(upbeat music) >> Hello and welcome to the Cube Studios in Palo Alto California, for another Cube conversation. Where we go in-depth with thought leaders driving innovation across the tech industry. I'm your host Peter Burris. Every Enterprise is saddled with the challenge of how to get more value out of their data. While at the same time trying to find new ways of associating value with product or value with service and to work with the different technology suppliers to create an optimal relationship for how they can move their business forward within a data-driven world. It's a tall order but 2020 is going to feature an enormous amount of progress and how enterprises think about how to handle the people, process and technology of improving their overall stance towards getting value out of their data. So to have that conversation today, we're joined by a Guy Churchward, who's the CEO of Datera. Guy welcome back to the cube. >> Thank You Peter, I appreciate it. >> So before we go any further give us a quick update what's going on with Datera? >> We're doing pretty well. I mean this year's we're just going to close it off. So we're in Q4 right at the end of it. You mentioned data-driven, you know I mean that was obviously one of my key excitements, years ago we kind of moved from a hardware resiliency or Hardware-driven to software resiliency, Software defined and I do think that we've hit that data-defined, data-driven infrastructure right now. I've been in the CEO role now just about a year. I've been on the board since August of a year and change ago and part of it is we had a little bit of an impedance mismatch of message, technology and basically I go to market. So the team quite brilliantly produced this data services platform to do data driven architectures. >> Mmmh. >> But customers don't wake up every morning and go, I need to go buy a data-driven, how do I buy one? And so when I came in I realized that you know what they had was an exceptional solution but the market isn't ready yet for that thought process, and what they were really buying still was SDS, software defined storage. >> So it almost in a connect way. so I'm going to buy an SDS and connect it to something and get a little bit of flexibility over here but still worry about the lock in every where else. >> Yeah, exactly and in fact even on the SDS side. What they weren't looking for is bring your own server storage. What they were looking for was automation and they were looking to basically break out and have more data mobility and data freedom. And so that was good and then the second one was our technology really sells directly to enterprises, directly to large scale organizations and it's very difficult as a start-up, small company to basically be able to punch straight into a global account, you know. Because they'll sit back and say, well you know would you trust your family jewels to a company that's got 40 employees in Silicon Valley. >> Right. >> And so what you really have is this and get the message right and then make sure you have to flow through to the customer credibility right and we were fortunate to land a very strategic relationship with HP. And so that was our focus point. Right. So we basically got on board with HP, got into their complete program, started selling very closely to them of which their sales team has been marvelous and then we're just finishing that that year. The good news is and you know I'll give you a spoiler I care about Billings, you know I mean we actually move from an appliance business to a software business exclusively, and so we basically sell term agreement. So if you think about it from a bookings perspective, that's important but basically how much you bill out is more important. From a Billings perspective I think we're going to run roughly 350% up year-over-year. >> Ooh. >> Yeah which is kind of good. Right I mean in other words it was a bit of a pat on the back that seems very happy with that and then even from new account acquisitions if I count the amount of accounts that we bought in this year and to date, entirely since 2013 we've only had one customer churn, so all the customers are coming with us but if I count this year, if I look at 16 17 and 18 we've actually bought more customers on board in 19 than all three pulled together. So we're actually finishing a very very strong year. >> Congratulations. Now if we think about going into 2020 you're closing this quarter, but every startup has to have a notion of what's going to happen next and what role you're going to play. And what happens next. So if I look back I I see the enterprise starting to assert themselves in the cloud businesses. That's having an effect on on everybody. But it really becomes concrete you know, the rubber really meets the road at the level of data. So as you start to grow you're talking more customers, as you talk to more customers and they expressed what they need out of this new cloud oriented world, what kinds of problems are they bringing to the table as far as you're concerned? >> Yeah, I mean they initially come to us so what I would say is every account that we've run we've replaced traditional arrays storage arrays and every account we've run, we've actually competed against SDS vendors and whether that's something like Dells, VxFlex or even vSAN, VMware's vSAN and which are probably the two most well-known ones. A lot of cases I mean we actually have 100% win rate against that in these competitive situations, but interestingly most customers now are putting dual source in place. So in fact the reason that we've ridden pretty quickly and we've run lots of deals, isn't because we're going in and saying VxFlex is failing or vSAN is failing, but they want something extra, they want automation, they want desegregation, they want scale >> They want second source. In many respects of sales is, it's succeeding but you have to push a little bit harder and that is ease most easily done by bringing in another platform with crucial functionality... >> Yeah >> ...and a second source. >> And I think you're on the money there Peter because if I look at second source in the traditional array business, no CIO worth their soul is a single source vendor so they they will have Dell and they'll have HP or they'll have HP and they'll have Pure, doesn't matter and and even on HCI you'll see the HCI vendors, Nutanix is doing very well, so is Dell. So therefore they'll have that from second source if its critical. So if an environment is critical they always have a second source and so even now when you look into software-defined, this market in 2019 was very much like the, let's get the second source in place. And that shows you where we are on the maturity curve because people is basically moving on this en mass. Now that's 2019 you're asking about 20, 21, 22 moving forward. The reason that the traditional arrays weren't working for them is whether it's flexibility or it's basically management costs or maintenance, but it's data freedom. It's what they're really looking for. You know, what is a data center? Is it on-premise, is it cloud? It's definitely cloud but the question is is it on-premise cloud? Is it hybrid cloud, is it public cloud? And then you mention edge. You know we actually find customers who are looking and are saying look, the most important thing for us is being data-driven and what data-driven basically articulates is we get data in, we analyze it, we make decisions on it and we win and lose against our competition as fast as we can be accurate on that data set. And a lot of the decisions are getting made at the edge. So a lot of people are looking at saying my data center is actually at the edge, it's not in the center in the cloud, right. >> Well in many respects, it's for the first time a data center actually is what it says it is, right. Because the data center used to be where the hardware was and now increasingly enterprises are realizing that the services and the capabilities have to be where the data is. >> Yeah. >> Where the data is being produced, where the data is being utilized and certainly where the data, where decisions are being made about what to keep what not to keep, how much of it etc, and that that does start to drive forward an increased recognition that at some point in time we are going to talk more about the services that these platforms, or these devices or these software-defined environments provide. Have I got that right? >> Yeah, yeah you have and even if you look at that, you know ... what the AI/ML, you know I mean if I if I kind of step back and I look at what a customer's trying to do which is to utilize as much data as possible, in a way that they have data freedom that allows them to make decisions and that's really where AI and machine learning comes in. Right you know everybody employs that. I recently bought a camera, shockingly inside the camera it's got ML functionality into it, it's got AI built into it, my new photo editing software on my iPad is actually an ML-based system. They don't do it because it's a buzz word, they do it because basically they can get a much higher level of accuracy and then use data for enrichment, right. And then in the ML track, the classic route was I'm going to create a data lake, right. So I got my data lake and I've got everything in it then I'm going to analyze off the back of it. But everybody was analyzing once it's in the data lake. And what they've realized is to compete, they actually have to analyze much quicker. >> Right. >> And that's at the edge, and that's in real-time and that string based. And so that's really where people are sort of saying I can't ... I'm not going to have any long pole in my technology tent. I'm not going to have anything slow me down, I have to beat my competition and as part of that they need complete fluidity on their data. So I don't care whether it's at the edge or it's in the center or in the cloud, I need instant access to it for enrichment purposes and to make fast and accurate decisions. So they don't want data silos. You know, so any product out there that basically says me me me me give me my data and therefore I'm going to encrypt in such a ways you can't read it and it's not available to anybody else. They are just trying to eradicate that. And and we've sort of moved. It's a weird way of putting it but we've moved from hardware-defined to software-defined and I think we've moved into this data-defined era. But at the same time, it's the most stupid thing for me to say, because we've never not been in a data-defined era. But it's the way in which people think with their architecture as they sign up a data center now or a cloud and they're not saying, hey so about the hardware, it's based on that or it's the software. It's always going to be about the data. The access to the data, however before you get excited. (laughs) The thing that I kind of look at I say so what has fundamentally changed? And it's the fact that we always used to have to make a decision. You know, I ran a security analytics business and when you do things like log management, it's about collecting as much as data so in other words accuracy beats speed. And then security event management is speed beats accuracy. Because you can't ask questions of the same data. But technology is caught up now. So we've actually moved from the do you want accuracy? Or do you want speed? It's like "or arena". So people were building architectures in this "or" world, you know. Do you want software-defined? If you want software-defined you can't have Enterprisilities. Why not? Well, if you want an enterprise application, I mean remember the age-old adage. You should never buy a version 1.0 of an app. >> Right. But what happens is they want they want this ... people are turning around saying I need an enterprise application, I want full data access to the back of it, I actually need it to be fluid, I need it Software-defined, I don't know where it's going to be based and I don't want to do forklift upgrades. I want and and and and and. Not or, so what we've actually moved to is a software-defined era you know, and a data-defined architecture in an "and arena". And where customers are truly winning and where they're going to beat their competition, is where they don't settle and say oh I remember back two years ago, this happened and therefore we should learn from that, and we shouldn't do that. They're actually just breaking through and saying I'm going to fire the application up I want it up and running within 30 days, I want it to be an enterprise application, I need it to be flexible, I needed to have a hype of scale and then I'm going to break it down and by the way I'm not going to pay contractually to an organization to build all that infrastructure. And that's really why soup to nuts, as we move forward not only they sort of building an infrastructure is data-defined infrastructure, they don't want lock-in. They want optionality and that means they want term licenses which is sure, they don't want these proprietary silos and they need data flexibility on the back of it. And those are the progressive customers, and by the way I've not had to convince a single customer to move to software-defined or data-defined. Every client knows they're going there, the question on the journey is, how fast they want to get. >> Right, when? >> Yeah. >> So if so look every single every single enterprise, every single business person takes a look at what are regarded as the most valuable assets and then they hire people to take care of those assets, to get value out of those assets, to maintain those assets, and when we move from a hardware world where the most valuable asset is hardware that leads to one organization, one set of processes, one set of activities. Move into a software world to get the same thing. But we agree with you, we think that we are moving to a world that is data first, where data is increasingly going to be the primary citizen and as a consequence we're seeing firms reinstitutionalize how work is done, redefine the type of people they have, alter their sourcing arrangements, I mean there's an enormous amount of change happening because data is now becoming the primary citizen. So how is Datera going to help accelerate that in 2020? >> Yeah I mean and again that's part of data access. And then also part of data scale. Back probably six seven eight years ago. EMC we were even I remember Steve Manley is a good buddy of mine, we went on stage and we talked about bringing sexy back to back up. We were trying to move away from backup admins just being backup admins to backup admins actually morphing their job into being AI/ML. You know, I remember a big client of mine, and it wasn't in the EMC days, it was before that were basically saying they have to educate their IT staff, they want to bring them up as they move forward. In other words, you can't ... what you don't want is you don't want your team, because it all comes down to people. You don't want them stuck in an area to say we can't innovate forward because we can't get you away from this product, right. So one of our customers at Datera is a SaaS vendor. And their challenge is they had traditional array business even though it was in a SaaS model, it was basically hardware in the background and they would buy instances and they found that their HR cost, their headcount cost was scaling, >> With the hardware. >> Exactly, and and they were looking at and going, what does that do to my business? It does one or two things, either one is it means that cost I mean do I bear that I don't make profitability and I can't drive my business or do I lay that on my customers and then the cost goes up and therefore I'm actually not a cloud scale. And I can't hire all the people I need to hire into it. So they really needed to move to a point of saying how do I get to hyper scale? How do I drive the automation that allows me to basically take staff and do what they need to do. And so our thing isn't removing staff, it's actually taking the work that you have and the people and put them in a way they really matter. So in other words if you think about the old days of I'm going to mess this up but, I talked to somebody recently about what IT stands for. And they said IT should stand for information technology, right. I mean that's really what it is. But, but you know for the last 20 years it stood for infrastructure technology? >> Yeah. >> And that's frustrating, because in essence we got way too many people managing a lot of crap. And what they really should be doing is focusing on what makes the business happen. >> Yeah. >> And for instance I like to run a business by money in and money out, everybody else does and then you look at it and you say well, how do I get more money coming in? By being smarter and quicker than somebody else. How do I do that? By data analytics. Where do I want to put my work? Well I want to put it into the ML/AI and I want more analysts to work on it. I want my IT staff to do that. Let's move them into that. I don't want them you know rooms and reams of people trying to make it you know manage arrays that don't function the way they should or... >> One more percent out of that array of productivity. >> Yeah, abnormally trying to scale HCI solutions to a hyper scale that actually is impossible for them to do it. >> Right. >> You know and and that was the thing that really what Mark, who was the founder of Datera and the team really did is they looked at it from a cloud perspective and said it's got to be easier than this. There must be a way of doing low lights-out automation on storage. And that's why I was saying when I took over, I kind of did the company an injustice by calling it an SDS Tier 1 vendor. But in reality that was what customers could assume. And we're basically a data services platform that allows them to scale and then if you hop forward you go how do you open up the platform? How do you become data movement? How do you handle multi-cloud? How do you make sure that they don't have this issue? And the policies that they put in place and the way in which they've innovated, it allows that open and flexible choice. So for me, one is you get the scale, two you don't have forklift upgrade three is you don't have human capital cost on every decision you make, and it actually fits in in a very fluid way. And so even though customers move to us and buy us as a second source for SDS, once they've got the power of this thing they realize actually now they've got a data service platform and they start then layering in other policies and other systems and what we've seen is then a good uptick of us being seen as a strategic part of their data movement infrastructure. >> You expand. >> Exactly. >> Guy Churchward, CEO of Datera, thanks again for being on the Cube. >> My pleasure. Thank you Peter. >> And thank you for joining us for another CUBEConversation. I'm Peter Burris, see you next time. (upbeat music)

>> From our studios in the heart of Silicon Valley. Holloway Alto, California. It is a cube conversation. >> He will come back and ready Geoffrey here with the Cuban Interpol about those details for acute conversation. We've got a really great guess. He's been on many, many times. We're always excited. Have them on to a bunch of different companies a lot of years and really a great perspective. So we're excited. Guy. Church word. The CEO of Da Terra. Back >> in the politest. EEO guy. Great to see you. >> Thank you, Jeff. Appreciate it. >> Absolutely. So I think last time you were here, I was looking it up. Actually, Was November of twenty eighteen. You were >> kind of just getting started on your day. Terror of the adventure. Give us kind of the update. >> Yeah, I was gonna say last time we had Mark in whose CEO when found a cofounder of Data and I was edging in. So I was executive chairman at the time, you know? And obviously I found the technology. I was looking for an organization that had some forward thinking on storage. Andi, we started to get very close with a large strategic and actually We re announced it on the go to market, I think in February with HP, and I thought that myself and Mark kind of sat down, did a pinky swear and said, OK, maybe it's time for me to step in and take the CEO role just to make sure that we had that sort of marriage of innovation and then some of the operations stuff they could bring inside the business. >> So you've been at this for a >> while, but in the industry for a long time. What was it that you saw? Um, that really wanted you to get deeper in with date. Eriks. Obviously, I'm sure you have tons of opportunities coming your way. You know, to kind of move from the board seat into the CEO position. >> Yeah. Yeah, a bad bet. Maybe stupidity or being drunk. It, to be honest, it was. You know, the first thing is, I was looking for this technology that basically spanned forward, and I had this gut hunch that organizations were looking for data freedom. There's why did the Data Analytics job before that? I did security analytics, and, you know, we were looking at that when we were you know, back when we talk to things like I'm seeing Del and so from appear technology standpoint, I wanted to be in that space, but in the last few months, because you know, jobs are all about learning and then adjusting and learning and adjusting and learning. Adjusting on what I saw is a great bunch of guys, good technology. But we were sort of flapping around on DH had an idea that we were an Advanced data services platform. It's to do with multi, you know, multi cloud. And in essence, I've kind of come to this fundamental kind of understanding because I've been on both sides, which is date era is a bunch of cloud people trying to solve storage needs for what the cloud needs. But they have the experience. They walked that mile. You know, when people say you've gotta learn by walking in their shoes, right? Right on DH there, Done that versus where? Bean. In the past, where we were a ray specialists pushing towards the future that we didn't quite understand, you know, and and there is a fundamental philosopher philosophical difference between the two. Andi weirdly, my analogy or my R har moment came with the Tessler piece. And I know that, you know, you've pinned me a few times on Twitter over this, right? I'm not a tesler. Bigger to the extent of, you know, and probably am now, I should have a test a T shirt on, But I always thought it was an electric car and all they've done is electrified a car and there was on DH, You know, I've resisted it for years and bean know exactly an advocate, but I ended up buying one because I just I felt from a technology standpoint, their platform that they were the right thing. And once I started to really understand what they were about, I saw these severe differences. And, you know, we've chatted a little bit about this Onda again. It's part of the analogy of what's happening in the storage industry, but what's happening in the industry in in a global position. But if you compare contrast something like Tesler, too, maybe Volkswagon and it might be a bad example. But you know, Audi there first trance into electric vehicles was the Audi A three, and I could imagine that they were traditional car people pushing their car forward sort is a combustion engine will if I change that and put some salt powertrain in place, which is the equivalent of a you know, a system to basically drive the wheels and then a bunch of batteries Job done or good, right? Right. And I assume the test it was the same. But I had a weird experience, which is, once you get it into autopilot, you can actually set the navigation direction, and then it will indicate it'll it'Ll hint to you went to change lanes. And so, for instance, I'm driving to the office and I'm going along eight eighty and I want to go toe Wanna one? It says, You know you need to pull across. They hit the indicator will change lanes and they'LL do some of the stuff and that's all well and good. But I was up going to a board meeting on two eighty, going off for the Rosewood. You know, Sandra El Santo and I was listening to a book one of these, you know, audiobooks, and I wasn't really paying much attention. I'm in the outside lane, obviously hitting the speed limit gnome or but I wasn't paying attention. And all of a sudden the car basically indicates form A changes lanes, slows down, change lane again and then takes a junction, slows down, comes up to a junction, and you start to realize that actually Tesla's know about electrified vehicles. It's actually about the telemetry and the analytics and then feeding that back into the system. And I always thought Tesler might be collecting how faster cars going when they break. You know the usual thing. Everybody has this conversation. It's always over worked. But if you've sort of look at it and he said no, maybe they collect everything and then maybe what they're doing is they're collecting, hitting the indicator stalk. So when I'm coming out to a junction and I indicate, How long do I stay? Indicating before I break? And then I changed lanes and then I basically slow down and I go into the junction. And then what they do is they take that live information and crowdsource it, pull it back into the system, and then when they're absolutely bulletproof, that junction, then is exactly as a human would normally do this. They then let the car take over So the difference between the two junctions is one they totally understood, the other one there still learning from right when you look at it and you go done. So they're basically an edge telemetry at a micro level organization, you know, And that is a massive difference between what Tesla's doing and a lot of the other car manufacturers are doing. They're catching up, which is really why I believe that they're going to be a head for a long time. >> It's really interesting. I was >> Elektronik wholesale for ten years before come back to school. Can't got in the tech industry. And so really distribution was king from the manufacturer point of view. Always. They just like ship their products for ages, right? These distribution to break bulk thes distribution, educate the customer these distribution just to get this stuff out. But they never knew how people actually operate their products. Whether that be a car, a washing machine. Ah, cassette player, whatever. So what? What What fascinates me about thes connected devices is what, what a fundamentally different set of data. Now manufacturers have people have in how people actually use the product. But even more importantly, that as you said, they could take that data and make adjustments on the fly because since so much of its software now, we talked again before we turned on some of your software upgrades that you've gotten in the Tesla over the last six months, which we're all driven by customers. But they had a platform in place that enabled them to update functionality and to basically repurpose hardware elements for a new function, which is which is, you know, so in sync with Dev ops and kind of this dev up culture in this continuous this continuous upgrade, this continuous innovation with actual data from real people operating the products that they should come to the market. >> Andi, think once you step back. And that was really why was keen to sit down and talk. And it's not specifically around software defined storage, which is the data. A piece in our example is yes, I am the Tessler because we can do all of the analytics and all of the telemetry versus of standard array. If you scratch that away and you say let's have a look at our whole lives are macro lives. Another example was my wife and I. We've got friends of ours always banging on about these sleep by number beds and and so we went past the store wandered in, and the sales rep got us lying on a bed and he was doing there, you know, pumping the bed up to a size. It's just Well, you are sixty five, a US seventy or seventy five, and I kind of got bored of that. And I went here, Okay, I'm that and he goes, Okay, your wife's of fifty and you're a seventy five, Andi said. But let's kind of daft. And he goes, Well, here's and he shows them a map and it shows a thermal image of me lying on the bed. I'm a side sleeper back sleeper, and then what they do is they feed the information so that comes back off their edge, which is now Abed. And then what they do is they then analyzing continuously prove it to try and increase my bed sleeping patterns. So you look at it and you say what they're not doing is just manufacturing of mattress and throwing it out. What they've done is they said, we're going to treat each individual that lies on the mattress differently on, we're going to take feedback and we're going to make that experience even better. So that the same thing, which is this asset telemetry my crisis telemetry happens to be on the edge is identical to what they have, you know. And then I look at it and I go, Why don't I like the array systems? Will, because the majority of stuff is I'm a far system. My brain is inherently looking at the Dr types underneath and saying, As long as that works fine, everything that sits inside that OK, it'LL do its thing right, and that was built around the whole process and premise of an application has a single function. But now applications create data. That data has multiple functions, and as people start to use it in different ways, you need to feed that data on the way in which is processed differently. And so it all has the intelligence houses in home automation. I'm a junkie on anything that has a plug on it, and I've now got to a point where I have light switches or light fittings would have multiple bulbs on every bulb now is actually Khun B has telemetry around it, which I can adjust it dynamically based on the environment. Right? Right. And I wish it got wine. You know, I got the wine. Fridge is that's my biggest beef right now is you gotta wine, fridge. You can have Jules, you know, you have jewels climates, which means that you don't fan to one side of it and they overheat the bottom right. But it'LL break the grapes down. Would it be really cool if the cork actually had some way of figuring out what it needs to be fed? And then each of them could be individual, right? But our entire being, you know, if you think about it's not just technology or technologies driving it, but it's not the IT industry, but our entire lives. And now driven around exactly what you just described, which is manufacturers dropping something out into the wild to the edge and then having enough telemetry to be able to enhance that experience and then provide over the air, you know, enhancements, >> right? And the other thing, I think it's fascinating as it's looking up. We interviewed Derek Curtain >> from the architect council on. That's a group locally that just try work, too, along with municipalities and car manufacturers, tech companies. But >> he made a really interesting >> comment because there's the individual adjustment to you to know that you want to get off it at Page Milan or sandhill on DH. You've got a counter on your point of this is meeting the Rosewood. But >> then the other thing is, when you aggregate >> that now back up. You know, not that you're going to be sharing other people's data, but when he start to get usage patterns from a large population that you can again incorporate best practices into upgrades of the product and used a really good example of this was right after the one pedestrian got killed by the test of the lady with the bike that ran across the front of the street and it it it literally happened a week before. I think the conference so very hot topic at an autonomous vehicle conference and >> what he said, which is really important. You know, if if I get >> in an automobile accident and I'm going to learn something, the person I hits pride gonna learn something. The insurance adjusters going to take some notes and we're going to learn it's a bad intersection. I made a mistake, whatever, but when an autonomous vehicle gets in a Brack when it's connected, all that telemetry goes back up into the system to feed the system, to make improvements for the whole system. So every car learns every time one car has a problem every time one car gets into a sticky situation. So again, kind of this crowd sourced. Learning an optimization opportunity is fundamentally different than I'm just shipping stuff out, and I don't know what's going to happen to it, and maybe a couple pieces come back. So I think people that are not into this into the direct connection are so missing out on those you said this whole different level of data, this whole different level of engagement, a whole different level of product improvement and road map that's not a PR D. It's not an M R G. It's all about Get it out there, you know, get feedback from the usage and make those improvements on this >> guy finish improvements and micro analytics. I mean, even, you know, we talk back when you were adjusting how you deliver content for the Cube, you know, rather than a big blob, You really want to say, Well, I need more value for that. My clients need more value for that. So you've almost done that Mike segmentation by taking the information and then met attacking every single word in every single interview right to enrich the customer's experience, you know, And it kind of Then you Matt back and you say, We've got to the age now where the staff, the execs that we talked to over the other side, the table there, us they're living our lives. They've got the same kids as we've got the same ages we've got. They do the same person's we've got. They understand the same things and they get frustrated when things naturally don't work the way they should. Like I've got a home theater system and I've still got three remote controls. I can't get down. I've got a universal remote control, but it won't work because the components don't think so. What's happened is we've got to a world where everything's kind of interconnected and everything kind of learns and everything gets enriched when something doesn't it now stands out like a sore thumb and goes, That doesn't That is not the right way to do business on DH. Then you look that you say, translate that then into it and then into data centers. And there's these natural big red flag that says That's an old way of doing things. That's the old economy that doesn't enable me to go forward. I need to go forward. I need more agility. You know, I've got to get data freedom and then how do I solve that issue? And then what? Cos they're going to take me there because they're thinking the same ways as we are. This is why Tesler screamingly successful. This is why something like these beds are there. This is why things like Philips Hue systems are good and the list just goes on. And right now we're naturally inclined to work with products that enable us to enrich our lives and actually give feedback and then benefit us over the air. We don't like things that are too static now, and actually, there is this whole philosophy of cloud, which I think from an economic standpoint, is superb, you know? I mean, our product is Tier one enterprise storage in an SD s fashion for public private hybrid clouds. But we're seeing a lot of people doing bring backs. You know, out of the cloud is a whole thread of it right now, but I would actually say maybe it's not because the cloud philosophy is right, but it's the business model of the cloud guise of God. Because a lot of people have looked at cloud as they're setting. Forget, dump my stuff in the cloud. I get good economics. But what we're talking about now is data gets poked and prodded and moved and adjusted constantly. But the movement of the data is such that if you put in, the cloud is going to impinge you based on the business model. So that whole thing is going to mature as well, right? >> You're such a good position to because >> the, you know the growth of date is going. Bananas were just at at Arcee a couple weeks ago. In one of the conversation was about smart smart buildings, another zip zip devices on shades that tie back to the HBC, and if anybody's in the room or not, should be open should be closed. Where's the sun? But >> there was really interesting comment about >> you know, if you look at things from a software to find way you take what was an independent system that ran the elevator and independent system that ran the HBC and independent system that ran the locks? One that ran the fire alarm. But guess what? If the fire alarm goes off, baby, it would be convenient to unlock all the doors and baby. It would convenient automatically throw the elevator control system into fire mode, which is don't move. Maybe, you know so in reconnecting these things in new and imaginative ways, and then you tie it back to the I T side of the house. You know, it's it's it's it's getting a one plus one makes three effect. With all these previously silent systems that now can be, you know, connected. They can be software defined, you know, they can kind of take the operation till I would have never thought of that one hundred years. I thought that just again this fascinating twist of the Linz and how to get more value out of the existing systems by adding some intelligence and adding this back and forth telemetry. >> Yeah, and and and again, part of May is being the CEO of date era. I want advocates the right platform for people to use. But part of this is my visceral obsession of this market is moving through this software defined pattern. So it's going from being hardware resilient to software resilient to allow youto have flexibility across it. But things have to kind of interconnecting work, as you just described on SDF software to find storage as an example comes in different forms. HD is an example of it and clouds an example. I mean, everything is utterly software defined in Amazon. It so is the term gets misused, could be suffered to find you could say data centric data to find or you could say software resilient. But the whole point is what you've just described, which is open it up, allow data freedom, allow access to it and then make sure that your business is agile and whatever you do, Khun, take the feedback in a continuous loop on at lashing. Move forward as opposed to I've just got this sentence forget or lock mentality that allows me just to sort of look down the stack and say, I've got the silo. I'm owning that customer of owning the data and by the way, that's the job. It's going to doe, right? So this is just the whole concept of kind of people opening their eyes on DH. My encouragement on DI we can encourage anybody, whether customers or basically vendors, is to look around your life and figure out what enriches it from a technology standpoint. On odds on it will be something in the arena that we've just described, right? >> Do you think it's It's because I think software defined, maybe in its early days was >> just kind of an alternative thought to somebody doing it to flipping switches. But as you said in the early example, with the car, propulsion wasn't kind of a fundamentally different way to attack the problem. It was just applying a different way to execute action. What we're talking about now is a is a totally higher order of magnitude because now you've got analytics. You actually want to enable action based on the analytics based on the data for your card. Actually take action, not just a guy. Maybe you should you know, give given alert and notice that pops up on your phone. So, you know, >> maybe we need something different because it's not just redoing >> what we did a different way. It's actually elevating the whole interaction on a whole different kind of love. >> And this is this is kind of thank you for that. It was the profound kind of high got wasn't joining data and watching it. It was I got a demo off the cloud. You I the callback piece of what cloud? What data has. And I was watching a dashboard off a live data stream. You know of information that we were getting back from multiple customers and in each of the customers, it would make recommendations of, you know, how many gets on, how many times it would hear cash on DH. So it was actually coming back dynamically and recommending moving workloads across onto or flash systems. You, Khun, do things where once you've got this freedom on application, a data set isn't unknown. It's now basically in a template, and you say this is what priority has. And so you say it's got high priority. So whatever the best legacy you could give me. Give me right, You drop it onto a disk. And at the moment I've got hybrid. That's all I've got, but I decide to addle flash. So I put some all flash into the into the system. Now it becomes part of this fabric and its spots it and goes well on our second. That will disservice me better and then migrates the workload across onto it without you touching it, right? So, in other words, complete lights out so that the whole thing of this is what Mark and the team have done is looked at and said the only way forward is running this massively agile data center based on a swarm of servers that will basically be plugged together into something that would look like a fabric array. But but you can't. Then you've got to assume that you can now handle application life cycles across onto it. It'LL make recommendations like the bed thing. You know what I was saying? I was lying there and what I liked about it. So So I set my thing to fifty nine, and then it realizes I'm not sleeping very well. It's not suggested. Sixty sixty one sixty. Sleeping well, OK, that's it. And then that's good. We'LL do the same thing where an application will actually say, Here's my template. This is what it looks like. The top priority, by the way. I need the most expensive drives you've got, drops it onto it, and then it look at it and go. Actually, we could do just as good a job if there's on hybrid and then migrated across and optimize the workload, right? And so it's not again. Part of it is not. Data is the best STDs, and it is for Tier one for enterprise storage. It's the fact that the entire industry, no matter where you look at it, not just our industry but everybody is providing tech is doing is exactly the same thing, which is, and you kind of look it and you go. It's kind of edge asset micro telemetry, and then that feedback loop and then continuous adjustment allows you to be successful. That's what products are basically getting underpants. >> Just, you know, it's when he's traveling. Just No, we're almost out of time, but I just can't help it but >> say it, you know, because we used to make decisions >> based on samples of old data with samples. And it was old. And now, because of where we are on the technology lifecycle of drives and networks and CPS and GPS, we can now make decisions based on all the data now. And what a fundamentally different, different decision that's going to drive this too. And then to your point, it's like, What do you optimizing for? And you don't necessarily optimize for the same thing all the time that maybe low priority work, load optimized for cost and maybe a super high value workload optimized for speeding late in sea. And that might change >> over time when Anu workload comes in. So it's such a different way to look at the world >> and it is temporal, right? I mean, again, I know you're going kick me off now, but think about it right the old days and writing a car building a car is you thought, well, what's going to need to be in the car in three years time, put it in now, build manufacture, coming out and then with a Tesler i by the current December. Since December, I've now got pinned based authentication I've got century mode. I've got Dash Cam, They've got all free. I've got a pet mode into it now. My car's got more range. It's got high performance. This guy highest top speed, and I haven't even taken the current or it's all over the air And this is all about, continues optimization. They've done around the platform and you just go. That's the way this linked in. Recently, someone posted something said, You know, keep the eyes are dead. Well, the reason there saying that isn't because there's a stupid thing to do Q. B. Ours is because if you're not measuring your business and adjusting on a continuous basis, you're gonna be dead anyway. So our whole economy is moving this way. So you need an infrastructure architecture to support that. But where everybody's the same, we're all thinking the same. And it doesn't matter what industry or, you know, proclivity have this. This adjustment and this speed of adjustment is what you need. And like I said, I'm That's why I wanted to get to date era. That's what I'm excited about it and that is the are hard I had I kinda looked. It went Oh my God, I'm now working with cloud people who understand what they've walked in the shoes And I kind of got this way of sense of can Imagine what it had been like if you were ill on the first time You saw a hundred thousand cars worth of life data spilling in of what power you have right to adjust and to basically help your client base. And you can't do that if you are in fixed things, right? And so that's That's the world moving forward >> just in time for twenty twenty one will all have great insight in a few short months. We'LL all know >> everything Well, guy great Teo Great to >> sit down Love to keep keeping tabs on you on Twitter and social And thanks for stopping by. I >> appreciate it. All >> right. He's guy. I'm Jeff. You're watching the cube within a cube conversation Or Paulo? What? The studio's thanks for watching >> we'LL see you next time

(orchestral music playing) >> Hi. I'm Peter Burris. Welcome to another Cube Conversation. Brought to you by theCUBE from our beautiful studios in Palo Alto, California. Great conversation today. We're going to be speaking with Datera about some of the new trends and how we're going to utilize data within the business, with greater success, generating more value to superior customer objectives. To do that, we've got Marc Fleischmann, who's the CEO and Founder of Datera. Marc, welcome to theCUBE. >> Thank you. >> And Guy Churchward, who's the Executive Chairman of Datera. >> Yeah, thank you Peter. >> So guys, this is a great topic, great conversation, very very timely industry. One of the reasons is we've heard a lot about the Cloud-native stack. Now the Cloud-native stack is increasingly going to reach into the enterprise and not just demand that everything come back to the cloud, but bring the cloud more to the enterprise. Well one of the things that's still something of a challenge is and how do we bring data given it's native attributes into that model more successfully. Marc, what are the issues? So look, ultimately we believe it's all about data freedom, the capability to extract the value of data across the enterprise. However, as long as we continue to think about proprietary systems silos, where data is trapped, where it can't move freely across the enterprise, we're not going to be able to get there. So ultimately what it requires is changing our thinking of infrastructure from a hard for centric prospective to a service centric prospective. Ready applications drive the needs from the data, where it's an application centric perspective that automatically drives how data is actually consumed across the enterprise. >> But the, we've been thinking about that through software defined, ECI, and other, you know, hyperconversion infrastructure in other things. But at the end of the day, we really have to make sure that we're doing so in a way that marries to the realities of data. >> Absolutely. >> Talk to us a little bit about how Datera is providing that substrate that is native to data, but also native to the cloud. >> Absolutely. So I would describe Datera as Datera is to data what Kubernetes is to compute. What do I mean by that? First of all, it's all about data orchestration. We orchestrate the data just like Kubernetes would orchestrate compute. That's the foundation of our platform. Now if we don't deliver enterprise performance, so that we can actually, you know, replace existing storage, we wouldn't be able to actually broadly deploy. So we have enterprise performance as well. And lastly, to get away from a hard for centric model, we offer wide variety, wide choice, future ready choice of Harver. Those are the three key tenants that we actually see as getting to that vision. >> So Guy, you've been in this business a long time. You've looked at a lot of changes in technology, for rays where we were mainly focused on persisting data to now some of the new technologies, we were more focusing on delivering data to new classes of applications. From your perspective, how does this message Marc's bringing line up with customer needs? >> Yeah, I know, appreciate it. I mean that was one of the reasons that when I had the opportunity to work closely with Datera, I kind of jumped into it. You know, because part of this is, as Marc said, data freedom. Unlocking, in other words, unlocking from the boundaries of basically a physical location. I think, you know, we always aspire and believe that we want to move towards a cloud, a pure cloud model. But we're going to be in this transition for five, six, seven years where we have on premise a bit of hybrid and a bit of distributed and things like Intelligent Edge. So in other words, the whole concept is to say how do I utilize data no matter where it is into a fabric or a mesh. And I think that the industry that we all live in sort of, by accident, tries to own the data, you know. It doesn't matter whether you own it in a physical construct of a data center or we own it in a physical construct of a piece of hardware or a proprietary format. But in essence you have these data silos absolutely everywhere. And so for me to move to a cloud, you've got the simplicity you need. You've got the orchestration that you actually need. But you need this freedom outside of the bounds of a physical location or a piece of tent. >> I want to return back to the issues of performance >> Yeah. >> and the need for performance because the world that you just laid out guys, makes an enormous amount of sense to me and the Wikibon community. But it does mean that this data generated by that application in this location may have value to some other applications somewhere else that may have completely different performance action. >> Absolutely. >> So let's talk about that need for ensuring, that again, this notion of a native data approach to incorporating data into the cloud. How does the performance angle really work? >> I would argue where traditional self defined storage, SDS, fell short was exactly on the promise of performance. We saw that we contributed a significant part of the Linux data path itself. The way we architected the system, we delivered true, primary application performance. So that in combination with the ability to orchestrate data across the data center, across multiple data centers, and ultimately across the data center and the cloud gives you the best of both worlds. It gives you primary workloads, the ability to actually serve primary workloads across multiple protocols, but to serve them location dependent, wherever you like, because we orchestrate the data through those places. >> And- >> So- >> Oops! Go ahead. >> Sorry. It's the coffee. It's going to kick in. (Peter laughs) So I mean part of it is not just that, but it's also the life cycle. >> Ah, very true. Right, I mean and, you know, this is the thing that kind of attracts me is, and you mentioned, you know, what you learn with the amount of hair I don't have now and the gray beard I've got is, you know, there's one thing about this sort of data boundaries and things getting locked in. The other one is the speed of which people want to build an application. They need it to be have the enterprisilities, and then they'll take the application down. You know, if you kind of think when we started in the industry and it would last 20 years. And then 10 years. And then five years. And now you look at it saying somebody wants an enterprisility application up and running within two or three months, which is preposterous, but needs to be done. And then it might be down within a month. Because- >> Oh 15 years ago it took us two or three months to create the test data required for the application to follow up. >> Right, and how many people would ever used to tell you never use an application if it's a window zero. But we're talking about, in a window zero period, they're actually going to serve their communities, the most critical thing. Data is it for a company. If you're analytics don't run as fast as your company's competitive space, you're behind. So if you're going to analyze something that application that you bring up to analyze has to be critical to your business. And that's going to go up and it's going to go down. So in other words, it's going to go from test and dev, up into production, tier zero, then tier one, tier two, tier three, and then out into an archive in a period of time that normally a window zero would gestate. And so you need a platform that has that ultimate agility and again it can't be bound by anything. And this is something that, you know, Datera has as unique. This was why I like software defined and why I believe that this market's place is now for this space. Everything prior to SDS is basically what I call new legacy. You know, it doesn't matter whether it's a ray or it's hyperconversion, and they're great and they've got their place. But each one of them has this fixed boundary that allows you to flex but inside of its own control. Businesses aren't like that. They can't be done like that and applications can't be done like that now. So it's all multi-cloud, it's all going to be versed. >> Well let's build on that. So the Kubernetes describes, as you said, a cluster of compute. When you pull away the- It's really a network of compute. >> That's right. >> It's a network of compute resources that Kubernetes has visibility into so we can move resources >> That's right. >> Or move elements where they need to be to be optimally utilized. Let's build on that. So what where is Datera in this relationship between resources as it starts to build a an orchestrator, a manager, a network of data elements, and pull that into something that makes it easier for developers to do what they need to do, operators to do what they need to do, and the business to do what it needs to do? >> Yeah, so you can call Kubernetes the network of compute or a swarm of compute, right? So the power of Kubernetes is that it abstracts the infrastructure to a level where it gets delivered continuously to the application on demand. We do exactly the same thing for data, for the ability to store, manage, and ultimately life cycle data. So simply label based, like Kubernetes is, you specify the service level objectives for every individual application, and Kubernetes pretty much does all the rest of the job, completely independent of the hardware underneath. Again, we do that for data. You have certain access requirements, protocols, authentications, security. You have certain performance requirements. You have certain reliability requirements. You articulate them simply in similar SLO, service level objectives. Datera does all the actual implementation automatically across the data center. So now you get to a point where in the modern data center and the soft defined data center, I would argue we are the data foundation in those kinds of scenarios, we can co-orchestrate data along, since you said Kubernetes specifically with Kubernetes, with its compute. Obviously we work in other environments as well. We work equally well for Enver. We work for some other, a number of other cloud orchestration frameworks. But Kubernetes is a really good example here. >> So who's going to buy it? I mean cause going back to this issue of the orchestrator, the developers clearly need this because they want access to real data, but they typically don't think in terms of underlying data structures. If it's available that's all they care about. Data administrators, business people. Who do you find your customers today are really making that, not the initial contact, but actually driving the adoption of this new data fabric? >> So Marc, I mean I know you'll answer it more accurately than I will. But just from a higher level to step down, there seems to be two types of people inside of large companies. One is a project owner. So for instance, you know, I've been blessed with a job inside of BMW that I have to do, autonomic cars. And I'm tying together a very complicated pipeline that has to be extremely agile. So that's the type of person that would basically look to buy and move us forward. And the other one is an internal service provider to the enterprise. So in other words, instead of being a group that has a physical job, what I'm actually doing is I'm saying I'm now going to be a service provider, or a cloud provider, or a resource provider to an organization that now has complexity that's moving into and embracing the digital economy or digital transformation. So if those are the two types of person inside of an organization, I think if you get a tie kicker, you know, there are places that we struggle with, I think it would be fair to say, is there's always going to be a geek somewhere that wants to kick the latest, cool technology, so we get involved with that. And then by the time you go all the way through it, there's no project there. They just really enjoyed themselves and so have we. But in essence there's enough people now who recognize my business is going through this transformation, I need to get out of my technical debt, I'm throwing business into, you know, this economy. It's normally around machine learning applications, Kubernetes, things that are fast moving, you know. And they need that level of ility that they're used to getting through fixed bounded technology, you know. And so we're actually seeing that as a service provider, both external and internal. But internal, inside the enterprises, is something which we're very key on. >> And let me give you perhaps a few examples. We're looking at Fortune 2000 companies. A good example, for instance, would be one of the top airlines in the world that is replatforming from a more rigid siloed IT to really deliver all their applications to internal and external customers as a service. It would also be digital businesses where there currency really is speed, agility, and obviously data is their currency. So if you're looking here at one of the top travel fare aggregators, that's one of the customers, actually interestingly we are in their tier zero at Storch. That's quite an endorsement of the performance aspect. We are also in one of, I would say, the leading service providers outside of the typical crowd you think, those are one of the up and coming guys. So those are typical markets and customers we're looking at. Really Fortune 2000 companies that are replatforming to cloud, hybrid cloud, and digital service businesses. Digital businesses. >> But it is most people who are basically going from, they're transforming their data center into a metadata center. They're embracing the distribution and then cloud. But they're not going wholesale and just saying (claps hands) we're over. They have this practicality of first thing I need to do is to free up my data, make my data center agile, and then decide how I want to distribute it across. >> Marc Fleischmann. Guy Churchward. Datera. Thank you very much for being on theCUBE. >> Thank you very much Peter. >> A pleasure. Thank you. >> And once again, this is Peter Burris from our CUBE studios in Palo Alto, California. Thanks very much for participating in this CUBE conversation with Datera. (orchestral music plays)

(inspirational music) >> Hello, I'm Peter Burris of Wikibon. Welcome to another Cube Conversation. We're going to have a great time talking over the next few minutes about the role that performance in the data plain is going to play at making possible both the options provided by the Cloud, but at the same time in a way that actually allows us to actually run the applications at the speed and the scale the business requires. To do that, we've got Marc Fleishman, who's the CEO and founder of Datera, and Marc Churchward who's the Executive Chairman at Datera. Welcome back to theCube, guys. >> Thank you for having us, Peter. >> So, Marc, I want to start with what I started with. That at the end of the day we've got this enormous agility that we're provided with in the CloudStack, but you still have to run on real computers that have real constraints and everybody knows that there is no greater constraint than maintaining the state of data and moving data. So how does Datera address those issues? >> Part of data freedom obviously is not only about automation, the the promise of software-defined storage is seamless automation. But unfortunately in many cases with unimpressive performance. In our case, we've engineered the whole data path down to the physical devices, ourselves. The levels of performance we can deliver is millions of IOPS across the data center at less than two hundred microseconds latency. Most importantly, on standard servers, over standard protocols, so nothing fancy in terms of hardware required. That's the true promise of software-defined storage. >> Now you mentioned automation. That kind of performance has got to open up new classes of automation potential, so that the storage or the data resources are that much easier to envision, that much easier to apply, that much easier to exploit by the development community. Tell us a little bit about how automation plays into this. >> Absolutely, once you've made data delivery frictionless, and you've made data orchestration and data automation frictionless, you do unlock new classes of applications. What we're specifically seeing is folks who traditionally run an array of databases on very dedicated proprietary hardware, and then again they get the data trapped in those silos, and they have a real hard time to extract the value off that data, we see a lot of database farms coming on our unified platform across the data center, basically being able to really extract the value of the data across a range of applications. >> Now we've been in the last few years investing pretty heavily in storage area networks and arrays and those types of resources. Flash is changing that, but it sounds as though you guys are actually making it easier to bring servers into the mix of this. What's the real direction you see? Where's this resource going to be managed by and what's the opportunity? >> Ultimately the resource should be managed by the applications, it should be driven by the applications and managed by machine learning. So as we understand the requirements of the applications, every individual application, it should be managed by machine learning in terms of the physical resources on the servers. The server capabilities you put underneath it, and then obviously start rolling the server hardware, as technology improves as well over time. >> So it's really being driven by the server, that's where the market opportunity's coming from. >> That's right, yes. >> The last question I have here is, when we think about new technology, new classes of automation, new trends in the industry, people always immediately go, "Yeah, but new companies?" Where does Datera fit in its lifecycle as it works with customers and as it delivers value out? >> If you look at the market today, server-based storage is already larger than traditional array-based storage. It's growing at five X, year by year. Since we've been on theCUBE the last time, about two years ago, we are now looking at a 240% kegger every year, so the market has clearly come our way. This is the time for this kind of product. >> So the market's good, company's good, trends are good. As we think ultimately about where this ends up in a few years, what role will Datera play within the evolved computing industry? What do you see for it? >> Given that we have the broad data orchestration, enterprise performance and choice on hardware, we really do see ourselves as the data foundation for the software-defined data center. What I mean by that, again, just in an operational model, we are to data what Cooper Ladies is to compute, across a number of operating environments. So it's a really broad data foundation for everyone who wants to deliver ITS service. >> Guy, I have a very simple question for you, very complex answer. One of the places where this seems to be especially important, where the need is especially great, is in that world of analytics. Especially as we try to close the loop between the analytical systems and the operational systems. How does Datera and analytics come together, not just in the use of analytics to make Datera better, but Datera in making analytics applications run better. >> Yeah, and as you said, an easy question, complicated answer. In reality, what companies are trying to do is to run the analytics at the speed of which they're competing in their market space. Which means that it has to get a lot faster. Today's classic environment is an ETL with a data leak, so parking stale data and analyzing it, post-event and tomorrow, in an environment where people are using AI and ML is now in stream and it's in real time. So part of that is you actually have very very fast applications, both from a performance perspective, but also how long their lifecycle is. Because people are doing AB testing on the web, they're doing analytics on the fly, and it really is a kind of a different world. It's a different pace. When I started this business, or when I was in business early and I had hair, we used to look at organizations that had applications that were lasting 10 or 20 years. Now we're looking at enterprise applications that are up and down within a period of months, if not weeks. So managing that lifecycle and not having to invest in infrastructure to support something, that age-old adage of you don't buy an application if it's in 1.0 is gone. Because by the time you're into 1.1, that opportunity's disappeared as well. So, part of what I saw in the attraction with Datera is because it's absolutely software-defined, and all the resilience handles in the software not the hardware. There's not the infrastructure burden. It has much more agility to get up. It can provide tier zero, tier one. Again, you land and expand, so in test endeavor you have the same environment. By a matter of flipping a few switches, you can have tier-one-ilities and then you can drop down in that lifecycle. It doesn't matter whether it's on premise, whether it's a distributed environment or on Cloud. It's the same infrastructure, same architecture, so, back to what Marc said, you have data freedom. >> So we're trying to tie the physical realities of data, to the virtual realities of machine resources in IT, to the Cloud realities of the new wave of applications. >> That's exactly right. >> Marc Fleishman, CEO and founder of Datera, Guy Churchward, Executive Chairman of Datera, thanks very much for being on theCUBE. >> Thanks for having us Peter. >> Thank you Peter. >> And once again, this is Peter Burris, Wikibon, thanks for watching theCUBE. (inspirational music)

>> Announcer: 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, continues, this is our first day. We are down the street from the Strata Data Conference. Come by, we're at this really cool venue, the Forager Tasting Room. We've got a cocktail party tonight. You're going to hear some insights there as well as tomorrow morning. I am Lisa Martin, joined by my co-host, George Gilbert, and we welcome back to theCUBE, for I think the 900 millionth time, the president and CEO of DataTorrent, Guy Churchward. Hey Guy, welcome back! >> Thank you, Lisa, I appreciate it. >> So you're one of our regular VIP's. Give us the update on DataTorrent. What's new, what's going on? >> We actually talked to you a couple of weeks ago. We did a big announcement which was around 3.10, so it's a new release that we have. In all small companies, and we're a small startup, in the big data and analytic space, there is a plethora of features that I can reel through. But it actually makes something a little bit more fundamental. So in the last year... In fact, I think we chatted with you maybe six months ago. We've been looking very carefully at how customers purchase and what they want and how they execute against technology, and it's very very different to what I expected when I came into the company about a year ago off the EMC role that I had. And so, although the features are there, there's a huge amount of underpinning around the experience that a customer would have around big data applications. I'm reminded of, I think it's Gartner that quoted that something like 80% of big data applications fail. And this is one of the things that we really wanted to look at. We have very large customers in production, and we did the analysis of what are we doing well with them, and why can't we do that en masse, and what are people really looking for? So that was really what the release was about. >> Let's elaborate on this a little bit. I want to drill into something where you said many projects, as we've all heard, have not succeeded. There's a huge amount of complexity. The terminology we use is, without tarring and feathering any one particular product, the open source community is kind of like, you're sort of harnessing a couple dozen animals and a zookeeper that works in triplicate... How does DataTorrent tackle that problem? >> Yeah, I mean, in fact I was desperately interested in writing a blog recently about using the word community after open source, because in some respects, there isn't a huge community around the open source movement. What we find is it's the du jour way in which we want to deliver technology, so I have a huge amount of developers that work on a thing called Apache Apex, which is a component in a solution, or in an architecture and in an outcome. And we love what we do, and we do the best we do, and it's better than anybody else's thing. But that's not an application, that's not an outcome. And what happens is, we kind of don't think about what else a customer has to put together, so then they have to go out to the zoo and pick loads of bits and pieces and then try to figure out how to stitch them all together in the best they can. And that takes an inordinately long time. And, in general, people who love this love tinkering with technologies, and their projects never get to production. And large enterprises are used to sitting down and saying, "I need a bulletproof application. "It has to be industrialized. "I need a full SLA on the back of it. "This thing has to have lights out technology. "And I need it quick." Because that was the other thing, as an aspect, is this market is moving so fast, and you look at things like digital economy or any other buzz term, but it really means that if you realize you need to do something, you're probably already too late. And therefore, you need it speedy, expedited. So the idea of being able to wait for 12 months, or two years for an application, also makes no sense. So the arch of this is basically deliver an outcome, don't try and change the way in which open source is currently developed, because they're in components, but embrace them. And so what we did is we sort of looked at it and said, "Well what do people really want to do?" And it's big data analytics, and 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. And so, we looked at it and said, "Okay, so the majority "of stuff we need is what we call a cache stack, "which is KAFKA, Apache Apex, Spark and Hadoop, "and then put complex compute on top." So you would have heard of terms like machine learning, and dimensional compute, so we have their modules. So we actually created an opinionated stack... Because otherwise you have a thousand to choose from and people get confused with choice. I equate it to going into a menu at a restaurant, there's two types of restaurants, you walk into one and you can turn pages and pages and pages and pages of stuff, and you think that's great, I got loads of choice, but the choice kind of confuses you. And also, there's only one chef at the back, and he can't cook everything well. So you know if he chooses the components and puts them together, you're probably not going to get the best meal. And then you go to restaurants that you know are really good, they generally give you one piece of paper and they say, "Here's your three entrees." And you know every single one of them. It's not a lot of choice, but at the end of the day, it's going to be a really good meal. >> So when you go into a customer... You're leading us to ask you the question which is, you're selling the prix fixe tasting menu, and you're putting all the ingredients together. What are some of those solutions and then, sort of, what happens to the platform underneath? >> Yeah, so what you don't want to do is to take these flexible, microdata services, which are open source projects, and hard glue them together to create an application that then has no flexibility. Because, again, one of the myths that I used to assume is applications would last us seven to 10 years. But what we're finding in this space is this movement towards consumerization of enterprise applications. In other words, I need an app and I need it tomorrow because I'm competitively disadvantaged, but it might be wrong, so I then need to adjust it really quick. It's this idea of continual developed, continual adjustment. But that flies in the face of all of this gluing and enterprise-ilities. And I want to base it on open source, and open source, by default, doesn't glue well together. And so what we did is we said okay, not only do you have to create an opinionated stack, and you do that because you want them all to scale into all industries, and they don't need a huge amount of choice, just pick best of breed. But you need to then put a sleeve around them so they all act as though they are a single application. And so we actually announced a thing calls Epoxy. It's a bit of a riff on gluing, but it's called DataTorrent Epoxy. So we have, it's like a microdata service bus, and you can then interchange the components. For instance, right now, Apache Apex is this string-based processing engine in that component. But if there's a better unit, we're quite happy to pull it out, chuck it away, and then put another one in. This isn't a ubiquitous snap-on toolset, because, again, the premise is use open source, get the innovation from there. It has to be bulletproof and enterprise-ility and move really fast. So those are the components I was working on. >> Guy, as CEO, I'm sure you speak with a lot of customers often. What are some of the buying patterns that you're seeing across industries, and what are some of the major business value that DataTorrent can help deliver to your customers? >> The buying patterns when we get involved, and I'm kind of breaking this down into a slightly different way, because we normally get involved when a project's in flight, one of the 80% that's failing, and in general, it's driven by a strategic business partner that has an agenda. And what you see is proprietary application vendors will say, "We can solve everything for you." So they put the tool in and realize it doesn't have the flexibility, it does have enterprise-ility, but it can't adjust fast. And then you get the other type who say, "Well we'll go to a distro or we'll go "to a general purpose practitioner, "and they'll build an application for us." And they'll take open source components, but they'll glue it together with proprietary mush, and then that doesn't then grow past. And then you get the other ones, which is, "Well if I actually am not guided by anybody, "I'll buy a bunch of developers, stick them in my company, "and I've got control on that." But they fiddle around a lot. So we arrive in and, in general, they're in this middle process of saying, "I'm at a competitive disadvantage, "I want to move forward and I want to move forward fast, "and we're working on one of those three channels." The types of outcomes, we just, and back to the expediency of this, we had a telco come to us recently, and it was just before the iPhone X launched, and they wanted to do AB testing on the launch on their platform. We got them up and running within three months. Subsequent from that launch, they then repurposed the platform and some of the components with some augmentation, and they've come out with three further applications. They've all gone into production. So the idea is then these fast cycles of microdata services being stitched together with the Epoxy resin type approach-- >> So faster time to value, lower TCO-- >> Exactly. >> Being able to get to meet their customers' needs faster-- >> Exactly, so it's outcome-based and time to value, and it's time to proof. Because this is, again, the thing that Gartner picked up on, is Hadoop's difficult, this market's complex and people kick the tires a lot. And I sort of joke with customers, "Hey if you want to "obsess about components rather than the outcome, "then your successor will probably come see us "once you're out and your group's failed." And I don't mean that in an obnoxious way. It's not just DataTorrent that solves this same thing, but this it the movement, right? Deal with open source, get enterprise-ilities, get us up and running within a quarter or two, and then let us have some use and agile repurposing. >> Following on that, just to understand going in with a solution to an economic buyer, but then having the platform be reusable, is it opinionated and focused on continuous processing applications, or does it also address both the continuous processing and batch processing? >> Yeah, it's a good answer. In general, and again Gatekeeper, you've got batch and you've got realtime and string, and so we deal with data in motion, which is string-based processing. A string-based processing engine can deal with batch as well, but a batch cannot deal with string. >> George: So you do both-- >> Yeah >> And the idea being that you can have one programming model for both. >> Exactly. >> It's just a window, batch is just a window. >> And the other thing is, a myth bust, is for the last maybe eight plus years, companies assume that the first thing you do in big data analytics is collect all the data, create a data lake, and so they go in there, they ingest the information, they put it into a data lake, and then they poke the data lake posthumously. But the data in the data lake is, by default, already old. So the latency of sticking it into a data lake and then sorting it, and then basically poking it, means that 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 it after at rest, right? So now the architecture of choice is ingest the information, use high performance storage and compute, and then, in essence, ingest, normalize, enrich, analyze, and act on data in motion, in memory. And then when I've used it, then throw it off into a data lake because then I can basically do posthumous analytics and use that for enrichment later. >> You said something also interesting where the DataTorrent customers, the initial successful ones sort of tended to be larger organizations. Those are typically the ones with skillsets to, if anyone's going to be able to put pieces together, it's those guys. Have you not... Well, we always expected big data applications, or sort of adaptive applications, to go mainstream when they were either packaged apps to take all the analysis and embed it, or when you had end to end integrated products to make it simple. Where do you think, what's going to drive this mainstream? >> Yeah, it depends on how mainstream you want mainstream. It's kind of like saying how fast is a fast car. If you want a contractor that comes into IT to create a dashboard, go buy Tableau, and that's mainstream analytics, but it's not. It's mainstream dashboarding of data. The applications that we deal with, by default, the more complex data, they're going to be larger organizations. Don't misunderstand when I say, "We deal with these organizations." We don't have a professional services arm. We work very closely with people like HCL, and we do have a jumpstart team that helps people get there. But our job is teach someone, it's like a kid with a bike and the training wheels, our job is to teach them how to ride the bike, and kick the wheels off, and step away. Because what we don't want to do is to put a professional services drip feed into them and just keep sucking the money out. Our job is to get them there. Now, we've got one company who actually are going to go live next month, and it's a kid tracker, you know like a GPS one that you put on bags and with your kids, and it'll be realtime tracking for the school and also for the individuals. And they had absolutely zero Hadoop experience when we got involved with them. And so we've brought them up, we've helped them with the application, we've kicked the wheels off and now they're going to be sailing. I would say, in a year's time, they're going to be comfortable to just ignore us completely, and in the first year, there's still going to be some handholding and covering up a bruise as they fall off the bike every so often. But that's our job, it's IP, technology, all about outcomes and all about time to value. >> And from a differentiation standpoint, that ability to enable that self service and kick off the training wheels, is that one of the biggest differentiators that you find DataTorret has, versus the Tableau's and the other competitors on the market? >> I don't want to say there's no one doing what we're doing, because that will sound like we're doing something odd. But there's no one doing what we're doing. And it's almost like Tesla. Are they an electric car or are they a platform? They've spurred an industry on, and Uber did the same thing, and Lyft's done something and AirBNB has. And what we've noticed is customer's buying patterns are very specific now. Use open source, get up their enterprise-ilities, and have that level of agility. Nobody else is really doing that. The only people that will do that is your contract with someone like Hortonworks or a Cloudera, and actually pay them a lot of money to build the application for you. And our job is really saying, "No, instead of you paying "them on professional services, we'll give you the sleeve, "we'll make it a little bit more opinionated, "and we'll get you there really quickly, "and then we'll let you and set you free." And so that's one. We have a thing called the Application Factory. That's the snap on toolset where they can literally go to a GUI and say, "I'm in the financial market, "I want a fraud prevention application." And we literally then just self assemble the stack, they can pick it up, and then put their input and output in. And then, as we move forward, we'll have partners who are building the spoke applications in verticals, and they will put them up on our website, so the customers can come in and download them. Everything is subscription software. >> Fantastic, I wish we had more time, but thanks so much for finding some time today to come by theCUBE, tell us what's new, and we look forward to seeing you on the show again very soon. >> I appreciate it, thank you very much. >> We want to thank you for watching theCUBE. Again, Lisa Martin with my co-host George Gilbert, we're live at our event, Big Data SV, in downtown San Jose, down the street from the Strata Data Conference. Stick around, George and I will be back after a short break with our next guest. (light electronic jingle)

(upbeat electronic music) >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're having a CUBE Conversation in the Palo Alto studio, a little bit of a break from the crazy conference season, so we can have a little more intimate conversation without the madness of some of the shows. So we're really excited to have many-time CUBE alumni, Guy Churchward, on. He's the president and CEO of DataTorrent. Guy, great to see you. >> Thank you, Jeff, 'preciate it. >> So how have you been surviving the crazy conference season? >> It's been crazy. This is very unusual. It's just calm and quiet and relaxed, and there's not people buzzing around, so it's different. >> So you've been at DataTorrent for a while now, so give us kind of the quick update, where you guys are, how things are moving along for you. >> Yeah, I mean, I've kicked in about five months, so I think I'm just coming up to sort of five and a half, six months, so it's a enough time to get my feet wet, understand whether I made a massive mistake or whether it's exciting. I'm still-- >> Jeff: Still here, you're wearing the T-shirt. >> Yeah, I'm pleased to say I'm still very excited about it. It's a great opportunity, and the space is just hot, hot. >> So you guys are involved in streaming data and streaming analytics, and you know, we had Hadoop, was kind of the hot thing in big data, and really the focus has shifted now to streaming analytics. You guys are playing right in that space and have been for a while, but you're starting to make some changes and come at the problem from a slightly different twist. Give us an update on what you guys are up to. >> Yeah, I mean, so when I dropped into DataTorrent, obviously, it's real-time data analytics, based on stream processing or event processing. So the idea is to say instead of doing things like analytics, insight, and action on data at rest, you know, traditional way of doing things is sucking data into a data store and then poking it litigiously at sort of a real-time analytics basis. And what the company decided to do, and again, this is around the founders, is to say if you could take the insight and action piece and shift it left of the data store in memory and then literally garner the insight and action when an event happens, then that's obviously faster and it's quicker. And it was interesting, a client said to us recently that batch, or stream, or near real-time, or microbatch, is sort of like real-time for a person, 'cause a person can't think that fast. So the latency is a factor of that, but what we do is real-time for a computer. So the idea here is that you literally have sub-second latency and response and actions and insight. But anyway, they built a toolkit, and they built a development platform, and it's completely extensible, and we've got a dozen customers on board, and they're high production, and people are running a billion events per second, so it's very cool. But there wasn't this repeatable business, and I think the deeper I got into it, you also look at it and you say, "Well, Hadoop isn't the easiest thing to deploy." >> Jeff: Right, right, consistently. >> And, the company had this mantra, really, of going to solve total cost of ownership and time to value, so in other words, how fast can I get to an outcome and how cheap is it to run it. So can you create unique IP on top of opensource that allows you to basically get up and running quickly, it's got a good budget constraint from a scale-up perspective and scale-out, but at the same time, you don't need these genius developers to work on it because there's only a small portion of people who basically can deploy a Hadoop cluster in a massive scale in a reliable way. So we thought, well, the thing to do is to really bring it into the masses. But again, if you bring a toolkit down, you're really saying here's a toolkit and an opportunity, and then build the applications and see what you can do. What we figured is actually what you want to do is to say, no, let's just see if we can take Hadoop out of the picture and the complexity of it, and actually provide an end-to-end application. So we looked to each of the customers' current deployments and then figured out, can we actually industrialize that pipeline? In other words, take the opensource components, ruggedize them, scale them, make sure that they stay up, they're full torrents, 7x24, and then provide them as an application. So we're actually shifting our focus, I think, from just what are called the apex platform and the stream-based processing platform to an application factory and actually producing end-to-end applications. >> 'Cause it's so interesting to think of batch and batch in not real-time compared to real-time streaming, right? We used to take action on a sample of old data, and now, you've got the opportunity to actually take action on all of the now data. Pretty significant difference. >> Yeah, I mean, it kills me. I've got to say, since the last time we met, I literally wrote a blog series, and one of them was called Analytics, Real-Time Analytics versus Real-Time Analytics. And I had this hilarious situation where I was talking to a client, and I asked then, and I said, "Do you do real-time analytics?" They go, "Yeah." And I said, "Do you work on real-time data?" And they said, "Yeah." And I said, "What's your latency between an event happening "and you being able to take an action on the event?" And he said, "Well, 60 milliseconds." It's just amazing. I said, "Well, tell me what your architecture looks like." And he says, "Well I take Kafka into Apex as a stream. "I then import it in essence into Cassandra, "and then I allow my customers to poke the data." So I said, "Well, but that's not 60 milliseconds." And he goes, "No, no, it is." And I said, "What are you measuring?" He goes, "Well, the customer basically puts "an inquiry onto the data store." And so literally, what he's doing is a real-time query against a stale data that's sitting inside of a date lake. But he swore blind. >> But it's fast though, right? >> And that's the thing is he's looking, he say, "Hey, well, I can get a really quick response." Well, I can as well. I mean, I can look at Google World and I can look at my house, and I can find out that my house is not real-time. And that's really what it was. So you then say to yourself, well look, the whole security market is based around this technology. It's classic ETL, and it's basically get the data, suck it in, park it into a data store, and then poke at it. >> Jeff: Right >> But that means that that latency, by just the sheer fact that you're taking the data in and you're normalizing it and dropping it into a data store, your latency's already out there. And so one of the applications that we looked at is around fraud, and specifically payment fraud and credit card fraud. And everything out there in the market today is basically, it's detection because of the latency. If you kind of think about it, credit card swipe, the transaction's happened, they catch the first one, they look at it and say, "Well, that's a bit weird." If another one of these ones comes up, then we know we've got fraud. Well, of course, what happens is they suck the data in, it sits inside a data store, they poke the data a little bit later, and they figure out, actually, it is fraud. But the second action has happened. So they detected fraud, but they couldn't prevent it, so everything out there is payment fraud prevention, or payment fraud detection because it's basically got that latency. So what we've done is we said to ourself, "No, we actually can prevent it." Because if you can move the insight and actions to the left-hand side of the data store, and as the event is happening, you literally can grab that card swipe and say no, no, no, you don't do it anymore, you prevent it. So, it's literally taking that whole market from, in essence, detection to prevention. And this is, it's kind of fascinating because there's other angles to this. There's a marketplace inside the credit card site that talks about card not present, and there's a thing called OmniChannel, and OmniChannel's interesting, 'cause most retailers have gone out there and they've got their bricks and mortar infrastructure and architecture and data centers, and they've gone and acquired an online company. And so, now, they have these two different architectures, and if you imagine if you got to hop between the two, it kind of has gaps. And so, the fraudsters will exploit OmniChannel because there's multiple different architectures around, right? So if you think about it, there's one side of saying, hey, if we can prevent that, so taking in a huge amount of data, having it talk, having a life cycle around it, and literally being able to detect and then prevent fraud before the fraudsters can actually figure out what to do, that's fantastic, and then on the plus side, you could take that same pipeline and that same application, and you can actually provide it to the retailers and say, well, what you'd want to do is things like, again, I wrote another blog on it, loyalty brand. You know, on the retail side, is for instance, my wife, we shop like crazy, everybody does. I try not to, but let's say she's been on the Nordstrom site, and we've got a Nordstrom. So Nordstrom has a cookie on their system and they can figure what had been done. And she's surfing around, and she finds a dress she kind of likes, but she doesn't buy it because she doesn't want to spend the money. Now, I'm in Nordstrom's about four weeks later, and I'm literally buying a pair of socks. A card swipe, and what it does is because you've got this OmniChannel and you can connect the two, what they want to do is to be able to turn around and say, "Oh, Guy, before we run this credit card, "we noticed that your wife was looking at this dress. "We know her birthday's coming up. "And by the way, we've checked our store, "and we've got the color and the size "she wants it in, and if you want, "we'll put it on the credit card." >> Don't tell her that, she already bought too much. She won't want you to get that dress. Nah, it's a great, it's a really interesting example, right? >> But it is that, and if you kind of think about it, and this where, when they say every second counts, it's like every millisecond counts. And so it really is machine-to-machine, real-time, and that's what we're providing. >> Well, that's the interesting, you know, a couple things just jump into mind as you're talking. One is by going the application route, right, you're reducing the overhead for just pure talent that we keep hearing about. It's such a shortage in some of these big data applications, Hadoop, specifically. So now, you're delivering a bunch of that, that's already packaged to do a degree in an application, is that accurate? >> Yeah, I mean I kind of look at the engineering talent inside an organization is like a triangle. And at the very top, you have talented engineers that basically can hard code and that's really where our technology has sat traditionally. So, we go to a large organization. They have a hundred people dedicated to this sport. The challenge is then it means the small organizations who don't have it can't take advantage. And then you've got at the base end, you have technologies like Tableau, you know, as a GUI that you can use by an IT guy. And in the middle you've got this massive swath of engineering talent that literally isn't the, Yoda hardcode on the analytics stuff and really can't do the Hadoop cluster. But they want to basically get dangerous on this technology, and if you can take your, you know, the top talent, and you bring that in to that center and then provide it at a cost economics that makes sense, then you're away. And that's really what we've seen is. So our client base is going to go from the 1410, 1420, 1450s, into the 14,000s and you bring it down, and that's really, if you think about it, that's where Splunk kind of got their roots. Which is really, get an application, allow people to use it, execute against it and then build that base up. >> That's ironically that you bring up Splunk 'cause George Gilbert, one of our Wikibon analysts, loves to say that Splunk is the best imitation of Hadoop that was ever created. He thinks of it really as a Hadoop application as opposed to Splunk, because they're super successful. They found a great application. They've been doing a terrific job. But the other piece that you brought up that triggered my mind was really the machine-to-machine. And real-time is always an interesting topic. What is real time? I always think of real time means in time to do something about it. That can be a wide spectrum depending on what you're actually doing. And the machine-to-machine aspect is really important because they do operate at a completely different level of speed. And time is very different for a machine-to-machine operation interaction interface than trying to provide some insight to a human, so they can start to make a decision. >> Yeah, I mean, you know, it was, again, one of those moments through the last five months I was looking at it. There's a very popular technology in our space called Spark, Apache Spark. And it's successful and it's great in batch and it's got micro-batch and there's actually a thing called Spark Streaming, which is micro-batch. But in essence, it's about a second latency, and so you look at it and you go, but what's in a second? You know what I mean? I mean, surely that's good enough. And absolutely, it's good enough for some stuff. But if you were, I mean we joke about it with things like autonomous cars. If you have cruise control, adaptive cruise control, you don't want that run on batch because that second is the difference between you slamming into a truck or not. If you have DHL, they're doing delivery drops to you, and you're actually measuring weather patterns against it, and correlating where you're going to drive and how and high and where, there's no way that you're going to run on a batch process. And then batch is just so slow in comparison. We actually built an application and it's a demo up on our web. And it's a live app, and when I sat down with the engineering team, and I said, "Look, I need people to understand "what real real-time does and the benefits of it." And it's simply doing is shifting the analytics and actions from the right-hand side of where the data store is, to the left-hand side. So you take all of the latency of parting the data and then go find the data. And what we did is we said, look, well, I want to do this really fair and, when you were a kid, there used to be games like Snap, you know, where the cards that you would turn over and you'd go snap and it's mine. So we're just looking and say, "Okay, "why don't we do something like that?" It's like fishing, you know, tickling fish and who sees the first fish, you grab it, it's yours. So we created an application that basically creates random numbers at a very, very huge speed, and whichever process, we have three processes running, whichever one sees it the first time, puts their hands up and says, "I got that." And if somebody else says, "I've got that," but they see a timestamp on the other one, they can't claim it. One wins, and the other two lose. And I did it, and we optimized around, basically, the Apache Apex code, which is ours in stream mode, the Apache Apex, believe it or not, in a micro-batch mode, and Spark Streaming, as fast as they can, and we literally engineered the hell out of them to get them as fast as possible. And if you look at the results, it literally is, win every time for stream, and a loss every time for the other two. So from a speed perspective, now the reality is like I said, is if I'm showing a dashboard to you, by the time you blink, all three have gotten you the data. It's immaterial, and this isn't knocking on Spark. Our largest deployments all run on what we call, like a cask-type architecture, which is basically Kafka Apache, Spark. So we see this in Hadoop, and it's always in there. So it's kind of this cache thing. So we like it for what it is, but where customers come unbundled, is where they try and force-fit a technology into the wrong space. And so again, you mentioned Splunk, these sort of waves of innovation. We find every client sitting there, going, "I want to get inside quicker". The amount of meetings that we're all in, where you sit there and go, "If I'd only known that now "or before, then I would've made a decision." And, you know, in the good old days, we worked at-rest data. At-rest was really the kingdom of Splunk. If you think about it, we're now in the tail end of batch, which is really where Spark's done. So Splunk and Spark are kind of there, and now you're into this real-time. So again, it's running at a fair pace, but the learnings that we've had over the last few months is toolkits are great, platforms are great, but to bring this out into a mass adoption, you really need to make sure that you've provided hardened application. So we see ourselves now as, you know, real-time big data applications company, not just Apache. >> And when you look at the application space that you're going to attack, do you look at it kind of vertically, do you look at it functionally, kind of, you mentioned fraud as one of the earlier ones. How are you kind of organizing yourself around the application space? >> Yeah, and so, the best way for me to describe it, and I want to spin it in a better way than this, but I'll tell you exactly as we've done it, which is, I've looked at what the customers have currently got and we have deployments in about a dozen big customers and they're all different use cases, and then I've looked at it and said, "What you really want to do is you want to go "to a market that people have a current problem, "and also in a vertical where they're prepared "to pay for something and solving a problem "that if they give you money, they either "make money quickly or they save money quickly." So it's actually-- >> So simple. (laughs) >> But it would be much better if I said it in a pure way and I made some magical thing up, but in reality is I'm looking and going, "You got to go where the hardest problems are," And right now, a few things like card not present, you look at roaming abuse and you look at OmniChannel from payment fraud, everybody is looking for something. Now, the challenge is the market's noisy there, and so what happens is everybody's saying, "But I've got it." >> That's what strikes me about the fraud thing is you would think that that's a pretty sophisticated market place in which to compete. So you clearly have to have an advantage to even get a meeting, I would imagine. >> Yeah, and again, we've tested the market. The market's pretty hard on the back of it. We've got an application coming out shortly, and we're actually doing design partnerships with a couple of big banks. So but we don't want to be seen as just a fraud, now, just a fraud, just a fraud prevention company. (chuckles) I'll stay with a fraud, myself. But you kind of look and you say, look, they'll be a set of fraud applications because there's about half a dozen only to be done, retail, like I mentioned on things like the loyalty brand stuff. We have a number of companies that are using us for ad tech. So again, I can't mention the names. Actually, we've just published one, Publix, no, PubMatic is one of the ad tech organizations that's using our products. But we'll literally come out and harden that pipeline as well. So we're going to strut along but instead of just saying, "Hey, we've solved absolutely everything," what I want to do is to solve a problem for someone and then just move forward. You know, most of our customers have somewhere between three to five different applications that are running up and that are in production. So once the platform's in, you know, then they see the value of it. But we really want to make sure that we're closer to the end result and to an outcome, because that's the du jour way that customers want to buy things now. >> Well, and they always have, right? Like you said, they've got a burning issue. You either got to make money or save money. And if it's not a burning issue, it falls to the bottom of the pile, 'cause there's something that's burning that they need to fix quickly. >> And the other thing, Jeff, is if you, and again, it's dirty laundry, but if you think about it, I go to an account and the account's got a fraud solution, and it's all right but it's not doing what they want, but we come along up with a platform, say, "We can do absolutely anything." And then they go, "Well, I've got this really difficult "problem that no one's solved for me, "but I'm not even sure if I've got a budget for it. "Let's spend two year messing around with it. And that's no good, you know? From a small company, you really want that tractionable event, so my thing is just say, "No, what we want to do is I want to go "talk to John about John's problem," and say, "I can solve it better than the current one." And there is nothing in the market today, on the payment fraud side, that will provide prevention. It is all detection. So, there's a unique value. The question is whether we can get the noise out. >> All right, well, we look forward to watching the progress and we'll check again in five months or so. >> Thank you, Jeff, 'preciate it. >> Guy Churchward, he's from DataTorrent, President and CEO. Took over about five months ago and kind of changed the course a little bit. Exciting to watch, thanks for stopping by. >> Guy: Thank you >> All right, Jeff Frick, you're watching the theCUBE. See you next time. Thanks for watching. (upbeat electronic music)

(techno music) >> Announcer: Live, from Silicon Valley, it's "the Cube," covering Mobile World Congress 2017. Brought to you by Mintel. >> Okay, welcome back everyone. We're here live in Palo Alto, California, covering Mobile World Congress, which is later in Spain right now, in Barcelona, it's gettin' close to bedtime, or, if you're a night owl, you're out hittin' the town, because Barcelona stays out very late, or just finishing your dinner. Of course, we'll bring in all theCube coverage here. News analysis, commentary, and of course, reaction to all the big mega-trends. And our next two guests is Guy Churchward who is the President and CEO of Data Torrent, formerly of EMC. You probably recognize him from theCube, from the EMC world, the many times he's been on. Cube alumni. And Phu Hoang, who's the co-founder and Chief Strategy Officer of Data Torrent. Co-founder, one of the founders. Also one of the early, early Yahoo engineers. I think he was the fourth engineer at Yahoo. Going way back on the 90s. Built that to a large scale. And Yahoo is credited for the invention of Hadoop, and many other great big data things. And we all know Yahoo was data-full. Guys, welcome to theCube's special coverage. Great to see you. >> Thank you so much. So I'm psyched that you guys came in, because, two things. I want to talk about the new opportunity at Data Torrent, and get some stories around the large scales experience that you guys have dealing with data. 'Cause you're in the middle of where this is intersecting with Mobile World Congress. Right now, Mobile World Congress is on the collision course between cloud-ready, classic enterprise network architectures with consumer, all happening at the same time. And data, with internet of things, is that going to be at the center of all the action? So, (laughing) these are not devices. So, that's the core theme. So, Guy, I want to get your take on, what attracted you to Data Torrent? What was the appeal for the opportunity? >> You mean, why am I here, why have I just arrived? >> I've always data-obsessed. You know this. From the days of running the storage business on their data protection, before that I was doing data analytics and security forensics. And if you look at, as you said, whether it's big data, or cloud, and the immersion of IOT, one thing's for sure, for me. It was never about big data, as in a big blob of stuff. It was all about small data sprawl. And the world's just getting more diverse by the second, and you can see that by Mobile World, right? The challenge then you have is, companies, they need to analyze their business. In other words, data analytics. About 30 years ago, when I was working for BA Systems, I remember meeting a general of the army. And he said the next war will be one in the data center, not on the battlegrounds. And so you really understand-- >> He's right about that. >> Yeah. And you have to be very, very close. So in other words, companies have started to obsess about what I call the do loop. And that really means, when data is created, and then ingesting the data, and getting insight from the data, and then actioning on that. And it's that do loop. And what you want to do, is you want to squeeze that down into a sub-second. And if you can run your analytics at the pace of your business, then you're in good shape. If you can't, you lose. And that means from a security perspective, or you're not going to win the bids. In any shape or form. That's not a business-- >> John: So speed is critical. >> Yeah, and people say, speed and accuracy. Because what you don't want to do is to run really really fast and fall off a cliff. So you really need to make sure that speed is there and accuracy is there. In the good old days, when I was running security forensics, you could either do complex end processing, which was a very small amount of information coming in and then querying it like crazy, or things like log management, where you would store data at rest, and then look at it afterwards. But now with the paradigm of all the technology catching up, so whether that's the disk space that you get, and the storage and the processing, and things like Hadoop with the clustering, you now break that paradigm. Where you can collect all the information from a business and process it before you land the data, and then get the insight out of it, and then action. So that was my thing, of looking and saying, look, this whole thing's going to happen. In last year -- >> And at large scale, too. I mean, what you're talking about in the theoretical side makes a lot of sense, but also putting that into large scale, is even more challenging. >> Yeah, we had, when I was going through the processes, dating, you know, to see whether was a company that made sense, I chatted one of our investors. And they're also a customer. And I said, why did you choose Data Torrent? And they said, "We tested everything in production, we tested all the competitive products out there, and we broke everything except Data Torrent. And actually, we tested you in production up to a billion events per second, and you didn't break. And we believe that that quantity is something that you need as a stepping stone to move forward." >> And what use cases does that fit for? Just give me some anecdotal (snaps fingers) billion transactions. At that speed, what's some use cases that really take advantage of that? >> They were mastering in, what I would call, industrialization of IT. So in other words, once you get into things like turbines, wind generation, train parts. We're going to be very very soon, looking out of a window and seeing -- >> John: So is it flow data? Is it the speed of the flow? Is it the feed of all the calculations, or both? >> It's a bit of both. And what I'll do, is I'll give Phu a chance, otherwise, we'll end up chatting about it. >> John: Phu, come on, you're the star. (laughing) When you founded this company, you had a background at Yahoo, which you built from scratch, but that was a first-mover opportunity, Web 1.0, as they say. That evolved up and then, everyone used Yahoo Finance. Everyone used Yahoo Search as a directory early on. And then everything just got bigger and bigger and bigger, and then you had to build your own stuff with Hadoop. >> Yeah. >> So you lived it. The telcos don't have the same problem. They actually got backed into the data, from being in the voice business, and then the data business. The data came after the voice. So what's the motivation behind Data Torrent? Tell us a little bit more. >> It's exactly what you say, actually. Going through the 12 years at Yahoo, and really, we learned big data the hard way. Making mistakes month after month, about how to do this thing right. We didn't have the money, and then we found out that, actually, proprietary systems of the shelf system that we thought were available, really couldn't do their jobs. So we had to invent our own technology, to deal with the kind of data processing that we had. At some point, Yahoo had a billion users using Yahoo at any given point in time, right? And the amount of impressions, the amount of clicks, the amount of activity, that a billion users have, onto the system. And all of the log files that you have to process to understand what's going on. On the other side of that, we need to be able to understand all of those activities in order to sell to our advertisers. Slice and dice behaviors and users, and so on. We didn't have the technology to do that. The only thing we knew how to do was, to have these cheap racks of cheap servers, that we were using to serve webpages. And we turned to that to say, this is what we're going to need to do, to solve these big data problems. And so, the idea of, okay we need to take this big problem and divide it into smaller pieces, so that we can run on these cheap servers, sort of became the core tenant of how we do distributor processing that became Hadoop, at the end of the day, right? >> You had big data come in because you were, big data-full, as we say. You weren't building software to solve someone else's problem. You had your own problem, you had a lot of data. You were full with data. >> Exactly. >> Had to go on a data diet, to your point. (crosstalk) >> And no one to turn to. >> And no one to turn to. >> All right. So let's spin this around or Mobile World Congress. 'Cause the big theme is, obviously, we all know what device is. In fact, we just released here on theCube early this morning Peter Burris pre-announced our new research initiative called IOTP. Which stands for Internet Of Things And People. And so now you add the complexity of people devices, whether that's going to be some sort of watch, phones, anything around them. That adds to the industrial aspect of turbines and what not. Internet of Things is a new edge architecture. So the data tsunami coming, besides the challenges of telcos to provision these devices, are going to be very challenging. So the question I want to ask you guys is, how do you see this evolving, because you have certainly connectivity. Yeah, you know, low latency, small little data coming from the windmills or whatever. Versus big high-dense bandwidth, mobility. And then you got network core issues, right. So how does this going to look like? Where does the data piece fit in? Because all aspects of this have data. What's your thoughts on this, and architecture. Tell us about your impressions, and the conversations you've had. >> First of all, I think data will exist everywhere. On the fringe, in the middle, at the center. And there's going to be data analytics and processing in every path of that. The challenge will be to kind of figure out what part of processing do you put on the fringe, what part do you put at the center. And I think that's a fluid thing that is going to be constantly changing. Going back to the telcos. We've had numbers of conversationw with telcos. And, yes we're helping them right now with their current set of issues around capacity management and billing, all those things. But they are also looking to the next step in their business. They're making all this money from provisioning, but they know they sit on top of this massive amount of really valuable data, from their customers. Every cellphone is sending them all of this data. And so there's a huge opportunity for them to monetize, or really produce value, back to their customers. And that could come in form of offers, to customers. But now you're talking about massive analytics targeting. That is also real-time, because if you're sending an offer to someone at a particular location, if you do that slowly, or in batch, and you give them an offer 10 minutes later, they're no longer where they are. They're 10 minutes away, right? >> Well, first two questions to follow up on that. One, do they know they have a data advantage opportunity here? Do they know that data is potentially a competitive advantage? >> From our conversation, they absolutely do. They're just trying to figure out, so what do we do here? It's new to them. >> I want to get both your perspectives. Guy, I want you to weigh in on this one, 'cause this is another theme that's coming out of the reporting and analysis from Mobile World Congress. This has come also from the cloud side as well. Integration now, is more important than ever, because, for instance, they might have an Oracle there, there might be Oracle databases outside their network. That they might want to tap into. So tapping other people's data. Not just what they can get, the telcos. It's going to be important. So how do you guys see the integration aspect, how we, top of the first inning, national anthem going on. I mean, where are we in this integration? There's a pregame, or, what inning are we in on this? >> Yeah, we're definitely not on the home run on it. I think our friend, and your friend Steve Manly, I sat down with him, and I gave him a brief, you know, what we were doing, and he was blown away by the technology and the opportunity, but he was certainly saying, but the challenge is the diversity of the data types. And then where they're going to be. Autonomic cars. You know each manufacturer will tell the car behind it, what it just experienced, but the question is, when will a Tesla tell a Range Rover, or tell a BMW? So you have actually -- >> They're different platforms, just different stats, it's a nightmare. >> Right. So in other words, >> And trackability. And whether it's going to be open APIs, whether it's technologies like Kafka. But the integration of that, and making sure that you can do transformation and then normalize it and drive it forward. It's kind of interesting, you know. You mentioned the telco space, and do they understand it. In some respects, what Phu went through with Yahoo, in other words, you go to a webpage, you pull it up, it knows you because of a cookie and it figures out, and then sells advertising to you on that page. Now think about you as a location, and you're walking past a Starbucks, and they want to sell you a coffee for ten cents less than they would normally do. They need to know you're there then. And this is the thing, and this is why real-time is going to be so critical. And similarly, like you said, you look out the window and you see DHL, or UPS, or FedEx drones out the window. You not only have an insight issue. You also have a security issue, you have a compliance issue, you have a locational issue. >> I think you're onto something. And I think I actually had this talk today with Steve Manly EMC World last year, around time series data. So this is interesting. Everyone wants to store everything, but it actually might not be worth anything anymore. If the drone is delivering your package, or whatever realtime data is in realtime, it's really important right there in realtime, or near realtime. It might not be worth anything after. But yet a purchase at a store, at a time, might be worth knowing that as a record to pull in. You get what I'm saying? So there's a notion of data that's interesting. >> And I think, and again, Phu's the expert. I'm still running up onto it. It's just a pet hobby, an obsession of mine. But the market has this term ETL. In other words, Extract, Transform, Land. Or load. But in essence, it's always talked about in that (mumbles) batch. In other words, I get the data, transform it, drop it, and then I have a look at it. We're going upside-down. So the idea now is to actually extract, transform, insight, action, then landing. So in other words, get the value at the fresh data, before it's the data late. Because if you set the data late, by default, it's actually stale. And actually, then there's the fascination of saying, if you're delivering realtime data to a person, you can't think fast enough to actually make a live decision. So therefore, you've almost got any information that comes to you, has to tier out. So it comes to a process. You get that fresh use of it, and then it drops into a data lake. And so I think there's using both, but I think what will you see in the market, and, again, you've experienced the disk flash momentum that happened last year. You're going to see that from a data source from at-rest, advanced, to real-time data streams on our applications next year. So I think the issue is, the formative year, and back to your, you know, get it right, get the integration, but make sure your APIs are there, talking to the right technologies. I think everything's going to be exciting this year and new and fresh and people really want to do it. Next year is going to be the year where you're going to see an absolute changing of the guards. >> And then also the SLA requirements, they'll start to get into this when you start looking at integration. >> You're absolutely right. Actually, the SLA part is actually very very important here. Because, as you move analytics from this back world, where it has, you do it once a day, and if it dies, it's okay, you just do it again. To where it is now continuous, 24 by 7, giving you insight continuously about your business, your people, your services, and so on. Then all of a sudden, it has to have the same characteristics as your business. Which is, it's 24 by 7, it can never go down, it can never lose data. So, all of a sudden you're putting tremendous requirements on an analytics system, which has, all the way from the beginning of history 'til now, been a very relaxed batch thing, to all of a sudden being something that is enterprise-grade, 24 by 7. And I think that that's actually where it's going to be the toughest nut to crack. >> So tell about some of the things that you've learned. And pretend for a second, let's pretend that you, as a co-founder at Data Torrent, and Guy, and you are teamed up. You guys run this telco. Let's just make one up, Verizon. Or AT&T, or pick one. And you sit there saying, okay, you've got the keys to the kingdom. And you can do whatever you want (laughing). You can be Donald Trump, or you can be whoever you want. You can fire everybody, or you can pick it over and run it. What would you do? You know you've got IOT. So this is business model innovation opportunities. I want you to put the technical hat on, plus knowing what you know around the business model opportunities. What do you do? You know IOT's an opportunity. Amazon is going after that heavily. Do you bolt a cloud together? Do you go after Amazon? Do you co-op with Amazon? Do you co-integrate? Do you grab the IOT? Do you use the data? I mean, given where we are today, what's the best move if we were consulting with this. >> You know, I will be the last person to be talking about giving advice to a telco. But since we are, we own our own telco here, and then we're pretending, I would say the following. IOT is going to happen, right? Earlier, when I say a billion people, that's just human beings. Once you now talk about censoring, you can program how many times they can send you data per second, then the growth in volume is immense, right? I think there's a huge opportunity, as a telco, in terms of the data that they have available and the insight that they could have about what's going on. That is not easy. I don't think that, as a telco, in the current DNA of a telco, I can go ahead and do all that analytics and really open up my business to the data insight layer. I would partner, and find a way-- >> Well, we're consulting, we're going to sit around and say hey, what do we have? We have relationship with the consumer, big marketing budgets. We can talk to them directly, we have access to their device. >> But you'll bifurcate the business. We're in the boardroom here, this is nothing more than that. But I would look at it and say look, you've got a consumer business, the same as in IOT. There's really, for me, there's three parts of IOT. There is the bit that I love which, you can geek out, which is basically the consumer market, which, there's no money in for a large-scale tenant, right, enterprise. And then you have the industrialization of IOT, which is I've got a leaky pipe, and I want a hardened device, ruggedized, which is wifi, so, now as a telco, I could create a IOT cloud, that allows me to put these devices out there, and in fact, I use Arlo, the little cameras. And they've got one now, where I can basically float it with its own cellular signal. So it's its own cellphone. That's a great use of IOT for that. And then you step to the consumer side of, I've got a cellphone, and then what I'll do is literally, in essence, riff off what Yahoo did in the early days and say, I'm now the new browser. The person's the browser. So in other words, follow the location, follow where he is, and then basically do locational-based advertising. >> By the way, you have to license the patent from our earlier guest, he'll say will he leak, 'cause he's got th6e patent on personal firewall for personal server. He's built a mobile personal server. >> Yeah. >> But this is the opportunity around wireless. Why I love the confusion, but the opportunity around wireless right now is, you can get bandwidth at high capacity. You have millimeter wave four, that doesn't go through walls, but you have other diverse frequencies and spectrum for instance, you can blend it all together to have that little drip signal, if you will, going into the cloud from the leaky pipe. Or if you need turbine, full-fat pipe, you maybe go somewhere. So, I think this is an interesting opportunity. >> And they're going to end up watching the data centers as well. There's still the gamut of saying our customer is going to continue to support their own data centers, or are there going to be one to a hundred data centers out there? And then how does selling a manufacturer or a telco play into that, and do they want to be that guy or not? >> Guy, Phu, thanks for coming in. I want to give you guys a chance to put a plug in for Data Torrent. Thanks for sharing some great commentary on the industry. So, what's up with you guys? Give us the update. Are you hiring? You growing? What are you guys doing? Customers? What's the update? Technology, innovations? >> So we've got a release coming out tomorrow which is a momentum release. I can't talk too much about the numbers, but in essence, from a fact base, we have a thing called a patchy apex. So it's open sourced, so you can use our product for free. But that's growing like gangbusters. From a top-level project, that's actually the fastest-growing one, and it's only been out for seven months. We just broke through 50,000 users on it. From our product, we're doing very well on the back of it. So we actually have subscription for the production side. >> So revenue is a subscription model. >> Yeah, and we meet both sides. So in other words, for the engineer who writes it, you've got the open source. And then when you put it into production, from the operations side, you can then license our products to enable you to manage an easy-- >> So when it gets commercialized, you pay as you go, when you use it. >> And you don't have to, if you don't want to. You've got all the tools to do it. But, we focus for our products group of, time to value, total cost of ownership. We're trying to bring Hadoop and real scale, realtime streaming to the masses. So what's the technology innovation? What's the disruptive enabler for you guys? >> I think we talked about it, right? You've got two really competing trends going on here. On one side, data is getting more and more and more massive. So it's going to take longer and longer to process it. Yet at the other side, business wants to be able to get data, have insight, and take action sub-second. So how do you get both at the same time? That's really the magic of the technology. >> Thanks for coming in. Great to meet you, Phu. I'd love to talk about the old Yahoo days, a total throwback, Web 1.0, a great time in history, pre-bubble bursting. Greatness happening in the valley and all around the world, and I remember those days clearly. Guy, great to see you. Congratulations on your new CEO committee. And great to have you on theCube. This is theCube bringing the coverage, and commentary, and reaction of Mobile World Congress here, in California. As everyone goes to bed in Barcelona, we're just gettin' down to the end of our day here in the afternoon in California. Be right back with more after this short break. (techno music)

>> Narrator: Live, from San Jose, in the heart of Silicon Valley, it's The Cube. Covering, DataWorks Summit, 2017. Brought to you by Hortonworks. >> Welcome back to The Cube. We are live on day two of the DataWorks Summit. From the heart of Silicon Valley. I am Lisa Martin, my co-host is George Gilbert. We're very excited to be joined by our next guest from DataTorrent, we've got Nathan Trueblood, VP of Product, hey Nathan. >> Hi. >> Lisa: And, the man who gave me my start in high tech, 12 years ago, the SVP of Marketing, Jeff Bettencourt. Welcome, Jeff. >> Hi, Lisa, good to see ya. >> Lisa: Great to see you, too, so. Tell us about the SVP of Marketing, who is DataTorrent, what do you guys do, what are doing in the big data space? >> Jeff: So, DataTorrent is all about real time streaming. So, it's really taken a different paradigm to handling information as it comes from the different sources that are out there, so you think, big IOT, you think, all of these different new things that are creating pieces of information. It could be humans, it could be machines. Sensors, whatever it is. And taking that in realtime, rather than putting it traditionally just in a data lake and then later on coming back and investigating the data that you stored. So, we started about 2011, started by some of the early founders, people that started Yahoo. And, we're pioneers in Hadoop with Hadoop yarn. This is one of the guys here, too. And so we're all about building realtime analytics for our customers, making sure that they can get business decisions done in realtime. As the information is created. And, Nathan will talk a little bit about what we're doing on the application side of it, as well. Building these hard application pipelines for our customers to assist them to get started faster. >> Lisa: Excellent. >> So, alright, let's turn to those realtime applications. Umm, my familiarity with DataTorrent started probably about five years ago, I think, where it was, I think the position is, I don't think that there was so much talk about streaming but it was like, you know, realtime data feed, but, now we have, I mean, streaming is sort of center of gravity. Sort of, appear to big data. >> Nathan: Yeah. >> So, tell us how someone whose building apps, should think about the two solution categories how they compliment each other and what sort of applications we can build now that we couldn't build before? >> So, I think the way I look at it, is not so much two different things that compliment each other, but streaming analytics and realtime data processing and analytics is really just a natural progression of where big data has been going. So, you know, when we were at Yahoo and we're running Hadoop in scale, you know, first thing on the scene was just simply the ability to produce insight out of a massive amount of data. But then there was this constant pressure, well, okay, now we've produced that insight in a day, can you do it in an hour? You know, can you do it in half an hour? And particularly at Yahoo at the time that Ah-mol, our CTO and I were there, there was just constant pressure of can you produce insight from a huge volume of data more quickly? And, so we kind of saw at that time, two major trends. One, was that we were kind of reaching a limit of where you could go with the Hadoop and batch architecture at that time. And so a new approach was required. And that's what really was sort of, the foundation of the Apache Apex project and of DataTorrent the company, was simply realizing that a new approach was required because the more that Yahoo or other businesses can take information from the world around them and take action on that as quickly as possible, that's going to make you more competitive. So I'd look at streaming as really just a natural progression. Where, now it's possible to get inside and take action on data as close to the time of data creation as possible and if you can do that, then, you're going to be competitive. And so we see this coming across a whole bunch of different verticals. So that's how I kind of look at the sort of it's not too much complimentary, as a trend in where big data is going. Now, the kinds of things that weren't possible before this, are, you know, the kinds of applications where now you can take insight whether it's from IOD or from sensors or from retail, all the things that are going on. Whereas before, you would land this in a data lake, do a bunch of analysis, produce some insight, maybe change your behavior, but ultimately, you weren't being as responsive as you could be to customers. So now what we are seeing, why I think the center of mass is moved into realtime and streaming, is that now it's possible to, you know, give the customer an offer the second they walk into a store. Based on what you know about them and their history. This was always something that the internet properties were trying to move towards, but now we see, that same technology is being made available across a whole bunch of different verticals. A whole bunch of different industries and that's why you know, when you look at Apex and DataTorrent, we're involved not only in things like adtech, but in industrial automation and IOT, and we're involved in, you know, retail and customer 360 because in every one of these cases, insurance, finance, security and fraud prevention, it's a huge competitive advantage if you can get insight and make a decision, close to the time of the data creation. So, I think that's really where the shift is coming from. And then the other thing I would mention here, is that a big thrust of our company, and of Apache Apex and this is, so we saw streaming was going to be something that every one was going to need. The other thing we saw from our experience at Yahoo, was that, really getting something to work at a POC level, showing that something is possible, with streaming analytics is really only a small part of the problem. Being able to take and put something into production at scale and run a business on it, is a much bigger part of the problem. And so, we put into both the Apache Apex problem as well as into our product, the ability to not only get insight out of this data in motion, but to be able to put that into production at scale. And so, that's why we've had quite a few customers who have put our product, in production at scale and have been running that way, you know, in some cases for years. And so that's another sort of key area where we're forging a path, which is, it's not enough to do POC and show that something is possible. You have to be able to run a business on it. >> Lisa: So, talk to us about where DataTorrent sits within a modern data architecture. You guys are kind of playing in a couple of, integrated in a couple of different areas. What goes through what that looks like? >> So, in terms of a modern data architecture, I mean part of it is what I just covered in that, we're moving sort of from a batch to streaming world where the notion of batch is not going away, but now when you have something, you know a streaming application, that's something that's running all the time, 24/7, there's no concept of batch. Batch is really more the concept of how you are processing data through that streaming application so, what we're seeing in the modern data architecture, is that, you know, typically you have people taking data, extracting it and eventually loading it into some kind of a data lake, right? What we're doing is, shifting left of the data lake. You know, analyzing information when it's created. Produce insight from it, take action on it, and then, yes, land it in the data lake, but once you land it in the data lake, now, all of the purposes of what you're doing with that data have shifted. You know, we're producing insight, taking action to the left of the data lake and then we use that data lake to do things, like train your you know, your machine learning model that we're then going to use to the left of the data lake. Use the data lake to do slicing and dicing of your data to better understand what kinds of campaigns you want to run, things like that. But ultimately, you're using the realtime portion of this to be able to take those campaigns and then measure the impacts you're having on your customers in realtime. >> So, okay, cause that was going to be my followup question, which is, there does seem to be a role, for a historical repository for richer context. >> Nathan: Absolutely. >> And you're acknowledging that. Like, did the low legacy analytics happen first? Then, store up for a richer model, you know, later? >> Nathan: Correct. >> Umm. So, there are a couple things then that seem to be like requirements, next steps, which is, if you're doing the modeling, the research model, in the cloud, how do you orchestrate its distribution towards the sources of the realtime data, umm, and in other words, if you do training up in the cloud where you have, the biggest data or the richest data. Is DataTorrent or Apex a part of the process of orchestrating the distribution and coherence of the models that should be at the edge, or closer to where the data sources are? >> So, I guess there's a couple different ways we can think about that problem. So, you know we have customers today who are essentially providing into the streaming analytics application, you know, the models that have been trained on the data from the data lake. And, part of the approach we take in Apex and DataTorrent, is that you can reload and be changing those models all of the time. So, our architecture is such that it's full tolerant it stays up all the time so you can actually change the application and evolve it over time. So, we have customers that are reloading models on a regular basis, so that's whether it's machine learning or even just a rules engine, we're able to reload that on a regular basis. The other part of your question, if I understood you, was really about the distribution of data. And the distribution of models, and the distribution of data and where do you train that. And I think that you're going to have data in the cloud, you're going to have data on premises, you're going to have data at the edge, again, what we allow customers to do, is to be able to take and integrate that data and make decisions on it, regardless kind of where it lives, so we'll see streaming applications that get deployed into the cloud. But they may be synchronized in some portion of the data, to on premises or vis versa. So, certainly we can orchestrate all of that as part of an overall streaming application. >> Lisa: I want to ask Jeff, now. Give us a cross section of your customers. You've got customers ranging from small businesses, to fortune 10. >> Jeff: Yep. >> Give us some, kind of used cases that really took out of you, that really showcased the great potential that DataTorrent gives. >> Jeff: So if you think about the heritage of our company coming out of the early guys that were in Yahoo, adtech is obviously one that we hit hard and it's something we know how to do really really well. So, adtech is one of those things where they're constantly changing so you can take that same model and say, if I'm looking at adtech and saying, if I applied that to a distribution of products, in a manufacturing facility, it's kind of all the same type of activities, right? I'm managing a lot of inventory, I'm trying to get that inventory to the right place at the right time and I'm trying to fill that aspect of it. So that's kind of where we kind of started but we've got customers in the financial sector, right, that are really looking at instantaneous type of transactions that are happening. And then how do you apply knowledge and information to that while you're bringing that source data in so that you can make decisions. Some of those decisions have people involved with them and some of them are just machine based, right, so you take the people equation out. We kind of have this funny thing that Guy Churchward our CEO talks about, called the do loop and the do loop is where the people come in and how do we remove people out of that do loop and really make it easier for companies to act, prevent? So then if you take that aspect of it, we've got companies like in the publishing space. We've got companies in the IOT space, so they're doing interview management, stuff like that, so, we go from very you know, medium sized customers all the way up to very very large enterprises. >> Lisa: You're really turning up a variety of industries and to tech companies, because they have to be these days. >> Nathan: Right, well and one other thing I would mention, there, which is important, especially as we look at big data and a lot of customer concern about complexity. You know, I mentioned earlier about the challenge of not just coming up with an idea but being able to put that into production. So, one of the other big ares of focus for DataTorrent, as a company, is that not only have we developed platform for streaming analytics and applications but we're starting to deliver applications that you can download and run on our platform that deliver an outcome to a customer immediately. So, increasingly as we see in different verticals, different applications, then we turn those into applications we can make available to all of our customers that solve business problems immediately. One of the challenges for a long time in IT is simply how do you eliminate complexity and there's no getting away from the fact that this is big data in its complex systems. But to drive mass adoption, we're focused on how can we deliver outcomes for our customers as quickly as possible and the way to do that is by making applications available across all these different verticals. >> Well you guys, this has been so educational. We wish you guys continued success, here. It sounds like you're really being quite disruptive in an of yourselves, so if you haven't heard of them, DataTorrent.com, check them out. Nathan, Jeff, thanks so much for giving us your time this afternoon. >> Great, thanks for the opportunity. >> Lisa: We look forward to having you back. You've been watching The Cube, live from day two of the DataWorks Summit, from the heart of Silicon Valley, for my co-host George Gilbert, I'm Lisa Martin, stick around, we'll be right back. (upbeat music)