>> Dave: Good morning everybody and welcome to Big Data SV. >> Come down, hang out with us today as we have continued conversations. >> Will this trend, this Big Data trend, solve the problems that decision support and business intelligence couldn't solve. We're going to talk about that today. Gentlemen, welcome to theCUBE. (energetic rock music) >> Dave: We're setting up for the digital business era. >> What do people really want to do? And it's big data analytics. I want to ingest a lot of information. I want to enrich it. I want to analyze it and I want to take actions and then I want to go park it. >> Leveraging everything that is open source to build models and put models in production. >> We talk a little bit like it's Google Docs for your data. >> So I no longer have to send daily data dumps to partners. They can simply query the data themselves. >> We've taken the two approaches of enterprise analytics and self-services and tried to create a scenario where you kind of get the best of both worlds. >> The epicenter of this whole data management has to move to cloud. >> It saves you a lot of time and effort. You can focus on more strategic projects. >> Do you agree it's kind of bifurcated. There's the Spotifys, and the Ubers, and the AirBnBs that are crushing it and then there's a lot of traditional enterprises that are still stovepipe and struggling. >> Marketing people, operational people, finance people, they need data to do their jobs. Their jobs are becoming more data-driven but they're not necessarily data people. >> They're depending on the vendor landscape to provide them with an entry level set of tools. >> Don't make me work harder and add new staff. Solve the problem. >> Yeah, it's all about solving problems. >> A lot more on machine learning now and artificial intelligence and frankly a lot of discussion around ethics. >> Data governance, it is in fact a business imperative. >> Marketers want all the customer data they can get, right? But there's social security numbers, PII-- Who should be able to see and use what because if this data is used inappropriately then it can cause a lot of problems. >> Creating that visibility is very important. >> The biggest casualty is going to be their customer relationship if they don't do this because most companies don't know their customers fully. >> The key that digital transformation is really a lauder on the concept of real time. >> If anybody deals with the data that's in motion, you lose because I'm analyzing as it's happening and then you would be analyzing after at rest. >> Speed is so important these days and the new companies that are grasping data aggressively, putting it somewhere where they can make decisions on it on a day-to-day basis, they're winning. >> Come on down, be part of our audience. We also have a great party tonight where you can network with some of our experts and analysts. (energetic rock music) >> Our expectation is that as the tooling gets better, we will see more people be able to present themselves truly as capable of doing this, and that will accelerate the process. >> To me, one of the first things a CDO has to do is understand how a company gets value out of its data. >> You can either run away from that data and say, look, I'm going to not, I'm going to bury my head in the sand, I'm going to be a business, I'm just going to forget about that data stuff and that's certainly a way to go. Right? It's a way to go away. >> It's easy to get overwhelmed for companies, you have to pick somewhere, right? >> You don't have to go sit in the basement for a year having something that is 'the thing', the unicorn in the business, it's small quick wins. >> We're not afraid of makin' mistakes. If we provision infrastructure and we don't get it right the first time, we just change it. >> That's something that we would just never be able to do previously in a data center. >> When companies get started with the right first project they can build on that success and invest more, whereas if you're not experimenting and trying things and moving, you're never going to get there. >> Dave: Thanks for watching, everybody. This is thCUBE. We're live from Big Data SV. >> And we're clear. Thank you. (audience applauds)

>> Narrator: Live from Las Vegas, it's theCUBE covering AWS reInvent 2019. Brought to you by Amazon Web Services and Intel along with it's Ecosystem partners. >> Well, welcome back, everyone theCUBE's live coverage in Las Vegas for AWS reInvent. It's theCUBE's 10th year of operations, it's our seventh AWS reInvent and every year, it gets better and better and every year, we've had theCUBE at reInvent, Jerry Chen has been on as a guest. He's a VIP, Jerry Chen, now a general partner at Greylock Tier One, one of the leading global Venture capitals at Silicon Valley. Jerry, you've been on the journey with us the whole time. >> I guess I'm your good luck charm. >> (laughs) Well, keep it going. Keep on changing the game. So, thanks for coming on. >> Jerry: Thanks for having me. >> So, now that you're a seasoned partner now at Greylock. You got a lot of investments under your belt. How's it going? >> It's great, I mean look, every single year, I look around the landscape thinking, "What else could be coming? "What if we surprise this year?" What's the new trends? What both macro-trends, also company trends, like, who's going to buy who, who's going to go public? Every year, it just gets busier and busier and bigger and bigger. >> All these new categories are emerging with this new architecture. I call it Cloud 2.0, maybe next gen Cloud, whatever you want to call it, it's clear visibility now into the fact that DevOps is working, Cloud operations, large scale operations with Cloud is certainly a great value proposition. You're seeing now multiple databases, pick the tool, I think Jassy got that right in his keynote, I believe that, but now the data equation comes over the top. So, you got DevOps infrastructure as code, you got data now looking like it's going to go down that same path of data as code where developers don't have to deal with all the different nuances of how data's stored, how it's handled, where is it, warm or cold or at glacier. So, developers still don't have that yet today. Seems to be an area of Amazon. What's your take on all this? >> I think you saw, so what drove DevOps? Speed, right? It's basically how developers shows you operations, merging of two groups. So, we're seeing the same trend DataOps, right? How data engineers and data scientists can now have the same speeds developers had for the past 10 years, DataOps. So, A, what does that mean? Give me the menu of what I want like, Goldilocks, too big, too small, just right. Too hot, too cold, just right. Like, give me the storage tier, the data tier, the size I want, the temperature I want and the speed I want. So, you're seeing DataOps give the same kind of Goldilocks treatment as developers. >> And on terms of like Cloud evolution again, you've seen the movie from the beginning at VM where now through Amazon, seventh year. What jumps out at you, what do you look at as squinting through the trend lines and the fashion of the features, it still seems to be the same old game, compute memory storage and software. >> Well I mean, compute memory storage, there's an atomic building blocks of a compute, right? So, regardless of services these high level frameworks, deep down, you still have compute networking and storage. So, that's the building blocks but I think we're seeing 10th year of reInvent this kind of, it's not one size fits all but this really big fat long tail, small instances, micro-instances, server lists, big instances for like jumbo VMs, bare metal, right? So, you're seeing not one architecture but folks can kind of pick and choose buy compute by the drip, the drop or buy compute by the whole VM or whole server full. >> And a lot of people are like, the builders love that. Amazon owns the builder market. I mean, if anyone who's doing a startup, they pretty much start on Amazon. It's the most robust, you pick your tools, you build, but Steve Malaney was just on before us says, "Enterprise don't want power tools, "they're going to cut their hand off." (laughs) Right so, Microsoft's been winning with this approach of consumable Cloud and it's a nice card to play because they're not yet there with capabilities with Amazon, so it's a good call, they got an Enterprise sales force. Microsoft playing a different game than AWS because they have to. >> Sure I mean, what's football now, you have a running game, you need a passing game, right? So, if you can't beat them with the running game, you go with a passing game and so, Amazon has kind of like the fundamental building blocks or power tools for the builders. There's a large segment of population out there that don't want that level of building blocks but they want us a little bit more prescriptive. Microsoft's been around Enterprise for many many years, they understand prescriptive tools and architectures. So, you're going to become a little bit more prefab, if you will. Here's how you can actually construct the right application, ML apps, AI apps, et cetera. Let me give you the building blocks at a higher level abstraction. >> So, I want to get your take on value creations. >> Jerry: Sure. >> So, if it's still early (mumbles), it's took a lot more growth, you start to see Jassy even admit that in his keynotes that he said quote, "There are two types "of developers and customers. "People want the building blocks "or people who want solutions." Or prefab or some sort of more consumable. >> More prescriptive, yeah. >> So, I think Amazon's going to start going that way but that being said, there's still opportunities for startups. You're an investor, you invest in startups. Where do you see opportunities? If you're looking at the startup landscape, what is the playbook? How should you advise startups? Because ya know, have the best team or whatever but you look at Amazon, it's like, okay, they got large scale. >> Jerry: Yeah. >> I'm going to be a little nervous. Are they going to eat my lunch? Do I take advantage of them? Do I draft off them? There are wide spaces as vertical market's exploding that are available. What's your view on how startups should attack the wealth creation opportunity value creation? >> There, I mean, Amazon's creating a new market, right? So, you look at their list of many services. There's just like 175 services out there, which is basically too many for any one company to win every single service. So, but you look at that menu of services, each one of those services themselves can be a startup or a collection of services can be a startup. So, I look at that as a roadmap for opportunity of companies can actually go in and create value around AI, around data, around security, around observability because Amazon's not going to naturally win all of those markets. What they do have is distribution, right? They have a lot of developer mind share. So, if you're a startup, you play one or three themes. So like, one is how do I pick one area and go deep for IP, right? Like, cheaper, better, faster, own some IP and though, they're going to execute better and that's doable over and over again in different markets. Number two is, we talked about this before, there's not going to be a one Cloud wins all, Amazon's clearly in the lead, they have won most of the Cloud, so far, but it'll be a multi-Cloud world, it'll be On Premise world. So, how do I play a multi-Cloud world, is another angle, so, go deep in IP, go multi-Cloud. Number three is this end to end solution, kind of prescriptive. Amazon can get you 80% of the way there, 70% of the way there but if you're like, an AI developer, you're a CMO, you're a marketing developer, you kind of want this end to end solution. So, how can I put together a full suite of tools from beginning to end that can give me a product that's a better experience. So, either I have something that's a deeper IP play a seam between multiple Clouds or give it end to end solutions around a problem and solve that one problem for our customer. >> And in most cases, the underlay is Amazon or Azure. >> Or Google or Alley Cloud or On Premises. Not going to wait any time soon, right? And so, how do I create a single fabric, if you will that looks similar? >> I want to riff with you in real time here on theCUBE around data. So, data scale is obviously a big discussion that's starting to happen now, data tsunami, we've heard that for years. So, there's two scale benefits, horizontal scale with data and then vertical specialism, vertical scale or ya know, using AI machine learning in apps, having data, so, how do you view that? What's your reaction to the notion of creating the horizontal scale value and vertical specialism value? >> Both are a great place for startups, right? They're not mutually exclusive but I think if you go horizontal, the amount of data being created by your applications, your infrastructure, your sensors, time stories data, ridiculously large amount, right? And that's not going away any time soon. I recently did investment in ChronoSphere, 'cause you guys covered over at CUBEcon a few weeks ago, that's talking about metrics and observability data, time stories data. So, they're going to handle that horizontal amount of data, petabytes and petabytes, how can we quarry this quickly, deeply with a lot of insight? That's one play, right? Cheaper, better, faster at scale. The next play, like you said, is vertical. It's how do I own data or slice the data with more contacts than I know I was going to have? We talked about the virtual cycle of data, right? Just the system of intelligence, as well. If I own a set of data, be it healthcare, government or self-driving car data, that no one else has, I can build a solution end to end and go deep and so either pick a lane or pick a geography, you can go either way. It's hard to do both, though. >> It's hard for startup. >> For a startup. >> Any big company. >> Very few companies can do two things well, startups especially, succeed by doing one thing very well. >> I think my observation is that I think looking at Amazon, is that they want the horizontal and they're leaving offers on the table for our startups, the vertical. >> Yeah, if you look at their strategy, the lower level Amazon gets, the more open-sourced, the more ubiquitous you try to be for containers, server lists, networking, S3, basic sub straits, so, horizontal horizontal, low price. As you get higher up from like, deep mind like, AI technologies, perception, prediction, they're getting a little bit more specialized, right? As you see these solutions around retail, healthcare, voice, so, the higher up in the stack, they can build more narrow solutions because like any startup of any product, you need the right wedge. What's the right wedge in the customers? At the base level of developers, building blocks, ubiquitous. For solutions marketing, healthcare, financial services, retail, how do I find a fine point wedge? >> So, the old Venture business was all enamored with consumers over the years and then, maybe four years ago, Enterprise got hot. We were lowly Enterprise guys where no one-- >> Enterprise has been hot forever in my mind, John but maybe-- >> Well, first of all, we've been hot on Enterprise, we love Enterprise but then all of a sudden, it just seemed like, oh my God, people had an awakening like, and there's real value to be had. The IT spend has been trillions and the stats are roughly 20 or so percent, yet to move to the Cloud or this new next gen architecture that you're investing companies in. So, a big market... that's an investment thesis. So, a huge enterprise market, Steve Malaney of Aviation called it a thousand foot wave. So, there's going to be a massive enterprise money... big bag of money on the table. (laughs) A lot of re-transformations, lot of reborn on the Cloud, lot of action. What's your take on that? Do you see it the same way because look how they're getting in big time, Goldman Sachs on stage here. It's a lot of cash. How do you think it's going to be deployed and who's going to be fighting for it? >> Well, I think, we talked about this in the past. When you look to make an investment, as a startup founder or as a VC, you want to pick a wave bigger than you, bigger than your competitors. Right so, on the consumer side, ya know, the classic example, your Instagram fighting Facebook and photo sharing, you pick the mobile first wave, iPhone wave, right, the first mobile native photo sharing. If you're fighting Enterprise infrastructure, you pick the Cloud data wave, right? You pick the big data wave, you pick the AI waves. So, first as a founder startup, I'm looking for these macro-waves that I see not going away any time soon. So, moving from BaaS data to streaming real time data. That's a wave that's happening, that's inevitable. Dollars are floating from slower BaaS data bases to streaming real time analytics. So, Rocksett, one of the investors we talked about, they're riding that wave from going BaaS to real time, how to do analytics and sequel on real time data. Likewise, time servers, you're going from like, ya know, BaaS data, slow data to massive amounts of time storage data, Chronosphere, playing that wave. So, I think you have to look for these macro-waves of Cloud, which anyone knows but then, you pick these small wavelettes, if that's a word, like a wavelettes or a smaller wave within a wave that says, "Okay, I'm going to "pick this one trend." Ride it as a startup, ride it as an investor and because that's going to be more powerful than my competitors. >> And then, get inside the wave or inside the tornado, whatever metaphor. >> We're going to torch the metaphors but yeah, ride that wave. >> All right, Jerry, great to have you on. Seven years of CUBE action. Great to have you on, congratulations, you're VIP, you've been with us the whole time. >> Congratulations to you, theCUBE, the entire staff here. It's amazing to watch your business grow in the past seven years, as well. >> And we soft launch our CUBE 365, search it, it's on Amazon's marketplace. >> Jerry: Amazing. >> SaaS, our first SaaS offering. >> I love it, I mean-- >> John: No Venture funding. (laughs) Ya know, we're going to be out there. Ya know, maybe let you in on the deal. >> But now, like you broadcast the deal to the rest of the market. >> (laughs) Jerry, great to have you on. Again, great to watch your career at Greylock. Always happy to have ya on, great commentary, awesome time, Jerry Chen, Venture partner, general partner of Greylock. So keep coverage, breaking down the commentary, extracting the signal from the noise here at reInvent 2019, I'm John Furrier, back with more after this short break. (energetic electronic music)

>> From Boston, Massachusetts. It's theCUBE. Covering Actifio 2019 Data Driven. (upbeat techno music) Brought to you by Actifio. >> Hello everyone and welcome to Boston and theCUBE's special coverage of Actifio Data Driven 19. I'm Dave Vellante. Stu Miniman is here. We've got a special guest, John Furrier is in the house from from Palo Alto. Guys, theCUBE we love to go out on the ground, you know, we go deep. We're here at this data theme, right? We were there in the early days, John, you called me up and say, "Get your butt here, we're going to cover the first of Doop World". And since then things have moved quite fast. Everybody thought, you know, Hadoop Big Data was going to take over the world. Nobody even uses that term anymore, right? It's kind of, now it's AI, and machine intelligence, and block chain, and everything else. So what do you think is happening? Did the early Big Data days fail? You know, Frank Genus this morning called it The experimentation phase. >> I mean, I don't really think Frank has a good handle on what's going on in my opinion, cause I think it's not an experimentation, it's real. That was a wave that was essentially the beginning of, not an experimentation, of realization and reality that data, unstructured data in particular was real and relevant. Hadoop looked good off the tee, mill the fairway as we say, but the thing about the Hadoop ecosystem is that validated big data. Every financial institution jumped on it. Everyone who knew anything about data or had data issues or had a lot of data, knew the value. It's just that the apparatus to build via Hadoop was too expensive. In comes Cloud computing at scale, so, as Cloud was accelerating, you look at the Amazon Web Services Revenue Chart you can almost see the D mark where the inflection point is on the hockey stick of Amazon's revenue numbers. And that is the point in time where Hadoop was on the declining of failure. Hortonworks sold the Cloudera. Cloudera's earnings are at an all-time low. A lot of speculation of their entire strategy, and their venture back company went public, but bet the ranch to be the next data warehouse. That wasn't the business model. The data business was a completely new industry, completely being re-transformed, and, far from experimentation, it is real and definitely growing like a weed, but changing because of the underpinning infrastructure dynamics of Cloud Native, Microservices, and that's only going to get highly accelerated and the people who talk about context of industry like Frank, are going to be off. Their predictions will be off because they don't really see the new picture clear enough, in my opinion, >> So, >> I think he's off. >> So it's not so much of a structural change like it was when we went from, you know, mainframes to PCs, it's more of a sort of flow, evolution into this new area which is being driven, powered by new technologies, we talk about block chain machine intelligence and other things. >> Well, I mean, the make up of companies that were building quote, "Big Data Solutions", were trying to build an apparatus or mechanisms to solve big data problems, but none of them actually had the big data problem. None of them were full of data. None of them had a lot of data. The ones that had problems were the financial institutions, the credit card companies, the people who were doing a lot of large scale, um, with Google, Facebook, and some of the hyperscalers. They were actually dealing with the data tsunami themselves, so the practitioners ended up driving it. You guys at Wikibomb, we pointed this out on theCUBE many times, that the value was going to come from the practitioners not the suppliers of so called technology. So, you know, the Clouderas of the world who thought Hadoop would be relevant and growing as a technology were right on one side, on the other side of the coin was the Cloud decimation of that sector. The Cloud computer just completely blew away that Hadoop market because you didn't have to hire a PhD, you didn't have to hire specialty skills to stand up Hadoop clusters. You could actually throw it in the Cloud and get agile quickly, and get value out of data very very quickly. That has been real, it has not been an experiment. There's been new case studies, new companies born, new brands, so it's not an experiment, it is reality, and it's only going to get more real every day. >> And I add of course now you've got, you mentioned Cloudera and Hortenworks, you also got Matt Bar reeling Stu. Let's talk about Actifio. So they coined the term Copy Data Management, they created the category, of course they do a lot of backup, I mean, everybody in this space does a lot of backup. And then you saw the Silicon Valley companies come in. Particularly Cohesity and Rubric, you know, to a lesser extent he got some other guys like Zerto and Durva, but it was really those two companies, Cohesity and Rubric, they raised more money in their D round than Actifio has since inception. But yet Actifio keeps, you know, plodding along, growing, you know, word is they're profitable, you know, they're not like this really sectioned very East Coast versus kind of West Coast mentality. What's your take on what's going on? >> Yeah, so, Dave right, you look at the early days of Actifio and you say great, Copy Data Management, I have all these copies of data, how do I reduce my cost, get greater utilization than I have and leverage the data? I love the title of the show here, Data Driven. You know, we know at the center of digital transformation if you can't become data driven, like the CMO Brian Regan got up on stage talk about that industrialization of data. How am I going along that journey being this, I collected data versus now, you know, data, you know, is the reason that I make decisions, how I make decisions, I get smarter. The Cloud of course is a huge enabler of this, there's all these services that I can instantly access to be able to get greater insight, and move along with that environment, and if you look underneath all of these backup companies, it's really how I can change that data into business value and drive my business, the metadata underneath and all those pieces, not just the wonky storage and technical solutions that make things better, and I get a faster ROI. It's that data at the core of what we do and how do I get that as a business to accelerate. Because we know IT needs to be able to respond back to the business and data needs to be that rocket fuel. >> Is it the case of data haves and data have-nots? I mean, Amazon has data >> I mean, you're right-- >> and Facebook has data. >> We're talking about Actifio, you brought that up, okay, on this segment, on the inside segment, which is cool, they're here at the event, but they have a good opportunity but they also, they got some challenges. I mean, the thing about Actifio is, to my earlier point, which side of the wave are they on? Are they out too much out front with virtualization and Amazon, the Cloud will take them away, or are they riding the Cloud wave, making that an enabler? And I think what really I like about Actifio is because they have a lot of virtualization capabilities, the question is can they scale that Stu, to containers and microservices, because, the real opportunity in this market, in my opinion, is going to build on the virtualization trend, and make container aware, microservices capabilities because if they don't, then that would be a tell sign. Now either way it's a hot M&A market right now, so I think being in the market, horse on the track as you say. You look at the tableau sales force deal monster numbers we are in clearly a hot IPO market and a major roll up market on the M&A side. I think clearly there's two types of companies, old and new, and that is really what people are looking at, are they part of the old guard, are they the new guard. So, you know, this to me is going to be a tell sign of what they do next, can they make the data driven value proposition, you articulated Stu, actually a reality It's going to come from the technology underneath. >> Well I think it's a really interesting point you're making because, Stu as you probably know, that Amazon announced the Amazon backup service right, and you talked about the backup guys and they're like, "Ah yeah it's backup, but it really doesn't do recovery, it's really not that robust". It's part of me says, "Uh oh"... >> Watch out. >> You better move fast", because Amazon has stated, "Hey if you don't move fast we're going to just keep gobbling", and you've seen Amazon do this. What are your thoughts on that? Can these specialists, can they survive, John's talking about M&A. Can the market support all these guys along with the big, you know, traditional guys like Veritas, and Dell EMC, and IBM and Combol? >> Right, well so Actifio started very much in the data center. They were before this Could wave really took off. It's really only in the last year that they've been sassifying their product. So the question is, does that underlying IP, which wasn't tied to hardware, but, you know, sat at really more of, you know, reminded us of that storage virtualization battles that we talked about for years, Dave, but now they are going in the Cloud. They've got all the partnerships in the Cloud, but they are competing against those new vendors that you talked about like Cohesity and Rubric out there, and there's big money chasing this environment. So, you know, I want to talk to the customers here and find out, you know, where they are using them, and especially some of those first customers using this--. >> Well they clearly need a Cloud play cause that's clearly where the action is. But if you look at what's going on with Amazon, Azure, and Google you see a lot of on premises, Stu, because that's where the customers are. So just because the customers are currently not migrating their existing workloads to the Cloud doesn't mean it's not going to happen. So I think there's an opportunity for any company like Actifio, who may or may not be on the curve on the tech side, one little misfire on a tech bet could cripple the company and also make the company. There's a lot of high risk, reward ratio. How they handle containers. How they build on virtualizations. Virtualization going to to be part of the future with Cloud. These are the kind of the dynamics that are going to be in play, and they got some time on their hands because the on premises growth is because the clients are trying to figure out what to do and they're not going to be migrating, lifting, and shifting workloads all off to the Cloud. New will be Cloud based, but enterprises have proven why we are in multi-Cloud and hybrid-Cloud conversation, that... The enterprise on premises is not going away anytime soon. >> I want to ask you guys, John you specifically, about this sort of new Silicon Valley growth model and how companies are achieving escape velocity. When you and I made our first trip to Barcelona, I was having dinner with David Scott who was the CEO of 3PAR and he said to me, When I came to 3PAR the board said, "Hey we're willing to invest 30 million dollars in this company". And David Scott said to them, "I need way more, I need 80 million dollars". Today 80 million dollars is nothing. You saw, you know, Pure Storage hit escape velocity, was just throwing money, and growing at the problem. You're seeing Cohesity-- >> Well you can debate that. I mean, If you have to build a rocket ship, hit critical mass and you want to fund that, you're going to to need an enterprise. However, there's arguments on the south side that you can actually get fly wheel effect going early with less capital. So again, that's 3PAR-- >> But so that's my point. >> Well so that's 3PAR, that was 2009. >> So, yeah that was early days so that's ancient history. But software is generally supposed to be a capital efficient market, yet these companies are raising many hundreds and hundreds of millions, you know, half a billion dollar raises and they are putting it largely in promotion. Is that the new model, is that sustainable, in your view? >> Well I think you're conflating capital market dynamics with viable companies to invest in. I think there's a robust seed in series A market but the series A market and Silicon Valley is you know, 15 to 25 million, it used to be 3 to 5. So the dynamics are changing on funding. There's just not enough companies, horses on the track, to deploy capital at tranches of 30, 50, 80 million. So the capital markets are clearly going to have the money available so it's a market for the startups and the broke companies. That's separate from actually winning. So you've got slacks going public this weeks, you have other companies who have built business on a sass fly wheel, and then everything else is gravy in terms of the go to market, they got a couple hundred million. I think slack got close to a billion dollars in cash that they've raised. So they're flooded with cash, they'll never spend it all. So there are some companies that can achieve success like that. Others have to buy market share, they got to push and build out a sales force, and it's going to be a function of the role of customer, customization, specialism, and whatnot. But with AI machine leaning there's more efficiencies coming in so I think the modern company can do more with less. >> What do you think of the ride sharing on IPOs, Uber and Lift, do you abol? Do you like 'em or do you think it's just, they're losing too money and can't sustain it? >> I was thinking about that this morning after looking at the article in the Wall Street Journal in our coverage on Silicon angle. You look at Zoom communications, I like models that actually can take a simple concept and an existing mature market and disrupt it by being Cloud efficient and completely sass and data driven. That is an example of success. That to me, Zoom Communications and Zscaler, another company that we talk to, these are companies that were built with a specific value proposition that made the product and they were targeting mature markets with leaders in it. Video conferencing, Webex, Citrix, Zoom came out of nowhere, optimized on simple value proposition, used Cloud scale and data, and crushed it. Uber, Lift, little bit different issue. They're losing money but I would bet on the long term that that is going to be the used case for how people will have transportation. I think that's the long game and I think that without regulatory kind of pressure, without, there's regulatory issues that's really the big risk. But I believe that Uber and Lift absolutely will be long brands and just like Facebook was early on, although they threw off a lot of cash, those guys are building for penetration, and that's where the funding matters. Penetration is critical. Now they're the standard, and people really don't take taxis anymore, but they're really using the ride sharing. And you get the scooters, you get the bikes, they're all sequencing into these adjacent markets which drains more cash but builds the brand, builds the footprint. >> Well that's what I want to ask you. So people compare the early Uber, Lift, Taxi, Ride sharing to Amazon selling books, but there's all these other adjacencies. You have a thought on this? >> Well, just, you know, right, Uber Eats is a huge opportunity for that environment and autonomous vehicles everybody talks about, but it's still quite a ways out. So there are a lot of different- >> Scooters are the same, we're in San Diego, there are 8 gazillion scooters. >> San Diego had fun, you know, going around on their electronic scooters, boy, talk about the gig economy, they pay people at the night, to like go pay by the recharge you do on that, what is the future of work, >> Yeah, that's a great point. >> and how can we have that-- >> Uber going to look a lot like Amazon. You subsidize the front end retail side of the business, but look at the data that they throw up. Uber's data that they're gathering on, not only customer behavior, but just mapping services, 3-D mapping is going to be huge, so you've got these cars that are essentially bots on the road, providing massive mapping and traffic analysis. So you're going to start to see data driven, like Actifio slogan here, be a big part of all design decisions and value proposition from any company out there. And if they're not data driven I think they're going to be toast. >> Probably could because there's that data and that machine learning underneath, that can optimize, you know, where the people are, how I use the system, such a huge wave that we're watching. >> How about one last topic which is heavily data driven, it's Facebook. Facebook is obviously a data driven company, the Facebook crypto play, I love it, I love Facebook. I'm a bull on Facebook, I think it's been beat up. I think, two billion users is hard to replicate, but what's your thoughts on their crypto play? >> Well it's kind of a middle finger to the United States of America but it's a great catalyst for the international market because crypto needed a whale to come in and bring all those users in. Bad timing, in my mind, for Facebook, because given all the anti-trust and regulatory conversations, what better way to show your threat to the world order when you say we're going to run a banking system with a collection of international companies. I think the US is going to look at this and say, "Oh my God! They can't even be trusted to handle personal information and we're going to now let them run a banking system? Run monetary, basically World Bank equivalent infrastructure?" No frickin way! I think this is going to to be a major road to home. I think Facebook has to really make this an ecosystem play if they want to make it work, that's their telegraphic move they're saying, "Hey we want to do for the community but we got our own wallet and we got our own network". But they bring a lot to the table so it's going to be a really interesting dynamic to see the coalescing around Facebook because they could make the market. Look what Instagram did to Snapchat. They literally killed the company, took all their users. That is what's going to happen in the digital money economy when Facebook brings billions of users user experience with money. What happened with Snapchat with Instagram is going to happen to the World Bank if this continues. >> Where do you stand on the government breaking up big tech? >> So Dave, you know, you look in these companies, it's not easy to pull those apart. I don't think our government understands how most of big tech works. You know, take Amazon and AWS, that's one company underneath it. You know, Facebook, Microsoft. You know, Microsoft went through all these issues. Question Dave, we've had lots of debates on Twitter you know, are they breaking the law, are they not doing trust? I have some trust issues with Facebook myself, but most of the big companies up there I don't think the anti-trust kicks in, I don't think it makes sense to pull them apart. >> Stu, the Facebook story and the YouTube story are simply this, they have been hiding under the platform rules, of the Digital Millennium Copyright Act, and they are an editing platform so you can't sue them. Okay, once they become a publisher they could be sued. Just like CNN, Fox News, and everybody else. And we're publishers. So they've been hiding behind the platform. That gig is up. They're going to have to address are you a platform or are you a publisher? You're making editing decisions around what users can see with software, you are essentially editing the feed, that is a publisher role, with that becomes responsibility, and then obviously regulartory. >> Well Facebook is conflicted right now. They're trying to figure out which side of the fence to go on. >> No no no! They want one side! The platform side! They're make billions of dollars! >> Yeah but so they're making decisions about you know, which content to show and whether they monetize it. And when it's controversial content, they'll turn down the ads a little bit but they won't completely eliminate it sometimes. >> So, Dave, the only thing that the partisans in politics seem to agree on though is that big tech has too much power. You know, What's your take on that? >> Well so I think that if they are breaking the law then they should be moderated. But I don't think the answer is to go hard after Elizabeth Warren. Hard after them and break them up. I think you got to start with okay, because you break these companies up what's going to happen is they're going to be worth more, it's going to be AT&T all over again. >> While you guys were at Sysco Live, we covered this at Amazon Web Service and Public Sector Summit. The real issue in government, Stu, is there's too much tech for bad on the PR side, and there's not enough tech for good. Tech is not bad, tech is good. There's not enough promotion around the apps around there. There's real venture funds being created to promote tech for good. That's going to where the tide will turn. When does the tech industry start doing good stuff, not bad stuff. >> All right we've got to wrap. John, thanks for sitting in. Thank you for watching. Be right back, we're here at Actifio Data Driven 2019. From Boston this is theCUBE, be right back. (upbeat techno music)

(upbeat techno music) >> Narrator: Live, from San Jose, it's theCUBE! Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to Big Data SV, everybody. My name is Dave Vellante and this is theCUBE, the leader in live tech coverage. You know, this is our 10th big data event. When we first started covering big data, back in 2010, it was Hadoop, and everything was a batch job. About four or five years ago, everybody started talking about real time and the ability to affect outcomes before you lose the customer. Lewis Kaneshiro was here. He's the CEO of Streamlio and he's joined by Karthik Ramasamy who's the chief product officer. They're both co-founders. Gentlemen, welcome to theCUBE. My first question is, why did you start this company? >> Sure, we came together around a vision that enterprises need to access the value around fast data. And so as you mentioned, enterprises are moving out of the slow data era, and looking for a fast data value to their data, to really deliver that back to their users or their use cases. And so, coming together around that idea of real time action what we did was we realized that enterprises can't all access this data with projects right now that are not meant to work together, that are very difficult, perhaps, to stitch together. So what we did was create an intelligent platform for fast data that's really accessible to enterprises of all sizes. What we do is we unify the core components to access fast data, which is messaging, compute and stream storage, accessing the best of breed open-source technology that's really open-source out of Twitter and Yahoo! >> It's a good thing I was going to ask why does the world need to know there are, you know, streaming platforms, but Lewis kind of touched on it, 'cause it's too hard. It's too complicated, so you guys are trying to simplify all that. >> Yep, the reason mainly we wanted to simplify it because, based on all our experiences at Twitter and Yahoo! one of the key aspects was to to simplify it so that it's conceivable by regular enterprise because Twitter and Yahoo! kind of our position can afford the talent and the expertise in order to do this real time platforms. But when it goes to normal enterprises, they don't have access to the expertise and the cost benefits that they might have to reincur. So, because of that we wanted to use these open-source projects, the Twitter and the Yahoo!'s provider, combine them, and make sure that you have a simple, easy, drag and drop kind of interface, so that it's easily conceivable for any enterprise. Essentially, what we are trying to do is reduce the (mumbles) for enterprises for real time, for all enterprises. >> Dave: Yeah, enterprises will pay up... >> Yes. >> For a solution. The companies that you used to work for, they all gladly throw engineering at the problem. >> Yeah. >> Sure. >> To save time, but most organizations, they don't have the resources and so. Okay, so how does it, would it work prior to Streamlio? Maybe take us through sort of how a company would attack this problem, the complexities of what they have to deal with, and what life is like with you guys. >> So, current state of the world is it's fragmented solution, today. So the state of the world is where you take multiple pieces of different projects and you'd assemble them together in formats so that you can do (mumbles) right? So the reason why people end up doing is each of these big data projects that people use was the same for completely different purpose. Like messaging is one, and compute is another one, and third one is storage one. So, essentially what we have done as company is to simplify this aspect by integrating this well-known, best-of-the-breed projects called, for messaging we use something called Apache Poser, for compute we use something called Apache Krem, from Twitter, and similarly for storage, for real time storage, we use something called Apache Bookkeeper, so and to unify them, so that, under the hoods, it may be three systems, but, as a user, when you are using it, it serves or functions as a single system. So you install the system, and ingest your data, express your computation, and get the results out, in one single system. >> So you've unified or converged these functions. If I understand it correctly, we talking off camera a little bit, the team, Lewis, that you've assembled actually developed a lot of these, or hugely committed to these open-source projects, right? >> Absolutely, co-creators of each of the projects and what that allows us to do is to really integrate, at a deep level, each project. For example, Pulsar is actually a pub/sub system that is built on Bookkeeper, and Bookkeeper, in our minds, is a pure list best-of-breed stream storage solution. So, fast and durable storage. That storage is also used in Apache Heron to store State. So, as you can see, enterprises, rather than stitching together multiple different solutions for queuing, streaming, compute, and storage, now have one option that they can install in a very small cluster, and operationally it's very simple to scale up. We simply add nodes if you get data spikes. And what this allows is enterprises to access new and exciting use cases that really weren't possible before. For example, machine learning model deployment to real time. So I'm a data scientist and what I found is in data science, you spend a lot of time training models in batch mode. It's a legacy type of approach, but once the model is trained, you want to put that model into production in real time so that you can deliver that value back to a user in real time. Let's call it under two second SLA. So, that has been a great use case for Streamlio because we are a ready made intelligent platform for fast data, for MLai deployment. >> And the use cases are typically stateful and your persisting data, is that right? >> Yes, use cases, it can be used for stateless use cases also, but the key advantage that we bring to a table is stateful storage. And since we ship along with the storage (mumbles) stateful storage becomes much easier because of the fact that it can be used to store a real intermediate state of the computation or it can be used for the staging (mumbles) data when it spills over from what the memory is it's automatically stored to disk or you can even in the data for as long as you want so that you can unlock the value later after the data has been processed for the fast data. You can access the lazy data later, in time. >> So give us the run-down on the company, funding, you know, VCs, head count. Give us the basics. >> Sure, we raise Series A from Lightspeed Venture Partners, lead by John Vrionis and Sudip Chakrabarti. We've raised seven and a half million and emerged from stealth back in August. That allowed us to ramp up our team to 17, now, mainly engineers, in order to really have a very solid product, but we launched post rev, prelaunch and some of our customers are really looking at geo replication across multiple data centers and so active, active geo replication is an open-source feature in Apache Pulsar, and that's been a huge draw, compared to some other solutions that are out there. As you can see, this theme of simplifying architecture is where Streamlio sits, so unifying, queuing and streaming allows us to replace a number of different legacy systems. So that's been one avenue to help growth. The other, obviously is on the compute piece. As enterprises are finding new and exciting use cases to deliver back to their users, the compute piece needs to scale up and down. We also announce Pulsar Functions, which is stream-native compute that allows very simple function computation in native Python and Java, so you spin out the Apache Python cluster or Streamlio platform, and you simply have compute functionality. That allows us to access edge use cases, so IOT is a huge, kind of exciting POC's for us right now where we have connected car examples that don't need heavyweight schedule or deployment at the edge. It's Pulsar Pulsar functions. What that allows us to do are things like fraud detection, anomaly detection at the edge, model deployment at the edge, interpolation, observability, and alerts. >> And, so how do you charge for this? Is it usage based. >> Sure. What we found is enterprise are more comfortable on a per node basis, simply because we have the ambition to really scale up and help enterprises really use Streamlio as their fast data platform across the entire enterprise. We found that having a per data charge rate actually would limit that growth, and so per node and shared architecture. So, we took an early investment in optimizing around Kubernetes. And so, as enterprises are adopting Kubernetes, we are the most simple installation on Kubernetes, so on-prem, multicloud, at the edge. >> I love it, so I mean for years we've just been talking about the complexity headwinds in this big data space. We certainly saw that with Hadoop. You know, Spark was designed to certainly solve some of those problems, but. Sounds like you're doing some really good work to take that further. Lewis and Karthik, thank you so much for coming on theCUBE. I really appreciate it. >> Thanks for having us, Dave. >> All right, thank you for watching. We're here at Big Data SV, live from San Jose. We'll be right back. (techno music)

>> Narrator: Live from San Jose, it's theCUBE. Presenting: Big Data Silicon Valley. Brought to you by SiliconANGLE Media, and its ecosystem partners. >> Welcome back, everyone, we're here live at San Jose for Big Data SV, Big Data Silicon Valley. I'm John Furrier, cohost of theCUBE. We're here with two great guests, Jaspreet Singh, founder and CEO of Druva, and Jake Burns, VP of Cloud Services of Live Nation Entertainment. Welcome to theCUBE, so what's going on with Cloud? Apps are out there, backup, recovery, what's going on? >> So, we went all in with AWS, and late 2015 and through 2016 we moved all of our corporate infrastructure into AWS, and I think we're a little bit unique in that situation, so in terms of our posture, we're 100% Cloud. >> John: Jaspreet, what's going on with you guys in the Cloud, because we've talked about this before, with a lot of the apps in the cloud, backup is really important. What's the key thing that you guys are doing together with Live Nation? >> Sure, so I think the notion of data is now pretty much everywhere. The data is captured, controlled in data center, now it's getting decentralized into getting into apps and ecosystems, and softwares and services deployed either at the edge or in the Cloud. As the data gets more and more decentralized, the notion of data management, bead backup, BD discovery. Anything has to get more and more centralized. And we strongly believe the epicenter of this whole data management has to move to Cloud. So, Druva is a size based provider for data management. And we work with Live Nation to predict the apps not just in the data center. But, also at the edge and also the Cloud data center. The applications deployed in the Cloud, be it Live Nation or Ticketmaster. >> And what are some of the workloads you guys are backing up? That's with Druva. >> Yeah so, it's pretty much all corporate, IT applications. You know, typical things you'd find in any IT shop really. So, you know, we have our financial systems and we have some of our smaller ticketing systems and you know, corporate websites. Things of that nature. So, it's like we have 120 applications that are running and it's just really kind of one of everything. >> We were talking before we came on camera about the history of computing and the Cloud has obviously changed the game. How would you compare the Cloud as a trend relative to operationalizing the role of data and obviously GDPR, Ransomware. These are things that now with the perimeter gone. There's worries. So now, how do you guys look at the Cloud? So Jake, I will start with you. If you can compare and contrast, where we have come from and where we are going. Role of the Cloud. Significant primary, expanding. How would you compare that? And how would you talk to someone who says Hey I'm still in the data center world? What's going on with Cloud? >> Well, yeah, it's significant and it's expanding, both. And you know, it's really transforming the way we do business. So you know just from a high level, things like shortening the time to market for applications, going from three to six months just to get a proof of concept started to today, you know, in the Cloud. Being able to innovate really by trying things trying to... we try 20 different things, decide what works, what doesn't work. And at very low cost. So, it allows us to really do things that just weren't possible before. So, also, we we move more quickly because, you know, we're not afraid of making mistakes. If we provision infrastructure and we don't get it right the first time, we just change it. You know, that's something that we would just never be able to do previously in the data center. So to answer your question, everything is different. >> And as a service model's been kind of key. Is the consumption on your end different like I mean radically different? Like give an example of like how much time would be saved or taken to use other the traditional approaches. >> Oh for sure. You know, in the role of IT has completely changed because you know, instead of worrying about nuts and bolts and servers and storage arrays and data centers. You know, we could really focus on the things that are important to the business. You know, those things delivering results for the business. So, bringing value, bringing applications online and trying things that are going to help you know, us do business rather than focusing on all the minutiae. All that stuff's now been outsourced to Cloud providers. So, really, we kind of have a similar head count and staff. But, we are focused on things that bring value rather than things that are just kind of frivolous. >> Jaspreet, you guys have been very successful startup growing rapidly. The Cloud been a good friend that trend is your friend with the Cloud. >> What's different operationally that you guys are tapping into? What's that tail wind for Druva that's making you guys successful? And is it the ease of use? Is it the ease of consumption? Is it the tech? What's the secret to success with Druva? >> Sure, so, we believe cloud is a very big business transformation trend more than a technology trend. It's how you consumer service with a fixed SLA, with a fixed service agreement across the globe. So, it's ease of consumption. It's simplicity of views. It's orchestration. It's cost control. All those things. So, our promise to our customers is the complexity of data management, backups, archives, data production, which is a risk mitigation project. You know, can be completely abstracted by a simple service. For example, you know, Live Nation consumers, consumer drove a service through Amazon Marketplace. So, think about consuming a critical service like data management through simplicity of marketplace, pay as you go, as you consume the service. Across the globe. In the US, in Australia, and Europe. And also, helps the vendors like us to innovate better. Because we have a control environment to understand how different customers are using the service and be able to orchestrate better security pusher, better threat prevention, better cost control. DevOps. So, it improves the pusher of the service being offered and helps the customer consumer. >> You both are industry veterans by today's standards unless you're like 24 doing some of the cryptocurrency stuff that, you know, doesn't know the old IT baggage. How would you guys view the multi-Cloud conversation? Because we hear that all the time. Multi-Cloud has come up so many times. What does it mean? Jake, what does multi-Cloud actually mean? Is it the same workload across multiple Clouds? Is it the fact that there is multiple Clouds? Certainly, there will be multiple Clouds? But, so, help us digest what that even means these days. >> Yeah, that's a great question and it's a really interesting topic. Multi-Cloud is one of those things where, you know, there's so many benefits to using more than one Cloud provider. But, there are also a lot of pitfalls. So, people really underestimate the difference in the technology and the complexity of managing the technology when you change Cloud providers. I'm talking primarily about infrastructure service providers like Amazon web services. So, you know, I think there's a lot of good reasons to be multi-Cloud to get the best features out of different providers, to not have, you know, the risk of having all your data in one place with one vendor. But, you know, it needs to be done in such a way where you don't take that hit in overhead and complexity and you know, I think that's kind of a prohibitive barrier for most enterprises. >> And what are the big pitfalls that you see? Is it mainly underestimating the stack complexity between them or is it more of just operational questions? I mean what is the pitfalls that you've observed? >> Yeah, so, moving from like a typical IT data center environment to public Cloud provider like AWS. You're essentially asking all your technical staff to start speaking in a new language. Now if you were to introduce a second Cloud provider to that environment, now you're asking them to learn a third language as well. And that's a lot to ask. So, you really have two scenarios where you can make that work today without using a third party. And that's ask all of your staff to know both and that's just not feasible. Or have two tech teams. One for each Cloud platform. That's really not something businesses want to do. So, I think the real answer is to rely on a third party that can come in and abstract one of those Cloud complexities Well, one of those Cloud providers out. So, you don't have to directly manage it. And in that way, you can get the benefit of being multi-Cloud, that data protection of being multi-Cloud. But, not have to introduce that complexity to your environment. >> To provide some abstraction layer. Some sort of software approach. >> Yeah, like for example, if you have your primary systems in AWS, and you use a software like Druva Phoenix to backup your data and you put that data into a second Cloud provider. You don't have to an account with that second Cloud provider. You don't have to have the risk of associating without a complexity associated without that is I think is a very >> And that's where you're looking for differentiation. We look at venues, say hey don't make me work harder. >> Right. >> And add new staff. Solve the problem. >> Yeah, it's all about solving problems right? And that's why we're doing this. >> So, Druva talk about this thing. Because we talked about it earlier about To me we could be oh we're on Azure. Well, they have Office 365 of course they're going to have Microsoft. A lot of people have a lot going on and AWS. So, maybe we're not there at the world where you can actually use provision across Clouds, the same workload, It would be nice to have that someday if it was seamless. But, I think that's might be the nirvana. But at the end of the day, an enterprise might have Office 365 and some Azure. But, I got some mostly Amazon over here I'm doing a lot of development on and doing a DevOps, and I'm on-prim. How do you talk to that? Because that's like you got to backup Office 365, you got to do the on-prim thing, you got to do the Amazon thing. How do you guys solve that problem? What's the conversation? >> Absolutely. I think over time we believe best of breed will win. So, people will deploy different type of cloud for different workloads. Pete's has hosted IaaS or platform like PaaS. When they do that, when they host multiple services, softwares to deploy services. I think its hard to control where the data will go. What we can orchestrate or anybody can orchestrate is the centralizing the data management part of it. So, Druva has the best pusher, has the best coverage across multiple heterogeneous Cloud breed. You know. Services like Office 365, Box, or Saleforce or B platforms like S3 or Dynono DB through our product called Apollo or hosted platforms like what Live Nation is using through our Phoenix product line. So getting the breadth of coverage, consistency of policies on a single platform is what will make enterprises adopt what's best out there without worrying about how you build abstraction for data management. >> Jake, what's the biggest thing you see people who are moving to the Cloud for the first time? What are they struggling with? Is it the idea that there's no perimeter? Is it staff training? I mean what are some of the as people move from Test Dev and or start to put in production the Cloud? What are some of the critical things they should think about? >> Yeah, there are so many of them. But first, really, its just getting buy in, you know, from your technical staff because, you know, in an enterprise environment you bring in a Cloud provider it's very easily framed to hold as if we're just being outsourced right? So, I think getting past that barrier first and really getting through to folks and letting them know that really this is good for you. This is not bad for you. You're going to be learning a new skill, very valuable skill, and you're going to be more effective at your job. So, I think that's the first thing. After that, once you start moving to the Cloud, then, the thing that becomes apparent very quickly is cost control. So, you know, the thing with public Cloud is you know, before you had this really kind of narrow range of what IT could cost. Now with the traditional data center, now we have this huge range. And yes, it can be cheaper than it was before. But, it can also be far more expensive than it was before. >> So, service is sprawled or just not paying attention? Both? >> Well, you essentially you're giving your engineers a blank check. So, you need to have some governance and, you know, you really need to think about things that you didn't have to think about before. You're paying for consumption. So, you really have to watch your consumption. >> So, take me thorough the mental model of D duplication in the Cloud. Because I'm trying to like visualize it or grok it a little bit. Okay, so, the Cloud is out there, data's everywhere. And do I move the compute to the data? How does the backup and recovery and data management work? And does D Doup change with Cloud? Because some people think I got my D Doup already and I'm on premise. I've been doing these old solutions. How does D Doup specifically change in the Cloud or does it? >> I know scale changes. You're looking at, you know, the best D Doup systems, if you look historically, you know, were 100 terabyte, 200 terabyte, Dedup indexes, data domain. The scale changes, you know, customers expect massive scale in Cloud. Our largest customer had 10 perabyte in a single Dedup index. It's 100x scale difference compared to what traditional systems could do. Number two, you could create a quality of service which is not really bound by a fixed, you know, algorithm like variable lent or whatever. So, you can optimize a Dedup very clearly for the right workload. The right Dedup for the right workload. So, you may Dedup off of 365 differently than your VMware instances, compared to your Oracle databases or your Endpoint workload. So, it helps you that as a service business model helps you create a custom, tailored solution for the right data. And bring the scale. We don't have the complexity of scale. But, to get the benefit of scale. All, you know, simply managing the cloud. >> Jake, what's it like working with Druve? What's the benefit that they bring to you guys? >> Yeah, so, specifically around backups for our enterprise systems, you know, that's a difficult challenge to solve natively in the Cloud. Especially if you're going to be limited to using Cloud native tools. So, it's really it's a really perfect use case for a third party provider. You know, people don't think about this much but in the old days, in the data center, you know, our backups went offsite into a vault. They were on tapes. It was very difficult for us to lose those or for them to be erased accidentally or even intentionally. Once you go into the Cloud, especially if you're all in with the Cloud, like we are. Everything is easier. And so, accidents are easier also. You know, deleting your data is easier. So, you know, what we really want and what a lot of enterprises want. >> And security too is a potential >> Absolutely, yeah. And so, what we want is we want to get some of that benefit, you know, back that we had from that inefficiency that we had beforehand. We love all the benefits of the Cloud. But, we want to have our data protected also. So, this is a great role for a company like Druva to come in and offer a product like Phoenix and say, you know, we're going to handle we're going to handle your backups for you essentially. So, you're going to put it in a safe place. We're going to secure it for you. And we're going to make sure it's secure for you. And doing it software is a service like Druva does with Phoenix. I think is the absolute right way to go. It's exactly what you need. >> Well, congratulations Jake Burns, Vice President in Cloud services. >> Thank you. >> At Live Nation entertainment. Jaspreet Singh, CEO of Druva, great to have you on. Congratulations on your success. >> Thank you. >> Inside the tornado called Cloud computing. A lot more stuff coming. More CUBE coverage coming up after this short break. Be right back. (electronic music)

>> Announcer: Live from San Jose, it's theCUBE, presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media and it's ecosystem partners. >> Welcome back to Big Data SV in San Jose. This theCUBE, the leader in live tech coverage. My name is Dave Vellante and this is day two of our wall-to-wall coverage. We've been here most of the week, had a great event last night, about 50 or 60 of our CUBE community members were here. We had a breakfast this morning where the Wikibon research team laid out it's big data forecast, the eighth big data forecast and report that we've put out, so check out that online. Jacques Nadeau is here. He is the CTO and co-founder of Dremio. Jacque, welcome to theCUBE, thanks for coming on. >> Thanks for having me here. >> So we were talking a little bit about what you guys do. Three year old company. Well, let me start. Why did you co-found Dremio? >> So, it was a very simple thing I saw, so, over the last ten years or so, we saw a regression in the ability for people to get at data, so you see all these really cool technologies that came out to store data. Data lakes, you know, SQL systems, all these different things that make developers very agile with data. But what we were also seeing was a regression in the ability for analysts and data consumers to get at that data because the systems weren't designed for analysts, they were designed for data producers and developers. And we said, you know what, there needs to be a way to solve this. We need to be able to empower people to be self-sufficient again at the data consumption layer. >> Okay, so you solved that problem how, you said, called it a self-service of a data platform. >> Yeah, yeah, so self-service data platform and the idea is pretty simple. It's that, no matter where the data is physically, people should be able to interact with a logical view of it. And so, we talk a little bit like it's Google Docs for your data. So people can go into the system, they can see the different data sets that are available to them, collaborate around those, create changes to those that they can then share with other people in the organization, always dealing with the logical layer and then, behind the scenes, we have physical capabilities to interact with all the different system we interact with. But that's something that business users shouldn't have to think as much about and so, if you think about how people interact with data today, it's very much about copies. So every time you want to do something, typically you're going to make a copy. I want to reshape the data, I make a copy. I want to make it go faster, I make a copy. And those copies are very, very difficult for people to manage and they could have mixed the business meaning of data with the physical, I'm making copies to make them faster or whatever. And so our perspective is that, if you can separate away the physical concerns from the logical, then business users have a much more, much more likelihood to be able to do something self-service. >> So you're essentially virtualizing my corpus of data, independent of location, is that right, I mean-- >> It's part of what we do, yeah. No, it's part of what we do. So, the way we look at it is, is kind of several different components to try to make something self-service. It starts with, yeah, virtualize or abstract away the details of the physical, right? But then, on top of that, expose a very, sort of a very user-friendly interface that allows people to sort of catalog and understand the different things, you know, search for things that they want to interact with, and then curate things, even if they're non-technical users, right? So the goal is that, if you talk to sort of even large internet companies in the Valley, it's very hard to even hire the amount of data engineering that you need to satisfy all the requests of your end-users of data. And so the, and so the goal of Dremio is basically to figure out different tools that can provide a non-technical experience for getting at the data. So that's sort of the start of it but then the second step is, once you've got access to this thing and people can collaborate and sort of deal with the data, then you've got these huge volumes of data, right? It's big data and so how do you make that go faster? And then we have some components that we deal with, sort of, speed and acceleration. >> So maybe talk about how people are leveraging this capability, this platform, what the business impact is, what have you seen there? >> So a lot of people have this problem, which is, they have data all over the place and they're trying to figure out "How do I expose this "to my end-users?" And those end-users might be analysts, they might be data scientists, they might be product managers that are trying to figure out how their product is working. And so, what they're doing today is they're typically trying to build systems internally that, to provide these capabilities. And so, for example, working with a large auto manufacturer. And they've got a big initiative where they're trying to make the data that they have, they have huge amounts of data across all sort of different parts of the organization and they're trying to make that available to different data consumers. Now, of course, there's a bunch of security concerns that you need to have around that, but they just want to make the data more accessible. And so, what they're doing is they're using Dremio to figure out ways to, basically, catalog all the data below, expose that to the different users, applying lots of different security rules around that, and then create a bunch of reflections, which make the things go faster as people are interacting with the things. >> Well, what about the governance factor? I mean, you heard this in the hadoop world years ago. "Ah, we're going to make, we're going to harden hadoop, "we're going to" and really, there was no governance and it became more and more important. How do you guys handle that? Do you partner with people? Is it up to the customer to figure that out? Do you provide that? >> It's several different things, right? It's a complex ecosystem, right? So it's a combination of things. You start with partnering with different systems to make sure that you integrate well with those things. So the different things that control some parts of credentials inside the systems all the way down to "What's the file system permissions?", right? "What are the permissions inside of something like Hive and the metastore there?" And then other systems on top of that, like Sentry or Ranger are also exposing different credentialing, right? And so we work hard to sort of integrate with those things. On top of that, Dremio also provides a full security model inside of the sort of virtual space that we work. And so people can control the permissions, the ability to access or edit any object inside of Dremio based on user roles and LDAP and those kinds of things. So it's, it's kind of multiple layers that have to be working together. >> And tell me more about the company. So founded three years ago, I think a couple of raises, >> Yep >> who's backing you? >> Yeah, yeah, yeah, so we founded just under three years ago. We had great initial investors, in Red Point and Lightspeed, so two great initial investors and we raised about 15 million on that round. And then we actually just closed a B round in January of this year and we added Norwest to the portfolio there. >> Awesome, so you're now in the mode of, I mean, they always say, you know, software is such a capital-efficient business but you see software companies raising, you know, 900 million dollars and so, presumably, that's to compete, to go to market and, you know, differentiate with your messaging and branding. Is that sort of what the, the phase that you're in now? You kind of developed a product, it's technically sound, it's proven in the marketspace and now you're scaling the, the go-to-market, is that right? >> That's exactly right. So, so we've had a lot of early successes, a lot of Fortune 100 companies using Dremio today. For example, we're working with TransUnion. We're working with Intel. We actually have a great relationship with OVH, which is the third-largest hosting company in the world, so a lot of great, Daimler is another one. So working with a lot of great companies, seeing sort of great early success with the product with those companies, and really looking to say "Hey, we're out here." We've got a booth for the first time at Strata here and we're sort of letting people know about, sort of, a better way, or easier way, for people to deal with data >> Yeah. >> A happier way. >> I mean, it's a crowded space, right? There's a lot of tools out there, a lot of companies. I'm interested in how you sort of differentiate. Obviously simplification is a part of that, the breadth of your capabilities. But maybe, in your words, you could share with me how you differentiate from the competition and how you break out from the noise. >> Yeah, yeah, yeah, so it's, you're absolutely right, it's a very crowded space. Everybody's using the same words and that makes it very hard for people to understand what's going on. And so, what we've found is very simple is that typically we will actually, the first meeting we deal with a customer, within the first 10 minutes we'll demo the product. Because so many technologies are technologies, not, they're not products and so you have to figure out how to use the product. You've got to figure out how you would customize it for your certain use-case. And what we've found with our product is, by making it very, very simple, people start, the light goes on in a very short amount of time and so, we also do things on our website so that you can see, in a couple of minutes, or even less than that, little animations that sort of give you a sense of what it's about. But really, it's just "Hey, this is a product "which is about", there's this light bulb that goes on, it's great. And you figure this out over the course of working with different customers, right? But there's this light bulb that goes on for people that are so confused by all the things that are going on and if we can just sit down with them, show them the product for a few minutes, all of a sudden they're like "Wait a minute, "I can use this", right? So you're frequently talking to buyers that are not the most technical parts of the organization initially, and so most of the technologies they look at are technologies that are very difficult to understand and they have to look to others to try to even understand how it would fit into their architecture. With Dremio, we have customers that can, that have installed it and gotten up, and within an hour or two, started to see real value. And that sort of excitement happens even in the demo, with most people. >> So you kind of have this bifurcated market. Since the big data meme, everybody says they're data-driven and you've got a bifurcated market in that, you've got the companies that are data-driven and you've got companies who say they're data-driven but really aren't. Who are your customers? Are they in both? Are they predominantly in the data-driven side? Are they predominantly in the trying to be data-driven? >> Well, I would say that they all would say that they're data-driven. >> Yeah, everyone, who's going to say "Well, we're not data-driven." >> Yeah, yeah, yeah. So I would say >> We're dead. >> I would say that everybody has data and they've got some ways that they're using it well and other places where they feel like they're not using it as well as they should. And so, I mean, the reason that we exist is to make it so it's easier for people to get value out of data, and so, if they were getting all the value they think they could get out of data, then we probably wouldn't exist and they would be fully data-driven. So I think that everybody, it's a journey and people are responding well to us, in part, because we're helping them down that journey. >> Well, the reason I asked that question is that we go to a lot of shows and everybody likes to throw out the digital transformation buzzword and then use Uber and Airbnb as an example, but if you dig deeper, you see that data is at the core of those companies and they're now beginning to apply machine intelligence and they're leveraging all this data that they've built up, this data architecture that they built up over the last five or 10 years. And then you've got this set of companies where all the data lives in silos and I can see you guys being able to help them. At the same time, I can see you helping the disruptors, so how do you see that? I mean, in terms of your role, in terms of affecting either digital transformations or digital disruptions. >> Well, I'd say that in either case, so we believe in a very sort of simple thing, which is that, so going back to what I said at the beginning, which is just that I see this regression in terms of data access, right? And so what happens is that, if you have a tightly-coupled system between two layers, then it becomes very difficult for people to sort of accommodate two different sets of needs. And so, the change over the last 10 years was the rise of the developer as the primary person for controlling data and that brought a huge amount of great things to it but analysis was not one of them. And there's tools that try to make that better but that's really the problem. And so our belief is very simple, which is that a new tier needs to be introduced between the consumers and the, and the producers of data. And that, and so that tier may interact with different systems, it may be more complex or whatever, for certain organizations, but the tier is necessary in all organizations because the analysts shouldn't be shaken around every time the developers change how they're doing data. >> Great. John Furrier has a saying that "Data is the new development kit", you know. He said that, I don't know, eight years ago and it's really kind of turned out to be the case. Jacques Nadeau, thanks very much for coming on theCUBE. Really appreciate your time. >> Yeah. >> Great to meet you. Good luck and keep us informed, please. >> Yes, thanks so much for your time, I've enjoyed it. >> You're welcome. Alright, thanks for watching everybody. This is theCUBE. We're live from Big Data SV. We'll be right back. (bright music)

>> Narrator: Live from San Jose it's theCUBE. Presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media and its ecosystem partners. (upbeat music) >> Welcome back to San Jose everybody, this is theCUBE, the leader in live tech coverage and you're watching BigData SV, my name is Dave Vellante. In the early days of Hadoop everything was batch oriented. About four or five years ago the market really started to focus on real time and streaming analytics to try to really help companies affect outcomes while things were still in motion. Steve Wilks is here, he's the co-founder and CTO of a company called Stream, a firm that's been in this business for around six years. Steve welcome to theCUBE, good to see you. Thanks for coming on. >> Thanks Dave it's a pleasure to be here. >> So tell us more about that, you started about six years ago, a little bit before the market really started talking about real time and streaming. So what led you to that conclusion that you should co-found Steam way ahead of its time? >> It's partly our heritage. So the four of us that founded Stream, we were executives at GoldenGate Software. In fact our CEO Ali Kutay was the CEO of GoldenGate Software. So when we were acquired by Oracle in 2009, after having to work for Oracle for a couple years, we were trying to work out what to do next. And GoldenGate was replication software right? So it's moving data from one place to another. But customers would ask us in customer advisory boards, that data seems valuable, it's moving. Can you look at it while it's moving and analyze it while it's moving, get value out of that moving data? And so that was kind of set in our heads. And then we were thinking about what to do next, that was kind of the genesis of the idea. So the concept around Stream when we first started the company was we can't just give people streaming data, we need to give them the ability to process that data, analyze it, visualize it, play with it and really truly understand the data. As well as being able to collect it and move it somewhere else. And so the goal from day one was always to build a full end-to-end platform that did everything customers needed to do for streaming integration analytics out of the box. And that's what we've done after six years. >> I got to ask a really basic question, so you're talking about your experience at GoldenGate moving data from point a to point b and somebody said well why don't we put that to work. But is there change data or was it static data? Why couldn't I just analyze it in place? >> GoldenGate works on change data. >> Okay so that's why, there was changes going through. Why wait until it hits its target, let's do some work in real time and learn from that, get greater productivity. And now you guys have taken that to a new level. That new level being what? Modern tools, modern technologies? >> A platform built from the ground up to be inherently distributed, scalable, reliable with exactly one's processing guarantees. And to be a complete end-to-end platform. There's a recognition that the first part of being able to do streaming data integration or analytics is that you need to be able to collect the data right? And while change data captured from databases is the way to get data out of databases in a streaming fashion, you also have to deal with files and devices and message queues and anywhere else the data can reside. So you need a large number of different data collectors that all turn the enterprise data sources into streaming data. And similarly if you want to store data somewhere you need a large collection of target adapters that deliver to things. Not just on premise but also in the cloud. So things like Amazon S3 or the cloud databases like Redshift and Google BigQuery. So the idea was really that we wanted to give customers everything they need and that everything they need isn't trivial. It's not just, well we take Apache Kafka and then we stuff things into it and then we take things out. Pretty often, for example, you need to be able to enrich data and that means you need to be able to join streaming data with additional context information, reference data. And that reference data may come form a database or from files or somewhere else. So you can't call out to the database and maintain the speeds of streaming data. We have customers that are doing hundreds of thousands of events per second. So you can't call out to a database for every event and ask for records to enrich it with. And you can't even do that with an external cache because it's just not fast enough. So we built in an in-memory data grid as part of our platform. So you can join streaming data with the context information in real time without slowing anything down. So when you're thinking about doing streaming integration, it's more than just moving data around. It's ability to process it and get it in the right form, to be able to analyze it, to be able to do things like complex event processing on that data. And also to be able to visualize it and play with it is an essential part of the whole platform. >> So I wanted to ask you about end-to-end. I've seen a lot of products from larger, maybe legacy companies that will say it's end-to-end but what it really is, is a cobbled together pieces that they bought in and then, this is our end-to-end platform, but it's not unified. Or I've seen others "Well we've got an end-to-end platform" oh really, can I see the visualization? "Well we don't have visualization "we use this third party for visualization". So convince me that you're end-to-end. >> So our platform when you start with it you go into a UI, you can start building data flows. Those data flows start from connectors, we have all the connectors that you need to get your enterprise data. We have wizards to help you build those. And so now you have a data stream. Now you want to start processing that, we have SQL-based processing so you can do everything from filtering, transformation, aggregation, enrichment of data. If you want to load reference data into memory you use a cache component to drag that in, configure that. You now have data in-memory you can join with your streams. If you want to now take the results of all that processing and write it somewhere, use one of our target connectors, drag that in so you've got a data flow that's getting bigger and bigger, doing more and more processing. So now you're writing some of that data out to Kafka, oh I'm going to also add in another target adaptor write some of it into Azure Blob Storage and some of it's going to Amazon Redshift. So now you have a much bigger data flow. But now you say okay well I also want to do some analytics on that. So you take the data stream, you build another data flow that is doing some aggregation of a Windows, maybe some complex event processing, and then you use that dashboard builder to build a dashboard to visualize all of that. And that's all in one product. So it literally is everything you need to get value immediately. And you're right, the big vendors they have multiple different products and they're very happy to sell you consulting to put them all together. Even if you're trying to build this from open source and you know, organizations try and do that, you need five or six major pieces of open source, a lot of support in libraries, and a huge team of developers to just build a platform that you can start to build applications on. And most organizations aren't software platform companies, they're finance companies, oil and gas companies, healthcare companies. And they really want to focus on solving business problems and not on reinventing the wheel by building a software platform. So we can just go in there and say look; value immediately. And that really, really helps. >> So what are some of your favorite use cases, examples, maybe customer examples that you can share with me? >> So one of the great examples, one of my customers they have a lot of data in our HP non-stop system. And they needed to be able to get visibility into that immediately. And this was like order processing, supply chain, ERP data. And it would've taken a very large amount of time to do analytics directly on the HP nonstop. And finding resources to do that is hard as well. So they needed to get the data out and they need to get it into the appropriate place. And they recognize that use the right technology to ask the right question. So they wanted some of it in Hadoop so they could do some machine learning on that. They wanted some of it to go into Kafka so they could get real time analytics. And they wanted some of it to go into HBase so they could query it immediately and use that for reference purposes. So they utilized us to do change data capture against the HP nonstop, deliver that datastream out immediately into Kafka and also push some of it into HEFS and some of it into HBase. So they immediately got value out of that, because then they could also build some real-time analytics on it. It would sent out alerts if things were taking too long in their order processing system. And allowed them to get visibility directly into their process that they couldn't get before with much fewer resources and more modern technologies than they could have used before. So that's one example. >> Can I ask you a question about that? So you talked about Kafka, HBase, you talk about a lot of different open source projects. You've integrated those or you've got entries and exits into those? >> So we ship with Kafka as part of our product. It's an optional messaging bus. So, our platform has two different ways of moving data around. We have a high-speed, in-memory only message bus and that works almost network speed and it's great for a lot of different use cases. And that is what backs our data streams. So when you build a data flow, you have streams in between each step, that is backed by an in-memory bus. Pretty often though, in use cases, you need to be able to potentially rewind data for recovery purposes or have different applications running at different speeds and that's where a persistent message bus like Kafka comes in but you don't want to use a persistent message bus for everything because it's doing IO and it's slowing things down. So you typically use that at the beginning, at the sources, especially things like IOT where you can't rewind into them. Things like databases and files, you can rewind into them and replay and recover but IOT sources, you can't do that. So you would push that into a Kafka backed stream and then subsequent processing is in-memory. So we have that as part of our product. We also have Elastic as part of our product for results storage. You can switch to other results storage but that's our default. And we have a few other key components that are part of our product but then on the periphery, we have adapters integrate with a lot of the other things that you mentioned. So we have adapters to read and write HDFS, Hive, HBase, Across, Cloudera, Autumn Works, even MapR. So we have the MapR versions of the file system and MapR streams and MapR DB and then there's lots of other more proprietary connectors like CVC from Oracle, and SQL server, and MySQL and MariaDB. And then database connectors for delivery to virtually any JDBC compliant database. >> I took you down a tangent before you had a chance. You were going to give us another example. We're pretty much out of time but if you can briefly share either that or the last word, I'll give it to you. >> I think the last word would be that that is one example. We have lots and lots of other types of use cases that we do including things like: migrating data from on-premise to the cloud, being able to distribute log data, and being able to analyze that log data being able to do in-memory analytics and get real-time insights immediately and send alerts. It's a very comprehensive platform but each one of those use cases are very easy to develop on their own and you can do them very quickly. And of course as the use case expands within a customer, they build more and more and so they end up using the same platform for lots of different use cases within the same account. >> And how large is the company? How many people? >> We are around 70 people right now. >> 70 People and you're looking for funding? What rounds are you in? Where are you at with funding and revenue and all that stuff? >> Well I'd have to defer to my CEO for those questions. >> All right, so you've been around for what, six years you said? >> Yeah, we have a number of rounds of funding. We had initial seed funding then we had the investment by Summit Partners that carried us through for a while. Then subsequent investment from Intel Capital, Dell EMC, Atlantic Bridge. And that's where we are right now. >> Good, excellent. Steve, thanks so much for coming on theCUBE, really appreciate your time. >> Great, it's awesome. Thank you Dave. >> Great to meet you. All right, keep it right there everybody, we'll be back with our next guest. This is theCUBE. We're live from BigData SV in San Jose. We'll be right back. (techno music)

>> Narrator: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media, and its ecosystem partners. (electronica flourish) >> We're back at Big Data SV. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante. Praveen Kankariya is here. He's the CEO of a company called Impetus. Company's been around the Big Data space before Hadoop, even. Praveen, thanks for back in theCUBE, good to see you. >> Thank you, Dave. >> So, as I said in the open, you've seen a lot. You kind of really got into the Big Data space in 2007, seen it blow through the Hadoop, you know, sort of batch world into the real time world, seen the data management headwinds. From your perspective, you know, what kind of problems are you solving today in the Big Data world? >> So I can go into the details of what we are doing, but at a high level, we are helping companies converge to a singular, enterprise-wide data model. 'Cause I think that is a crisis in the Fortune 500 today, and there'll be have and have-nots. >> Dave: What do you mean a crisis? >> I routinely run into companies who do not have their data model stitched. So they know the same customer, they know me by five different handles, and they don't have it figured out, that I'm the same guy. So, that I think is a major problem. So I think the C-suite is, they would not like to hear this, but they are flying partially blind. >> I have a theory on this, but I want to hear yours-- >> Sure. >> Why is that such a big problem? >> So, the most efficient business in the world is a one-man business, because everything is flowing in the same brain. The moment you hire your first employee, you start having communication breakdowns. And now these companies have hundreds and thousands of employees. Hundreds of thousands of employees. There's a lot of breakdown. There are airlines that, when I'm upgraded to first class, are offering me an economy-plus seat when I go to check in. That's ... they're turning me off, and they're losing an opportunity to, real opportunity to upsell something else to me. So. >> Okay, well, so let's bring this into the world of digital transformation. Everybody talks about those buzzwords, so let's try to put some sort of meat on that bone. If you look at the top five companies by market cap, Amazon, Apple, Facebook, Google. I'm missing somebody. Anyway, they're big. 500 billion, 700 billion dollars. They're all sort of what we would call data-driven. What does that mean? Data is at the core of their enterprise. A lot of the companies you're talking about, human expertise is the core of their enterprise, and they've got data that's sort of in silos, surrounding it. >> Praveen: Yes, yes. >> Is that an accurate description? >> That's-- And how can you help close that gap? >> So they have data in silos, and even that data in silos is not being used at velocity, with velocity. That data is, you know, it's taking much longer for them to even clean up that data, get access to that data, derive insights from that data. >> Dave: Right. >> So there's a lot of sluggishness, overall. >> Dave: So how do you help? >> How do we help? Great question. We help in many different ways. So we actually, so my company provides solutions. So we have some, a few products of our own, and then we work with all kinds of product companies. But we're about solving a problem, so when the customers we engage with, we actually solve a problem, so that there's a business outcome before we walk out. That's the big difference. We're not here to just sell the next sexy platform, or this or that, you know. We're not just here to excite the developers. >> So, maybe you could give me some of your favorite examples of where you've helped some of your clients. >> So there's one fairly large company, it's a household name around the world. And we have helped them create a single source of truth using a Big Data infrastructure. This has about six and a half thousand feeds of data coming in, continuously. Some continuously, some every few minutes, every few hours, whatnot. But then all their data is stitched together, and it's got guardrails, there's full governance. So, and now this platform is available to every business unit, to run their own applications. There's a set of APIs who go in and develop their own applications. So shadow idea is being promoted in this environment. It's not being looked down upon. >> So it's not sitting in one box, presumably, it's distributed throughout the organization? >> It is distributed. And you know, there're are some, you know, as long as you stay within the governance structure, you can derive, you know, somebody wants a graph database, they can derive a graph database from this massive, fully-connected data set, which is an enterprise-wide data set. >> Don't you see as some of the challenges, as well as cultural, there are some industries that might say, or some executives that say, "Well, you know my industry, "healthcare is an example, really hasn't been disrupted. "We're maybe insulated from that." I feel as though that's somewhat risky thinking, and it's easy to maybe sit back say, "Well, I'm going to wait, see what happens." What are your thoughts on that? >> Look at the data. The week Jeff Bezos announced that he is tying up with JPMC and Warren Buffet, some of the largest healthcare companies, and I'm talking of Fortune 10 companies, they lost about 20% of their market cap that week. So, you don't have to listen to me. Listen to the markets. >> Well, that's true. We see what happens in grocery, see what happens in... We haven't really seen, as I say, the disruption in healthcare, financial services, but it's all data, and that changes the equation. So why, let's see, not why. How when, if you get to this, so it sounds like step one is to get that sort of single data model across the organization, but there's other steps. You got to figure out how to monetize the data, not necessarily by selling it, but how data contributes to the monetization of the company. You got to it accessible, you got to make it of high quality, you've got to get the right skill sets. So there's a lot to it, and more than just the technology. Maybe you could talk about that. >> So the way, I would like to preach, if I'm allowed to-- >> Dave: Please, it's theCUBE... (laughs) >> No, no, I mean, I don't mean here, but if any CEO was listening to me, what I would like to tell them is, just create a vision of your ultimate connected data model. And then start looking at how do you converge out of that vision. It may not happen in one day, one week, one year. It's going to take time, and you know, every business is in flight, so they have to operate continuously, but they have to keep gravitating. And the biggest casualty is going to be their customer relationship if they don't do this. Because most companies don't know their customers fully. I mean, that little example of the airline which was showing me, flashing an ad for economy seats, premium economy seats when I'm already in first class, they don't know me. Some part of that company doesn't know me. So they're not able to service me well. Here now they lost an opportunity to monetize, but I think from another perspective, they lost an opportunity to really offer me something which would've made my flight way more comfortable. >> Well. >> So. >> Then you wonder if that's the dynamic that you encountered, what's the speed to market, the agility of that organization? They're hampered by their ability to, whether it's roll out new apps, identify new data sources, create new products for the customers. Have you seen, what kind of impacts have you seen within your customers? You gave the example before, of that sort of single data model, the single version of the truth. What business impacts have been able to affect for your customers? >> So, there, I mean I can go on giving you anecdotes from my observations, my front row observations into these companies. >> Yeah, it'd be good to have some kind of proof points, right? Our audience would love to hear that. >> So, you know there's a company not too far from here. They've stitched every click stream, right to product usage data. To support data, to every marketing email opened. And they can tell who's buying, what happened, what is their support experience, who's upgrading, who's upgrading faster because they had a positive support experience, or not. So everything is tied. Any direction you want to look into your customer space, you can go and get visibility from every perspective you can think of. That's customer 360. We worked with a credit card company where they had a massive rules engine, which had been developed over generations to report fraud, to catch fraud, while a transaction's being processed. We actually, once they got all their data together, we could apply a massive machine learning engine. And we started learning from customers' own behavior, so we completely discarded the rules engine, and now we have a learning system which is flagging fraudulent transactions. So they managed to cut down their false positives tremendously, and in turn reduced inconvenience. It used to be embarrassing for me to give out a card and get it declined in front of a customer. >> So, as I said at the top, you've seen sort of the evolution of this whole Big Data meme before it was called Big Data. What are the things that may be exciting you? We seem to be entering a new era we call digital. There's a cognitive era, AI, machine intelligence. What do you see that's exciting, and real? >> So number one, so I like to divide this space into two parts, the whole space of data analytics. There's the data plumbing, which we call data management, and whatnot. I have to plumb all my data together. Only then I can feed this data into my AI models. Now I can do in my silos today, but for me to do at a global level for my entire corporation, I need it all stitched together. And then, of course, these models are very real. My son, my 22-year old son is using TensorFlow for some little startup that he's cooking. And it took him just a month to pick it up and start applying it. So why can't our large companies do so? And in turn, bring down the cost of services, cost of products, the velocity of delivering those things to us, and make life better. >> So, the barriers to technology deployment are getting lower. >> And this is all feasible, Dave, right now. >> Yeah. >> You know, I mean, this is all, this is a dream 10 years ago. If somebody had said, you know, for an old corporation to stitch all its data, "What're you talking about? "It's not going to happen." But now, this is possible, and it's feasible. It's not going to require, make a massive hole in their budgets. >> But don't you think it's also table stakes to compete in over, the next 10 years? >> It is, there is table stakes. It's actually kind of late, from my perspective. If I had to go invest in the market, I mean, I would invest in companies who have their data act together. >> Yeah, yeah. So, what's the, how do you tell, when a company has its data act together? When you walk into a prospect, how do you know, what do you see, what're the characteristics of somebody who has that act together? >> It's hard for me to give you a few characteristics, but you know, you can tell what is the mandate they're operating under, if there are clear mandates. Because, for most companies, this is lost because of turf battle. This whole battle is lost due to turf issues. And the moment you see senior executives working together, with a massive willingness to bring everything together. You know, they'll have different turfs, and they're willing to contribute data, and bring it together. That's a phenomenally positive sign, because once that happens, then every large company has the wherewithal to go hire 50 data scientists, or work with all kinds of companies, including mine, to get data science help. >> Yeah, it comes back to the culture, doesn't it? >> Yes, absolutely. >> All right, Praveen, we have to leave it right there. Thanks very much for coming back in theCUBE. >> Thank you Dave, thank you. Thank you for the opportunity. >> You're very welcome. All right, keep it right there, everybody. This is theCUBE. We're live from the Forager in San Jose, Big Data SV. We'll be right back. (electronica flourish)

>> Narrator: Live from San Jose, it's theCUBE. Presenting Big Data, Silicon Valley. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Hi, I'm George Gilbert, and we are broadcasting from the Strata Data Conference, we're right around the corner at the Forager Tasting Room & Eatery. We have this wonderful location here, and we are very lucky to have with us Michael Nixon, from Snowflake, which is a leading cloud data warehouse. And David Abercrombie from Sharethrough which is a leading ad tech company. And between the two of them, they're going to tell us some of the most advance these cases we have now for cloud-native data warehousing. Michael, why don't you start with giving us some context for how on a cloud platform one might rethink a data warehouse? >> Yeah, thank you. That's a great question because let me first answer it from the end-user, business value perspective, when you run a workload on a cloud, there's a certain level of expectation you want out of the cloud. You want scalability, you want unlimited scalability, you want to be able to support all your users, you want to be able to support the data types, whatever they may be that comes in into your organization. So, there's a level of expectation that one should expect from a service point of view once you're in a cloud. So, a lot of the technology that were built up to this point have been optimized for on-premises types of data warehousing where perhaps that level of service and currency and unlimited scalability was not really expected but, guess what? Once it comes to the cloud, it's expected. So those on-premises technologies aren't suitable in the cloud, so for enterprises and, I mean, companies, organizations of all types from finance, banking, manufacturing, ad tech as we'll have today, they want that level of service in the cloud. And so, those technologies will not work, and so it requires a rethinking of how those architectures are built. And it requires being built for the cloud. >> And just to, alright, to break this down and be really concrete, some of the rethinking. We separate compute from storage, which is a familiar pattern that we've learned in the cloud but we also then have to have this sort of independent elasticity between-- >> Yes. Storage and the compute, and then Snowflake's taken it even a step further where you can spin out multiple compute clusters. >> Right. >> Tell us how that works and why that's so difficult and unique. >> Yeah, you know, that's taking us under the covers a little bit, but what makes our infrastructure unique is that we have a three-layer architecture. We separate, just as you said, storage from the compute layer, from the services layer. And that's really important because as I mentioned before, you want unlimited capacity, unlimited resources. So, if you scale, compute, and today's world on on-premises MPP, what that really means is that you have to bring the storage along with the compute because compute is tied to the storage so when you scale the storage along with the compute, usually that involves a lot of burden on the data warehouse manager because now they have to redistribute the data and that means redistributing keys, managing keys if you will. And that's a burden, and by the reverse, if all you wanted to do was increase storage but not the compute, because compute was tied to storage. Why you have to buy these additional compute notes, and that might add to the cost when, in fact, all you really wanted to pay for was for additional storage? So, by separating those, you keep them independent, and so you can scale storage apart from compute and then, once you have your compute resources in place, the virtual warehouses that you're talking about that have completed the job, you spun them up, it's done its job, and you take it down, guess what? You can release those resources, and of course, in releasing those resources, basically you can cut your cost as well because, for us, it's pure usage-based pricing. You only pay for what you use, and that's really fantastic. >> Very different from the on-prem model where, as you were saying, tied compute and storage together, so. >> Yeah, let's think about what that means architecturally, right? So if you have an on-premises data warehouse, and you want to scale your capacity, chances are you'll have to have that hardware in place already. And having that hardware in place already means you're paying that expense and, so you may pay for that expense six months prior to need it. Let's take a retailer example. >> Yeah. >> You're gearing up for a peak season, which might be Christmas, and so you put that hardware in place sometime in June, you'll always put it in advanced because why? You have to bring up the environment, so you have to allow time for implementation or, if you will, deployment to make sure everything is operational. >> Okay. >> And then what happens is when that peak period comes, you can't expand in that capacity. But what happens once that peak period is over? You paid for that hardware, but you don't really need it. So, our vision is, or the vision we believe you should have when you move workloads to the cloud is, you pay for those when you need them. >> Okay, so now, David, help us understand, first, what was the business problem you were trying to solve? And why was Snowflake, you know, sort of uniquely suited for that? >> Well, let me talk a little bit about Sharethrough. We're ad tech, at the core of our business we run an ad exchange, where we're doing programmatic training with the bids, with the real-time bidding spec. The data is very high in volume, with 12 billion impressions a month, that's a lot of bids that we have to process, a lot of bid requests. The way it operates, the bids and the bid responses and programmatic training are encoded in JSONs, so our ad exchange is basically exchanging messages in JSON with our business partners. And the JSONs are very complicated, there's a lot of richness and detail, such that the advertisers can decide whether or not they want to bid. Well, this data is very complicated, very high-volume. And advertising, like any business, we really need to have good analytics to understand how our business is operating, how our publishers are doing, how our advertisers are doing. And it all depends upon this very high-volume, very complex JSON event data stream. So, Snowflake was able to ingest our high-volume data very gracefully. The JSON parsing techniques of Snowflake allow me to expose the complicated data structure in a way that's very transparent and usable to our analysts. Our use of Snowflake has replaced clunkier tools where the analysts basically had to be programmers, writing programs in Scala or something to do in analysis. And now, because we've transparently and easily exposed the complicated structures within Snowflake in a relational database, they can use good old-fashioned SQL to run their queries, literally, afternoon analysis is now a five-minute query. >> So, let me, as I'm listening to you describe this. We've had various vendors telling us about these workflows in the sort of data prep and data science tool change. It almost sounds to me like Snowflake is taking semi-structured or complex data and it's sort of unraveling it and normalizing is kind of an overloaded term but it's making it business-ready, so you don't need as much of that manual data prep. >> Yeah, exactly, you don't need as much manual data prep, or you don't need as much expertise. For instance, Snowflake's JSON capabilities, in terms of drilling down the JSON tree with dot path notation, or expanding nested objects is very expressive, very powerful, but still your typical analyst or your BI tool certainly wouldn't know how to do that. So, in Snowflake, we sort of have our cake and eat it too. We can have our JSONs with their full richness in our database, but yet we can simplify and expose the data elements that are needed for analysis, so that an analyst, their first day on the job, they can get right to work and start writing queries. >> So let me ask you about, a little more about the programmatic ad use case. So if you have billions of impressions per month, I'm guessing that means you have quite a few times more, in terms of bids, and then there's the, you know once you have, I guess a successful one, you want to track what happens. >> Correct. >> So tell us a little more about that, what that workload looks like, in terms of, what analytics you're trying to perform, what's your tracking? >> Yeah, well, you're right. There's different steps in our funnel. The impression request expands out by a factor of a dozen as we send it to all the different potential bidders. We track all that data, the responses come back, we track that, we track our decisions and why we selected the bidder. And then, once the ad is shown, of course there's various beacons and tracking things that fire. We'd have to track all of that data, and the only way we could make sense out of our business is by bringing all that data together. And in a way that is reliable, transparent, and visible, and also has data integrity, that's another thing I like about the Snowflake database is that it's a good old-fashioned SQL database that I can declare my primary keys, I can run QC checks, I can ensure high data integrity that is demanded by BI and other sorts of analytics. >> What would be, as you continue to push the boundaries of the ad tech service, what's some functionality that you're looking to add, and Snowflake as your partner, either that's in there now that you still need to take advantage of or things that you're looking to in the future? >> Well, moving forward, of course, we, it's very important for us to be able to quickly gauge the effectiveness of new products. The ad tech market is fast-changing, there's always new ways of bidding, new products that are being developed, new ways for the ad ecosystem to work. And so, as we roll those out, we need to be able to quickly analyze, you know, "Is this thing working or not?" You know, kind of an agile environment, pivot or prove it. Does this feature work or not? So, having all the data in one place makes that possible for that very quick assessment of the viability of a new feature, new product. >> And, dropping down a little under the covers for how that works, does that mean, like you still have the base JSON data that you've absorbed, but you're going to expose it with different schemas or access patterns? >> Yeah, indeed. For instance, we make use of the SQL schemas, roles, and permissions internally where we can have the different teams have their own domain of data that they can expose internally, and looking forward, there's the share house feature of Snowflake that we're looking to implement with our partners, where, rather than sending them data, like a daily dump of data, we can give them access to their data in our database through this top layer that Michael mentioned, the service layer, essentially allows me to create a view grant select onto another customer. So I no longer have to send daily data dumps to partners or have some sort of API for getting data. They can simply query the data themselves so we'll be implementing that feature with our major partners. >> I would be remiss in not asking at a data conference like this, now that there's the tie-in with CuBOL and Spark Integration and Machine Learning, is there anything along that front that you're planning to exploit in the near future? >> Well, yeah, Sharethrough, we're very experimental, playful, we're always examining new data technologies and new ways of doing things but now with Snowflake as sort of our data warehouse of curated data. I've got two petabytes of referential integrity data, and that is reliable. We can move forward into our other analyses and other uses of data knowing that we have captured every event exactly once, and we know exactly where it fits in a business context, in a relational manner. It's clean, good data integrity, reliable, accessible, visible, and it's just plain old SQL. (chuckles) >> That's actually a nice way to sum it up. We've got the integrity that we've come to expect and love from relational databases. We've got the flexibility of machine-oriented data, or JSON. But we don't have to give up the query engine, and then now you have more advanced features, analytic features that you can take advantage of coming down the pipe. >> Yeah, again we're a modern platform for the modern age, that's basically cloud-based computing. With a platform like Snowflake in the backend, you can now move those workloads that you're accustomed to to the cloud and have in the environment that you're familiar with, and it saves you a lot of time and effort. You can focus on more strategic projects. >> Okay, well, with that, we're going to take a short break. This has been George Gilbert, we're with Michael Nixon of Snowflake, and David Abercrombie of Sharethrough listening to how the most modern ad tech companies are taking advantage of the most modern cloud data warehouses. And we'll be back after a short break here at the Strata Data Conference, thanks. (quirky music)

>> Announcer: Live from San Jose, it's theCUBE. Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. (upbeat music) >> Welcome back to theCUBE, I'm Lisa Martin with John Furrier. We are covering our second day of our event Big Data SV. We've had some great conversations, John, yesterday, today as well. Really looking at Big Data, digital transformation, Big Data, plus data science, lots of opportunity. We're excited to welcome back to theCUBE an alumni, Satyen Sangani, the co-founder and CEO of Alation. Welcome back! >> Thank you, it's wonderful to be here again. >> So you guys finish up your fiscal year end of December 2017, where in the first quarter of 2018. You guys had some really strong results, really strong momentum. >> Yeah. >> Tell us what's going on at Alation, how are you pulling this momentum through 2018. >> Well, I think we have had an enterprise focused business historically, because we solve a very complicated problem for very big enterprises, and so, in the last quarter we added customers like American Express, PepsiCo, Roche. And with huge expansions from our existing customers, some of whom, over the course of a year, I think went 12 X from an initial base. And so, we found some just incredible momentum in Q4 and for us that was a phenomenal cap to a great year. >> What about the platform you guys are doing? Can you just take a minute to explain what Alation does again just to refresh where you are on the product side? You mentioned some new accounts, some new use cases. >> Yeah. >> What's the update? Take a minute, talk about the update. >> Absolutely, so, you certainly know, John, but Alation's a data catalog and a data catalog essentially, you can think of it as Yelp or Amazon for data and information side of the enterprise. So if you think about how many different databases there are, how many different reports there are, how many different BI tools there are, how many different APIs there are, how many different algorithms there are, it's pretty dizzying for the average analyst. It's pretty dizzying for the average CIO. It's pretty dizzying for the average chief data officer. And particularly, inside of Fortune 500s where you have hundreds of thousands of databases. You have a situation where people just have too much signal or too much noise, not enough signal. And so what we do is we provide this Yelp for that information. You can come to Alation as a catalog. You can do a search on revenue 2017. You'll get all of the reports, all of the dashboards, all of the tables, all of the people that you might need to be able to find. And that gives you a single place of reference, so you can understand what you've got and what can answer your questions. >> What's interesting is, first of all, I love data. We're data driven, we're geeks on data. But when I start talking to folks that are outside the geek community or nerd community, you say data and they go, "Oh," because they cringe and they say, "Facebook." They see that data issues there. GDPR, data nightmare, where's the store, you got to manage it. And then, people are actually using data, so they're realizing how hard (laughs) it is. >> Yeah >> How much data do we have? So it's kind of like a tropic disillusionment, if you will. Now they got to get their hands on it. They've got to put it to work. >> Yeah. >> And they know that So, it's now becoming really hard (laughs) in their mind. This is business people. >> Yeah. >> They have data everywhere. How do you guys talk to that customer? Because, if you don't have quality data, if you don't have data you can trust, if you don't have the right people, it's hard to get it going. >> Yeah. >> How do you guys solve that problem and how do you talk to customers? >> So we talk a lot about data literacy. There is a lot of data in this world and that data is just emblematic of all of the stuff that's going on in this world. There's lots of systems, there's lots of complexity and the data, basically, just is about that complexity. Whether it's weblogs, or sensors, or the like. And so, you can either run away from that data, and say, "Look, I'm going to not, "I'm going to bury my head in the sand. "I'm going to be a business. "I'm just going to forget about that data stuff." And that's certainly a way to go. >> John: Yeah. >> It's a way to go away. >> Not a good outlook. >> I was going to say, is that a way of going out of business? >> Or, you can basically train, it's a human resources problem fundamentally. You've got to train your people to understand how to use data, to become data literate. And that's what our software is all about. That's what we're all about as a company. And so, we have a pretty high bar for what we think we do as a business and we're this far into that. Which is, we think we're training people to use data better. How do you learn to think scientifically? How do you go use data to make better decisions? How do you build a data driven culture? Those are the sorts of problems that I'm excited to work on. >> Alright, now take me through how you guys play out in an engagement with the customer. So okay, that's cool, you guys can come in, we're getting data literate, we understand we need to use data. Where are you guys winning? Where are you guys seeing some visibility, both in terms of the traction of the usage of the product, the use cases? Where is it kind of coming together for you guys? >> Yeah, so we literally, we have a mantra. I think any early stage company basically wins because they can focus on doing a couple of things really well. And for us, we basically do three things. We allow people to find data. We allow people to understand the data that they find. And we allow them to trust the data that they see. And so if I have a question, the first place I start is, typically, Google. I'll go there and I'll try to find whatever it is that I'm looking for. Maybe I'm looking for a Mediterranean restaurant on 1st Street in San Jose. If I'm going to go do that, I'm going to do that search and I'm going to find the thing that I'm looking for, and then I'm going to figure out, out of the possible options, which one do I want to go to. And then I'll figure out whether or not the one that has seven ratings is the one that I trust more than the one that has two. Well, data is no different. You're going to have to find the data sets. And inside of companies, there could be 20 different reports and there could be 20 different people who have information, and so you're going to trust those people through having context and understanding. >> So, trust, people, collaboration. You mentioned some big brands that you guys added towards the end of calendar 2017. How do you facilitate these conversations with maybe the chief data officer. As we know, in large enterprises, there's still a lot of ownership over data silos. >> Satyen: Yep. >> What is that conversation like, as you say on your website, "The first data catalog designed for collaboration"? How do you help these organizations as large as Coca-Cola understand where all the data are and enable the human resources to extract values, and find it, understand it, and trust it? >> Yeah, so we have a very simple hypothesis, which is, look, people fundamentally have questions. They're fundamentally curious. So, what you need to do as a chief data officer, as a chief information officer, is really figure out how to unlock that curiosity. Start with the most popular data sets. Start with the most popular systems. Start with the business people who have the most curiosity and the most demand for information. And oh, by the way, we can measure that. Which is the magical thing that we do. So we can come in and say, "Look, "we look at the logs inside of your systems to know "which people are using which data sets, "which sources are most popular, which areas are hot." Just like a social network might do. And so, just like you can say, "Okay, these are the trending restaurants." We can say, "These are the trending data sets." And that curiosity allows people to know, what data should I document first? What data should I make available first? What data do I improve the data quality over first? What data do I govern first? And so, in a world where you've got tons of signal, tons of systems, it's totally dizzying to figure out where you should start. But what we do is, we go these chief data officers and say, "Look, we can give you a tool and a catalyst so "that you know where to go, "what questions to answer, who to serve first." And you can use that to expand to other groups in the company. >> And this is interesting, a lot of people you mentioned social networks, use data to optimize for something, and in the case of Facebook, they they use my data to target ads for me. You're using data to actually say, "This is how people are using the data." So you're using data for data. (laughs) >> That's right. >> So you're saying-- >> Satyen: We're measuring how you can use data. >> And that's interesting because, I hear a lot of stories like, we bought a tool, we never used it. >> Yep. >> Or people didn't like the UI, just kind of falls on the side. You're looking at it and saying, "Let's get it out there and let's see who's using the data." And then, are you doubling down? What happens? Do I get a little star, do I get a reputation point, am I being flagged to HR as a power user? How are you guys treating that gamification in this way? It's interesting, I mean, what happens? Do I become like-- >> Yeah, so it's funny because, when you think about search, how do you figure out that something's good? So what Google did is, they came along and they've said, "We've got PageRank." What we're going to do is we're going to say, "The pages that are the best pages are the ones "that people link to most often." Well, we can do the same thing for data. The data sources that are the most useful ones are the people that are used most often. Now on top of that, you can say, "We're going to have experts put ratings," which we do. And you can say people can contribute knowledge and reviews of how this data set can be used. And people can contribute queries and reports on top of those data sets. And all of that gives you this really rich graph, this rich social graph, so that now when I look at something it doesn't look like Greek. It looks like, "Oh, well I know Lisa used this data set, "and then John used it "and so at least it must answer some questions "that are really intelligent about the media business "or about the software business. "And so that can be really useful for me "if I have no clue as to what I'm looking at." >> So the problem that you-- >> It's on how you demystify it through the social connections. >> So the problem that you solve, if what I hear you correctly, is that you make it easy to get the data. So there's some ease of use piece of it, >> Yep. >> cataloging. And then as you get people using it, this is where you take the data literacy and go into operationalizing data. >> Satyen: That's right. >> So this seems to be the challenge. So, if I'm a customer and I have a problem, the profile of your target customer or who your customers are, people who need to expand and operationalize data, how would you talk about it? >> Yeah, so it's really interesting. We talk about, one of our customers called us, sort of, the social network for nerds inside of an enterprise. And I think for me that's a compliment. (John laughing) But what I took from that, and when I explained the business of Alation, we start with those individuals who are data literate. The data scientists, the data engineers, the data stewards, the chief data officer. But those people have the knowledge and the context to then explain data to other people inside of that same institution. So in the same way that Facebook started with Harvard, and then went to the rest of the Ivies, and then went to the rest of the top 20 schools, and then ultimately to mom, and dad, and grandma, and grandpa. We're doing the exact same thing with data. We start with the folks that are data literate, we expand from there to a broader audience of people that don't necessarily have data in their titles, but have curiosity and questions. >> I like that on the curiosity side. You spent some time up at Strata Data. I'm curious, what are some of the things you're hearing from customers, maybe partners? Everyone used to talk about Hadoop, it was this big thing. And then there was a creation of data lakes, and swampiness, and all these things that are sort of becoming more complex in an organization. And with the rise of myriad data sources, the velocity, the volume, how do you help an enterprise understand and be able to catalog data from so many different sources? Is it that same principle that you just talked about in terms of, let's start with the lowest hanging fruit, start making the impact there and then grow it as we can? Or is an enterprise needs to be competitive and move really, really quickly? I guess, what's the process? >> How do you start? >> Right. >> What do people do? >> Yes! >> So it's interesting, what we find is multiple ways of starting with multiple different types of customers. And so, we have some customers that say, "Look, we've got a big, we've got Teradata, "and we've got some Hadoop, "and we've got some stuff on Amazon, "and we want to connect it all." And those customers do get started, and they start with hundreds of users, in some case, they start with thousands of users day one, and they just go Big Bang. And interestingly enough, we can get those customers enabled in matters of weeks or months to go do that. We have other customers that say, "Look, we're going to start with a team of 10 people "and we're going to see how it grows from there." And, we can accommodate either model or either approach. From our prospective, you just have to have the resources and the investment corresponding to what you're trying to do. If you're going to say, "Look, we're going to have, two dollars of budget, and we're not going to have the human resources, and the stewardship resources behind it." It's going to be hard to do the Big Bang. But if you're going to put the appropriate resources up behind it, you can do a lot of good. >> So, you can really facilitate the whole go big or go home approach, as as well as the let's start small think fast approach. >> That's right, and we always, actually ironically, recommend the latter. >> Let's start small, think fast, yeah. >> Because everybody's got a bigger appetite than they do the ability to execute. And what's great about the tool, and what I tell our customers and our employees all day long is, there's only metric I track. So year over year, for our business, we basically grow in accounts by net of churn by 55%. Year over year, and that's actually up from the prior year. And so from my perspective-- >> And what does that mean? >> So what that means is, the same customer gave us 55 cents more on the dollar than they did the prior year. Now that's best in class for most software businesses that I've heard. But what matters to me is not so much that growth rate in and of itself. What it means to me is this, that nobody's come along and says, "I've mastered my data. "I understand all of the information side of my company. "Every person knows everything there is to know." That's never been said. So if we're solving a problem where customers are saying, "Look, we get, and we can find, and understand, "and trust data, and we can do that better last year "than we did this year, and we can do it even more "with more people," we're going to be successful. >> What I like about what you're doing is, you're bringing an element of operationalizing data for literacy and for usage. But you're really bringing this notion of a humanizing element to it. Where you see it in security, you see it in emerging ecosystems. Where there's a community of data people who know how hard it is and was, and it seems to be getting easier. But the tsunami of new data coming in, IOT data, whatever, and new regulators like GDPR. These are all more surface area problems. But there's a community coming together. How have you guys seen your product create community? Have you seen any data on that, 'cause it sounds like, as people get networked together, the natural outcome of that is possibly usage you attract. But is there a community vibe that you're seeing? Is there an internal collaboration where they sit, they're having meet ups, they're having lunches. There's a social aspect in a human aspect. >> No, it's humanal, no, it's amazing. So in really subtle but really, really powerful ways. So one thing that we do for every single data source or every single report that we document, we just put who are the top users of this particular thing. So really subtly, day one, you're like, "I want to go find a report. "I don't even know "where to go inside of this really mysterious system". Postulation, you're able to say, "Well, I don't know where to go, but at least I can go call up John or Lisa," and say, "Hey, what is it that we know about this particular thing?" And I didn't have to know them. I just had to know that they had this report and they had this intelligence. So by just discovering people in who they are, you pick up on what people can know. >> So people of the new Google results, so you mentioned Google PageRank, which is web pages and relevance. You're taking a much more people approach to relevance. >> Satyen: That's right. >> To the data itself. >> That's right, and that builds community in very, very clear ways, because people have curiosity. Other people are in the mechanism why in which they satisfy that curiosity. And so that community builds automatically. >> They pay it forward, they know who to ask help for. >> That's right. >> Interesting. >> That's right. >> Last question, Satyen. The tag line, first data catalog designed for collaboration, is there a customer that comes to mind to you as really one that articulates that point exactly? Where Alation has come in and really kicked open the door, in terms of facilitating collaboration. >> Oh, absolutely. I was literally, this morning talking to one of our customers, Munich Reinsurance, largest reinsurance customer or company in the world. Their chief data officer said, "Look, three years ago, "we started with 10 people working on data. "Today, we've got hundreds. "Our aspiration is to get to thousands." We have three things that we do. One is, we actually discover insights. It's actually the smallest part of what we do. The second thing that we do is, we enable people to use data. And the third thing that we do is, drive a data driven culture. And for us, it's all about scaling knowledge, to centers in China, to centers in North America, to centers in Australia. And they've been doing that at scale. And they go to each of their people and they say, "Are you a data black belt, are you a data novice?" It's kind of like skiing. Are you blue diamond or a black diamond. >> Always ski in pairs (laughs) >> That's right. >> And they do ski in pairs. And what they end up ultimately doing is saying, "Look, we're going to train all of our workforce to become better, so that in three, 10 years, we're recognized as one of the most innovative insurance companies in the world." Three years ago, that was not the case. >> Process improvement at a whole other level. My final question for you is, for the folks watching or the folks that are going to watch this video, that could be a potential customer of yours, what are they feeling? If I'm the customer, what smoke signals am I seeing that say, I need to call Alation? What are some of the things that you've found that would tell a potential customer that they should be talkin' to you guys? >> Look, I think that they've got to throw out the old playbook. And this was a point that was made by some folks at a conference that I was at earlier this week. But they basically were saying, "Look, the DLNA's PlayBook was all about providing the right answer." Forget about that. Just allow people to ask the right questions. And if you let people's curiosity guide them, people are industrious, and ambitious, and innovative enough to go figure out what they need to go do. But if you see this as a world of control, where I'm going to just figure out what people should know and tell them what they're going to go know. that's going to be a pretty, a poor career to go choose because data's all about, sort of, freedom and innovation and understanding. And we're trying to push that along. >> Satyen, thanks so much for stopping by >> Thank you. >> and sharing how you guys are helping organizations, enterprises unlock data curiosity. We appreciate your time. >> I appreciate the time too. >> Thank you. >> And thanks John! >> And thank you. >> Thanks for co-hosting with me. For John Furrier, I'm Lisa Martin, you're watching theCUBE live from our second day of coverage of our event Big Data SV. Stick around, we'll be right back with our next guest after a short break. 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(royal music) >> Announcer: John Cleese. >> There's a lot of people out there who have no idea what they're doing, but they have absolutely no idea that they have no idea what they're doing. Those are the ones with the confidence and stupidity who finish up in power. That's why the planet doesn't work. >> Announcer: Knowledgeable, insightful, and a true gentleman. >> The guy at the counter recognized me and said... Are you listening? >> John Furrier: Yes, I'm tweeting away. >> No, you're not. >> I tweet, I'm tweeting away. >> He is kind of rude that way. >> You're on your (bleep) keyboard. >> Announcer: John Cleese joins the Cube alumni. Welcome, John. >> John Cleese: Have you got any phone calls you need to answer? >> John Furrier: Hold on, let me check. >> Announcer: Live from San Jose, it's the Cube, presenting Big Data Silicon Valley, brought to you by Silicon Angle Media and its ecosystem partners. (busy music) >> Hey, welcome back to the Cube's continuing coverage of our event, Big Data SV. I'm Lisa Martin with my co-host, George Gilbert. We are down the street from the Strata Data Conference. This is our second day, and we've been talking all things big data, cloud data science. We're now excited to be joined by the CEO of a company called Data Science, Ian Swanson. Ian, welcome to the Cube. >> Thanks so much for having me. I mean, it's been a awesome two days so far, and it's great to wrap up my trip here on the show. >> Yeah, so, tell us a little bit about your company, Data Science, what do you guys do? What are some of the key opportunities for you guys in the enterprise market? >> Yeah, absolutely. My company's called datascience.com, and what we do is we offer an enterprise data science platform where data scientists get to use all they tools they love in all the languages, all the libraries, leveraging everything that is open source to build models and put models in production. Then we also provide IT the ability to be able to manage this massive stack of tools that data scientists require, and it all boils down to one thing, and that is, companies need to use the data that they've been storing for years. It's about, how do you put that data into action. We give the tools to data scientists to get that data into action. >> Let's drill down on that a bit. For a while, we thought if we just put all our data in this schema-on-read repository, that would be nirvana. But it wasn't all that transparent, and we recognized we have to sort of go in and structure it somewhat, help us take the next couple steps. >> Ian: Yeah, the journey. >> From this partially curated data sets to something that turns into a model that is actionable. >> That's actually been the theme in the show here at the Strata Data Conference. If we went back years ago, it was, how do we store data. Then it was, how do we not just store and manage, but how do we transform it and get it into a shape that we can actually use it. The theme of this year is how do we get it to that next step, the next step of putting it into action. To layer onto that, data scientists need to access data, yes, but then they need to be able to collaborate, work together, apply many different techniques, machine learning, AI, deep learning, these are all techniques of a data scientist to be able to build a model. But then there's that next step, and the next is, hey, I built this model, how do I actually get it in production? How does it actually get used? Here's the shocking thing. I was at an event where there's 500 data scientists in the audience, and I said, "Stand up if you worked on a model for more than nine months "and it never went into production." 90% of the audience stood up. That's the last mile that we're all still working on, and what's exciting is, we can make it possible today. >> Wanting to drill down into the sort of, it sounds like there's a lot of choice in the tools. But typically, to do a pipeline, you either need well established APIs that everyone understands and plugs together with, or you need an end to end sort of single vendor solution that becomes the sort of collaboration backbone. How are you organized, how are you built? >> This might be self-serving, but datascience.com, we have enterprise data science platform, we recommend a unified platform for data science. Now, that unified platform needs to be highly configurable. You need to make it so that that workbench, you can use any tool that you want. Some data scientists might want to use a hammer, others want to be able to use a screwdriver over here. The power is how configurable, how extensible it is, how open source you can adopt everything. The amazing trends that we've seen have been proprietary solutions going back decades, to now, the rise of open source. Every day, dozens if not hundreds of new machine learning libraries are being released every single day. We've got to give those capabilities to data scientists and make them scale. >> OK, so the, and I think it's pretty easy to see how you would have incorporate new machine learning libraries into a pipeline. But then there's also the tools for data preparation, and for like feature extraction and feature engineering, you might even have some tools that help you with figuring out which algorithm to select. What holds all that together? >> Yeah, so orchestrating the enterprise data science stack is the hardest challenge right now. There has to be a company like us that is the glue, that is not just, do these solutions work together, but also, how do they collaborate, what is that workflow? What are those steps in that process? There's one thing that you might have left out, and that is, model deployment, model interpretation, model management. >> George: That's the black art, yeah. >> That's where this whole thing is going next. That was the exciting thing that I heard in terms of all these discussion with business leaders throughout the last two days is model deployment, model management. >> If I can kind of take this to maybe shift the conversation a little bit to the target audience. Talked a lot about data scientists and needing to enable them. I'm curious about, we just talked with, a couple of guests ago, about the chief data officer. How, you work with enterprises, how common is the chief data officer role today? What are some of the challenges they've got that datascience.com can help them to eliminate? >> Yeah, the CIO and the chief data officer, we have CIOs that have been selecting tools for companies to use, and now the chief data officer is sitting down with the CEO and saying, "How do we actually drive business results?" We work very closely with both of those personas. But on the CDO side, it's really helping them educate their teams on the possibilities of what could be realized with the data at hand, and making sure that IT is enabling the data scientists with the right tools. We supply the tools, but we also like to go in there with our customers and help coach, help educate what is possible, and that helps with the CDO's mission. >> A question along that front. We've been talking about sort of empowering the data scientist, and really, from one end of the modeling life cycle all the way to the end or the deployment, which is currently the hardest part and least well supported. But we also have tons of companies that don't have data science trained people, or who are only modestly familiar. Where do, what do we do with them? How do we get those companies into the mainstream in terms of deploying this? >> I think whether you're a small company or a big company, digital transformation is the mandate. Digital transformation is not just, how do I make a taxi company become Uber, or how do I make a speaker company become Sonos, the smart speaker, it's how do I exploit all the sources of my data to get better and improved operational processes, new business models, increased revenue, reduced operation costs. You could start small, and so we work with plenty of smaller companies. They'll hire a couple data scientists, and they're able to do small quick wins. You don't have to go sit in the basement for a year having something that is the thing, the unicorn in the business, it's small quick wins. Now we, my company, we believe in writing code, trained, educated, data scientists. There are solutions out there that you throw data at, you push a button, it gets an output. It's this magic black box. There's risk in that. Model interpretation, what are the features it's scoring on, there's risk, but those companies are seeing some level of success. We firmly believe, though, in hiring a data science team that is trained, you can start small, two or three, and get some very quick wins. >> I was going to say, those quick wins are essential for survivability, like digital transformation is essential, but it's also, I mean, to survival at a minimum, right? >> Ian: Yes. >> Those quick wins are presumably transformative to an enterprise being able to sustain, and then eventually, or ideally, be able to take market share from their competition. >> That is key for the CDO. The CDO is there pitching what is possible, he's pitching, she's pitching the dream. In order to be able to help visualize what that dream and the outcome could be, we always say, start small, quick wins, then from there, you can build. What you don't want to do is go nine months working on something and you don't know if there's going to be outcome. A lot of data science is trial and error. This is science, we're testing hypotheses. There's not always an outcome that's to be there, so small quick wins is something we highly recommend. >> A question, one of the things that we see more and more is the idea that actionable insights are perishable, and that latency matters. In fact, you have a budget for latency, almost, like in that short amount of time, the more sort of features that you can dynamically feed into a model to get a score, are you seeing more of that? How are the use cases that you're seeing, how's that pattern unfolding? >> Yeah, so we're seeing more streaming data use cases. We work with some of the biggest technology companies in the world, so IoT, connected services, streaming real time decisions that are happening. But then, also, there are so many use cases around org that could be marketing, finance, HR related, not just tech related. On the marketing side, imagine if you're customer service, and somebody calls you, and you know instantly the lifetime value of that customer, and it kicks off a totally new talk track, maybe get escalated immediately to a new supervisor, because that supervisor can handle this top tier customer. These are decisions that can happen real time leveraging machine learning models, and these are things that, again, are small quick wins, but massive, massive impact. It's about decision process now. That's digital transformation. >> OK. Are you seeing patterns in terms of how much horsepower customers are budgeting for the training process, creating the model? Because we know it's very compute intensive, like, even Intel, some people call it, like, high performance compute, like a supercomputer type workload. How much should people be budgeting? Because we don't see any guidelines or rules of thumb for this. >> I still think the boundaries are being worked out. There's a lot of great work that Nvidia's doing with GPU, we're able to do things faster on compute power. But even if we just start from the basics, if you go and talk to a data scientist at a massive company where they have a team of over 1,000 data scientists, and you say to do this analysis, how do you spin up your compute power? Well, I go walk over to IT and I knock on the door, and I say, "Set up this machine, set up this cluster." That's ridiculous. A product like ours is able to instantly give them the compute power, scale it elastically with our cloud service partners or work with on-prem solutions to be able to say, get the power that you need to get the results in the time that's needed, quick, fast. In terms of the boundaries of the budget, that's still being defined. But at the end of the day, we are seeing return on investment, and that's what's key. >> Are you seeing a movement towards a greater scope of integration for the data science tool chain? Or is it that at the high end, where you have companies with 1,000 data scientists, they know how to deal with specialized components, whereas, when there's perhaps less of, a smaller pool of expertise, the desire for end to end integration is greater. >> I think there's this kind of thought that is not necessarily right, and that is, if you have a bigger data science team, you're more sophisticated. We actually see the same sophistication level of 1,000 person data science team, in many cases, to a 20 person data science team, and sometimes inverse, I mean, it's kind of crazy. But it's, how do we make sure that we give them the tools so they can drive value. Tools need to include collaboration and workflow, not just hammers and nails, but how do we work together, how do we scale knowledge, how do we get it in the hands of the line of business so they can use the results. It's that that is key. >> That's great, Ian. I also like that you really kind of articulated start small, quick ins can make massive impact. We want to thank you so much for stopping by the Cube and sharing that, and what you guys are doing at Data Science to help enterprises really take advantage of the value that data can really deliver. >> Thanks so much for having datascience.com on, really appreciate it. >> Lisa: Absolutely. George, thank you for being my co-host. >> You're always welcome. >> We want to thank you for watching the Cube. I'm Lisa Martin with George Gilbert, and we are at our event Big Data SV on day two. Stick around, we'll be right back with our next guest after a short break. (busy music)

>> Live from San Jose, it's theCUBE! Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to theCUBE. Our continuing coverage of our event, Big data SV. I'm Lisa Martin with my co-host George Gilbert. We're down the street from the Strata Data Conference, hearing a lot of interesting insights on big data. Peeling back the layers, looking at opportunities, some of the challenges, barriers to overcome but also the plethora of opportunities that enterprises alike have that they can take advantage of. Our next guest is no stranger to theCUBE, she was just on with me a couple days ago at the Women in Data Science Conference. Please welcome back to theCUBE, Ziya Ma. Vice President of Software and Services Group and the Director of Big Data Technologies from Intel. Hi Ziya! >> Hi Lisa. >> Long time, no see. >> I know, it was just really two to three days ago. >> It was, well and now I can say happy International Women's Day. >> The same to you, Lisa. >> Thank you, it's great to have you here. So as I mentioned, we are down the street from the Strata Data Conference. You've been up there over the last couple days. What are some of the things that you're hearing with respect to big data? Trends, barriers, opportunities? >> Yeah, so first it's very exciting to be back at the conference again. The one biggest trend, or one topic that's hit really hard by many presenters, is the power of bringing the big data system and data science solutions together. You know, we're definitely seeing in the last few years the advancement of big data and advancement of data science or you know, machine learning, deep learning truly pushing forward business differentiation and improve our life quality. So that's definitely one of the biggest trends. Another thing I noticed is there was a lot of discussion on big data and data science getting deployed into the cloud. What are the learnings, what are the use cases? So I think that's another noticeable trend. And also, there were some presentations on doing the data science or having the business intelligence on the edge devices. That's another noticeable trend. And of course, there were discussion on security, privacy for data science and big data so that continued to be one of the topics. >> So we were talking earlier, 'cause there's so many concepts and products to get your arms around. If someone is looking at AI and machine learning on the back end, you know, we'll worry about edge intelligence some other time, but we know that Intel has the CPU with the Xeon and then this lower power one with Atom. There's the GPU, there's ASICs, FPGAS, and then there are these software layers you know, with higher abstraction layer, higher abstraction level. Help us put some of those pieces together for people who are like saying, okay, I know I've got a lot of data, I've got to train these sophisticated models, you know, explain this to me. >> Right, so Intel is a real solution provider for data science and big data. So at the hardware level, and George, as you mentioned, we offer a wide range of products from general purpose like Xeon to targeted silicon such as FPGA, Nervana, and other ASICs chips like Nervana. And also we provide adjacencies like networking the hardware, non-volatile memory and mobile. You know, those are the other adjacent products that we offer. Now on top of the hardware layer, we deliver fully optimized software solutions stack from libraries, frameworks, to tools and solutions. So that we can help engineers or developers to create AI solutions with greater ease and productivity. For instance, we deliver Intel optimized math kernel library. That leverage of the latest instruction set gives us significant performance boosts when you are running your software on Intel hardware. We also deliver framework like BigDL and for Spark and big data type of customers if they are looking for deep learning capabilities. We also optimize some popular open source deep learning frameworks like Caffe, like TensorFlow, MXNet, and a few others. So our goal is to provide all the necessary solutions so that at the end our customers can create the applications, the solutions that they really need to address their biggest pinpoints. >> Help us think about the maturity level now. Like, we know that the very most sophisticated internet service providers who are sort of all over this machine learning now for quite a few years. Banks, insurance companies, people who've had this. Statisticians and actuaries who have that sort of skillset are beginning to deploy some of these early production apps. Where are we in terms of getting this out to the mainstream? What are some of the things that have to happen? >> To get it to mainstream, there are so many things we could do. First I think we will continue to see the wide range of silicon products but then there are a few things Intel is pushing. For example, we're developing this in Nervana, graph compiler that will encapsulate the hardware integration details and present a consistent API for developers to work with. And this is one thing that we hope that we can eventually help the developer community with. And also, we are collaborating with the end user. Like, from the enterprise segment. For example, we're working with the financial services industry, we're working with a manufacturing sector and also customers from the medical field. And online retailers, trying to help them to deliver or create the data science and analytics solutions on Intel-based hardware or Intel optimized software. So that's another thing that we do. And we're seeing actually very good progress in this area. Now we're also collaborating with many cloud service providers. For instance, we work with some of the top seven cloud service providers, both in the U.S. and also in China to democratize the, not only our hardware, but also our libraries and tools, BigDL, MKL, and other frameworks and libraries so that our customers, including individuals and businesses, can easily access to those building blocks from the cloud. So definitely we're working from different factors. >> So last question in the last couple of minutes. Let's kind of vibe on this collaboration theme. Tell us a little bit about the collaboration that you're having with, you mentioned customers in some highly regulated industries, for as an example. But a little bit to understand what's that symbiosis? What is Intel learning from your customers that's driving Intel's innovation of your technologies and big data? >> That's an excellent question. So Lisa, maybe I can start my sharing a couple of customer use cases. What kind of a solution that we help our customer to address. I think it's always wise not to start a conversation with the customer on technology that you deliver. You want to understand the customer's needs first. And then so that you can provide a solution that really address their biggest pinpoint rather than simply selling technology. So for example, we have worked with an online retailer to better understand their customers' shopping behavior and to assess their customers' preferences and interests. And based upon that analysis, the online retailer made different product recommendations and maximized its customers' purchase potential. And it drove up the retailer's sales. You know, that's one type of use case that we have worked. We also have partnered with the customers from the medical field. Actually, today at the Strata Conference we actually had somebody highlighting, we had a joint presentation with UCSF where we helped the medical center to automate the diagnosis and grading of meniscus lesions. And so today actually, that's all done manually by the radiologist but now that entire process is automated. The result is much more accurate, much more consistent, and much more timely. Because you don't have to wait for the availability of a radiologist to read all the 3D MRI images. And that can all be done by machines. You know, so those are the areas that we work with our customers, understand their business need, and give them the solution they are looking for. >> Wow, the impact there. I wish we had more time to dive into some of those examples. But we thank you so much, Ziya, for stopping by twice in one week to theCUBE and sharing your insights. And we look forward to having you back on the show in the near future. >> Thanks, so thanks Lisa, thanks George for having me. >> And for my co-host George Gilbert, I'm Lisa Martin. We are live at Big Data SV in San Jose. Come down, join us for the rest of the afternoon. We're at this cool place called Forager Tasting and Eatery. We will be right back with our next guest after a short break. (electronic outro music)

>> Announcer: Live from San Jose, it's The Cube, presenting Big Data, Silicon Valley. Brought to you by Silicon Angle Media and its ecosystem partners. >> Welcome back to The Cube. Our continuing coverage of our event, Big Data SV continues. I am Lisa Martin joined by Peter Burris. We're in downtown San Jose at a really cool place called Forager Tasting and Eatery. Come down, hang out with us today as we have continued conversations around all things big data, everything in between. This is our second day here and we're excited to welcome to The Cube the CMO of VANTIQ, Blaine Mathieu. Blaine, great to meet you, great to have you on the program. >> Great to be here, thanks for inviting me. >> So, VANTIQ, you guys are up the street in Walnut Creek. What do you guys do, what are you about, what makes VANTIQ different? >> Well, in a nutshell, VANTIQ is a so called high productivity application development platform to allow developers to build, deploy, and manage so called event driven real time applications, the kind of applications that are critical for driving many of the digital transformation initiatives that enterprises are trying to get on top of these days. >> Digital trasformation, it's a term that can mean so many different things, but today, it's essential for companies to be able to compete, especially enterprise companies with newer companies that are more agile, more modern. But if we peel apart digital transformation, there's so many elements that are essential. How do you guys help companies, enterprises, say, evolve their application architectures that might currently not be able to support an actual transformation to a digital business? >> Well, I think that's a great question, thank you. I think the key to digital trasformation is really a lot around the concept of real time, okay. The reason Uber is disrupting or has disrupted the taxi industry is the old way of doing it was somebody called a taxi and then they waited 30 minutes for a taxi to show up and then they told the taxi where to go and hopefully they got there. Whereas, Uber, turned that into a real time business, right? You called, you pinged something on your phone. They knew your location. They knew the location of the driver. They matched those up, brought 'em together in real time. Already knew where to bring you to and ensured you had the right route and that location. All of this data flowing, all of these actions have been taken in real time. The same thing applies to a disruptor like Netflix, okay? In the old days, Blockbuster used to send you, you know, a leaflet in the mail telling you what the new movies are. Maybe it was personalized for you. Probably not. No, Netflix knows who you are instantly, gives you that information, again, in real time based on what you've done in the past and is able to give you, deliver the movie also, in real time pretty well. Every disruptor you look at around digital transformation is bringing a business or a process that was done slowly and impersonally to make it happen in real time. Unfortunately, enterprise applications and the architectures, as you said a second ago, that are being used in most applications today weren't designed to enable these real time use cases. A great example is sales force. So, a sales force is a pretty standard, what you'd call a request application. So, you make a request, a person, generally, makes a request of the system, system goes into a database, queries that database, find information and then returns it back to the user. And that whole process could take, you know, significant amounts of time, especially if the right data isn't in the database at the time and you have to go request it or find it or create it. A new type of application needs to be created that's not fundamentally database centric, but it's able to take these real time data streams coming in from devices, from people, from enterprise systems, process them in real time and then take an action. >> So, let's pretend I'm a CEO. >> Yeah. >> One of the key things you said, and I want you to explain it better, is event. What is event? What is an event and how does that translate into a digital business decision? >> This notion of complex event processing CEP has been around in technology for a long time and yet, it surprises me still a lot of folks we talk to, CEOs, have never heard of the concept. And, it's very simple really. An event is just something that happens in the context of business. That's as complex and as simple as it is. An event could be a machine increases in temperature by one degree, a car moves from one location to another location. It could be an enterprise system, like an ERP system, you know, approves a PO. It could be a person pressing a button on a mobile device. All of those, or it could be an IOT device putting off a signal about the state of a machine. Increasingly, we're getting a lot of events coming from IOT devices. So, really, any particular interesting business situation or a change in a situation that happens is an event And increasingly driven, as you know, by IOT, by augmented reality, by AI and machine learning, by autonomous vehicles, by all these new real time technologies are spinning off more and more events, streams of these events coming off in rapid fashion and we have to be able to do something about them. >> Let me take a crack at it and you tell me if I've got this right. That, historically, applications have been defined in terms of processes and so, in many respects, there was a very concrete, discreet, well established program, set of steps that were performed and then the transaction took place. And event, it seems to me is, yeah, we generally described it, but it changes in response to the data. >> Right, right. >> So, an event is kind of like an outside in driven by data. >> Right, right. >> System response, whereas, your traditional transaction processing is an inside out driven by a sequence of programmed steps, and that decision might have been made six years ago. So, the event is what's happening right now informed by data versus a transaction, traditional transaction is much more, what did we decide to do six years ago and it just gets sustained. Have I got that right? >> That's right. Absolutely right or six hours ago or even six minutes ago, which might seem wow, six minutes, that's pretty good, but take a use case for a field service agent trying to fix a machine or an air conditioner on top of a building. In today's world now, that air conditioner has hundreds of sensors that are putting off data about the state of that air conditioner in real time. A service tech has the ability to, while the machine is still putting off that data, be able to make repairs and changes and fixes, again, in the moment, see how that is changing the data coming off the machine, and then, continue to make the appropriate repairs in collaboration with a smart system or an application that's helping them. >> That's how identifying patterns about what the problem is, versus some of the old ways was where we had recipe of, you know, steps that you went through in the call center. >> Right, right. And the customer is getting more and more frustrated. >> They got their clipboard out and had the 52 steps they followed to see oh that didn't work, now the next step. No, data can help us do that much more efficiently and effectively if we're able to process it in real time. >> So, in many respects, what we're really talking about is an application world or a world looking forward where the applications, which historically have been very siloed, process driven, to a world where the application function is much more networked together and the application, the output of one application is having a significant impact through data on the performance of an application somewhere else. That seems like it's got the potential to be an extremely complex fabric. (laughing) So, do I wait until I figure all that out (laughing) and then I start building it? Or do I, I mean, how do I do it? Do I start small and create and grow into it? What's the best way for people to start working on this? >> Well, you're absolutely right. Building these complex, geeking out a little bit, you know, asynchronous, non-blocking, so called reactive applications, that's the concept that we've been using in computer science for some time, is very hard, frankly. Okay, it's much easier to build computing systems that process things step one, step, two, step three, in order, but if you have to build a system that is able to take real time inputs or changes at any point in the process at any time and go in a different direction, it's very complex. And, computer scientists have been writing applications like this for decades. It's possible to do, but that isn't possible to do at the speed that companies now want to transform themselves, right? By the time you spec out an application and spend two years writing it, your business competitors have already disrupted you. The requirements have already changed. You need to be much more rapid and agile. And so, the secret sauce to this whole thing is to be able to write these transformative applications or create them, not even write is actually the wrong word to use, to be able to create them. >> Generate them. >> Yeah, generate them in a way which is very fast, does not require a guru level developer and reactive Java or some super low level code that you'd have to use to otherwise do it, so that you can literally have business people help design the applications, conceptually build them almost in real time, get them out into the market, and then be able to modify them as you need to, you know, on the fly. >> If I can build on that for just one second. So, it used to be we had this thing called computer assisted software engineer. >> (laughs) Right, right. >> We were going to operate this very very high level language. It's kind of-- But then, we would use code and build a code and the two of them were separated and so the minute that we deployed, somebody would go off and maintain and the whole thing would break. >> Right, right. >> Do you have that problem? >> No, well, that's exactly right. So, the old, you know, the old, the previous way of doing it was about really modeling an application, maybe visually, drag and drop, but then fundamentally, you created a bunch of code and then your job, as you said after, was to maintain and deploy and manage. >> Try to sustain some connection back up to that beautiful visual model. >> And you probably didn't because that was too much. That was too much work, so forget about the model after that. Instead, what we're able to do these days is to build the applications visually, you know, really for the most part with either super low code or, in many cases, no code because we have the ability to abstract away a lot of the complexity, a lot of the complex code that you'd have to write, we can represent that, okay, with these logical abstractions, create the applications themselves, and then continue to maintain, add to, modify the application using the exact same structure. You're not now stuck on, now you're stuck with 20,000 lines of code that you have to, that you have to edit. You're continuing to run and maintain the application just the way you built it, okay. We've now got to the place in computer science where we can actually do these things. We couldn't do them, you know, 20 years ago with case, but we can absolutely do them now. >> So, I'm hearing from a customer internal perspective a lot of operational efficiencies that VANTIQ can drive. Let's look now from a customer's perspective. What are the business impacts you're able to make? You mentioned the word reactive a minute ago when you were talking about applications, but do you have an example where you've, VANTIQ, has enabled a customer, a business, to be more, to be proactive and be able to identify through, you know, complex event processing, what their customers are doing to be able to deliver relevant messages and really drive revenue, drive profit? >> Right, right. So many, you know, so many great examples. And, I mentioned field service a few minutes ago. I've got a lot of clients in that doing this real time field service using these event processing applications. One that I want to bring up right now is one of the largest global shoe manufacturers, actually, that's a client of VANTIQ. I, unfortunately, can't say the name right now 'cause they want to keep what they're doing under wraps, but we all definitely know the company. And they're using this to manage the security, primarily, around their real time global supply chain. So, they've got a big challenge with companies in different countries redirecting shipments of their shoes, selling them on the gray market, at different prices than what are allowed in different regions of the world. And so, through both sensorizing the packages, the barcode scanning, the enterprise systems bringing all that data together in real time, they can literally tell in the moment is something is be-- If a package is redirected to the wrong region or if literally a shoe or a box of shoes is being sold where it shouldn't be sold at the wrong price. They used to get a monthly report on the activities and then they would go and investigate what happened last month. Now, their fraud detection manager is literally sitting there getting this in real time, saying, oh, Singapore sold a pallet of shoes that they should not have been able to sell five minute ago. Call up the guy in Singapore and have him go down and see what's going on and fix that issue. That's pretty powerful when you think about it. >> Definitely, so like reduction in fraud or increase in fraud detection. Sounds like, too, there's a potential for a significant amount of cost savings to the business, not just meeting the external customer needs, but from a, from a cost perspective reduction. Not just some probably TCO, but in operational expenses. >> For sure, although, I would say most of the digital transformation initiatives, when we talk to CEOs and CIOs, they're not focused as much on cost savings, as they're focused on A, avoiding being disrupted by the next interesting startup, B, creating new lines of business, new revenue streams, finding out a way to do something differently dramatically better than they're currently doing it. It's not only about optimizing or squeezing some cost out of their current application. This thing that we are talking about, I guess you could say it's an improvement on their current process, but really, it's actually something they just weren't even really doing before. Just a total different way of doing fraud detection and managing their global supply chain that they just fundamentally weren't even doing. And now, of course, they're looking at many other use cases across the company, not just in supply chain, but, you know, smart manufacturing, so many use cases. Your point about savings, though, there's, you know, what value does the application itself bring? Then, there's the question of what does it cost to build and maintain and deploy the application itself, right? And, again, with these new visual development tools, they're not modeling tools, you're literally developing the application visually. You know, I've been in so many scenarios where we talked to large enterprises. You know, we talk about what we're doing, like we talk about right now, and they say, okay, we'd love to do a POC, proof of concept. We want to allocate six months for this POC, like normally you would probably do for building most enterprise applications. And, we inevitably say, well, how about Friday? How about we have the POC done by Friday? And, you know, we get the Germans laugh, you know, laugh uncomfortably and we go away and deliver the POC by Friday because of how much different it is to build applications this way versus writing low level Java or C-sharp code and sticking together a bunch of technologies and tools 'cause we abstract all that away. And, you know, the eyes drop open and the mouth drops open and it's incredible what modern technology can do to radically change how software is being developed. >> Wow, big impact in a short period of time. That's always a nice thing to be able to deliver. >> It is, it is to-- It's great to be able to surprise people like that. >> Exactly, exactly. Well, Blaine, thank you so much for stopping by, sharing what VANTIQ is doing to help companies be disruptive and for sharing those great customer examples. We appreciate your time. >> You're welcome. Appreciate the time. >> And for my co-host, Peter Burris, I'm Lisa Martin. You're watching The Cube's continuing coverage of our event, Big Data SV Live from San Jose, down the street from the Strata Data Conference. Stick around, we'll be right back with our next guest after a short breal. (techy music)

>> Male Narrator: Live from San Jose, it's theCube. Presenting Big Data Silicon Valley, brought to you by SilconANGLE Media and it's ecosystem partners. >> Welcome back to theCube's continuing coverage of our event, Big Data SV in downtown San Jose. I'm Lisa Martin, my co-host is Dave Vellante. Hey Dave. >> Hey Lisa, how's it going? >> Good. >> Doing a great job here, by the way. >> Well thank you, sir. >> Keeping the trains going. >> Yeah. >> Well done. >> We've had a really interesting couple of days, we started here yesterday interviewing lots of great guys and gals on Big Data and everything in between. A lots of different topics there, opportunities, challenges, digital transformation, how can customers really evolve on this journey? We're excited to welcome back to theCube, one of our distinguished alumni, Matt Maccaux, the Global Big Data Practice Lead from Dell EMC. Welcome back. >> Well thanks for having me, appreciate it, it's a pleasure to be here. >> Yeah, so lots of stuff going on. We've been here, as I mentioned, we're down the street from the Strata Data Conference and we've had a lot of great conversations, very educational, informative. You've been with the whole Dell EMC family for a while now. We'd love to get your perspective on, kind of, what's going on from your team's standpoint. What are you seeing in the enterprises with respect to Big Data and being able to really leverage data across the business as a value driver and a revenue generator? >> Yeah, it's interesting that what we see across the business in terms of, especially in the big enterprises, there, many organizations, even the more mature ones, are still struggling to get that extra dollar, that extra level of monetization out of their data assets. They, everyone talks about monetizing data and using data, treating it as an asset, but organizations are struggling with that, not because of the technology, the technology's been put in, they've ramped up their teams, their skills. It's, what we tend to see inhibiting this digital transformation growth is process. It's organizational strife and it's not looking to best practices, even within own, their own organization, we're doing things like DevOps. So, why would we treat the notion of creating a data model any different than we would regular application development? Well, organizations still carry that weight, that inertia, they still treat Big Data and analytics like they do the data warehouse, and the most effective organizations are starting to incorporate that agile methodology and agile thinking, no snowflakes, infrastructure's code, these concepts of quickly and rapidly repeatedly doing these things, those are the organizations that are really starting to pull away from their competitors in industry. So, Dell EMC, our consulting group and our product lines are all there to support that transformation journey by taking those best practices and DevOps DataOps and bringing that to the analytical space. >> Do you think that companies, Matt, have a pretty good sense as to how applications that they develop are going to affect, create value, creating value is, let's simplify it, increasing revenue or cutting cost? Generally people can predict with the impact, they can write a business case around it. My observation was that certainly in the early days of so-called Big Data, people really didn't have an understanding as to the relationship between their data and that value, and so, many companies mistakenly thought, "Well I need to figure out how to sell my data," versus understand how data affects monetization. I wonder if you could comment on that and how has that progressed throughout the years? >> Yeah, that's a good point, we, from a consulting practice, used to do a lot of, what we call, proof of values, where organizations, after they kicked the tires and covered some use cases, we took them through a very slow, methodical business case RY analysis. You're going to spend this much on infrastructure, you're going to hire these people, you're going to take this data, and poof, you're going to make this much money, you're going to save this much money. Well, we're doing less and less of that these days because organizations have a good feel for where they want to go and the potential upside for doing this where they're now tend to struggle is, "Well, how do I actually get there?" "There's still a lot of tools and a lot of technologies and which is right for my business?" "What is the right process and how do I build that consensus in the organization?" And so, from a business consulting perspective, we're doing less of the RY work and more of the governance, the sort of, governance work by aligning stakeholders, getting those repeatable patterns and architectures in place to help organizations take that first few wins and then scale it. >> Where do you see the action these days? I mean there's somehow I profile use cases, obviously getting people to click on ads, Big Data has helped with that, fraud detection has come such a long way in the last 10 years, ya know, no doubt, certainly risk assessment, ya know, from the financial services industry. Those are the obvious ones, where else do you see Big Data analytics to the changing the world, if you will? >> Yeah, so I'd say those static or batch-type workloads are well understood. That, hey, is there fraud on transactions that occurred yesterday or last night? What is the customer score, lifetime value score for customer? Where we see more trends in the enterprise space is streaming. So, what can we catch in real time and help our people make real time decisions? So, and that is dealing with unstructured data. So, I've got a call center and I'm listening to the voice that's coming in, putting some sentiment analysis on that and then providing a score or script to the customer call agent in real time. And those, sort of, streaming use cases, whether it's images or voice, that, I think, is the next paradigm for use cases that organizations want to tackle. 'Cause if you can prevent a customer from leaving in real time, right, say, you know what, it sounds like you're upset, what if we did X to help retain you, it's going to be significant. All these organizations have a good idea of the cost it takes to acquire a new customer and the cost of losing a customer, so if they can put that intelligence in upstream, they no longer have to spend so much money trying to capture new customers 'cause they can focus on the ones they have. So, I think that, sort of, time between customer and streaming is where the next set of, I think, money's to be found. >> So customer experience is critical for businesses in any organization, I'm wondering, kind of, what the juxtaposition is of businesses going, "Yes, we have to be able "to do things in real time, in enterprise, "we have to be agile, yet we have, in order "to really facilitate a really effective, relevant, "timely customer experience, many departments "and organizations in a business need access to data." From a political perspective, how does Dell EMC, how does your consulting practice help an enterprise be able to start opening up these barriers internally to be able to enable data sharing so that they can drive and take advantage of things like real-time streaming to ultimately improve the customer experience, revenue, et cetera? >> Yeah, it's going to sound really trite, but the first step is getting everyone in a room and talking about what good looks like, what are the low-hanging... And everyone's going to agree on those use cases, there going to say, "These are the things we have to do," right, "We want to lose fewer customers, we want to..." You know, whatever the case may be, so everyone will agree on that. So, the politics don't come into play there. So, "Well, what data do we require for that?" "Okay, well, we've got all this data, great, "no disagreement there." Well, where is the data located? Who's the owner or the steward of that data? And now, who's going to be responsible for monetizing that? And that's where we tend to see the breakdown because when these things cross the line of business and customer always crosses the line of business, you end up with turf wars. And so this, the emergence of the Chief Data Officer, who's responsible for the policy and the prioritization and the ownership of these things is such a key role now, that, and it's not a CIO responsible for data, it is a business aligned executive reporting to the chief, CEO, COO, CFO. Again, business alignment, that tends to be the decision maker or at least the thing that solves for those conflicts across those BUs. And when that happens, then we see real change. But, if there's not that role or that person that can put that line in the sand and say, "This is how we're going to do it," you end up with that political strife and then you end up with silos of information or point solutions across the enterprise and it doesn't serve anyone. >> What are you seeing in terms of that CDO role? I mean, initially the Chief Data Officer was really within regulated businesses, financial services, healthcare, government. And then you've seen it permeate, ya know, to more mainstream. Do you see that role as having legs? A lot of people have questioned that role. What Chief Digital Officer, Chief Data Officer is encroaching on the CIO territory? I'm inferring from your comments that you're optimistic about that role going forward. >> I am, as long as it's well-defined as having unique capabilities that's different than the CIO. Again, I think the first generation of Chief Data Officers were very CIO-liked or CIO-for-data and that's when you ended up with the turf wars. And then it was like, "Okay, well this is "what we're doing." But then you had someone who was sort of a peer for infrastructure and so, it just didn't seem to work out. And so, now we're seeing that role being redefined, it's less about the technology and the tools and the infrastructure, and it's more about the policies, the consistency, the architectures. >> You know I'd observe, I wonder if we can talk about this for a little bit, it's the CDO role. To me, one of the first things a CDO has to do is understand how a company gets value out of its data, what is the, and if it's a full profit company, what's the monetization, where does that come from? Not selling the data, as we were talking about earlier. And then there is what data, what data, where are, what data architecture, data sources, how do we give access to that? And then quality, data quality seems to be something that they worry about. And then skills, not, none, no technology in here. And then somehow they're going to form relationships with the line of business and it's simultaneous to figuring that out. Does that seem like a reasonable framework for the CIO, CDOs job? >> It does, and you call them Chief Data Governance Officer, I mean, it really falls under the umbrella of governance. It's about standards and consistency, but also these policies of, there are finite resources, whether we're talking people or computes. What do you do when there's not enough resources and more demand? How do you prioritize the things that the business does? Well, do you have policies and matrices that say, "Okay, well, is it material, actionable, timely?" "Then yes, then we'll proceed with this." "No, it doesn't pass." And it doesn't have to be about money. However the organization judges itself is what it should be based on. So, whether we're talking non-profit, we helped a school system recently better align kids with schedules and also learning abilities by sitting them next to each other in classes, there's no profit in that other than the education of children, so every organization judges itself or measures itself a little differently, but it comes back to those KPIs. What are your KPIs, how does that align to business initiatives? And then everything should flow from there. Now, I'm not saying it's easy work. Data governance is the hardest thing to do in this space and that's why I think so few organizations take it on 'cause it's a long, slow process and, ya know, you should've started 10 years ago on it and if you haven't, it feels like this mountain that is really high to climb. >> What you're saying is outcome driven. >> Yeah. >> Independent of the types of organizations. I want to talk about innovation, I've been asking a lot of people this week, do you feel like Big Data, ya know, the meme of Big Data that was created eight, 10 years ago, do you feel like it lived up to its promises? >> That's a loaded question. I think if you were to ask the back office enterprises, I would say yes. In terms of customers feeling it, probably not, because when you use an Uber app to hail a cab and pay $3.75 to go across town, it feels like a quality of life, but you don't know that that's a data-driven decision. As a consumer, your average consumer, you probably don't feel that. As you're clicking through Amazon and they know, sort of, the goods that you need, or the fact that they know what you're going to need and they've got it in a warehouse that they can get to you later that day, it doesn't feel like a Big Data solution, it just feels like, "Hey, the people I'm doing business with, they know me better." People don't really understand that that's a Big Data and analytics concept, so, has it lived up to the hype? Externally, I think the perception is that it has not, but the businesses that really get it, feel that absolutely it has. That's 'cause you, do you agree it's kind of bifurcated? >> Matt Maccaux: Yeah, it is. >> The Spotify's and the Ubers and the Airbnb's that are crushing it and then there's a lot of traditional enterprises that are still stove pipe and struggling. >> Yeah, it's funny, when we talk to customers, we've got our introductory power points, right, it always talks about the new businesses and the old businesses and, and I'm finding that that doesn't play very well anymore with enterprise customers. They're like, "We're never going to be the Uber "of our industry, it's not going to happen "if I'm a fortune 100 legacy, it's not going to happen." "What I really want to do, though, "is help my customers or make more money here, "I'm not going to be the Uber, it's just not going to happen." "We're not the culture, we're not the, we're not set up "that way, we have all of this technical legacy stuff, "but I really want to get more value out of my data, "how do I do that?" And so that message resonates. >> Isn't that in some ways, though, how do you feel about this, is it a recipe for disruption, where that's not going to happen, but something could happen where somebody digitizes your business? >> Yes, absolutely, if there are organizations, if you're in the fortune 500 and you are not worried about someone coming along and disrupting you, then you are probably not doing the right job. I would be kept awake every night, whether it was financial services or industrial manufacturing. >> Dave Vellante: Grocery. >> Nobody thought that the taxis, who the hell would come in and disrupt the cab industry? Ya got to hire all these people, the cars are junk, the customer experience is awful. Well, someone has come along and there's been an industry related to this, now they have their bumps in the road, so are they going to be disrupted again or what's the next level of disruption? But, I think it is technology that fuels that, but it's also the cultural shift as part of that, which is outside the technologies, the socioeconomic trends that I think drive that, as well. >> But even, ya know, and we've got just a few seconds left, the cultural shift internally. It sounds like, from what you're describing, if an enterprise is going to recognize, "I'm not going to compete with an Uber or an Airbnb "or a Netflix, but I've got to be able to compete "with my existing peers of enterprise organizations," the CDO role sounds like it's a matter of survivability. >> Yes. >> Without putting that in place, you can't capitalize on the value of data monetized and et cetera. Well guys, I wish we had more time 'cause I think we're opening a can of worms here, but Dave, Matt thanks so much for having this conversation. Thank you for stopping by. >> Thanks for having me here, it was a real pleasure. >> Likewise. We want to thank you for watching theCube. We are continuing our coverage of our event, Big Data SV in downtown San Jose. For Dave Vellante, my co-host, I'm Lisa Martin. Stick around, we'll be right back with our next guest after a short break. (upbeat music)

>> Announcer: Live from San Jose it's The Cube presenting Big Data, Silicon Valley brought to you by SiliconANGLE Media and its ecosystem partners. >> Good morning. Welcome to The Cube. We are on day two of our coverage our event Big Data SV. I'm Lisa Martin with my cohost Dave Vellante. We're down the street from the Strata Data Conference. This is The Cube's tenth big data event and we had a great day yesterday learning a lot from myriad guests on very different nuances of big data journey where things are going. We're excited to welcome back to The Cube an alumni, Octavian Tanase, the Senior Vice President of Data ONTAP fron Net App. Octavian, welcome back to The Cube. >> Glad to be here. >> So you've been at the Strata Data Conference for the last couple of days. From a big data perspective, what are some of the things that you're hearing, in terms of from a customer's perspective on what's working, what challenges, opportunities? I'm very excited to be here and learn about the innovation of our partners in the industry and share with our partners and our customers what we're doing to enable them to drive more value out of that data. The reality is that data has become the 21st Century gold or oil that powers the business and everybody's looking to apply new techniques, a lot of times machine learning, deep learning, to draw more value of the data, make better decisions and compete in the marketplace. Octavian, you've been at NetApp now eight years and I've been watching NetApp, as we were talking about offline, for decades and I've seen the ebb and flow and this company has transformed many, many times. The latest, obviously cloud came in, flash came into play and then you're also going through a major transition in the customer based to clustered ONTAP. You seemed to negotiate that. NetApp is back, thriving, stock's up. What's happening at NetApp? What's the culture like these days? Give us the update. >> I think we've been very fortunate to have a CEO like George Kurian, who has been really focused on helping us do basically fewer things better, really focus on our core business, simplify our operations and continue to innovate and this is probably the area that I'm most excited about. It's always good to make sure that you accelerate the business, make it simpler for your customers and your partners to do business with you, but what you have to do is innovate. We are a product company. We are passionate about innovation. I believe that we are innovating with more pace than many of the startups in the space so that's probably the most exciting thing that has been part of our transformation. >> So let's talk about big data. Back in the day if you had a big data problem you would buy a big Unix box, maybe buy some Oracle licenses, try to put all your data into that box and that became your data warehouse. The brilliance of Hadoop was hey we can leave the data where it is. There's too much data to put into the box so we're going to bring five megabytes to code to a petabyte of data. And the other piece of it is CFOs loved it, because we're going to reduce the cost of our expensive data warehouse and we're going to buy off the shelf components: white box, servers and off the shelf disk drives. We're going to put that together and life will be good. Well as things matured, the old client-server days, it got very expensive, you needed enterprise grade. So where does NetApp fit into that equation, because originally big storage companies like NetApp, they weren't part of the equation? Has that changed? >> Absolutely. One of the things that has enabled that transformation, that change is we made a deliberate decision to focus on software defined and making sure that the ONTAP operating system is available wherever data is being created: on the edge in an IoT device, in the traditional data center or in the cloud. So we are in the unique position to enable analytics, big data, wherever those applications reside. One of the things that we've recently done is we've partnered with IDC and what the study, what the analysis has shown is that deploying in analytics, a Hadoop or NoSQL type of solution on top of NetApp is half the cost of DAS. So when you consider the cost of servers, the licenses that you're going to have to pay for, these commercial implementations of Hadoop as well as the storage and the data infrastructure, you are much better off choosing NetApp than a white box type of solution. >> Let's unpack that a little bit, because if I infer correctly from what you said normally you would say the operational costs are going to be dramatically lower, it's easier to manage a professional system like a NetApp ONTAP, it's integrated, great software, but am I hearing you correctly, you're saying the acquisition costs are actually less than if I'm buying white box? A lot of people are going to be skeptical about that, say Octavian no way, it's cheaper to buy white box stuff. Defend that statement. >> Absolutely. If you're looking at the whole solution that includes the server and the storage, what NetApp enables you to do if you're running the solution on top of ONTAP you reduce the need for so many servers. If you reduce that number you also reduce the licensing cost. Moreover, if you actually look at the core value proposition of the storage layer there, DAS typically makes three copies of the data. We don't. We are very greedy and we're making sure that you're using shared storage and we are applying a bunch of storage efficiency techniques to further compress, compact that data for world class storage efficiency. >> So cost efficiency is obviously a great benefit for any company when they're especially evolving, from a digital perspective. What are some of the business level benefits? You mentioned speed a minute ago. What is Data ONTAP and even ONTAP in the cloud enabling your enterprise customers to achieve at the business level, maybe from faster time to market, identifying with machine learning and AI new products? Give me an example of maybe a customer that you think really articulates the value that ONTAP in the cloud can deliver. >> One of the things that's really important is to have your data management capability, whatever the data is being produced so ONTAP being consumed either as a VM or a service ... I don't know if you've seen some of the partnerships that we have with AWS and Azure. We're able to offer the same rich data management capabilities, not only the traditional data center, but in the cloud. What that really enables customers to do is to simplify and have the same operating system, the same data management platform for the both the second platform traditional applications as well as for the third platform applications. I've seen a company like Adobe be very successful in deploying their infrastructure, their services not only on prem in their traditional data center, but using ONTAP Cloud. So we have more than about 1,500 customers right now that have adopted ONTAP in the AWS cloud. >> What are you seeing in terms of the adoption of flash and I'm particularly interested in the intersection of flash adoption and the developer angle, because we've seen, in certain instances, certain organizations are able to share data off of flash much more efficiently that you would be, for instance, of a spinning disk? Have you seen a developer impact in your customer base? >> Absolutely I think most of customers initially have adopted flash, because of high throughput and low latency. I think over time customers really understood and identified with the overall value proposition in cost of ownership in flash that it enables them to consolidate multiple workloads in a smaller footprint. So that enables you to then reduce the cost to operate that infrastructure and it really gives you a range of applications that you can deploy that you were never able to do that. Everybody's looking to do in place, in line analytics that now are possible, because of this fast media. Folks are looking to accelerate old applications in which they cannot invest anymore, but they just want to run faster. Flash also tends to be more reliable than traditional storage, so customers definitely appreciate that fewer things could go wrong so overall the value proposition of flash, it's all encompassing and we believe that in the near future flash will be the defacto standard in everybody's data center, whether it's on prem or in the cloud. >> How about backup and recovery in big data? We obviously, in the enterprise, very concerned about data protection. What's similar in big data? What's different and what's NetApp's angle on that? >> I think data protection and data security will never stop being important to our customers. Security's top of mind for everybody in the industry and it's a source of resume changing events, if you would, and they're typically not promotions. So we have invested a tremendous deal in certifications for HIPAA, for FIPS, we are enabling encryption, both at rest and in flight. We've done a lot of work to make sure that the encryption can happen in software layer, to make sure that we give the customers best storage class efficiency and what we're also leveraging is the innovation that ONTAP has done over many years to protect the data, replicate its snapshots, peering the data to the cloud. These are techniques that we're commonly using to reduce the cost of ownership, also protect the data the customers deploy. >> So security's still a hot topic and, like you said, it probably always will be, but it's a shared responsibility, right? So customers leveraging NetApps safe or on prem hybrid also using Azure or AWS, who's your target audience? If you're talking to the guys and gals that are still managing storage are you also having the CSO or the security guys come in, the gals, to understand we've got this appointment in Azure or AWS so we're going to bring in ONTAP to facilitate this? There's a shared responsibility of security. Who's at the table, from your perspective, in your customers that you need to help understand how they facilitate true security? >> It's definitely been a transformative event where more and more people in IQ organizations are involved in the decisions that are required to deploy the applications. There was a time when we would talk only to the storage admin. After a while we started talking to the application admin, the virtualization admin and now you're talking to the line of business who has that vested interest to make sure that they can harness the power of the data in their environment. So you have the CSO, you have the traditional infrastructure people, you have the app administration and you have the app owner, the business owner that are all at the table that are coming and looking to choose the best of breed solution for their data management. >> What are the conversations like with your CXO, executives? Everybody talks about digital transformation. It's kind of an overused term, but there's real substance when you actually peel the onion. What are you seeing as NetApp's role in effecting digital transformations within your customer base? >> I think we have a vision of how we can help enterprises take advantage of the digital transformation and adopt it. I think we have three tenants of that vision. Number one is we're helping customers harness the power of the cloud. Number two, we're looking to enable them to future proof their investments and build the next generation data center. And number three, nobody starts with a fresh slate so we're looking to help customers modernize their current infrastructure through storage. We have a lot of expertise in storage. We've helped, over time, customers time and again adopt disruptive technologies in nondisruptive ways. We're looking to adopt these technologies and trends on behalf of our customers and then help them use them in a seamless safe way. >> And continue their evolution to identify new revenue streams, new products, new opportunities and even probably give other lines of business access to this data that they need to understand is there value here, how can we harness it faster than our competitors, right? >> Absolutely. It's all about deriving value out of the data. I think earlier I called it the gold of the 21st Century. This is a trend that will continue. I believe there will be no enterprise or center that won't focus on using machine learning, deep learning, analytics to derive more value out of the data to find more customer touch points, to optimize their business to really compete in the marketplace. >> Data plus AI plus cloud economics are the new innovation drivers of the next 10, 20 years. >> Completely agree. >> Well Octavian thanks so much for spending time with us this morning sharing what's new at NetApp, some of the visions that you guys have and also some of the impact that you're making with customers. We look forward to having you back on the program in the near future. >> Thank you. Appreciate having the time. >> And for my cohost Dave Vellante I'm Lisa Martin. You're watching The Cube live on day two of coverage of our event, Big Data SV. We're at this really cool venue, Forager Tasting Room. Come down here, join us, get to hear all these great conversations. Stick around and we'll be right back with our next guest after a short break. (electronic music)