 Hey, welcome back everybody. Jeff Rick 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 could have a little more intimate conversation without the madness of some of the show. So we're really excited to have many time CUBE alumni. Guy Church Ward on. He's the president and CEO of DataTorrent. Guy, great to see you. Thank you, Jeff, appreciate it. So 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 was enough time to get my feet wet, understand whether I made a massive mistake or whether it was exciting. I'm pleased. You're 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 happened 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, you know, real-time data analytics, you know, 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, you know, litigiously at a 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 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, you know, batch or stream or near real-time or micro-batch is sort of like real-time for a person because a person can't think that fast. So the latency is a fact of that. But what we do is real-time for a computer. So the idea is that you literally have sub-second, you know, 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 in a billion events per second. So it's very cool. But there wasn't this, you know, 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. And so, you know, 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 open source 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, you know, 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 a dupe 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, you know, can we actually industrialize that pipeline? In other words, take the open source components, ruggedize them, scale them, make sure that they stay up, their fault tolerance, send by 24, and then provide them as an application. So we're actually shifting our focus, I think, from just the, you know, what I call the Apex platform and this stream-based processing platform to an application factory and actually producing, you know, end-to-end applications. It's so interesting to think of, you know, batch and 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, different. Yeah, I mean, it kills me. I mean, 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, and I said, you do real-time analytics? They go, yeah. And I said, you work on real-time data? And they said, yeah. And I said, what's your latency between an event happening and you've been able to take an action on the event? And he said, what, 60 milliseconds? It's just amazing. So he said, well, tell me what your architecture looks like. And he says, well, I take Kafka as 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, 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 data lake. But he swore blind. And it's fast, though, right? And that's the thing is he's looking. He's like, hey, well, I can get a really quick response. Well, I can as well. 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. 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 is already out there. And so one of the applications that we looked at is around fraud, you know, and specifically payment fraud and credit card fraud. And everything out there in the market today is basically its detection. Because of the latency, if you kind of think about it, credit card swipe, the transactions 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 ourselves, 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 a, it's kind of fascinating because there's other angles to this, you know, there's a marketplace inside the credit card side that talks about Cardinal Present. And there's a thing called OmniChannel. And OmniChannel is interesting because most retailers have gone out there and they've got their bricks and mortar infrastructure in 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've got a 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 lifecycle 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, you know, 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, I mean, for instance, my wife, we shop like crazy, everybody does. I try not to. But let's say she's been on a Nordstrom site and we've got a Nordstrom. So Nordstrom has a cookie on a system and they can figure out what we've 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 Nordstroms about four weeks later and I've literally, you know, buying a pair of socks, quite 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, well, guy, before we've 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 in. And if you want, we'll put it on the credit card. That'll creep her out too much. She won't want you to get that dress. Well, actually. It's great. It's a really interesting example, right? But it is that, and if you kind of think about it, and this is where, you know, 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 thing. So, you know, a couple of 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 to do specifically. So now you're delivering a bunch of that that's already packaged to a degree in an application. Is that accurate? Yeah, I mean, I kind of look at the engineering talent inside organizations like a triangle, you know? 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 that small organizations 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, you know, Yoda hard code on the analytic 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 into 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, right? Which is really get an application, allow people to use it, execute against it and then build that base up. That's ironically bring up Splunk. Because George Gilbert, one of our Wikibon analysts loves to say that Splunk is the best implementation 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 was like real time means in time to do something about it. And that could 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, 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 a cruise control, it's adaptive cruise control, you don't want them run on batch because that second is the difference between you slamming into a truck or not. You know, if you have DHL 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 what I sat down with the engineering team and I said, look, I need to, I need people to understand what real, real time does and the benefits of it. And it's simply doing is shifting their analytics and actions from the right hand side of where the data store is to the left hand side. So you take all of that latency of park the data and then go find the data. And what we did is we said, look, what I want to do this really fair. And when you're a kid, there used to be games like snap, you know, with the cards that you turn over and you go snap in its mind. So you just look at it 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 which everyone sees at 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 and 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 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 blinked, all three have got you the data. So it's immaterial, you know, and this isn't knocking on Spark. Our largest deployments all run on what we call like a Cask type architecture, which is basically, you know, Kafka Apache Spark. So we see this and Hadoop is always in there. So it's kind of this cash thing. So we like it for what it is, but where customers come unbundled is where they try and force fit technology into the wrong space. And so again, you mentioned Splunk, you know, these sort of ways of innovation, you know, we find every client sitting there going, I want to get insight quicker, you know, 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 have made a decision. And, you know, in the good old days, we worked at at rest data. At rest was really the kingdom of Splunk, you know, and then 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 the fair pace, but I think the learnings that we've had over the last few months is, Toolkit's are great and platforms are great, but to bring this out into a mass adoption, you really need to make sure that you provided hardened applications. So we see ourselves now as, you know, real-time big data applications company, not, you know, 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 is one of the earlier ones. How are you kind of organizing yourself around the application space? Yeah, and so I kind of, 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 are 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, but it would be much better if I said in a pure way and I made some magical thing up. So in reality, as I'm looking and going, you got to go where the hottest problems are. And right now, if you think of 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. Well, that's what strikes me about the fraud thing is you would think that that's a pretty sophisticated marketplace in which to compete. So you clearly have to have an advantage to even get a meeting out of that. Yeah, and again, we've tested the market. The market's pretty hot 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 we'll, just a fraud, just a fraud prevention company. I'll stay with the fraud myself. But you kind of look and you say, look, there'll be a set of fraud applications because there's about half a dozen all need 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. We actually, we've just published one, Publix. Pubmatics 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 just, we're going to slot 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. Most of our customers have somewhere between three to five different applications that are running up in production. So once the platform's in, 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 digital way that customers want to buy things. Well, and they always have, right? Like you said, they got a burning issue to either you got to make money or save money. If it's not a burning issue, it falls to the bottom of the pile because 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 with a platform and say, hey, we can do absolutely anything. And they go, well, I've got this really difficult problem that no one's sold for me, but I'm not even sure if I've got a budget for it. Let's spend two years 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 saying, 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. Well, we look forward to watching the progress and we'll check in again in five months or so. Thank you, Jeff, appreciate it. Got your torrent. He's from Native Torrent, 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. Thank you. All right, Jeff Frick, you're watching theCUBE. See you next time. Thanks for watching.