here. And I really, really, really admire what Kent has done. I joined the snowflake like less than two months ago, and for me it's been a huge learning experience. And I look up to Kent a lot on how we deliver the message and how do we deliver all of that. So I would love to hear his answer first. >> Okay, that's cool. Okay Kent later on. So talk of data cloud, that's a catchy phrase, right? But it vectors into at least two of the components of my innovation, innovation cocktail. What, what are the substantive substantive aspects behind the data cloud? >> I mean, it's a, it's a new concept, right? We've been talking about infrastructure clouds and SAS applications living in an application clouds so data cloud is the ability to really share all that data that we've been collecting. You know, we've, we've spent what, how many days a decade or more with big data now, but have we been able to use it effectively? And that's, that's really where the data cloud is coming in and snowflake in making that a more seamless, friendly, easy experience to get access to the data. I've been in data warehousing for nearly 30 years now. And our dream has always been to be able to augment an organization's analytics with data from outside their organization. And that's just been a massive pain in the neck with having to move files around and replicate the data and maybe losing track of where it came from or where it went. And the data cloud is really giving our customers the ability to do that in a much more governed way, a much more seamless way, and really make it push button to give anyone access to the data they need and have the performance to do the analytics in near real time. It's it's total game changer as, as you already know, and just it's crazy what we're able to do today, compared to what we could do when I started out in my career. >> Well, I'm going to come back to that cause I want to tap your historical perspective, but Filipe, let me ask you. So why did you join snowflake? You're you're the newbie here. What attracted you? >> Exactly, I'm the newbie. I used to work at Google until August. I was there for 10 years. I was a developer advocate there also for data, you might have heard about a big query. I was doing a lot of that and then as time went by, Snowflake started showing up more and more in my feeds, within my customers, in my community. And it came the time. When, I felt that like, you know, when wherever you're working, once in a while you think I should leave this place, I should try something new. I should move my career forward. While at Google, I thought that so many times as anyone would do, and it was only when snowflake showed up, like where snowflake is going now, how snowflake is, is being received by all the customers that I saw this opportunity. And I decided that moving to Snowflake would be a step forward for me. And so far I'm pretty happy. Like the timing has been incredible, but more than the timing and everything, it's really, really a great place for data. What I love first is data sharing data, analyzing data and how Snowflake is doing it it promotes me in phenomena. >> So Ken, I want to come back to you and I say, tap, maybe your historical perspective here. And you said, you know, it's always been a dream that you could do these other things bring in external data. I would say this, that I don't want to push a little bit on this because I have often said that the EDW marketplace really never lived up to its promises of 360 degree views of the customer in real time or near real time analytics. And, and it really has been, as you kind of described are a real challenge for a lot of organizations when Hadoop came in you know, we had, we we we got excited that it was kind of going to actually finally live up to that vision and and and we duped it a lot. And it don't get me wrong. I mean, the whole concept of, you know, bring the compute to data and the lowering the cost and so forth, but it certainly didn't minimize complexity. And, and it seems like, feels like Snowflake is on the cusp of actually delivering that promise that we've been talking about for 30 years. I wonder if you could share your perspective, is it, are we going to get there this time? >> Yeah. And as far as I can tell working with all of our customers, some of them are there. I mean, they're, they Fought through those struggles that you were talking about that I saw throughout my career and now with getting on Snowflake they're, they're delivering customer 360, they're integrating weblogs and IOT data with structured data from their ERP systems or CRM systems, their supply chain systems. And it really is coming to fruition. I mean, the, you know, the industry leaders, you know, Bill Inman and Claudia M Hoff, they've had this vision the whole time, but the technology just wasn't able to support it. And the cloud, as we said about the internet, changed everything and then Ben Y and Terry, in their vision and building the system, taking the best concepts from the Hadoop world and the data Lake world and the enterprise data warehouse world, and putting it all together into this, this architecture, that's now, you know Snowflake and the data cloud solved it. I mean, it's the, you know, the, the classic benefit of her insight is 2020 after years in the industry, they had seen these problems and said like, how can we solve them? Does the cloud let us solve these problems? And the answer was yes, but it did require writing everything from scratch and starting over with because the architecture the cloud just allows you to do things that you just couldn't do before. Yeah I'm glad you brought up, you know, some of the originators of the data warehouse, because it really wasn't their fault. They were trying to solve a problem. That was the marketers that took it and really kind of made promises that they couldn't keep. But the reality is when you talk to customers in the, in the, so the old EDW days, and this is the other thing I want to, I want to tap your guys' brains on. It was very challenging. I mean, one, one customer, one time referred to it as a snake, swallowing a basketball. And what he meant by that is you know, every time there's a change, you know, Sarbanes Oxley comes and we have to ingest all this new data. It's like, Oh, it's just everything slows down to a grinding halt. Every time Intel came out with a new microprocessor, they would go out and grab a new server as fast as they possibly could. He called it chasing the chips, and it was this endless cycle of pain. And so, you know, the originators of the data whereas they didn't, they didn't have you know the compute power, they didn't have the cloud. >> Yeah. >> And so, and of course they didn't have the 30- 40 years of pain to draw upon. But, but I wonder if you could, could maybe talk a little bit about the kinds of things that can be done now that we haven't been able to do here before. >> Well, yeah I remember early on having a conversation with, with Bill about this idea of near real time data warehousing and saying, is this real? Is this something really need people need? And at the time it was, was a couple of decades ago, he said no to them they just want to load their data sooner than once a month. >> Yeah. >> That was the goal. And that was going to be near real time for them. And, but now I'm seeing it with our customers. It's like, now we can do it, you know, with things like the Kafka technology and snow pipe in, in Snowflake, that people are able to get that refresh way faster and have near real time analytics access to that data in a much more timely manner. And so it really is coming true. And the, the compute power that's there, as you said, you know we, we've now got this compute power in the cloud that we never dreamed of. I mean, you would think of only certain very large, massive global companies or governments could afford supercomputers. And that's what it would have taken. And now we've got nearly the power of a supercomputer in our mobile device that we all carry around with us. So being able to harness all that now in the cloud is really opening up opportunities to do things with data and access data in a way that again really we just kind of dreamed of before. It's like, we can, we can democratize data when we get to this point. And I think that's the, that's where we are, we're at that inflection point where now it's, it's possible to do it. So the challenge on organizations is going to be, how do we do it effectively? How do we do it with agility? And how do we do it in a governed manner? You mentioned Sarbanes Oxley, GDPR, CCPA, all of those are out there. And so we have all of that as well. And so that's where, that's where we're going to get into it, right. Is into the governance and being able to do that in a very quick, flexible, extensible manner and you know, Snowflakes really letting people do it now. >> Well, yeah and you know, again, we've been talking about Hadoop and again, for all my, my fond thoughts of that era, and it's not like hadoop is gone, but, but it was a lot of excitement around it but but governance was a huge problem and it was kind of a ball tough enough. Felipe I got to ask you, like when you think about a company like Google your former employer, you know, data is at the core of their business. And so many companies, the data is not at the core of their business. Something else is it's a process or a manufacturing facility or you know whatever it is. And the data is sort of on the outskirts. You know, we often talk about in, in stove pipes. And so we're now seeing organizations really put data at the core of their it becomes, you know, central to their, to their DNA. I'm curious as to your thoughts on that. And also if you've got a lot of experience with developers, is there, is there a developer angle here in this new data world? >> Oh, for sure. I mean, I love seeing every, like throughout my career at Google and my two months here and talking to so many companies, you never thought before, like these are database companies, but the the ones that keep rowing. The ones that keep moving to the next stage of their development is because they are focusing on data. They are adapting the processes they learning from it. And me, I focus a lot on developers. So I mean when I started This career as an advocate. First I was a software engineer and my work so far, has been work, I really loved talking to the engineers on the other companies. Like maybe I'm not the one solving the business problem, but at the end of the day, when these companies have a business problem that they want to row, they want to have data. There are other engineers that are scientists likes me that are, that, that want to work for work for the company and bring the best technology to solve the problems. Yeah, there's so much where data can help as we evolve the system for the company. And also for us for understanding the systems, things like observability and recently, there was a big company, a big launch on observability the company name is observable, where they are running all of their data warehousing needs. And all of their data needs on Snowflake, just because running these massive systems and being able to see how they're working generates a lot of data. And then how do you manage it? How do you analyze it? Or snowflake is already there to help. >> Well you know >> I covered the two areas. >> It's interesting my, my business partner, John farrier, cohost of the cube, he said, gosh, I would say middle of the last decade, maybe even around the time, you know, 2013, when Snowflake was just coming out, he said, he predicted the data would be the new development kit. And you know, it's really at the center of a lot of, you know, the data life cycle, the, the, what I call the data pipelines. I know people use that term differently, but, but I'm, I'm very excited about the data cloud summit and what we're going to learn there. And I get to interview a lot of really cool people. And so I appreciate you guys coming on, but Kent, who, who should attend the data cloud summit, I mean, what, what are the, what should they expect to learn? >> Well, as you said earlier, Dave, there's, there's so many tracks and there's really kind of something for everyone. So we've got a track on unlocking the value of the data cloud, which is really going to speak to, you know, the business leaders, you know, as to what that vision is, what can we do from an organizational perspective, with the data cloud to get that value from the data to, to move our businesses forward. But we've also got, you know, for the technicians migrating to Snowflake training sessions on how to do the migration, modernizing your data like data science, you know how to do analytics with the, and data science in Snowflake and in the data cloud and even down to building apps. So the developers and building data products. So, you know, we've got stuff for developers, we've got stuff for data scientists. We've got stuff for the, the data architects like myself and the data engineers on how to, how to build all of this out. And then there's going to be some industry solutions spotlights as well. So we can talk about different verticals of folks in FinTech and, and in healthcare. There's going to be stuff for them. And then for our, our data superheroes, we have a hallway track where we're going to get talks from the folks that are in our data superheroes, which is really our community advocacy program. So these are folks who are out there in the trenches using Snowflake, delivering value at, at their organizations. And they're going to talk you know down and dirty. How did they make this stuff happen? So there's going to be just really something for everyone, fireside chats with our executives, of course, something I'm really looking forward to in myself. It's always fun to, to hear from Frank and Christian. And Benwah about, you know, what's the next big thing, you know, what are we doing now? Where are we going with all of this? And then there is going to be some awards. We'll be giving out our data driver awards for our most innovative customers. So this is going to be a lot, a lot for everybody to consume and enjoy and learn about this, this new space of, of the data cloud. >> Well, thank you for that Kent. And I'll second that, I mean, there's going to be a lot for everybody. If you're an existing Snowflake customer, there's going to be plenty of two on one content we can get in to the how to's and the best practice. If you're really not that familiar with Snowflake, or you're not a customer, there's a lot of one-on-one content going on. If you're an investor and you want to figure out, okay, what is this vision? And can, you know, will this company grow into its massive valuation and how are they going to do that? I think you're going to, you're going to hear about the data cloud and really try get a perspective. And you can make your own judgment as to, to, you know, whether or not you think that it's going to be as large a market as many people think. So Felipe, I'd love to hear from you what people can expect at the data cloud summit. >> Totally, so I would love to plus one to everyone that Kent said. We have a phenomenal schedule that the the executive will be there. And I really wanted to specially highlight the session I'm preparing with Trevor Noah. I'm sure you might have heard of him. And we are having him at the data cloud summit, and we are going to have a session. We're going to talk about data. We are preparing a session, That's all about how people that love data, that people that want to make data actionable. How can they bring storytelling and make it more, have more impact as he has well learned to do through his life. >> That's awesome, So yeah, Trevor Noah, we're not just going to totally geek out here. We're going to, we're going to have some great entertainment as well. So I want you to go to snowflake.com and click on data cloud summit, 2020 there's four geos. It starts on November 17th and then runs through the week and then the following week in Japan. So, so check that out. We'll see you there. This is Dave Volante for the cube. Thanks for watching. (soft music)

>> Narrator: From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE Conversation. >> Hi everyone, this is Dave Vellante at theCUBE, and we're getting ready for the Snowflake Data Cloud Summit. four geographies, eight tracks, more than 40 sessions for this global event. starts on November 17th, where we're tracking the rise of the data cloud. You're going to hear a lot about that. Now, by now, you know the story of Snowflake or, you know what? Maybe you don't. But a new type of cloud-native database was introduced in the middle part of the last decade. And a new set of analytics workloads has emerged, that is powering a transformation within the organizations. And it's doing this by putting data at the core of businesses and organizations. For years, we marched to the cadence of Moore's law. That was the innovation engine of our industry, but now that's changed. It's data, plus machine intelligence, plus cloud. That's the new innovation cocktail for the technology industry and industries overall. And in the Data Cloud Summit, we'll hear from Snowflake executives, founders, technologists, customers, and ecosystems partners. And of course, you're going to hear from interviews on theCUBE. So let's dig in a little bit more. And to help me are two Snowflake experts. Felipe Hoffa is a data cloud advocate and Kent Graziano is a chief technical evangelist, both at Snowflake. Gents, great to see you, thanks for coming on. >> Thanks for having us on, this is great. >> Thank you. >> So guys, first, I got to congratulate you on getting to this point. You've achieved beyond escape velocity and obviously one of the most important IPOs of the year, but you got a lot of work to do and I know that. Felipe, let me start with you. Data cloud, what's a data cloud and what are we going to learn about it at the Data Cloud Summit? >> Oh, that's an excellent question. And, let me tell you a little bit about our story here. And I really, really, really admire what Kent has done. I joined Snowflake like less than two months ago and for me, it's been a huge learning experience. And I look up to Kent a lot on how we deliver the method here, how do we deliver all of that? So, I would love to hear his answer first. >> Dave: Okay, that's cool. Okay Kent, leader on. (Kent laughing) So we took it. Data cloud, that's a catchy phrase, right? But it vectors into at least two of the components of my innovation cocktail. What are the substantive aspects behind the data cloud? >> I mean, it's a new concept, right? We've been talking about infrastructure clouds and SaaS applications living in the application cloud, so data cloud is the ability to really share all that data that we've been collecting. We've spent what? How many da-- A decade or more with big data now, but have we been able to use it effectively? And that's really where the data cloud is coming in and Snowflake, in making that a more seamless, friendly, easy experience to get access to the data. I've been in data warehousing for nearly 30 years now. And our dream has always been to be able to augment an organization's analytics with data from outside their organization. And that's just been a massive pain in the neck with having to move files around and replicate the data and maybe losing track of where it came from or where it went. And the data cloud is really giving our customers the ability to do that in a much more governed way, a much more seamless way, and really make it push button to give anyone access to the data they need and have the performance to do the analytics in near real-time. It's a total game changer as you already know. And just, it's crazy what we're able to do today compared to what we could do when I started out in my career. >> Well, I'm going to come back to that 'cause I want to tap your historical perspective. But Felipe, let me ask you, so why did you join Snowflake? You're the newbie here, what attracted you? >> And finally, I'm the newbie. I used to work at Google until August. I was there for 10 years, I was a developer advocate there also for data, you might have heard about the BigQuery, I was doing a lot of that. And though as time went by, Snowflake started showing up more and more in my feeds, within my customers, in my community. And it came the time when I felt like-- Wherever you're working, once in a while you think, "I should leave this place, "I should try something new, "I should move my career forward." While at Google, I thought that so many times as anyone will do. And it was only when Snowflake showed up, like where Snowflake is going now, how Snowflake is being received by all the customers, that I saw this opportunity. And I decided that moving to Snowflake would be a step forward for me. And so far I'm pretty happy, like the timing has been incredible, but more than the timing and everything, it's really, really a great place for data. What I love first is data, sharing data, analyzing data and how Snowflake is doing it, its promising phenomena. >> So, Kent, I want to come back to you and I said, tap maybe your historical perspective here. And you said, it's always been a dream that you could do these other things, bring in external data. I would say this, that I would want to push a little bit on this because I have often said that the EDW marketplace really never lived up to its promises of 360 degree views of the customer, in real-time or near real-time analytics. And it really has been, as you kind of described it, a real challenge for a lot of organizations. When Hadoop came in, we had-- We got excited that it was going to actually finally live up to that vision and Hadoop did a lot. And don't get me wrong, I mean, the whole concept of, bring the computer data and lowering the cost and so forth. But it certainly didn't minimize complexity. And it seems like, feels like Snowflake is on the cusp of actually delivering on that promise that we've been talking about for 30 years. I wonder if you could share your perspective as an o-- Are we going to get there this time? >> Yeah. And as far as I can tell working with all of our customers, some of them are there. I mean, they thought through those struggles that you were talking about, that I saw throughout my career. And now with getting on Snowflake they're delivering customer 360, they're integrating weblogs and IOT data with structured data from their ERP systems or CRM systems, their supply chain systems and it really is coming to fruition. I mean, the industry leaders, Bill Inmon and Claudia Imhoff, they've had this vision the whole time, but the technology just wasn't able to support it and the cloud, as we said about the internet, changed everything. And then Benoit and Thierry in their vision in building the system, taking the best concepts from the Hadoop world and the data lake world and the enterprise data warehouse world, and putting it all together into this architecture, that's now Snowflake and the data cloud, solved it. I mean, it's-- The classic benefit of hindsight is 20/20, after years in the industry, they had seen these problems and said like, "How can we solve them? "Does the cloud let us solve these problems?" And the answer was, yes, but it did require writing everything from scratch and starting over with, because the architecture of the cloud just allows you to do things that you just couldn't do before. >> Yeah, I'm glad you brought up some of the originators of the data warehouse, because it really wasn't their fault, they were trying to solve a problem. It was the marketers that took it and really kind of made promises that they couldn't keep. But, the reality is when you talk to customers in the sort of the old EDW days, and this is the other thing I want to tap you guys' brains on, it was very challenging. I mean, and one customer one time referred to it as a snake swallowing a basketball. And what he meant by that is, every time there's a change, or Sarbanes-Oxley comes and we have to ingest all this new data. It's like aargh! It's just everything slows down to a grinding halt. Every time Intel came out with a new microprocessor they would go out and grab a new server as fast as they possibly could, he called it chasing the chips. And it was this endless cycle of pain. And so, the originators of the data warehouse, they didn't have the compute power, they didn't have the cloud. And so-- And of course they didn't have like 30, 40 years of pain to draw upon. But I wonder if you could maybe talk a little bit about the kinds of things that can be done now that we haven't been able to do here tofore. >> Well, yeah. I remember early on having a conversation with Bill about this idea of near real-time data warehousing and saying, "Is this real? "Is this something really people need?" And at the time, it was a couple of decades ago, he said, "No, to them, they just want to load their data "sooner than once a month." That was the goal. And they-- That was going to be near real-time for them. And, but now I'm seeing it with our customers. It's like, now we can do it. With things like the Kafka technology and Snowpipe in Snowflake, that people are able to get that refresh way faster and have near real-time analytics access to that data in a much more timely manner. And so it really is coming true. And the compute power that's there, as you said, we've now got this compute power in the cloud that we never dreamed of. I mean, you would think of only certain, very large, massive global companies or governments could afford supercomputers. And that's what it would have taken. And now we've got nearly the power of a super computer in our mobile device that we all carry around with us. So being able to harness all of that now in the cloud, is really opening up opportunities to do things with data and access data in a way that, again, really, we just kind of dreamed of before. Its like, we can democratize data when we get to this point. And I think that's where we are, we're at that inflection point, where now it's possible to do it. So the challenge on organizations is going to be how do we do it effectively? How do we do it with agility? And how do we do it in a governed manner? You mentioned Sarbanes-Oxley, GDPR, CCPA, all of those are out there. And so we have all of that as well. And so that's where we're going to get into it, ride us into the governance and being able to do that in a very quick, flexible, extensible manner. And Snowflakes really letting people do it now. >> Well, yeah. And again, we've been talking about Hadoop, and again, for all my fond thoughts of that era, and it's not like Hadoop is gone, but there was a lot of excitement around it, but governance was a huge problem. And it was kind of a bolt on. And now, Felipe I got to ask you, when you think about a company like Google, your former employer, data is at the core of their business. And so many companies, the data is not at the core of their business, something else is, it's a process or a manufacturing facility or whatever it is. And the data is sort of on the outskirts. We often talk about in stovepipes. And so we're now seeing organizations really, put data at the core of their... And it becomes central to their DNA. I'm curious as to your thoughts on that. And also, if you've got a lot of experience with developers, is there a developer angle here in this new data world? >> Oh, for sure. I mean, I love seeing every-- Like throughout my career at Google and my two months here, I'm talking to so many companies, that you never thought before, like these are database companies. But the ones that keep growing, the ones that keep moving to the next stage of their development is because they are focusing on data, they are adopting the processes, They are learning from it. And, me per-- I focus a lot on developers, so I mean, when I started this career as an advocate, first, I was a software engineer. And my work so far, has been... (mumbles) I really love talking to the engineers on the other companies, like... Maybe I'm not the one solving the business problem, but at the end of the day, when these companies have a business problem through out the world, they want to have data. There are other engineers that are scientists like me that are... That want to work for the company and bring the best technology to solve the problems. Yeah, for example, there's so much where data can help. If, as we evolve the systems for the company and also for us for understanding these systems, things like observability. And recently, there was a big company, a big launch on observability, on the company names of Cyberroam, where they are running all of their data warehousing needs and all of their data needs on Snowflake. Just because running these massive systems and being able to see how they're working, generates a lot of data. And then how do you manage it? How do you analyze it? Snowflake is ready there to help and support the two areas. >> It's interesting, my business partner, John Furrier, co-host of theCUBE, he said, gosh, I would say the middle of the last decade, maybe even around the time, 2013, when Snowflake was just coming out. He said... He predicted that data would be the new development kit. And, it's really at the center of a lot of the data life cycle, the-- What I call the data pipelines, I know people use that term differently. But, I'm very excited about the Data Cloud Summit and what we're going to learn there. And I get to interview a lot of really cool people. And so I appreciate you guys coming on. But Kent, who should attend the Data Cloud Summit? I mean, what are the-- What should they expect to learn? >> Well, as you said earlier Dave, there's so many tracks and there's really kind of something for everyone. So we've got a track on unlocking the value of the data cloud, which is really going to speak to the business leaders, as to what that vision is, what can we do from an organizational perspective with the data cloud to get them value from the data to move our businesses forward? But we've also got for the technicians, migrating to Snowflake. Training sessions on how to do the migration and modernizing your data lake, data science. How to do analytics with, and data science in Snowflake and in the data cloud. And even down to building apps, for the developers and building data products. So, we've got stuff for developers, we've got stuff for data scientists, we've got stuff for the data architects like myself and the data engineers, on how to build all of this out. And then there's going to be some industry solutions spotlights as well. So we can talk about different verticals, folks in FinTech and in healthcare, there's going to be stuff for them. And then for our data superheroes, we have a hallway track where we're going to get talks from the folks that are in our data superheroes, which is really our community advocacy program. So these are folks that are out there in the trenches using Snowflake, delivering value at their organizations. And they're going to talk down and dirty of how did they make this stuff happen? So there's going to be just really, something for everyone. Fireside chats with our executives, of course, something I'm really looking forward to myself. It's always fun to hear from Frank and Christian and Benoit, about what's the next big thing, what are we doing now? Where are we going with all of this? And then there is going to be some awards. We'll be giving out our Data Driver Awards for our most innovative customers. So there's going to be a lot for everybody to consume and enjoy and learn about this new space of the data cloud. >> Well, thank you for that Kent and I'll second that, and there's going to be a lot for everybody. If you're an existing Snowflake customer, there's going to be plenty of two of one content, where we can get in to the how tos and the best practice. If you're really not that familiar with Snowflake or you're not a customer, there's a lot of one-on-one content going on. If you're an investor and you want to figure out, "Okay, what is this vision? "And can, will this company grow into its massive valuation? "And how are they going to do that?" I think you're going to hear about the data cloud and really try to get a perspective and you can make your own judgment as to whether or not you think that it's going to be as large a market as many people think. So Felipe, I'd love to hear from you what people can expect at the Data Cloud Summit. >> Totally. So I would love to plus one to every one that Kent said, we have a phenomenal schedule that day, the executives will be there. But I really wanted to especially highlight the session I'm preparing with Trevor Noah. I'm sure you must have heard of him. And we are having him at the Data Cloud Summit, and we are going to have a session. We are going to talk about data. We are preparing a session that's all about how people that love data, that people that want to make that actionable, how can they bring storytelling and make it have more impact as he has well learned to do through his life. >> That's awesome. So, yeah, Trevor Noah, we're not just going to totally geek out here. We're going to have some great entertainment as well. So I want you to go to snowflake.com and click on Data Cloud Summit 2020. There's four geos. It starts on November 17th and then runs through the week and then the following week in Japan. So, check that out, we'll see you there. This is Dave Vellante for theCUBE. Thanks for watching. (upbeat music)

>>from around the globe. It's the Cube with digital coverage of VM World 2020 brought to you by VM Ware and its ecosystem partners. Hello and welcome back to the cubes. Virtual coverage of VM World 2020 Virtual I'm John for your host of the Cube, our 11th year covering V emeralds. Not in person. It's virtual. I'm with my coast, Dave. A lot, of course. Ah, guest has been on every year since the cubes existed. Sanjay Putin, who is now the chief operating officer for VM Ware Sanjay, Great to see you. It's our 11th years. Virtual. We're not in person. Usually high five are going around. But hey, virtual fist pump, >>virtual pissed bump to you, John and Dave, always a pleasure to talk to you. I give you more than a virtual pistol. Here's a virtual hug. >>Well, so >>great. Back at great. >>Great to have you on. First of all, a lot more people attending the emerald this year because it's virtual again, it doesn't have the face to face. It is a community and technical events, so people do value that face to face. Um, but it is virtually a ton of content, great guests. You guys have a great program here, Very customer centric. Kind of. The theme is, you know, unpredictable future eyes is really what it's all about. We've talked about covert you've been on before. What's going on in your perspective? What's the theme of your main talks? >>Ah, yeah. Thank you, John. It's always a pleasure to talk to you folks. We we felt as we thought, about how we could make this content dynamic. We always want to make it fresh. You know, a virtual show of this kind and program of this kind. We all are becoming experts at many Ted talks or ESPN. Whatever your favorite program is 60 minutes on becoming digital producers of content. So it has to be crisp, and everybody I think was doing this has found ways by which you reduce the content. You know, Pat and I would have normally given 90 minute keynotes on day one and then 90 minutes again on day two. So 180 minutes worth of content were reduced that now into something that is that entire 180 minutes in something that is but 60 minutes. You you get a chance to use as you've seen from the keynote an incredible, incredible, you know, packed array of both announcements from Pat myself. So we really thought about how we could organize this in a way where the content was clear, crisp and compelling. Thekla's piece of it needed also be concise, but then supplemented with hundreds of sessions that were as often as possible, made it a goal that if you're gonna do a break out session that has to be incorporate or lead with the customer, so you'll see not just that we have some incredible sea level speakers from customers that have featured in in our pattern, Mikey notes like John Donahoe, CEO of Nike or Lorry beer C I, a global sea of JPMorgan Chase partner Baba, who is CEO of Zuma Jensen Wang, who is CEO of video. Incredible people. Then we also had some luminaries. We're gonna be talking in our vision track people like in the annuity. I mean, one of the most powerful women the world many years ranked by Fortune magazine, chairman, CEO Pepsi or Bryan Stevenson, the person who start in just mercy. If you watch that movie, he's a really key fighter for social justice and criminal. You know, reform and jails and the incarceration systems. And Malala made an appearance. Do I asked her personally, I got to know her and her dad's and she spoke two years ago. I asked her toe making appearance with us. So it's a really, really exciting until we get to do some creative stuff in terms of digital content this year. >>So on the product side and the momentum side, you have great decisions you guys have made in the past. We covered that with Pat Gelsinger, but the business performance has been very strong with VM. Where, uh, props to you guys, Where does this all tie together for in your mind? Because you have the transformation going on in a highly accelerated rate. You know, cov were not in person, but Cove in 19 has proven, uh, customers that they have to move faster. It's a highly accelerated world, a lot. Lots changing. Multi cloud has been on the radar. You got security. All the things you guys are doing, you got the AI announcements that have been pumping. Thean video thing was pretty solid. That project Monterey. What does the customer walk away from this year and and with VM where? What is the main theme? What what's their call to action? What's what do they need to be doing? >>I think there's sort of three things we would encourage customers to really think about. Number one is, as they think about everything in infrastructure, serves APS as they think about their APS. We want them to really push the frontier of how they modernize their athletic applications. And we think that whole initiative off how you modernized applications driven by containers. You know, 20 years ago when I was a developer coming out of college C, C plus, plus Java and then emerge, these companies have worked on J two ee frameworks. Web Logic, Be Aware logic and IBM Web Street. It made the development off. Whatever is e commerce applications of portals? Whatever was in the late nineties, early two thousands much, much easier. That entire world has gotten even easier and much more Micro service based now with containers. We've been talking about kubernetes for a while, but now we've become the leading enterprise, contain a platform making some incredible investments, but we want to not just broaden this platform. We simplified. It is You've heard everything in the end. What works in threes, right? It's sort of like almost t shirt sizing small, medium, large. So we now have tens Ooh, in the standard. The advanced the enterprise editions with lots of packaging behind that. That makes it a very broad and deep platform. We also have a basic version of it. So in some sense it's sort of like an extra small. In addition to the small medium large so tends to and everything around at modernization, I think would be message number one number two alongside modernization. You're also thinking about migration of your workloads and the breadth and depth of, um, er Cloud Foundation now of being able to really solve, not just use cases, you are traditionally done, but also new ai use cases. Was the reason Jensen and us kind of partner that, and I mean what a great company and video has become. You know, the king maker of these ai driven applications? Why not run those AI applications on the best infrastructure on the planet? Remember, that's a coming together of both of our platforms to help customers. You know automotive banking fraud detection is a number of AI use cases that now get our best and we want it. And the same thing then applies to Project Monterey, which takes the B c f e m A Cloud Foundation proposition to smart Knicks on Dell, HP Lenovo are embracing the in video Intel's and Pen Sandoz in that smart make architectural, however, that so that entire world of multi cloud being operative Phobia Macleod Foundation on Prem and all of its extended use cases like AI or Smart Knicks or Edge, but then also into the AWS Azure, Google Multi Cloud world. We obviously had a preferred relationship with Amazon that's going incredibly well, but you also saw some announcements last week from, uh, Microsoft Azure about azure BMR solutions at their conference ignite. So we feel very good about the migration opportunity alongside of modernization on the third priority, gentlemen would be security. It's obviously a topic that I most recently taken uninterested in my day job is CEO of the company running the front office customer facing revenue functions by night job by Joe Coffin has been driving. The security strategy for the company has been incredibly enlightening to talk, to see SOS and drive this intrinsic security or zero trust from the network to end point and workload and cloud security. And we made some exciting announcements there around bringing together MAWR capabilities with NSX and Z scaler and a problem black and workload security. And of course, Lassiter wouldn't cover all of this. But I would say if I was a attendee of the conference those the three things I want them to take away what BMR is doing in the future of APS what you're doing, the future of a multi cloud world and how we're making security relevant for distributed workforce. >>I know David >>so much to talk about here, Sanjay. So, uh, talk about modern APS? That's one of the five franchise platforms VM Ware has a history of going from, you know, Challenger toe dominant player. You saw that with end user computing, and there's many, many other examples, so you are clearly one of the top, you know. Let's call it five or six platforms out there. We know what those are, uh, and but critical to that modern APS. Focus is developers, and I think it's fair to say that that's not your wheelhouse today, but you're making moves there. You agree that that is, that is a critical part of modern APS, and you update us on what you're doing for that community to really take a leadership position there. >>Yeah, no, I think it's a very good point, David. We way seek to constantly say humble and hungry. There's never any assumption from us that VM Ware is completely earned anyplace off rightful leadership until we get thousands, tens of thousands. You know, we have a half a million customers running on our virtualization sets of products that have made us successful for 20 years 70 million virtual machines. But we have toe earn that right and containers, and I think there will be probably 10 times as many containers is their virtual machines. So if it took us 20 years to not just become the leader in in virtual machines but have 70 million virtual machines, I don't think it will be 20 years before there's a billion containers and we seek to be the leader in that platform. Now, why, Why VM Where and why do you think we can win in their long term. What are we doing with developers Number one? We do think there is a container capability independent of virtual machine. And that's what you know, this entire world of what hefty on pivotal brought to us on. You know, many of the hundreds of customers that are using what was formerly pivotal and FDR now what's called Tan Xue have I mean the the case. Studies of what those customers are doing are absolutely incredible. When I listen to them, you take Dick's sporting goods. I mean, they are building curbside, pick up a lot of the world. Now the pandemic is doing e commerce and curbside pick up people are going to the store, That's all based on Tan Xue. We've had companies within this sort of world of pandemic working on contact, tracing app. Some of the diagnostic tools built without they were the lab services and on the 10 zoo platform banks. Large banks are increasingly standardizing on a lot of their consumer facing or wealth management type of applications, anything that they're building rapidly on this container platform. So it's incredible the use cases I'm hearing public sector. The U. S. Air Force was talking about how they've done this. Many of them are not public about how they're modernizing dams, and I tend to learn the best from these vertical use case studies. I mean, I spend a significant part of my life is you know, it s a P and increasingly I want to help the company become a lot more vertical. Use case in banking, public sector, telco manufacturing, CPG retail top four or five where we're seeing a lot of recurrence of these. The Tan Xue portfolio actually brings us closest to almost that s a P type of dialogue because we're having an apse dialogue in the in the speak of an industry as opposed to bits and bytes Notice I haven't talked at all about kubernetes or containers. I'm talking about the business problem being solved in a retailer or a bank or public sector or whatever have you now from a developer audience, which was the second part of your question? Dave, you know, we talked about this, I think a year or two ago. We have five million developers today that we've been able to, you know, as bringing these acquisitions earn some audience with about two or three million from from the spring community and two or three million from the economic community. So think of those five million people who don't know us because of two acquisitions we don't. Obviously spring was inside Vienna where went out of pivotal and then came back. So we really have spent a lot of time with that community. A few weeks ago, we had spring one. You guys are aware of that? That conference record number of attendees okay, Registered, I think of all 40 or 50,000, which is, you know, much bigger than the physical event. And then a substantial number of them attended live physical. So we saw a great momentum out of spring one, and we're really going to take care of that, That that community base of developers as they care about Java Manami also doing really, really well. But then I think the rial audience it now has to come from us becoming part of the conversation. That coupon at AWS re invent at ignite not just the world, I mean via world is not gonna be the only place where infrastructure and developers come to. We're gonna have to be at other events which are very prominent and then have a developer marketplace. So it's gonna be a multiyear effort. We're okay with that. To grow that group of about five million developers that we today Kate or two on then I think there will be three or four other companies that also play very prominently to developers AWS, Microsoft and Google. And if we're one among those three or four companies and remembers including that list, we feel very good about our ability to be in a place where this is a shared community, takes a village to approach and an appeal to those developers. I think there will be one of those four companies that's doing this for many years to >>come. Santa, I got to get your take on. I love your reference to the Web days and how the development environment change and how the simplicity came along very relevant to how we're seeing this digital transformation. But I want to get your thoughts on how you guys were doing pre and now during and Post Cove it. You already had a complicated thing coming on. You had multi cloud. You guys were expanding your into end you had acquisitions, you mentioned a few of them. And then cove it hit. Okay, so now you have Everything is changing you got. He's got more complex city. You have more solutions, and then the customer psychology is change. You got to spectrums of customers, people trying to save their business because it's changed, their customer behavior has changed. And you have other customers that are doubling down because they have a tailwind from Cove it, whether it's a modern app, you know, coming like Zoom and others are doing well because of the environment. So you got your customers air in this in this in this, in this storm, you know, they're trying to save down, modernized or or or go faster. How are you guys changing? Because it's impacted how you sell. People are selling differently, how you implement and how you support customers, because you already had kind of the whole multi cloud going on with the modern APS. I get that, but Cove, it has changed things. How are you guys adopting and changing to meet the customer needs who are just trying to save their business on re factor or double down and continue >>John. Great question. I think I also talked about some of this in one of your previous digital events that you and I talked about. I mean, you go back to the last week of February 1st week of March, actually back up, even in January, my last trip on a plane. Ah, major trip outside this country was the World Economic Forum in Davos. And, you know, there were thousands of us packed into the small digits in Switzerland. I was sitting having dinner with Andy Jassy in a restaurant one night that day. Little did we know. A month later, everything would change on DWhite. We began to do in late February. Early March was first. Take care of employees. You always wanna have the pulse, check employees and be in touch with them. Because the health and safety of employees is much more important than the profits of, um, where you know. So we took care of that. Make sure that folks were taking care of older parents were in good place. We fortunately not lost anyone to death. Covert. We had some covert cases, but they've recovered on. This is an incredible pandemic that connects all of us in the human fabric. It has no separation off skin color or ethnicity or gender, a little bit of difference in people who are older, who might be more affected or prone to it. But we just have to, and it's taught me to be a significantly more empathetic. I began to do certain things that I didn't do before, but I felt was the right thing to do. For example, I've begun to do 25 30 minute calls with every one of my key countries. You know, as I know you, I run customer operations, all of the go to market field teams reporting to me on. I felt it was important for me to be showing up, not just in the big company meetings. We do that and big town halls where you know, some fractions. 30,000 people of VM ware attend, but, you know, go on, do a town hall for everybody in a virtual zoom session in Japan. But in their time zone. So 10 o'clock my time in the night, uh, then do one in China and Australia kind of almost travel around the world virtually, and it's not long calls 25 30 minutes, where 1st 10 or 15 minutes I'm sharing with them what I'm seeing across other countries, the world encouraging them to focus on a few priorities, which I'll talk about in a second and then listening to them for 10 15 minutes and be, uh and then the call on time or maybe even a little earlier, because every one of us is going to resume button going from call to call the call. We're tired of T. There's also mental, you know, fatigue that we've gotta worry about. Mental well, being long term. So that's one that I personally began to change. I began to also get energy because in the past, you know, I would travel to Europe or Asia. You know, 40 50%. My life has travel. It takes a day out of your life on either end, your jet lag. And then even when you get to a Tokyo or Beijing or to Bangalore or the London, getting between sites of these customers is like a 45 minute, sometimes in our commute. Now I'm able to do many of these 25 30 minute call, so I set myself a goal to talk to 1000 chief security officers. I know a lot of CEOs and CFOs from my times at S A P and VM ware, but I didn't know many security officers who often either work for a CEO or report directly to the legal counsel on accountable to the audit committee of the board. And I got a list of these 1,002,000 people we called email them. Man, I gotta tell you, people willing to talk to me just coming, you know, into this I'm about 500 into that. And it was role modeling to my teams that the top of the company is willing to spend as much time as possible. And I have probably gotten a lot more productive in customer conversations now than ever before. And then the final piece of your question, which is what do we tell the customer in terms about portfolio? So these were just more the practices that I was able to adapt during this time that have given me energy on dial, kind of get scared of two things from the portfolio perspective. I think we began to don't notice two things. One is Theo entire move of migration and modernization around the cloud. I describe that as you know, for example, moving to Amazon is a migration opportunity to azure modernization. Is that whole Tan Xue Eminem? Migration of modernization is highly relevant right now. In fact, taking more speed data center spending might be on hold on freeze as people kind of holding till depend, emmick or the GDP recovers. But migration of modernization is accelerating, so we wanna accelerate that part of our portfolio. One of the products we have a cloud on Amazon or Cloud Health or Tan Xue and maybe the other offerings for the other public dog. The second part about portfolio that we're seeing acceleration around is distributed workforce security work from home work from anywhere. And that's that combination off workspace, one for both endpoint management, virtual desktops, common black envelope loud and the announcements we've now made with Z scaler for, uh, distributed work for security or what the analysts called secure access. So message. That's beautiful because everyone working from home, even if they come back to the office, needs a very different model of security and were now becoming a leader in that area. of security. So these two parts of the portfolio you take the five franchise pillars and put them into these two buckets. We began to see momentum. And the final thing, I would say, Guys, just on a soft note. You know, I've had to just think about ways in which I balance work and family. It's just really easy. You know what, 67 months into this pandemic to burn out? Ah, now I've encouraged my team. We've got to think about this as a marathon, not a sprint. Do the personal things that you wanna do that will make your life better through this pandemic. That in practice is that you keep after it. I'll give you one example. I began biking with my kids and during the summer months were able to bike later. Even now in the fall, we're able to do that often, and I hope that's a practice I'm able to do much more often, even after the pandemic. So develop some activities with your family or with the people that you love the most that are seeing you a lot more and hopefully enjoying that time with them that you will keep even after this pandemic ends. >>So, Sanjay, I love that you're spending all this time with CSOs. I mean, I have a Well, maybe not not 1000 but dozens. And they're such smart people. They're really, you know, in the thick of things you mentioned, you know, your partnership with the scale ahead. Scott Stricklin on who is the C. C so of Wyndham? He was talking about the security club. But since the pandemic, there's really three waves. There's the cloud security, the identity, access management and endpoint security. And one of the things that CSOs will tell you is the lack of talent is their biggest challenge. And they're drowning in all these products. And so how should we think about your approach to security and potentially simplifying their lives? >>Yeah. You know, Dave, we talked about this, I think last year, maybe the year before, and what we were trying to do in security was really simplified because the security industry is like 5000 vendors, and it's like, you know, going to a doctor and she tells you to stay healthy. You gotta have 5000 tablets. You just cannot eat that many tablets you take you days, weeks, maybe a month to eat that many tablets. So ah, grand simplification has to happen where that health becomes part of your diet. You eat your proteins and vegetables, you drink your water, do your exercise. And the analogy and security is we cannot deploy dozens of agents and hundreds of alerts and many, many consoles. Uh, infrastructure players like us that have control points. We have 70 million virtual machines. We have 75 million virtual switches. We have, you know, tens of million's off workspace, one of carbon black endpoints that we manage and secure its incumbent enough to take security and making a lot more part of the infrastructure. Reduce the need for dozens and dozens of point tools. And with that comes a grand simplification of both the labor involved in learning all these tools. Andi, eventually also the cost of ownership off those particular tool. So that's one other thing we're seeking to do is increasingly be apart off that education off security professionals were both investing in ah, lot of off, you know, kind of threat protection research on many of our folks you know who are in a threat. Behavioral analytics, you know, kind of thread research. And people have come out of deep hacking experience with the government and others give back to the community and teaching classes. Um, in universities, there are a couple of non profits that are really investing in security, transfer education off CSOs and their teams were contributing to that from the standpoint off the ways in which we can give back both in time talent and also a treasure. So I think is we think about this. You're going to see us making this a long term play. We have a billion dollar security business today. There's not many companies that have, you know, a billion dollar plus of security is probably just two or three, and some of them have hit a wall in terms of their progress sport. We want to be one of the leaders in cybersecurity, and we think we need to do this both in building great product satisfying customers. But then also investing in the learning, the training enable remember, one of the things of B M worlds bright is thes hands on labs and all the training enable that happened at this event. So we will use both our platform. We in world in a variety of about the virtual environments to ensure that we get the best education of security to professional. >>So >>that's gonna be exciting, Because if you look at some of the evaluations of some of the pure plays I mean, you're a cloud security business growing a triple digits and, you know, you see some of these guys with, you know, $30 billion valuations, But I wanted to ask you about the market, E v m. Where used to be so simple Right now, you guys have expanded your tam dramatically. How are you thinking about, you know, the market opportunity? You've got your five franchise platforms. I know you're very disciplined about identifying markets, and then, you know, saying, Okay, now we're gonna go compete. But how do you look at the market and the market data? Give us the update there. >>Yeah, I think. Dave, listen, you know, I like davinci statement. You know, simplicity is the greatest form of sophistication, and I think you've touched on something that which is cos we get bigger. You know, I've had the great privilege of working for two great companies. s a P and B M where the bulk of my last 15 plus years And if something I've learned, you know, it's very easy. Both companies was to throw these TLS three letter acronyms, okay? And I use an acronym and describing the three letter acronyms like er or s ex. I mean, they're all acronyms and a new employee who comes to this company. You know, Carol Property, for example. We just hired her from Google. Is our CMO her first comments like, My goodness, there is a lot of off acronyms here. I've gotta you need a glossary? I had the same reaction when I joined B. M or seven years ago and had the same reaction when I joined the S A. P 15 years ago. Now, of course, two or three years into it, you learn everything and it becomes part of your speed. We have toe constantly. It's like an accordion like you expanded by making it mawr of luminous and deep. But as you do that it gets complex, you then have to simplify it. And that's the job of all of us leaders and I this year, just exemplifying that I don't have it perfect. One of the gifts I do have this communication being able to simplify things. I recorded a five minute video off our five franchise pill. It's just so that the casual person didn't know VM where it could understand on. Then, when I'm on your shore and when on with Jim Cramer and CNBC, I try to simplify, simplify, simplify, simplify because the more you can talk and analogies and pictures, the more the casual user. I mean, of course, and some other audiences. I'm talking to investors. Get it on. Then, Of course, as you go deeper, it should be like progressive layers or feeling of an onion. You can get deeper. It's not like the entire discussion with Sanjay Putin on my team is like, you know, empty suit. It's a superficial discussion. We could go deeper, but you don't have to begin the discussion in the bowels off that, and that's really what we don't do. And then the other part of your question was, how do we think about new markets? You know, we always start with Listen, you sort of core in contact our borough come sort of Jeffrey Moore, Andi in the Jeffrey more context. You think about things that you do really well and then ask yourself outside of that what the Jason sees that are closest to you, that your customers are asking you to advance into on that, either organically to partnerships or through acquisitions. I think John and I talked about in the previous dialogue about the framework of build partner and by, and we always think about it in that order. Where do we advance and any of the moves we've made six years ago, seven years ago and I joined the I felt VM are needed to make a move into mobile to really cement opposition in end user computing. And it took me some time to convince my peers and then the board that we should by Air One, which at that time was the biggest acquisition we've ever done. Okay. Similarly, I'm sure prior to me about Joe Tucci, Pat Nelson. We're thinking about nice here, and I'm moving to networking. Those were too big, inorganic moves. +78 years of Raghu was very involved in that. The decisions we moved to the make the move in the public cloud myself. Rgu pack very involved in the decision. Their toe partner with Amazon, the change and divest be cloud air and then invested in organic effort around what's become the Claudia. That's an organic effort that was an acquisition fast forward to last year. It took me a while to really Are you internally convinced people and then make the move off the second biggest acquisition we made in carbon black and endpoint security cement the security story that we're talking about? Rgu did a similar piece of good work around ad monetization to justify that pivotal needed to come back in. So but you could see all these pieces being adjacent to the core, right? And then you ask yourself, Is that context meaning we could leave it to a partner like you don't see us get into the hardware game we're partnering with. Obviously, the players like Dell and HP, Lenovo and the smart Knick players like Intel in video. In Pensando, you see that as part of the Project Monterey announcement. But the adjacent seas, for example, last year into app modernization up the stack and into security, which I'd say Maura's adjacent horizontal to us. We're now made a lot more logical. And as we then convince ourselves that we could do it, convince our board, make the move, We then have to go and tell our customers. Right? And this entire effort of talking to CSOs What am I doing is doing the same thing that I did to my board last year, simplified to 15 minutes and get thousands of them to understand it. Received feedback, improve it, invest further. And actually, some of the moves were now making this year around our partnership in distributed Workforce Security and Cloud Security and Z scaler. What we're announcing an XDR and Security Analytics. All of the big announcements of security of this conference came from what we heard last year between the last 12 months of my last year. Well, you know, keynote around security, and now, and I predict next year it'll be even further. That's how you advance the puck every year. >>Sanjay, I want to get your thoughts. So now we have a couple minutes left. But we did pull the audience and the community to get some questions for you, since it's virtually wanted to get some representation there. So I got three questions for you. First question, what comes after Cloud and number two is VM Ware security company. And three. What company had you wish you had acquired? >>Oh, my goodness. Okay, the third one eyes gonna be the turkey is one, I think. Listen, because I'm gonna give you my personal opinion, and some of it was probably predates me, so I could probably safely So do that. And maybe put the blame on Joe Tucci or somebody else is no longer here. But let me kind of give you the first two. What comes after cloud? I think clouds gonna be with us for a long time. First off this multi cloud world, you just look at the moment, um, that AWS and azure and the other clouds all have. It's incredible on I think this that multi cloud from phenomenon. But if there's an adapt ation of it, it's gonna be three forms of cloud. People are really only focus today in private public cloud. You have to remember the edge and Telco Cloud and this pendulum off the right balance of workloads between the data center called it a private cloud. The public cloud on one end and the telco edge on the other end. I think we're in a really good position for workloads to really swing between all three of those locations. Three other part that I think comes as a sequel to Cloud is cloud native. All of the capabilities a serverless functions but also containers that you know. Obviously the one could think of that a sister topics to cloud but the entire world of containers. The other seat, uh, then cloud a cloud native will also be topics, but these were all fairly connected. That's how I'd answer the first question. A security company? Absolutely. We you know, we aspire to be one of the leading companies in cyber security. I don't think they will be only one. We have to show this by the wealth on breath of our customers. The revenue momentum we have Gartner ranking us or the analysts ranking us in top rights of magic quadrants being viewed as an innovator simplifying the stack. But listen, we weren't even on the radar. We weren't speaking of the security conferences years ago. Now we are. We have a billion dollar security business, 20,000 plus customers, really strong presences and network endpoint and workload and Cloud Security. The three Coppola's a lot more coming in Security analytics, Cloud Security distributed workforce Security. So we're here to stay. And if anything, BMR persist through this, we're planning for multi your five or 10 year timeframe. And in that course I mean, the competition is smaller. Companies that don't have the breadth and depth of the n words are Andy muscle and are going market. We just have to keep building great products and serving customer on the third man. There's so many. But I mean, I think Listen, when I was looking back, I always wondered this is before I joined so I could say the summit speculatively on. Don't you know, make this This is BMR. Sorry. This is Sanjay one's opinion. Not VM. I gotta make very, very clear. Well, listen, I would have if I was at BMO in 2012 or 2013. I would love to about service now then service. It was a great company. I don't even know maybe the company's talk, but then talk about a very successful company at that time now. Maybe their priorities were different. I wasn't at the company at the time, but I can speculate if that had happened, that would have been an interesting Now I think that was during the time of Paul Maritz here and and so on. So for them, maybe there were other priorities the company need to get done. But at that time, of course, today s so it's not as big of a even slightly bigger market cap than us. So that's not happening. But that's a great example of a good company that I think would have at that time fit very well with VM Ware. And then there's probably we don't look back and regret we move forward. I mean, I think about the acquisitions we have made the big ones. Okay, Nice era air watch pop in black. Pivotal. The big moves we've made in terms of partnership. Amazon. What? We're announcing this This, you know, this week within video and Z scaler. So you never look back and regret. You always look for >>follow up on that To follow up on that from a developer, entrepreneurial or partner Perspective. Can you share where the white spaces for people to innovate around vm Where where where can people partner and play. Whether I'm an entrepreneur in a garage or venture back, funded or say a partner pivoting and or resetting with Govind, where's the white spaces with them? >>I think that, you know, there's gonna be a number off places where the Tan Xue platform develops, as it kind of makes it relevant to developers. I mean, there's, I think the first way we think about this is to make ourselves relevant toe all of that ecosystem around the C I. C. D type apply platform. They're really good partners of ours. They're like, get lab, You know, all of the ways in which open source communities, you know will play alongside that Hash E Corp. Jay frog there number of these companies that are partnering with us and we're excited about all of their relevancy to tend to, and it's our job to go and make that marketplace better and better. You're going to hear more about that coming up from us on. Then there's the set of data companies, you know, con fluent. You know, of course, you've seen a big I p o of a snowflake. All of those data companies, we'll need a very natural synergy. If you think about the old days of middleware, middleware is always sort of separate from the database. I think that's starting to kind of coalesce. And Data and analytics placed on top of the modern day middleware, which is containers I think it's gonna be now does VM or play physically is a data company. We don't know today we're gonna partner very heavily. But picking the right set of partners been fluent is a good example of one on. There's many of the next generation database companies that you're going to see us partner with that will become part of that marketplace influence. And I think, as you see us certainly produce out the VM Ware marketplace for developers. I think this is gonna be a game changing opportunity for us to really take those five million developers and work with the leading companies. You know, I use the example of get Lab is an example get help there. Others that appeal to developers tie them into our developer framework. The one thing you learn about developers, you can't have a mindset. With that, you all come to just us. It's a very mingled village off multiple ecosystems and Venn diagrams that are coalescing. If you try to take over the world, the developer community just basically shuns you. You have to have a very vibrant way in which you are mingling, which is why I described. It's like, Listen, we want our developers to come to our conferences and reinvent and ignite and get the best experience of all those provide tools that coincide with everybody. You have to take a holistic view of this on if you do that over many years, just like the security topic. This is a multi year pursuit for us to be relevant. Developers. We feel good about the future being bright. >>David got five minutes e. >>I thought you were gonna say Zoom, Sanjay, that was That was my wildcard. >>Well, listen, you know, I think it was more recently and very fast catapult Thio success, and I don't know that that's clearly in the complete, you know, sweet spot of the anywhere. I mean, you know, unified collaboration would have probably put us in much more competition with teams and, well, back someone you always have to think about what's in the in the bailiwick of what's closest to us, but zooms a great partner. Uh, I mean, obviously you love to acquire anybody that's hot, but Eric's doing really well. I mean, Erica, I'm sure he had many people try to come to buy him. I'm just so proud of him as a friend of all that he was named to Time magazine Top 100. But what he's done is phenomenon. I think he could build a company that's just his important, his Facebook. So, you know, I encourage him. Don't sell, keep building the company and you'll build a company that's going to be, you know, the enterprise version of Facebook. And I think that's a tremendous opportunity to do this better than anybody else is doing. And you know, I'm as an immigrant. He's, you know, China. Born now American, I'm Indian born, American, assim immigrants. We both have a similar story. I learned a lot from him. I learned a lot from him, from on speed on speed and how to move fast, he tells me he learns a thing to do for me on scale. We teach each other. It's a beautiful friendship. >>We'll make sure you put in a good word for the Kiwi. One more zoom integration >>for a final word or the zoom that is the future Facebook of the enterprise. Whatever, Sanjay, Thank >>you for connecting with us. Virtually. It is a digital foundation. It is an unpredictable world. Um, it's gonna change. It could be software to find the operating models or changing you guys. We're changing how you serve customers with new chief up commercial customer officer you have in place, which is a new hire. Congratulations. And you guys were flexing with the market and you got a tailwind. So congratulations, >>John and Dave. Always a pleasure. We couldn't do this without the partnership. Also with you. Congratulations of Successful Cube. And in its new digital format, Thank you for being with us With VM world here on. Do you know all that you're doing to get the story out? The guests that you have on the show, they look forward, including the nonviable people like, Hey, can I get on the Cuban like, Absolutely. Because they look at your platform is away. I'm telling this story. Thanks for all you're doing. I wish you health and safety. >>I'm gonna bring more community. And Dave is, you know, and Sanjay, and it's easier without the travel. Get more interviews, tell more stories and tell the most important stories. And thank you for telling your story and VM World story here of the emerald 2020. Sanjay Poon in the chief operating officer here on the Cube I'm John for a day Volonte. Thanks for watching Cube Virtual. Thanks for watching.

>> from our studios in the heart of Silicon Valley, Palo Alto, California It is a cute conversation. >> Welcome to this cube competition here at the Palo Alto Cube Studios. I'm John for a host of the Cube. Were here a special guests to keep alumni investor An entrepreneur who do Sudhakar, would you Good to see you again, John. Always a pleasure. You've been on as an entrepreneur, founder. As an investor, you're always out. Scour in the Valley was a great conversation. I want to get your thoughts as kind of a guest analyst on this segment around the state of the Union for Enterprise Tech. As you know, we covering the price tag. We got all the top enterprise B to B events. The world has changed and get reinvent coming up. We got VM World before that. The two big shows, too to cap out this year got sprung a variety of other events as well. So a lot of action cloud now is pretty much a done deal. Everyone's validating it. Micro cells gaining share a lot of growth areas around cloud that's been enable I want to get your thoughts first. Question is what are the top growth sectors in the enterprise that you're seeing >> papers. Thank you for having me. It's always a pleasure talking to you over the years. You and me have done this so many times. I'm learning a lot from you. So thank you. You are so yeah, I think Let's dig into the cloud side and in general market. So I think that there are 34 areas that I see a lot that's happening a lot. Cloud is still growing, a lot 100% are more growth and cloud and dog breeders. And what is the second? I see, a lot of I T services are close services. This includes service management. The areas that service now isn't They're >> still my ops was Maybe >> they opt in that category. E I said With management, the gutter is coming with the new canticle a service management. So they're replacing idea some with a different. So that's growing 800% as a category tourist. RP according to again, the industry analysts have seen that it's going at 65 to 70% so these three areas are going a lot in the last one that I see a lot of user experience. Can you build? It's like it's a 20,000,000,000 market cap, something. So if you let it out, it's a cloud service Management services RP user experience cos these are the four areas I see a lot dating all the oxygen rest. Everybody is like the bread crumbs. >> Okay, and why do you think the growth in our P A. So how's the hype? Is it really what? What is going on in our pee, In your opinion, >> on the rumors I'm hearing or there is some companies are already 1,000,000,000 revenue run great wise. That's a lot in our piece. So it's not really a hype that really so that if you look and below that, what's happening is I'd be a Companies are automating automation. The key for here is if I can improve the user experience and also automate things. RPS started doing screen scraping right in their leaders, looking at any reservations supply chain any workflow automation. So every company is so complex. Now somebody has to automate the workflow. How can you do this with less number of people, less number, resources, and improve the productivity >> coming? R P A. Is you know, robotic process automation is what it stands for, but ultimately it's software automation. I mean, it's software meets cloud meets automation. It seems to be the big thing. That's also where a I can play a part. Your take on the A I market right now. Obviously, Cloud and A I are probably the two biggest I think category people tend to talk about cloud and a eyes kind of a big kind of territories. RPG could fall under a little bit of bulls, but what you take on a guy, >> Yeah, so I think if you look at our pier, I actually call the traditional appears to be historical legacy. Wonders and R P companies are doing a good job to transform themselves to the next level, right? But our pianist Rocky I score. It's no longer the screen skipping tradition, making the workflow understanding. So there are new technology called conversational Rp. There's actually a separate market. Guys been critical conversation within a Can I talk to in a dialogue manner like what you experienced Instagram are what using what's up our dialogue flow? How can I make it? A conversational RPS is a new secretary is evolving it, but our becomes have done a good job. They leave all their going out. A >> lot has been has great success. We've been covering them like a blanket on a single cube. Um, I got it. I got to get your take on how this all comes into the next generation modern era because, um, you know, we're both been around the block. We've seen the waves of innovation. The modern error of clouds certainly cloud one Dato Amazon. Now Microsoft has your phone. Google anywhere else really goes. Dev Ops, The devil's movement cloud native amazing, create a lot of value continues to do well, but now there's a big culture on cloud 2.0, what is your definition of cloud two point? Oh, how do you see Cloud 2.0, evolving. But >> I like the name close to party. I think it's your third. It is going to continue as a trained. So look, throw two point with eyes. I don't know what it will be, but I can tell you what it should be and what it can have. Some other things that should do in the cloud is cloud is still very much gun to human beings. Lot of develops people. Lot of human being The next addition to a daughter should have things done programmatically I don't need tens of thousands off Assad ease and develops people. So back to your air, upside and everything. Some of those things should become close to become proactive. I don't want to wait until Amazon. Easter too is done. If I'm paying him is on this money. Amazon should be notifying me when my service is going to be done. The subsidy eaters They operated Chlo Trail Cloudwatch Exeter. But they need to take it to a notch level. But Amazon Azure. >> So making the experience of deploying, running and building APS scalable. Actually, that's scales with Clavet. Programmable kind of brings in the RPI a mean making a boat through automation edge of the network is also interesting. Comes up a lot like Okay, how do you deal with networking? Amazons Done computing storage and meet amazing. Well, cloud and networking has been built in, I guess to me, the trend of networking kicks in big because now it's like, OK, if you have no perimeter, you have a service area with I o t. >> There's nothing that >> cloud to point. It has to address riel time programming ability. Things like kubernetes continues to rise. You're gonna need to have service has taken up and down automatically know humans. So this >> is about people keep on fur cloak. What should be done before the human in the to rate still done. It develops. People are still using terror from lot of scripting. Lot of manual. Can you automata? That's one angle The second angle I see in cloud 2.0 is if you step back and say What, exactly? The intrinsic properties of Claude Majors. It's the work floor. It's automation, but it's also able to do it. Pro, actually. So what I don't have to raise if I'm playing club renders this much money. Tell me what outrageous are happening. Don't wait until outage happens. Can you predict voted? Yes, they have the capability to women. It should be Probably steal it. No, not 100%. So I want to know what age prediction. I wonder what service are going down. Are notified the user's that will become a a common denominator and solutions will be start providing, even though you see small startups doing this. Eventually they become features all these companies, and they'll get absorbed by the I called his aircraft carriers. You have Masson agile DCP. They're going to absorb all this, a ups to the point that provide that as the functionality. >> Yeah, let's get the consolidation in second. I want to get your thoughts on the cloud to point because we really getting at is that there's a lot of white space opportunity coming in. So I gotta ask you to start up. Question as you look at your investor, prolific investor in start ups. Also, you're an entrepreneur yourself. What >> is? >> They have opportunities out there because we'll get into the big the big whales Amazon, who were building and winning at scale. So embarrassed entry or higher every day, even though it's open sources, They're Amazons, betting on open source. Big time. We had John Thompson talk about that. That was excessive. Something Nutella. And so what? What if I was a printer out there? Would what do I do? I mean, is there Is there any real territory that I could create a base camp on and make money? >> That's plenty. So there's plenty of white faces to create. Look, first of all your look at what's catering, look at what's happening. IBM is auto business in service management, CSL itself to Broadcom. BMC is sold twice to private companies. Even the CEO got has left our war It is. Then you have to be soldiers of the Micro Focus. The only company that's left is so it's not so in that area, you can create plenty of good opportunities. That's a big weight. >> Sensors now just had a bad quarter. So actually, clarity will >> eventually they're gonna enough companies to go in that space. That play that's based can support 23 opportunities so I can see a publicly traded company in service. No space in next five years. My production is they'll be under company will go a p o in the service management space. Same things would happen. Rp, Rp vendors won't get acquired A little cleared enough work for automation. They become the next day because of the good. I can see a next publicly traded company. What happened in the 80 operations? Patriotism Probably. Computer company Pedro is doing really well. Watch it later. Don't. They're going to go public next. So that area also, you see plenty of open record companies in a UPS. >> So this is again back to the growth areas. Cloud hard to compete on Public Cloud. Yes, the big guys are out there. There's a cloud enablers, the people who don't have the clouds. So h p tried to do a cloud hp They had to come out, they'll try to cloud couldn't do It s a P technically is out there with a cloud. They're trying to be multi cloud. So you have a series of people who made it an oracle still on the fence. They still technically got a cloud, but it's really more Oracle and Oracle. So they're kind of stuck in the middle between the cloud and able nervous. The Cloud player. If you're not a cloud player large enterprise, what is the strategy? Because you got HP, IBM, Cisco and Dell. >> So I don't know. You didn't include its sales force in that If I'm Salesforce, I want sales force to get in. They have a sales cloud marketing cloud commerce code. Mark is not doing anything in the area of fighting clothes. They cannot go from 100,000,000,000 toe, half a trillion trillion market cap. Told I D. They have to embrace that and that's 100% growth area. You know, people get into this game at some point. It'll be is already hard and 50,000,000,000 market cap. Then that leaves. What is this going to do? Cisco has been buying more security software assets, but they don't wanna be a public company, their hybrid club. But they have to figure out How can they become an arms dealer in escape and by ruining different properties off close services? And that's gonna happen. And I've been really good job by acquiring Red Heart. So I think some place really figuring out this what is happening. But they have to get in the gaming club they have to do. Other service management have begun and are here. They have to get experience. None of these guys have experienced in this day and age that you killed and who are joining the workforce. They care for Airbnb naked for we work. They care for uber. They care for Netflix. It is not betting unders. So if I'm on the border, Francisco, I'm not talking about experience That's a problem to me. Hey, tree boredom is not talking about that. That's what if I'm I know Mark is on the board. Paramount reason. But Mark is investing in all the slack. Cos then why is it we are doing it either hit special? Get a separate board member. They should get somebody else. >> Why? He wouldn't tell. You have to move. Maybe. I don't know. We don't talk about injuries about that. But I want to get back to this experience thing because experience has become the new expectation. Yes, that's been kind of a design principle kind of ethos. Okay, so let's take that. The next little younger generation, they're consuming Airbnb. They're using the serious like their news and little chunks be built a video service for that. So things are changing. What is? I tease virgin as the consumption is a product issue. So how does I t cater to these new experience? What are some of those experiences? I >> think all of them. But I think I d for Social Kedrick, every property, every product should figure out how to offer to the young dreamers how they were contributed offer to the businesses on the B two baby to see. So the eye has to think every product or not. Should I start thinking about how my user should consume this and how should out for new experiences and how they want to see this in a new way, right? It's not in the same the same computer networking. How can a deluded proactively How can a dealer to a point where people can consume it and make other medications so darn edition making? That's where the air comes in. Don't wait for me toe. Ask the question. Suggest it's like Gmail auto complete. Every future should be thinking through problem. Still, what can I do to improve the experience that changes the product? Management's on? And that's what I'm looking at, companies who are thinking like that connection and see Adam Connection security. But that has to happen in the product. >> I was mentioning the people who didn't have clouds HP, IBM, Cisco and Dell you through sales force in there, I kind of would think sales were six, which is technically a cloud. They were cloud before cloud was even cloud. They built basically oracle for the cloud that became sales force. But you mentioned service now. Sales force. You got adobe, You got work day. These are application clouds. So they're not public clouds per se they get Amazon Web service is, you know, at Adobe runs on AWS, right? A lot of other people do. Microsoft has their own cloud, but they also have applications as well. Office 3 65 So what if some of these niche cloud these application clouds have to do differently? Because if you think about sales force, you mentioned a good point. Why isn't sales were doing more? People generally don't like Salesforce. You think that it's more of a lock inspect lesson with a wow. They've done really innovative things. I mean, I don't People don't really tend to talk about sales force in the same breath as innovation. They talk about Well, we run sales for us. We hate it or we use it and they never really break into these other markets. What's your take on them? >> I think Mark has done a good job to order. Yes, acquiring very cos it has to start from the top and at the market. His management team should say, I want to get in a new space. He got in tow. Commerce. Claudia got into marketing. He has to know, decide to get into idea or not. Once he comes out, he's really taken because today, science. What is below the market cap? Com Part of it'll be all right. If I am sales force, I need to go back down. Should I go after service? No. Industry should go after entire 80 services industry. Yes or no, But they have to make a suggestion. Something with Toby Toby is not gonna be any slower. They will get into. I decide. They're already doing the eyesight and experience. They're king of experience. Their king off what they're doing. Marketing site. They will expand. Writing. >> What does something We'll just launched a platform. Yes, that's right. The former executive from IBM. That's an interesting direction. They all have these platforms. Okay, so I got together to the Microsoft Amazon, Um, Google, the big clouds and then everybody else. A lot of discussion around consolidation. A lot of people say that the recession's coming next year. I doubt that. No, nos. The consolidation continues to happen. You can almost predict that. But where do you see the consolidation of you got some growth areas as you laid out cloud I t service is our p a experience based off where looks like where's the consolidation happening? If growth is happening, they're words to tell. >> It was happening. Really Like I see a lot in cyber security. I'm in Costa Rica, live in public. You have the scaler, the whole bunch of companies. So the next level of cos you always saw Sisko Bart, do your security followed has been buying aggressively companies. So secret is already going to a lot of consolidation. You're not seeing other people taking it, but in the I T services industry, you'll start seeing that you're already seeing that in the community space. That game is pretty much over right. Even the ember barred companies, even Net are barred companies and the currency. So I think console is always going to happen. People are picking up the right time. It's happening across the board. It's a great time to be an entrepreneur creator value. They come this public. So it's like I think it's cannot anymore very time. Look to your point where the decision happens or not. Nobody can predict. But if a chance now, it's best time to raise money. Build a company. >> Well, we do. I think the analysis, at least from my perspective, is looking at all the events we go to is the same theme comes up over and over. And Andy Jassy this heat of a tigress always talks about Old Garden new Guard. I think there's two sides of the streets developing old way in a new way, and I think the modern architect of the modern era of computer industry is coming, and it looks a lot different than it. Waas. So I think the consolidate is happening on those companies that didn't make the right bets, either technically or business model wise, for they took on too much technical debt and could not convert over to the cloud world or these really robust software environment. So I think consolidations from just just the passing of holder >> seems pretty set up for a member of the first men. First Main Computing was called mainframe Era, then, with clients Herrera and Kim, the club sodas 6 2009 13 years old, the new Errol called. Whatever the name, it will be something with a n mission in India that things would be so automated. That's what we have new area of computing, So that's I would like to see. So that's a new trick, this vendetta near turn. So even though we go through this >> chance all software software sales data 11. Yeah, it's interesting. And I think the opportunity, for starters is to build a new brands. His new branch would come out. Let's take an example of a company that but after our old incumbent space dying market share not not very attractive from a VC standpoint. From market space standpoint, Zoom Zoom went after Web conferencing, and they took on WebEx and portability. And they did it with a very simple formula. Be fast, be cloud native and go after that big market and just beat them on speed and simple >> experience. They give your greatest experience just on the Web, conferencing it and better than sky better than their backs better than anybody else in that market. Paid them with reward. Thanks, Vic. He had a good >> guy and he's very focused. He used clouds. Scale took the value proposition of WebEx. Get rid of all the other stuff brought its simple to video conference. And Dr Mantra is one >> happening. The A applying to air for 87 management. A ops A customer surveys. >> So this is what our Spurs could do. They can target big markets debt and go directly at either a specific differentiation. Whether it's experience or just a better mouse trap in this case could win, >> right? And one more thing we didn't talk about is where their underpants go after is the area number. Many of these abs are still enterprise abs. Nobody really focused on moving this enterprise after the club. Hollis Clubbers are still struggling with the thing. How can I move my workload number 10%. We're closing the club 90% still on track. So somebody needs to figure out how to migrate these clouds to the cloud really seamlessly. The Alps are gonna be born in the cloud club near the apse. So how do you address truckload in here? So there's enough opportunity to go after enterprise applications clouded your application. Yeah, >> I mean, I do buy the argument that they will still be on premises activity, but to your point will be stealing massive migration to the cloud either sunsetting absent being born the cloud or moving them over on Prem All in >> all the desert I keep telling the entree and follow the money. When there is a thing you look for it Is there a big market? Are people catering there? If people are dying and the old guard is there to your point and is that the new are you? God will happen. And if you can bet on the new guard in your experience, market will reward you. >> Where is the money? Follow the money. Worse. What do we follow? Show me where it is. Tell me where it is >> That all of the clothes, What is the big I mean, if you're not >> making money in the club for the cloud, you are a fool right now. If there any company on making out making in the club as a CEO, a board member, you need to think through it. Second automation whether you go r p a IittIe automation here to make money on, said his management. Whether it's from customer service to support the operation, you got to take the car. Start off it if you are Jesse ever today and you're not making birds that cementing. I see it mostly is that still don't want to take it back. They want to build empires. The message to see what's right, Nice. Either you do it or get out. Get the job to somebody that >> I hold a lot of sea cells and prayer. Preparing for reinforce Amazon's new security cloud security conference and overwhelmingly response from the sea. So's chief security officer is we are building stacks internally. When I asked him about multi cloud, you know what they said? Multi cloud is B s. I said, Why? Because Well, we have a secondary cloud, but I don't want to fork my development team. I want to keep my people focused on one cloud. It's Amazon. Go Amazon. It's azure. We stay with Azure. I don't wanna have three development teams. So this a trend to keep the stack building internally. That means they're investing in building their own text. Axe your thoughts on that >> look, I mean, that's again. There's no one size fits all. There will be some CEOs who want to have three different silos. Some people have a hard, gentle stack like I've seen companies. Right now. They write, the court wants it, compiles, and it's got an altar cloth. That's a new irritability you're not. We locate a stack for each of them. You're right. The court order to users and NATO service is but using the same court base. That's the whole The new startups are building it. If somebody's writing it like this, that's all we have. Thing is the CEO. So there's that. The news he always have to think through. How can you do? One court works on our clothes? >> Great. You do. Thank you for coming on again. Always great to get your commentary. I learned a lot from you as well. Appreciate it. I gotta ask the final question as you go around the VC circles. You don't need to mention any names you can if you want, but I want to get a taste of the market size of rounds, Seed Round A and B. What are hot rounds? What sizes of Siri's am seeing? Maur? No. 10,000,000? 15,000,000? Siri's >> A. >> Um >> Siri's bees are always harder to get than Siri's. A seeds. I always kind of easier. What's your take on the hot rounds that are hot right now. And what's the sizes of the >> very good question? So I'm in the series the most easy one, right? Your concept. But the seed sizes went up from 200 K to know mostly drones are 1,000,000 2 1,000,000 Most city says no oneto $10,000,000. So if you're a citizen calmly, you're not getting 10 to 15. Something's wrong because that become the norm because there's more easy money. It also helps entrepreneurs. You don't have to look for money. See, this beast are becoming $2025 $5,000,000 pounds, Siri sees. If you don't raise a $50,000,000 then that means you're in good company. So the minimum amount of dries 50,000,000 and CDC Then after that, you're really looking for expansions. $100,000,000 except >> you have private equity or secondary mortgage >> keys, market valuations, all the rent. So I tell entrepreneurs when there is an opportunity, if you have something, you can command the price. So if you're doing a serious be a $20,000,000 you should be commanding $100,000,000.150,000,000 dollars, 2,000,000 evaluations right if you're not other guys are getting that you're giving too much of your company, so you need to think through all of that. >> So serious bees at 100,000,000 >> good companies are much higher than that. That'll be 1 52 100 And again, this is a buyer's market. The underpinnings market. So he says, more money in the cash. Good players they're putting. Whether you have 1,000,000 revenue of 5,000,000 revenue, 10,000,000 series is the most hardest, but its commanding good premium >> good time to be in our prayers were with bubble. Always burst when it's a bite, mark it on the >> big money. Always start a company >> when the market busts. That's always my philosophy. Voodoo. Thanks for coming. I appreciate your insight. Always as usual. Great stuff way Do Sudhakar here on the Q investor friend of the Cube Entrepreneur, I'm John for your Thanks >> for watching. Thank you.

>> from our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Welcome to this Special Cube conversation here in Palo Alto, California at the Cube Studios at the Cube headquarters. I'm John for the host, like you were a Dustin Kirkland product manager and Google friend of the Cuban. The community with Cooper Netease been on the Cube Cube alumni. Dustin. Welcome to the Cube conversation. >> Thanks. John's a beautiful studio. I've never been in the studio and on the show floor a few times, but this is This is fun. >> Great to have you on a great opportunity to chat about Cooper Netease yet of what you do out some product man's working Google. But really more importantly on this conversation is about the fifth anniversary, the birthday of Cuba Netease. Today we're celebrating the fifth birthday of Cooper Netease. Still, it's still a >> toddler, absolutely still growing. You think about how you know Lennox has been around for a long time. Open stack has been around these other big projects that have been around for, you know, going on decades and Lenox this case and Cooper nineties. It's going so fast, but It's only five years old, you know. >> You know, I remember Adam Open Stack event in Seattle many, many years ago. That was six years ago. Pubes on his 10th year. So many of these look backs moments. This is one of them. I was having a beer with Lou Tucker. J J Kiss Matic was like one of the first comes at the time didn't make it, But we were talking about open stagger like this Cooper Netease thing. This is really hot. This paper, this initiative this could really be the abstraction layer to kind of bring all this cloud Native wasn't part of the time, but it was like more of an open stack. Try and move up to stack. And it turned out it ended up happening. Cooper Netease then went on to change the landscape of what containers did. Dr. Got a lot of credit for pioneering that got the big VC funding became a unicorn, and then containers kind of went into a different direction because of Cooper duties. >> Very much so. I mean, the modernization of software infrastructure has been coming for a long time, and Cooper nutty sort of brings it all brings it all together at this point, but putting software into a container. We've been doing that different forest for for a lot of time, uh, for a long time, but But once you have a lot of containers, what do you do with that? Right? And that was the problem that Cooper Nettie solved so eloquently and has, you know, now for a couple of years, and it just keeps getting better. >> You know, you mentioned modernization. Let's talk about that because I think the modernization the theme is now pretty much prevalent in every vertical. I'll be in D. C. Next week for the Amazon Webster was public sector Summit, where modernization of governments and nations are being discussed. Education, modernization of it. We've seen it here. The media business that were participating in is about not where you store the code. It's how you code. How you build is a mindset shift. This has been the rial revelation around the Dev Ops Movement Infrastructures Code, now called Cloud Native. Share your thoughts on this modernization mindset because it really is how you build. >> Yeah, I think the cross pollination actually across industries and we even we see that even just in the word containers, right and all the imagery around shipping and shipping containers, we've applied these age old concepts that have been I don't have perfected but certainly optimized over decades of, actually centuries or millennia of moving things across water in containers. Right. But we apply that to software and boom. We have the step function difference in the way that we we manage and we orchestrated and administer code. That's one example of that cross pollination, and now you're talking about, like optimizing optimized governments or economies but being able to maybe then apply other concepts that we've come a long way in computer science do de bop set a good example? You know, applying Dev ops principles to non computer feels. Just think about that for a second. >> It's mind blowing. And if you think about also the step function you mentioned because I think this actually changed a lot of the entrepreneurial landscape as well and also has shaped open source and, you know, big news this this quarter is map are going to shut down due one of the biggest do players. Cloudera merge with Horton Works fired their CEO, the founder Michael. So has retired, Some say forced out. I don't think so. I think it's more of his time. I'm Rodel still there. Open source is a business model, you know. Can we be the red hat for her? Duped the red? Not really kind of the viable, but it's evolving. So open source has been impacted by this step function. There's a business impact. Talk about the dynamics with step function both on the business side and on how software's built specifically open source. >> You know, you and I have been around open source for a long, long time. I think it started when I was in college in the late nineties on then through my career at IBM. And it's It's interesting how on the fringe open source was for so long and such so so much of my BM career. And then early time spent onside it at Red Hat. It was it was something that was it was different, was weird. It was. It was very much fringe where the right uh, but now it's in mainstream and it's everywhere, and it's so mainstream that it's almost the defacto standard to just start with open source. But you know, there's some other news that's been happening lately that she didn't bring up. But it's a really touchy aspect of open source right now on that's on some of the licenses and how those licenses get applied by software, especially databases. When offered as a service in the cloud. That's one of the big problems. I think that that's that we're we're working within the open >> source, summarize the news and what it means. What's what's happening? What's the news and what's the really business? Our technical impact to the licensing? What's the issue? What's the core issue? >> Yeah, eso without taking judgment any any way, shape or form on this, the the the TL D are on. This is a number of open source database is most recently cockroach D. B. I have adopted a different licensing model that is nonstandard from an open source perspective. Uh, and from one perspective, they're they're adopting these different licensing models because other vendors can take that software and offered as a service, yes, and in some some cases, like Amazon like Sure, you said, uh, and offered as a as a service, uh, and maybe contribute. Maybe pay money to the smaller startup or the open source community behind it. But not necessarily. Uh, and it's in some ways is quite threatening to open source communities and open source companies on other cases, quite empowering. And it's going to be interesting to see how that plays out. The tension between open sourcing software and eventually making money off of it is something that we've we've seen for, you know, at least 25. >> And it continues to go on today, and this is, to me a real fascinating area that I think is going to be super important to keep an eye on because you want to encourage contribution and openness. Att the same time we look at the scale of just the Lenox foundations numbers. It's pretty massive in terms of now, the open source contribution. When you factor in even China and other nations, it's it's on exponential growth, right? So is it just open source? Is the model not necessarily a business? Yeah. So this is the big question. No one knows. >> I think we crossed that. And open source is the model. Um, and this is where me is a product manager. That's worked around open source. I've spent a lot of time thinking about how to create commercial offerings around open source. I spent 10 years at Economical, the first half of which, as an engineer, the second half of which, as a product manager around, uh, about building services, commercial services around 12 And I learned quite a few things that now apply absolutely to communities as well as to a number of open source startups. That that I've advised on DH kind of given them some perspective on maybe some successful and unsuccessful ways to monetize that that opens. >> Okay, so doesn't talk about Let's get back to Coburg. And so I think this is the next level Talk track is as Cooper Netease has established itself and landed in the industry and has adoption. It's now an expansion votes the land adopted expand. We've seen adoption. Now it's an expansion mode. Where does it go from here? Because you look at the tale signs things like service meshes server. Listen, you get some interesting trends that going to support this expansionary stage of uber netease. What is your view about the next expansion everyway what >> comes next? Yeah, I I think I think the next stage is really about democratizing communities for workloads that you know. It's quite obvious where when communities is the right answer at the scale of a Google or a Twitter or Netflix or, you know, some of these massive services that it is obviously and clearly the best answer to orchestrating containers. Now I think the next question is, how does that same thing that works at that massive scale Also worked for me as a developer at a very small scale helped me develop my software. My small team of five or 10 people. Do I need a coup? Burnett. He's If I'm ah five or 10 person startup. Well, I mean, not the original sort of borde vision of communities. It's probably overkill, but actually the tooling has really advanced, and we now >> have >> communities that makes sense on very small scales. You've got things like a three s from from Rancher. You've got micro Kates from from my colleagues at economical other ways of making shrinking communities down to something that fits, perhaps on devices perhaps at the edge, beyond just the traditional data center and into remote locations that need to deploy manage applications >> on the Cooper Netease clustering the some of the tech side. You know, we've seen some great tech trends as mentioned in Claudia Horton. Works and map Our Let's Take Claudia and Horton work. Remember back in the old days when it was booming? Oh, they were so proud to talk about their clusters. I stood up all these clusters and then I would ask them, Well, what do you doing with it? Well, we're storing data. I think so. That became kind of this use case where standing up the cluster was the use case and they're like, OK, now let's put some data in it. It's a question for you is Coburn. Eddie's a little bit different. I'm not seeing they were seeing real use cases. What are people standing up? Cuban is clusters for what specific Besides the same Besides saying I've done it. Yeah, What's the what's the main use case that you're seeing this that has real value? >> Yeah, actually, there's you just jog t mind of really funny memory. You know, back in those big data days, I was CEO of a startup. We were encrypting data, and we were helping encrypt healthcare data for health care companies and the number of health care companies that I worked with at that time who said they had a big data problem and they had all of I don't know, 33 terabytes worth of worth of data that they needed to encrypt. It was kind of humorous sometimes like, Is that really a big, big data problem? This fits on a single disc, you know, Uh, but yeah, I mean, it's interesting how >> that the hype of of the tech was preceding. The reality needs needs, says Cooper Nettie. So I have a Cuban Eddie's cluster for blank. Fill in the blank. What are people saying? >> Yeah, uh, it's It's largely about the modernization. So I need to modernize my infrastructure. I'm going to adopt the platform. That's probably not, er, the old er job, a Web WebSphere type platform or something like that. I'm investing in hardware investing in Software Middle, where I'm investing in people, and I want all of those things to line up with where industry is going from a software perspective, and that's where Cooper Nighties is sort of the cornerstone piece of that Lennox Of course, that's That's pretty well established >> canoes delivery in an integration piece of is that the pipeline in was, that was the fit on the low hanging fruit use cases of Cooper Netease just development >> process. Or it's the operations it's the operations of now got software that I need to deploy across multiple versions, perhaps multiple sites. Uh, I need to handle that upgrade ideally without downtime in a way that you said service mash in a way that meshes together makes sense. I've got a roll out new certificates I need to address the security, vulnerability, thes air, all the things that Cooper and I used to such a better job at then, what people were doing previously, which was a whole lot of four loops, shell strips and sshh pushing, uh, pushing tar balls around. Maybe Debs or rpm's around. That is what Cooper not he's actually really solves and does an elegant job of solving as just a starting point. And that's just the beginning and, you know, without getting ve injury here, you know, Anthros is the thing that we had at Google have built around Cooper Netease that brings it to enterprise >> here the other day did a tweet. I called Anthem. I just typing too fast. I got a lot of crap on Twitter for that mission. And those multi cloud has been a big part of where Cubans seems to fit. You mentioned some of the licensing changes. Cloud has been a great resource for a lot of the new Web scale applications from all kinds of companies. Now, with several issues seeing a lot more than capabilities, how do you see the next shift with data State coming in? Because God stateless date and you got state full data. Yeah, this has become a conversation point. >> Yeah, I think Kelsey Hightower has said it pretty eloquently, as he usually does around the sort of the serval ist movement and lets lets developers focus on just their code and literally just their code, perhaps even just their function in just their piece of code, without having to be an expert on all of the turtles all the way, all the way down. That's the big difference about service have having written a couple of those functions. I can I can really invest my time on the couple of 100 lines of code that matter and not choosing a destro choosing a cougar Nati is choosing, you know, all the stack underneath. I simply choose the platform where I'm gonna drop that that function, compile it, uploaded and then riff and rub. On that >> fifth anniversary, Cooper Netease were riffing on Cooper Netease. Dustin Circle here inside the Cube Cube Alumni you were recently at the coop con in overseas in Europe, Barcelona, Barcelona, great city. Keeps been there many times. Do was there covering for us. Couldn't make this trip, Unfortunately, had a couple daughter's graduating, so I didn't make the trip. Sorry, guys. Um, what was the summary? What was the takeaway? Was the big walk away from that event? What synthesized? The main stories were the most important stories being >> told. >> Big news, big observations. >> It was a huge event to start with. It was that fear of Barcelona. Um, didn't take over the whole space. But I've been there a number of times from Mobile World Congress. But, you know, this is this is cube con in the same building that hosts all of mobile world Congress. So I think 8,000 attendees was what we saw. It's quite celebratory. You know, I think we were doing some some pre fifth birthday bash celebrations, Key takeaways, hybrid hybrid, Cloud, multi Cloud. I think that's the world that we've evolved into. You know, there was a lot of tension. I think in the early days about must stay on. Prem must go to the cloud. Everything's there's gonna be a winner and a loser and everything's gonna go one direction or another. I think the chips have fallen, and it's pretty obvious now that the world will exist in a very hybrid, multi cloud state. Ultimately, there's gonna be some stuff on Prem that doesn't move. There's going to be some stuff better hosted in one arm or public clouds. That's the multi cloud aspect, Uh, and there will be stubborn stuff at the edge and remote locations and vehicles on oil rigs at restaurants and stores and >> so forth. What's most exciting from a trans statement? What do you what? What's what's getting you excited from what you see on the landscape out there? >> So the tying all of that to Cooper Netease, Cuban aunties, is the thing that basically normalizes all of that. You write your application put it in a container and expect to communities to be there to scale that toe. Operate that top grade that to migrate that over time. From that perspective, Cooper nineties has really ticked, ticked all the boxes, and you've got a lot of choices now about which companies here, you're going to use it and where >> beyond communities, a lot of variety of projects coop flow, you got service messes out there a lot of difference. Project. What's What's a dark horse? What's something that sets out there that people should be paying attention to? That you see emerging? That's notable. That should be paying attention. To >> think is a combination of two things. One is pretty obvious, and that's a ML is coming like a freight train and is sort of the next layer of excitement. I think after Cooper, Netease becomes boring, which hopefully if we've done our jobs well, that communities layer gets settled and we'll evolve. But the sort of the hockey stick hopefully settles down and it becomes something super stable. Uh, the application of machine learning to create artificial intelligence conclusions, trends from things that is sort of the next big trend on then I would say another one If you really want the dark horse. I think it's around communications. And I think it's around the difference in the way that we communicate with one another across all forms of media voice, video chat, writing, how we interact with people, how we interact with our our tools with our software and in fact, how our software in Iraq's with us in our software acts with with other software that communications industry is, it's ripe for some pretty radical disruption. And you know some of the organizations and they're doing that. It's early early days on those >> changes. Final point you mentioned earlier in our conversation here about how Dev Ops is influencing impacting non tech and computer science. Really? What did you mean by that? >> Uh, well, I think you brought up unexpectedly and that that you were looking at the way Uh, some other industries are changing, and I think that cross pollination is actually quite quite powerful when you take and apply a skill and expertise you have outside of your industry. But it adds something new and interesting, too, to your professional environment. That's where you get these provocative operations. He's really creative, innovative things that you know. No one really saw it coming. >> Dave Ops principles apply to other disciplines. Yeah, agility. That's that's pointing down waterfall based processes. That's >> one phenomenal example. Imagine that for governments, right to remove some of the like the pain that you and I know. I've got to go and renew my license. My birthday's coming up. I gotta go to renew my driver's license. You know much. I'm dreading going to the the DMV Root >> Canal driver's license on the same. Exactly >> how waterfall is that experience. And could we could we beam or Mohr Agile More Dev Autopsy and some of our government across >> the U. S. Government's procurement practices airbase upon 1990 standards they still want Request a manual, a physical manual for every product violent? Who does that? >> I know that there are organizations trying to apply some open source principles to government. But I mean, think about, you know, just democracy and how being a little bit more open and transparent in the way that we are in open source code, the ability to accept patches. I have a side project, a passion for brewing beer and I love applying open source practices to the industry of brewing. And that's an example of where use professional work, Tio. Compliment a hobby. >> All right, we got to bring some cubic private label, some Q beer. >> If you like sour beer, I'm in the sour beer. >> That's okay. We like to get the pus for us. Final question for you. Five years from now, Cooper needs to be 10 years old. What's the world gonna look like when we wake up five years from now with two Cuban aunties? >> Yeah, I think, uh, I don't think we're struggling with the Cooper nutties. Uh, the community's layer. At that point, I think that's settled science, inasmuch as Lennox is pretty settled. Science, Yes, there's a release, and it comes out with incremental features and bug fixes. I think Cuban aunties is settled. Science management of of those containers is pretty well settled. Uh, five years from now, I think we end up with software, some software that that's writing software. And I don't quite mean that in the way That sounds scary, uh, and that we're eliminating developers, but I think we're creating Mohr powerful, more robust software that actually creates that that software and that's all built on top of the really strong, robust systems we have underneath >> automation to take the heavy lifting. But the human creation still keeping one of the >> humans Aaron the look it's were We're many decades away from humans being out of the loop on creative processes. >> Dustin Kirkland, he a product manager of Google Uh, Cooper Netease guru also keep alumni here in the studio talking about the coup. Burnett. He's 50 year anniversary. Of course, the kid was president creation during the beginning of the wave of communities. We love the trend we love Cloud would left home a tec. I'm Sean for here in Palo Alto. Thanks for watching.

>> Narrator: Live from New York, it's the Cube, covering big data New York City 2016. Brought to you by headline sponsors, CISCO, IBM, NVIDIA, and our ecosystem sponsors, now here's your host, Dave Valante. >> Welcome back to New York City, everybody, this is the Cube, the worldwide leader in live tech coverage, and this is a cube first, we've got a nine person, actually eight person panel of experts, data scientists, all alike. I'm here with my co-host, James Cubelis, who has helped organize this panel of experts. James, welcome. >> Thank you very much, Dave, it's great to be here, and we have some really excellent brain power up there, so I'm going to let them talk. >> Okay, well thank you again-- >> And I'll interject my thoughts now and then, but I want to hear them. >> Okay, great, we know you well, Jim, we know you'll do that, so thank you for that, and appreciate you organizing this. Okay, so what I'm going to do to our panelists is ask you to introduce yourself. I'll introduce you, but tell us a little bit about yourself, and talk a little bit about what data science means to you. A number of you started in the field a long time ago, perhaps data warehouse experts before the term data science was coined. Some of you started probably after Hal Varian said it was the sexiest job in the world. (laughs) So think about how data science has changed and or what it means to you. We're going to start with Greg Piateski, who's from Boston. A Ph.D., KDnuggets, Greg, tell us about yourself and what data science means to you. >> Okay, well thank you Dave and thank you Jim for the invitation. Data science in a sense is the second oldest profession. I think people have this built-in need to find patterns and whatever we find we want to organize the data, but we do it well on a small scale, but we don't do it well on a large scale, so really, data science takes our need and helps us organize what we find, the patterns that we find that are really valid and useful and not just random, I think this is a big challenge of data science. I've actually started in this field before the term Data Science existed. I started as a researcher and organized the first few workshops on data mining and knowledge discovery, and the term data mining became less fashionable, became predictive analytics, now it's data science and it will be something else in a few years. >> Okay, thank you, Eves Mulkearns, Eves, I of course know you from Twitter. A lot of people know you as well. Tell us about your experiences and what data scientist means to you. >> Well, data science to me is if you take the two words, the data and the science, the science it holds a lot of expertise and skills there, it's statistics, it's mathematics, it's understanding the business and putting that together with the digitization of what we have. It's not only the structured data or the unstructured data what you store in the database try to get out and try to understand what is in there, but even video what is coming on and then trying to find, like George already said, the patterns in there and bringing value to the business but looking from a technical perspective, but still linking that to the business insights and you can do that on a technical level, but then you don't know yet what you need to find, or what you're looking for. >> Okay great, thank you. Craig Brown, Cube alum. How many people have been on the Cube actually before? >> I have. >> Okay, good. I always like to ask that question. So Craig, tell us a little bit about your background and, you know, data science, how has it changed, what's it all mean to you? >> Sure, so I'm Craig Brown, I've been in IT for almost 28 years, and that was obviously before the term data science, but I've evolved from, I started out as a developer. And evolved through the data ranks, as I called it, working with data structures, working with data systems, data technologies, and now we're working with data pure and simple. Data science to me is an individual or team of individuals that dissect the data, understand the data, help folks look at the data differently than just the information that, you know, we usually use in reports, and get more insights on, how to utilize it and better leverage it as an asset within an organization. >> Great, thank you Craig, okay, Jennifer Shin? Math is obviously part of being a data scientist. You're good at math I understand. Tell us about yourself. >> Yeah, so I'm a senior principle data scientist at the Nielsen Company. I'm also the founder of 8 Path Solutions, which is a data science, analytics, and technology company, and I'm also on the faculty in the Master of Information and Data Science program at UC Berkeley. So math is part of the IT statistics for data science actually this semester, and I think for me, I consider myself a scientist primarily, and data science is a nice day job to have, right? Something where there's industry need for people with my skill set in the sciences, and data gives us a great way of being able to communicate sort of what we know in science in a way that can be used out there in the real world. I think the best benefit for me is that now that I'm a data scientist, people know what my job is, whereas before, maybe five ten years ago, no one understood what I did. Now, people don't necessarily understand what I do now, but at least they understand kind of what I do, so it's still an improvement. >> Excellent. Thank you Jennifer. Joe Caserta, you're somebody who started in the data warehouse business, and saw that snake swallow a basketball and grow into what we now know as big data, so tell us about yourself. >> So I've been doing data for 30 years now, and I wrote the Data Warehouse ETL Toolkit with Ralph Timbal, which is the best selling book in the industry on preparing data for analytics, and with the big paradigm shift that's happened, you know for me the past seven years has been, instead of preparing data for people to analyze data to make decisions, now we're preparing data for machines to make the decisions, and I think that's the big shift from data analysis to data analytics and data science. >> Great, thank you. Miriam, Miriam Fridell, welcome. >> Thank you. I'm Miriam Fridell, I work for Elder Research, we are a data science consultancy, and I came to data science, sort of through a very circuitous route. I started off as a physicist, went to work as a consultant and software engineer, then became a research analyst, and finally came to data science. And I think one of the most interesting things to me about data science is that it's not simply about building an interesting model and doing some interesting mathematics, or maybe wrangling the data, all of which I love to do, but it's really the entire analytics lifecycle, and a value that you can actually extract from data at the end, and that's one of the things that I enjoy most is seeing a client's eyes light up or a wow, I didn't really know we could look at data that way, that's really interesting. I can actually do something with that, so I think that, to me, is one of the most interesting things about it. >> Great, thank you. Justin Sadeen, welcome. >> Absolutely, than you, thank you. So my name is Justin Sadeen, I work for Morph EDU, an artificial intelligence company in Atlanta, Georgia, and we develop learning platforms for non-profit and private educational institutions. So I'm a Marine Corp veteran turned data enthusiast, and so what I think about data science is the intersection of information, intelligence, and analysis, and I'm really excited about the transition from big data into smart data, and that's what I see data science as. >> Great, and last but not least, Dez Blanchfield, welcome mate. >> Good day. Yeah, I'm the one with the funny accent. So data science for me is probably the funniest job I've ever to describe to my mom. I've had quite a few different jobs, and she's never understood any of them, and this one she understands the least. I think a fun way to describe what we're trying to do in the world of data science and analytics now is it's the equivalent of high altitude mountain climbing. It's like the extreme sport version of the computer science world, because we have to be this magical unicorn of a human that can understand plain english problems from C-suite down and then translate it into code, either as soles or as teams of developers. And so there's this black art that we're expected to be able to transmogrify from something that we just in plain english say I would like to know X, and we have to go and figure it out, so there's this neat extreme sport view I have of rushing down the side of a mountain on a mountain bike and just dodging rocks and trees and things occasionally, because invariably, we do have things that go wrong, and they don't quite give us the answers we want. But I think we're at an interesting point in time now with the explosion in the types of technology that are at our fingertips, and the scale at which we can do things now, once upon a time we would sit at a terminal and write code and just look at data and watch it in columns, and then we ended up with spreadsheet technologies at our fingertips. Nowadays it's quite normal to instantiate a small high performance distributed cluster of computers, effectively a super computer in a public cloud, and throw some data at it and see what comes back. And we can do that on a credit card. So I think we're at a really interesting tipping point now where this coinage of data science needs to be slightly better defined, so that we can help organizations who have weird and strange questions that they want to ask, tell them solutions to those questions, and deliver on them in, I guess, a commodity deliverable. I want to know xyz and I want to know it in this time frame and I want to spend this much amount of money to do it, and I don't really care how you're going to do it. And there's so many tools we can choose from and there's so many platforms we can choose from, it's this little black art of computing, if you'd like, we're effectively making it up as we go in many ways, so I think it's one of the most exciting challenges that I've had, and I think I'm pretty sure I speak for most of us in that we're lucky that we get paid to do this amazing job. That we get make up on a daily basis in some cases. >> Excellent, well okay. So we'll just get right into it. I'm going to go off script-- >> Do they have unicorns down under? I think they have some strange species right? >> Well we put the pointy bit on the back. You guys have in on the front. >> So I was at an IBM event on Friday. It was a chief data officer summit, and I attended what was called the Data Divas' breakfast. It was a women in tech thing, and one of the CDOs, she said that 25% of chief data officers are women, which is much higher than you would normally see in the profile of IT. We happen to have 25% of our panelists are women. Is that common? Miriam and Jennifer, is that common for the data science field? Or is this a higher percentage than you would normally see-- >> James: Or a lower percentage? >> I think certainly for us, we have hired a number of additional women in the last year, and they are phenomenal data scientists. I don't know that I would say, I mean I think it's certainly typical that this is still a male-dominated field, but I think like many male-dominated fields, physics, mathematics, computer science, I think that that is slowly changing and evolving, and I think certainly, that's something that we've noticed in our firm over the years at our consultancy, as we're hiring new people. So I don't know if I would say 25% is the right number, but hopefully we can get it closer to 50. Jennifer, I don't know if you have... >> Yeah, so I know at Nielsen we have actually more than 25% of our team is women, at least the team I work with, so there seems to be a lot of women who are going into the field. Which isn't too surprising, because with a lot of the issues that come up in STEM, one of the reasons why a lot of women drop out is because they want real world jobs and they feel like they want to be in the workforce, and so I think this is a great opportunity with data science being so popular for these women to actually have a job where they can still maintain that engineering and science view background that they learned in school. >> Great, well Hillary Mason, I think, was the first data scientist that I ever interviewed, and I asked her what are the sort of skills required and the first question that we wanted to ask, I just threw other women in tech in there, 'cause we love women in tech, is about this notion of the unicorn data scientist, right? It's been put forth that there's the skill sets required to be a date scientist are so numerous that it's virtually impossible to have a data scientist with all those skills. >> And I love Dez's extreme sports analogy, because that plays into the whole notion of data science, we like to talk about the theme now of data science as a team sport. Must it be an extreme sport is what I'm wondering, you know. The unicorns of the world seem to be... Is that realistic now in this new era? >> I mean when automobiles first came out, they were concerned that there wouldn't be enough chauffeurs to drive all the people around. Is there an analogy with data, to be a data-driven company. Do I need a data scientist, and does that data scientist, you know, need to have these unbelievable mixture of skills? Or are we doomed to always have a skill shortage? Open it up. >> I'd like to have a crack at that, so it's interesting, when automobiles were a thing, when they first bought cars out, and before they, sort of, were modernized by the likes of Ford's Model T, when we got away from the horse and carriage, they actually had human beings walking down the street with a flag warning the public that the horseless carriage was coming, and I think data scientists are very much like that. That we're kind of expected to go ahead of the organization and try and take the challenges we're faced with today and see what's going to come around the corner. And so we're like the little flag-bearers, if you'd like, in many ways of this is where we're at today, tell me where I'm going to be tomorrow, and try and predict the day after as well. It is very much becoming a team sport though. But I think the concept of data science being a unicorn has come about because the coinage hasn't been very well defined, you know, if you were to ask 10 people what a data scientist were, you'd get 11 answers, and I think this is a really challenging issue for hiring managers and C-suites when the generants say I was data science, I want big data, I want an analyst. They don't actually really know what they're asking for. Generally, if you ask for a database administrator, it's a well-described job spec, and you can just advertise it and some 20 people will turn up and you interview to decide whether you like the look and feel and smell of 'em. When you ask for a data scientist, there's 20 different definitions of what that one data science role could be. So we don't initially know what the job is, we don't know what the deliverable is, and we're still trying to figure that out, so yeah. >> Craig what about you? >> So from my experience, when we talk about data science, we're really talking about a collection of experiences with multiple people I've yet to find, at least from my experience, a data science effort with a lone wolf. So you're talking about a combination of skills, and so you don't have, no one individual needs to have all that makes a data scientist a data scientist, but you definitely have to have the right combination of skills amongst a team in order to accomplish the goals of data science team. So from my experiences and from the clients that I've worked with, we refer to the data science effort as a data science team. And I believe that's very appropriate to the team sport analogy. >> For us, we look at a data scientist as a full stack web developer, a jack of all trades, I mean they need to have a multitude of background coming from a programmer from an analyst. You can't find one subject matter expert, it's very difficult. And if you're able to find a subject matter expert, you know, through the lifecycle of product development, you're going to require that individual to interact with a number of other members from your team who are analysts and then you just end up well training this person to be, again, a jack of all trades, so it comes full circle. >> I own a business that does nothing but data solutions, and we've been in business 15 years, and it's been, the transition over time has been going from being a conventional wisdom run company with a bunch of experts at the top to becoming more of a data-driven company using data warehousing and BI, but now the trend is absolutely analytics driven. So if you're not becoming an analytics-driven company, you are going to be behind the curve very very soon, and it's interesting that IBM is now coining the phrase of a cognitive business. I think that is absolutely the future. If you're not a cognitive business from a technology perspective, and an analytics-driven perspective, you're going to be left behind, that's for sure. So in order to stay competitive, you know, you need to really think about data science think about how you're using your data, and I also see that what's considered the data expert has evolved over time too where it used to be just someone really good at writing SQL, or someone really good at writing queries in any language, but now it's becoming more of a interdisciplinary action where you need soft skills and you also need the hard skills, and that's why I think there's more females in the industry now than ever. Because you really need to have a really broad width of experiences that really wasn't required in the past. >> Greg Piateski, you have a comment? >> So there are not too many unicorns in nature or as data scientists, so I think organizations that want to hire data scientists have to look for teams, and there are a few unicorns like Hillary Mason or maybe Osama Faiat, but they generally tend to start companies and very hard to retain them as data scientists. What I see is in other evolution, automation, and you know, steps like IBM, Watson, the first platform is eventually a great advance for data scientists in the short term, but probably what's likely to happen in the longer term kind of more and more of those skills becoming subsumed by machine unique layer within the software. How long will it take, I don't know, but I have a feeling that the paradise for data scientists may not be very long lived. >> Greg, I have a follow up question to what I just heard you say. When a data scientist, let's say a unicorn data scientist starts a company, as you've phrased it, and the company's product is built on data science, do they give up becoming a data scientist in the process? It would seem that they become a data scientist of a higher order if they've built a product based on that knowledge. What is your thoughts on that? >> Well, I know a few people like that, so I think maybe they remain data scientists at heart, but they don't really have the time to do the analysis and they really have to focus more on strategic things. For example, today actually is the birthday of Google, 18 years ago, so Larry Page and Sergey Brin wrote a very influential paper back in the '90s About page rank. Have they remained data scientist, perhaps a very very small part, but that's not really what they do, so I think those unicorn data scientists could quickly evolve to have to look for really teams to capture those skills. >> Clearly they come to a point in their career where they build a company based on teams of data scientists and data engineers and so forth, which relates to the topic of team data science. What is the right division of roles and responsibilities for team data science? >> Before we go, Jennifer, did you have a comment on that? >> Yeah, so I guess I would say for me, when data science came out and there was, you know, the Venn Diagram that came out about all the skills you were supposed to have? I took a very different approach than all of the people who I knew who were going into data science. Most people started interviewing immediately, they were like this is great, I'm going to get a job. I went and learned how to develop applications, and learned computer science, 'cause I had never taken a computer science course in college, and made sure I trued up that one part where I didn't know these things or had the skills from school, so I went headfirst and just learned it, and then now I have actually a lot of technology patents as a result of that. So to answer Jim's question, actually. I started my company about five years ago. And originally started out as a consulting firm slash data science company, then it evolved, and one of the reasons I went back in the industry and now I'm at Nielsen is because you really can't do the same sort of data science work when you're actually doing product development. It's a very very different sort of world. You know, when you're developing a product you're developing a core feature or functionality that you're going to offer clients and customers, so I think definitely you really don't get to have that wide range of sort of looking at 8 million models and testing things out. That flexibility really isn't there as your product starts getting developed. >> Before we go into the team sport, the hard skills that you have, are you all good at math? Are you all computer science types? How about math? Are you all math? >> What were your GPAs? (laughs) >> David: Anybody not math oriented? Anybody not love math? You don't love math? >> I love math, I think it's required. >> David: So math yes, check. >> You dream in equations, right? You dream. >> Computer science? Do I have to have computer science skills? At least the basic knowledge? >> I don't know that you need to have formal classes in any of these things, but I think certainly as Jennifer was saying, if you have no skills in programming whatsoever and you have no interest in learning how to write SQL queries or RR Python, you're probably going to struggle a little bit. >> James: It would be a challenge. >> So I think yes, I have a Ph.D. in physics, I did a lot of math, it's my love language, but I think you don't necessarily need to have formal training in all of these things, but I think you need to have a curiosity and a love of learning, and so if you don't have that, you still want to learn and however you gain that knowledge I think, but yeah, if you have no technical interests whatsoever, and don't want to write a line of code, maybe data science is not the field for you. Even if you don't do it everyday. >> And statistics as well? You would put that in that same general category? How about data hacking? You got to love data hacking, is that fair? Eaves, you have a comment? >> Yeah, I think so, while we've been discussing that for me, the most important part is that you have a logical mind and you have the capability to absorb new things and the curiosity you need to dive into that. While I don't have an education in IT or whatever, I have a background in chemistry and those things that I learned there, I apply to information technology as well, and from a part that you say, okay, I'm a tech-savvy guy, I'm interested in the tech part of it, you need to speak that business language and if you can do that crossover and understand what other skill sets or parts of the roles are telling you I think the communication in that aspect is very important. >> I'd like throw just something really quickly, and I think there's an interesting thing that happens in IT, particularly around technology. We tend to forget that we've actually solved a lot of these problems in the past. If we look in history, if we look around the second World War, and Bletchley Park in the UK, where you had a very similar experience as humans that we're having currently around the whole issue of data science, so there was an interesting challenge with the enigma in the shark code, right? And there was a bunch of men put in a room and told, you're mathematicians and you come from universities, and you can crack codes, but they couldn't. And so what they ended up doing was running these ads, and putting challenges, they actually put, I think it was crossword puzzles in the newspaper, and this deluge of women came out of all kinds of different roles without math degrees, without science degrees, but could solve problems, and they were thrown at the challenge of cracking codes, and invariably, they did the heavy lifting. On a daily basis for converting messages from one format to another, so that this very small team at the end could actually get in play with the sexy piece of it. And I think we're going through a similar shift now with what we're refer to as data science in the technology and business world. Where the people who are doing the heavy lifting aren't necessarily what we'd think of as the traditional data scientists, and so, there have been some unicorns and we've championed them, and they're great. But I think the shift's going to be to accountants, actuaries, and statisticians who understand the business, and come from an MBA star background that can learn the relevant pieces of math and models that we need to to apply to get the data science outcome. I think we've already been here, we've solved this problem, we've just got to learn not to try and reinvent the wheel, 'cause the media hypes this whole thing of data science is exciting and new, but we've been here a couple times before, and there's a lot to be learned from that, my view. >> I think we had Joe next. >> Yeah, so I was going to say that, data science is a funny thing. To use the word science is kind of a misnomer, because there is definitely a level of art to it, and I like to use the analogy, when Michelangelo would look at a block of marble, everyone else looked at the block of marble to see a block of marble. He looks at a block of marble and he sees a finished sculpture, and then he figures out what tools do I need to actually make my vision? And I think data science is a lot like that. We hear a problem, we see the solution, and then we just need the right tools to do it, and I think part of consulting and data science in particular. It's not so much what we know out of the gate, but it's how quickly we learn. And I think everyone here, what makes them brilliant, is how quickly they could learn any tool that they need to see their vision get accomplished. >> David: Justin? >> Yeah, I think you make a really great point, for me, I'm a Marine Corp veteran, and the reason I mentioned that is 'cause I work with two veterans who are problem solvers. And I think that's what data scientists really are, in the long run are problem solvers, and you mentioned a great point that, yeah, I think just problem solving is the key. You don't have to be a subject matter expert, just be able to take the tools and intelligently use them. >> Now when you look at the whole notion of team data science, what is the right mix of roles, like role definitions within a high-quality or a high-preforming data science teams now IBM, with, of course, our announcement of project, data works and so forth. We're splitting the role division, in terms of data scientist versus data engineers versus application developer versus business analyst, is that the right breakdown of roles? Or what would the panelists recommend in terms of understanding what kind of roles make sense within, like I said, a high performing team that's looking for trying to develop applications that depend on data, machine learning, and so forth? Anybody want to? >> I'll tackle that. So the teams that I have created over the years made up these data science teams that I brought into customer sites have a combination of developer capabilities and some of them are IT developers, but some of them were developers of things other than applications. They designed buildings, they did other things with their technical expertise besides building technology. The other piece besides the developer is the analytics, and analytics can be taught as long as they understand how algorithms work and the code behind the analytics, in other words, how are we analyzing things, and from a data science perspective, we are leveraging technology to do the analyzing through the tool sets, so ultimately as long as they understand how tool sets work, then we can train them on the tools. Having that analytic background is an important piece. >> Craig, is it easier to, I'll go to you in a moment Joe, is it easier to cross train a data scientist to be an app developer, than to cross train an app developer to be a data scientist or does it not matter? >> Yes. (laughs) And not the other way around. It depends on the-- >> It's easier to cross train a data scientist to be an app developer than-- >> Yes. >> The other way around. Why is that? >> Developing code can be as difficult as the tool set one uses to develop code. Today's tool sets are very user friendly. where developing code is very difficult to teach a person to think along the lines of developing code when they don't have any idea of the aspects of code, of building something. >> I think it was Joe, or you next, or Jennifer, who was it? >> I would say that one of the reasons for that is data scientists will probably know if the answer's right after you process data, whereas data engineer might be able to manipulate the data but may not know if the answer's correct. So I think that is one of the reasons why having a data scientist learn the application development skills might be a easier time than the other way around. >> I think Miriam, had a comment? Sorry. >> I think that what we're advising our clients to do is to not think, before data science and before analytics became so required by companies to stay competitive, it was more of a waterfall, you have a data engineer build a solution, you know, then you throw it over the fence and the business analyst would have at it, where now, it must be agile, and you must have a scrum team where you have the data scientist and the data engineer and the project manager and the product owner and someone from the chief data office all at the table at the same time and all accomplishing the same goal. Because all of these skills are required, collectively in order to solve this problem, and it can't be done daisy chained anymore it has to be a collaboration. And that's why I think spark is so awesome, because you know, spark is a single interface that a data engineer can use, a data analyst can use, and a data scientist can use. And now with what we've learned today, having a data catalog on top so that the chief data office can actually manage it, I think is really going to take spark to the next level. >> James: Miriam? >> I wanted to comment on your question to Craig about is it harder to teach a data scientist to build an application or vice versa, and one of the things that we have worked on a lot in our data science team is incorporating a lot of best practices from software development, agile, scrum, that sort of thing, and I think particularly with a focus on deploying models that we don't just want to build an interesting data science model, we want to deploy it, and get some value. You need to really incorporate these processes from someone who might know how to build applications and that, I think for some data scientists can be a challenge, because one of the fun things about data science is you get to get into the data, and you get your hands dirty, and you build a model, and you get to try all these cool things, but then when the time comes for you to actually deploy something, you need deployment-grade code in order to make sure it can go into production at your client side and be useful for instance, so I think that there's an interesting challenge on both ends, but one of the things I've definitely noticed with some of our data scientists is it's very hard to get them to think in that mindset, which is why you have a team of people, because everyone has different skills and you can mitigate that. >> Dev-ops for data science? >> Yeah, exactly. We call it insight ops, but yeah, I hear what you're saying. Data science is becoming increasingly an operational function as opposed to strictly exploratory or developmental. Did some one else have a, Dez? >> One of the things I was going to mention, one of the things I like to do when someone gives me a new problem is take all the laptops and phones away. And we just end up in a room with a whiteboard. And developers find that challenging sometimes, so I had this one line where I said to them don't write the first line of code until you actually understand the problem you're trying to solve right? And I think where the data science focus has changed the game for organizations who are trying to get some systematic repeatable process that they can throw data at and just keep getting answers and things, no matter what the industry might be is that developers will come with a particular mindset on how they're going to codify something without necessarily getting the full spectrum and understanding the problem first place. What I'm finding is the people that come at data science tend to have more of a hacker ethic. They want to hack the problem, they want to understand the challenge, and they want to be able to get it down to plain English simple phrases, and then apply some algorithms and then build models, and then codify it, and so most of the time we sit in a room with whiteboard markers just trying to build a model in a graphical sense and make sure it's going to work and that it's going to flow, and once we can do that, we can codify it. I think when you come at it from the other angle from the developer ethic, and you're like I'm just going to codify this from day one, I'm going to write code. I'm going to hack this thing out and it's just going to run and compile. Often, you don't truly understand what he's trying to get to at the end point, and you can just spend days writing code and I think someone made the comment that sometimes you don't actually know whether the output is actually accurate in the first place. So I think there's a lot of value being provided from the data science practice. Over understanding the problem in plain english at a team level, so what am I trying to do from the business consulting point of view? What are the requirements? How do I build this model? How do I test the model? How do I run a sample set through it? Train the thing and then make sure what I'm going to codify actually makes sense in the first place, because otherwise, what are you trying to solve in the first place? >> Wasn't that Einstein who said if I had an hour to solve a problem, I'd spend 55 minutes understanding the problem and five minutes on the solution, right? It's exactly what you're talking about. >> Well I think, I will say, getting back to the question, the thing with building these teams, I think a lot of times people don't talk about is that engineers are actually very very important for data science projects and data science problems. For instance, if you were just trying to prototype something or just come up with a model, then data science teams are great, however, if you need to actually put that into production, that code that the data scientist has written may not be optimal, so as we scale out, it may be actually very inefficient. At that point, you kind of want an engineer to step in and actually optimize that code, so I think it depends on what you're building and that kind of dictates what kind of division you want among your teammates, but I do think that a lot of times, the engineering component is really undervalued out there. >> Jennifer, it seems that the data engineering function, data discovery and preparation and so forth is becoming automated to a greater degree, but if I'm listening to you, I don't hear that data engineering as a discipline is becoming extinct in terms of a role that people can be hired into. You're saying that there's a strong ongoing need for data engineers to optimize the entire pipeline to deliver the fruits of data science in production applications, is that correct? So they play that very much operational role as the backbone for... >> So I think a lot of times businesses will go to data scientist to build a better model to build a predictive model, but that model may not be something that you really want to implement out there when there's like a million users coming to your website, 'cause it may not be efficient, it may take a very long time, so I think in that sense, it is important to have good engineers, and your whole product may fail, you may build the best model it may have the best output, but if you can't actually implement it, then really what good is it? >> What about calibrating these models? How do you go about doing that and sort of testing that in the real world? Has that changed overtime? Or is it... >> So one of the things that I think can happen, and we found with one of our clients is when you build a model, you do it with the data that you have, and you try to use a very robust cross-validation process to make sure that it's robust and it's sturdy, but one thing that can sometimes happen is after you put your model into production, there can be external factors that, societal or whatever, things that have nothing to do with the data that you have or the quality of the data or the quality of the model, which can actually erode the model's performance over time. So as an example, we think about cell phone contracts right? Those have changed a lot over the years, so maybe five years ago, the type of data plan you had might not be the same that it is today, because a totally different type of plan is offered, so if you're building a model on that to say predict who's going to leave and go to a different cell phone carrier, the validity of your model overtime is going to completely degrade based on nothing that you have, that you put into the model or the data that was available, so I think you need to have this sort of model management and monitoring process to take this factors into account and then know when it's time to do a refresh. >> Cross-validation, even at one point in time, for example, there was an article in the New York Times recently that they gave the same data set to five different data scientists, this is survey data for the presidential election that's upcoming, and five different data scientists came to five different predictions. They were all high quality data scientists, the cross-validation showed a wide variation about who was on top, whether it was Hillary or whether it was Trump so that shows you that even at any point in time, cross-validation is essential to understand how robust the predictions might be. Does somebody else have a comment? Joe? >> I just want to say that this even drives home the fact that having the scrum team for each project and having the engineer and the data scientist, data engineer and data scientist working side by side because it is important that whatever we're building we assume will eventually go into production, and we used to have in the data warehousing world, you'd get the data out of the systems, out of your applications, you do analysis on your data, and the nirvana was maybe that data would go back to the system, but typically it didn't. Nowadays, the applications are dependent on the insight coming from the data science team. With the behavior of the application and the personalization and individual experience for a customer is highly dependent, so it has to be, you said is data science part of the dev-ops team, absolutely now, it has to be. >> Whose job is it to figure out the way in which the data is presented to the business? Where's the sort of presentation, the visualization plan, is that the data scientist role? Does that depend on whether or not you have that gene? Do you need a UI person on your team? Where does that fit? >> Wow, good question. >> Well usually that's the output, I mean, once you get to the point where you're visualizing the data, you've created an algorithm or some sort of code that produces that to be visualized, so at the end of the day that the customers can see what all the fuss is about from a data science perspective. But it's usually post the data science component. >> So do you run into situations where you can see it and it's blatantly obvious, but it doesn't necessarily translate to the business? >> Well there's an interesting challenge with data, and we throw the word data around a lot, and I've got this fun line I like throwing out there. If you torture data long enough, it will talk. So the challenge then is to figure out when to stop torturing it, right? And it's the same with models, and so I think in many other parts of organizations, we'll take something, if someone's doing a financial report on performance of the organization and they're doing it in a spreadsheet, they'll get two or three peers to review it, and validate that they've come up with a working model and the answer actually makes sense. And I think we're rushing so quickly at doing analysis on data that comes to us in various formats and high velocity that I think it's very important for us to actually stop and do peer reviews, of the models and the data and the output as well, because otherwise we start making decisions very quickly about things that may or may not be true. It's very easy to get the data to paint any picture you want, and you gave the example of the five different attempts at that thing, and I had this shoot out thing as well where I'll take in a team, I'll get two different people to do exactly the same thing in completely different rooms, and come back and challenge each other, and it's quite amazing to see the looks on their faces when they're like, oh, I didn't see that, and then go back and do it again until, and then just keep iterating until we get to the point where they both get the same outcome, in fact there's a really interesting anecdote about when the UNIX operation system was being written, and a couple of the authors went away and wrote the same program without realizing that each other were doing it, and when they came back, they actually had line for line, the same piece of C code, 'cause they'd actually gotten to a truth. A perfect version of that program, and I think we need to often look at, when we're building models and playing with data, if we can't come at it from different angles, and get the same answer, then maybe the answer isn't quite true yet, so there's a lot of risk in that. And it's the same with presentation, you know, you can paint any picture you want with the dashboard, but who's actually validating when the dashboard's painting the correct picture? >> James: Go ahead, please. >> There is a science actually, behind data visualization, you know if you're doing trending, it's a line graph, if you're doing comparative analysis, it's bar graph, if you're doing percentages, it's a pie chart, like there is a certain science to it, it's not that much of a mystery as the novice thinks there is, but what makes it challenging is that you also, just like any presentation, you have to consider your audience. And your audience, whenever we're delivering a solution, either insight, or just data in a grid, we really have to consider who is the consumer of this data, and actually cater the visual to that person or to that particular audience. And that is part of the art, and that is what makes a great data scientist. >> The consumer may in fact be the source of the data itself, like in a mobile app, so you're tuning their visualization and then their behavior is changing as a result, and then the data on their changed behavior comes back, so it can be a circular process. >> So Jim, at a recent conference, you were tweeting about the citizen data scientist, and you got emasculated by-- >> I spoke there too. >> Okay. >> TWI on that same topic, I got-- >> Kirk Borne I hear came after you. >> Kirk meant-- >> Called foul, flag on the play. >> Kirk meant well. I love Claudia Emahoff too, but yeah, it's a controversial topic. >> So I wonder what our panel thinks of that notion, citizen data scientist. >> Can I respond about citizen data scientists? >> David: Yeah, please. >> I think this term was introduced by Gartner analyst in 2015, and I think it's a very dangerous and misleading term. I think definitely we want to democratize the data and have access to more people, not just data scientists, but managers, BI analysts, but when there is already a term for such people, we can call the business analysts, because it implies some training, some understanding of the data. If you use the term citizen data scientist, it implies that without any training you take some data and then you find something there, and they think as Dev's mentioned, we've seen many examples, very easy to find completely spurious random correlations in data. So we don't want citizen dentists to treat our teeth or citizen pilots to fly planes, and if data's important, having citizen data scientists is equally dangerous, so I'm hoping that, I think actually Gartner did not use the term citizen data scientist in their 2016 hype course, so hopefully they will put this term to rest. >> So Gregory, you apparently are defining citizen to mean incompetent as opposed to simply self-starting. >> Well self-starting is very different, but that's not what I think what was the intention. I think what we see in terms of data democratization, there is a big trend over automation. There are many tools, for example there are many companies like Data Robot, probably IBM, has interesting machine learning capability towards automation, so I think I recently started a page on KDnuggets for automated data science solutions, and there are already 20 different forums that provide different levels of automation. So one can deliver in full automation maybe some expertise, but it's very dangerous to have part of an automated tool and at some point then ask citizen data scientists to try to take the wheels. >> I want to chime in on that. >> David: Yeah, pile on. >> I totally agree with all of that. I think the comment I just want to quickly put out there is that the space we're in is a very young, and rapidly changing world, and so what we haven't had yet is this time to stop and take a deep breath and actually define ourselves, so if you look at computer science in general, a lot of the traditional roles have sort of had 10 or 20 years of history, and so thorough the hiring process, and the development of those spaces, we've actually had time to breath and define what those jobs are, so we know what a systems programmer is, and we know what a database administrator is, but we haven't yet had a chance as a community to stop and breath and say, well what do we think these roles are, and so to fill that void, the media creates coinages, and I think this is the risk we've got now that the concept of a data scientist was just a term that was coined to fill a void, because no one quite knew what to call somebody who didn't come from a data science background if they were tinkering around data science, and I think that's something that we need to sort of sit up and pay attention to, because if we don't own that and drive it ourselves, then somebody else is going to fill the void and they'll create these very frustrating concepts like data scientist, which drives us all crazy. >> James: Miriam's next. >> So I wanted to comment, I agree with both of the previous comments, but in terms of a citizen data scientist, and I think whether or not you're citizen data scientist or an actual data scientist whatever that means, I think one of the most important things you can have is a sense of skepticism, right? Because you can get spurious correlations and it's like wow, my predictive model is so excellent, you know? And being aware of things like leaks from the future, right? This actually isn't predictive at all, it's a result of the thing I'm trying to predict, and so I think one thing I know that we try and do is if something really looks too good, we need to go back in and make sure, did we not look at the data correctly? Is something missing? Did we have a problem with the ETL? And so I think that a healthy sense of skepticism is important to make sure that you're not taking a spurious correlation and trying to derive some significant meaning from it. >> I think there's a Dilbert cartoon that I saw that described that very well. Joe, did you have a comment? >> I think that in order for citizen data scientists to really exist, I think we do need to have more maturity in the tools that they would use. My vision is that the BI tools of today are all going to be replaced with natural language processing and searching, you know, just be able to open up a search bar and say give me sales by region, and to take that one step into the future even further, it should actually say what are my sales going to be next year? And it should trigger a simple linear regression or be able to say which features of the televisions are actually affecting sales and do a clustering algorithm, you know I think hopefully that will be the future, but I don't see anything of that today, and I think in order to have a true citizen data scientist, you would need to have that, and that is pretty sophisticated stuff. >> I think for me, the idea of citizen data scientist I can relate to that, for instance, when I was in graduate school, I started doing some research on FDA data. It was an open source data set about 4.2 million data points. Technically when I graduated, the paper was still not published, and so in some sense, you could think of me as a citizen data scientist, right? I wasn't getting funding, I wasn't doing it for school, but I was still continuing my research, so I'd like to hope that with all the new data sources out there that there might be scientists or people who are maybe kept out of a field people who wanted to be in STEM and for whatever life circumstance couldn't be in it. That they might be encouraged to actually go and look into the data and maybe build better models or validate information that's out there. >> So Justin, I'm sorry you had one comment? >> It seems data science was termed before academia adopted formalized training for data science. But yeah, you can make, like Dez said, you can make data work for whatever problem you're trying to solve, whatever answer you see, you want data to work around it, you can make it happen. And I kind of consider that like in project management, like data creep, so you're so hyper focused on a solution you're trying to find the answer that you create an answer that works for that solution, but it may not be the correct answer, and I think the crossover discussion works well for that case. >> So but the term comes up 'cause there's a frustration I guess, right? That data science skills are not plentiful, and it's potentially a bottleneck in an organization. Supposedly 80% of your time is spent on cleaning data, is that right? Is that fair? So there's a problem. How much of that can be automated and when? >> I'll have a shot at that. So I think there's a shift that's going to come about where we're going to move from centralized data sets to data at the edge of the network, and this is something that's happening very quickly now where we can't just hold everything back to a central spot. When the internet of things actually wakes up. Things like the Boeing Dreamliner 787, that things got 6,000 sensors in it, produces half a terabyte of data per flight. There are 87,400 flights per day in domestic airspace in the U.S. That's 43.5 petabytes of raw data, now that's about three years worth of disk manufacturing in total, right? We're never going to copy that across one place, we can't process, so I think the challenge we've got ahead of us is looking at how we're going to move the intelligence and the analytics to the edge of the network and pre-cook the data in different tiers, so have a look at the raw material we get, and boil it down to a slightly smaller data set, bring a meta data version of that back, and eventually get to the point where we've only got the very minimum data set and data points we need to make key decisions. Without that, we're already at the point where we have too much data, and we can't munch it fast enough, and we can't spin off enough tin even if we witch the cloud on, and that's just this never ending deluge of noise, right? And you've got that signal versus noise problem so then we're now seeing a shift where people looking at how do we move the intelligence back to the edge of network which we actually solved some time ago in the securities space. You know, spam filtering, if an emails hits Google on the west coast of the U.S. and they create a check some for that spam email, it immediately goes into a database, and nothing gets on the opposite side of the coast, because they already know it's spam. They recognize that email coming in, that's evil, stop it. So we've already fixed its insecurity with intrusion detection, we've fixed it in spam, so we now need to take that learning, and bring it into business analytics, if you like, and see where we're finding patterns and behavior, and brew that out to the edge of the network, so if I'm seeing a demand over here for tickets on a new sale of a show, I need to be able to see where else I'm going to see that demand and start responding to that before the demand comes about. I think that's a shift that we're going to see quickly, because we'll never keep up with the data munching challenge and the volume's just going to explode. >> David: We just have a couple minutes. >> That does sound like a great topic for a future Cube panel which is data science on the edge of the fog. >> I got a hundred questions around that. So we're wrapping up here. Just got a couple minutes. Final thoughts on this conversation or any other pieces that you want to punctuate. >> I think one thing that's been really interesting for me being on this panel is hearing all of my co-panelists talking about common themes and things that we are also experiencing which isn't a surprise, but it's interesting to hear about how ubiquitous some of the challenges are, and also at the announcement earlier today, some of the things that they're talking about and thinking about, we're also talking about and thinking about. So I think it's great to hear we're all in different countries and different places, but we're experiencing a lot of the same challenges, and I think that's been really interesting for me to hear about. >> David: Great, anybody else, final thoughts? >> To echo Dez's thoughts, it's about we're never going to catch up with the amount of data that's produced, so it's about transforming big data into smart data. >> I could just say that with the shift from normal data, small data, to big data, the answer is automate, automate, automate, and we've been talking about advanced algorithms and machine learning for the science for changing the business, but there also needs to be machine learning and advanced algorithms for the backroom where we're actually getting smarter about how we ingestate and how we fix data as it comes in. Because we can actually train the machines to understand data anomalies and what we want to do with them over time. And I think the further upstream we get of data correction, the less work there will be downstream. And I also think that the concept of being able to fix data at the source is gone, that's behind us. Right now the data that we're using to analyze to change the business, typically we have no control over. Like Dez said, they're coming from censors and machines and internet of things and if it's wrong, it's always going to be wrong, so we have to figure out how to do that in our laboratory. >> Eaves, final thoughts? >> I think it's a mind shift being a data scientist if you look back at the time why did you start developing or writing code? Because you like to code, whatever, just for the sake of building a nice algorithm or a piece of software, or whatever, and now I think with the spirit of a data scientist, you're looking at a problem and say this is where I want to go, so you have more the top down approach than the bottom up approach. And have the big picture and that is what you really need as a data scientist, just look across technologies, look across departments, look across everything, and then on top of that, try to apply as much skills as you have available, and that's kind of unicorn that they're trying to look for, because it's pretty hard to find people with that wide vision on everything that is happening within the company, so you need to be aware of technology, you need to be aware of how a business is run, and how it fits within a cultural environment, you have to work with people and all those things together to my belief to make it very difficult to find those good data scientists. >> Jim? Your final thought? >> My final thoughts is this is an awesome panel, and I'm so glad that you've come to New York, and I'm hoping that you all stay, of course, for the the IBM Data First launch event that will take place this evening about a block over at Hudson Mercantile, so that's pretty much it. Thank you, I really learned a lot. >> I want to second Jim's thanks, really, great panel. Awesome expertise, really appreciate you taking the time, and thanks to the folks at IBM for putting this together. >> And I'm big fans of most of you, all of you, on this session here, so it's great just to meet you in person, thank you. >> Okay, and I want to thank Jeff Frick for being a human curtain there with the sun setting here in New York City. Well thanks very much for watching, we are going to be across the street at the IBM announcement, we're going to be on the ground. We open up again tomorrow at 9:30 at Big Data NYC, Big Data Week, Strata plus the Hadoop World, thanks for watching everybody, that's a wrap from here. This is the Cube, we're out. (techno music)