 Live from Silicon Valley, it's theCUBE. Covering Mobile World Congress 2017, brought to you by Intel. 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 getting close to bedtime or if you're a night owl, you're out hitting the town because Barcelona stays out very late or just finishing your dinner. Of course, we're bringing in all the CUBE coverage here, news analysis, commentary, and of course, reaction to all the big mega trends. And our next two guests is Guy Churchwood, who's the president CEO of Data Tourant, formerly of EMC, you probably recognize him from the CUBE, from EMC world, and many times he's been on CUBE alumni. And Foe Wong, who's the co-founder and chief strategy officer of Data Tourant, co-founder, one of the founders. And also one of the early, early Yahoo engineers, I think he was the fourth engineer at Yahoo going way back in 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 dataful. Guys, welcome to the CUBE special coverage. Great to see you. Thank you so much. So I'm excited you guys came in because one, two things, I want to talk about the new opportunity of Data Tourant and get some stories around the large scale experience that you guys have dealing with data. Because you're in the middle of where this is intersecting with Mobile World Congress. Right now, Mobile World Congress is on a collision course between cloud-ready, classic enterprise network architectures with consumer. All happening at the same time. And data with Internet of Things is going to be at the center of all the action. So these are now devices. So that's the core theme. So Guy, I want to get your take on. What attracted you to Data Tourant? What was the appeal for the opportunity? I mean, why am I here? Why did I just arrive? I mean, I've always been data assessed. I mean, you know this from the days of running the storage business of the data protection. Before that, I was doing data analytics and security forensics. And if you look at, as you said, is 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. And you know, there was 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 won in the data center, not on the battlegrounds. And so you really understand. He was 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 wanna do is you wanna squeeze that down to 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 either from a security perspective or you're not gonna win the bids in any shape or form. That's not a business. So speed is critical. Yeah, I mean, and people say speed. I mean, speed and accuracy. Because what you don't wanna do is to run really, really fast and fall off cliff. So you really need to make sure that speeds there and accuracies there. And in the good old days when I was running security forensics, you could either do complex and 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 a paradigm of all the technology catching up. So whether that's the disk speeds 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. And so that was my thing of looking at saying, look, this whole thing is gonna happen. And 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, I mean, we had, when I was going through the process of dating, to see whether this was a company that made sense, I chatted to one of our investors and they're also a customer. And I said, you know, why did you choose data torrent? And they said, well, look, 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, billion transactions, you know, that speed. What's some use cases that really take advantage of? Well, and they were mastering in what I would call industrialization of IT. So in other words, once you get into things like turbines, wind generation, you know, train parts. And, you know, we're going to be very, very soon looking out of a window and seeing. 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 Foo a chance otherwise we'll end up chatting about it. Foo, come on, you're the star, co-founder. When you found this company, obviously you had a background at Yahoo, which you built from scratch because that was a first mover opportunity, Web 1.0, as they say, but evolved up. And then everyone used Yahoo Finance. Everyone used Yahoo Search as a directory early on. Thank you. And then everything just got bigger and bigger and bigger and then you had to build your own stuff with a dupe. Yeah, yeah. So you've lived it. The telcos don't have the same problem. They actually got it backed into the data from being in the voice business and then the data business. So the data came after the voice. So, you know, what's the motivation behind data turrent? 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 Shell system that we thought were available really couldn't do the job. So we had to invent our own technology to deal with the kind of data processing that we had. I mean, at some point, you know, 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 activities 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. Because on the other side of that, we need to be able to understand all those activities in order to sell to our advertisers, sites and dice, behaviors and users and so on. And 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 web pages. And we turned to that to say, this is what we're gonna 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 small pieces so that we can run on these cheap servers sort of became the core tenant of how we do distributed processing that became Hadoop at the end of the day, right? You became a big data company because you were big data full, as we say. You didn't have a, 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. They had to trim down that. Yeah, and no one to turn to. Yeah, no one to turn to. All right, so let's spin this around from Mobile World Commerce. Because the big theme is, obviously we all know what device is. And in fact, we just released here on theCUBE earlier 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, you know, the watch, phones, anything around them. That adds to the industrial aspect of turbines and whatnot. Internet of Things is a new edge architecture. Yes. And the biggest 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? I mean, where's the data piece fitting in? Because all aspects of this will have data. What's your thoughts on this end-to-end architecture? Foo, tell us about your impression and conversations you've had. Yeah, 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 on every path of that. The challenge will be to kind of figure out what part of processing you put on the fringe, what part 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 conversations with telcos. And yes, we're helping them right now with their current set of issues around capacity management, 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 cell phone 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, the two questions on the 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. All right, so I want to get both of your perspective, Guy. I want you to weigh in on this one because this is another thing that's come out of the reporting and analysis from Mobile World Congress. And 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 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. That's right, that's right. It's going to be important. So how do you guys see the integration aspect? Are we top of the first inning, national anthem going on? I mean, where are we on the integration? There's a pregame, or what inning are we in on this? Yeah, yeah, we're definitely not on the home run on it. And I think our friend and your friend, Steve Manley, I sat down with him and I gave him a briefing on what we were doing. He was looking, it 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 the Tesla tell a Range Rover, tell a BMW? And so you have actually done it. Because they have different platforms, it's different stacks, it's a nightmare. Right, so in other words, interoperability. 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. And it's kind of interesting. You know, you mentioned the telco space and do they understand it? In some respects, what Foo 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 10 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 I said, you look at the window and you see DHL or UPS or FedEx drones out of 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 right, I think I actually had this conversation with Steve Manley at EMC World last year around time series data. So this is interesting. So like everyone wants a story thing but it actually might not be worth anything anymore. Because if the drone is delivering your package or whatever real time data is in real time, it's really important right there in real time or near real time. 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 know what I'm saying? So there's a notion of data that's interesting. And I think Jeff, and again, foos the expert in this. I'm still running up onto it. It's just a pet hobby and 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 an at rest or 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 land it. So in other words, get the value out of the fresh data before it hits the data lake. Because if it's hit the data lake by default, it's actually stale. And actually then there's the fascination of saying, if you're delivering real time 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 tear 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 use in both. But I think what will you see in the market and again, you've experienced the disc to flash momentum that happened last year. You're going to see that from a data source, from at rest to batch to real time data streams on applications next year. So I think this year is the formative year and back to your, get it right, get the integration right, 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 change in the gods. And then also the SLA requirements are going to start to get into this. We started looking at integration. You're absolutely right. Actually the SLA parties actually very, very important here because as you move analytics from this batch world where it has, you know, you do it once a day and if it dies, it's okay to do it again to where it is now continuous 24 by seven, giving you insight continuously about your business, your people, the 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 seven. It can never go down. It can never lose data. So all of a sudden you're putting tremendous requirement on an analytic system, which has all the way from the beginning of history till now been a very relaxed batch thing to all of a sudden being something that is enterprise grade 24 by seven. And actually, and I think that that's actually where it's going to be the toughest not to crack. So talk about some of the things that you've learned and pretend for a second, let's pretend that you're not the co-founder, data turner and guy and you're teamed up. You guys run the telco. Let's just make one up Verizon or AT&T or pick one. And you said they're saying, okay, you got the keys to the kingdom and you can do whatever you want. You could be Donald Trump or you could be whoever you want. You can fire everybody or you can take it over and run it. What would you do? Because you know you got IoT. So there's some 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? I mean, you know, IoT is an opportunity. Amazon's going after that heavily. Do you bolt the cloud together? Do you go after Amazon? Do you co-opt Amazon? Do you co-integrate? Do you go after IoT? Do you use the data? I mean, given where we are today, what's the best move if we were consulting the concos? You know, I will be the last person to be talking about giving advice to a telco but since we own our own telco here. We own our own telco in there. And we're pretending. I would say the following, you know, IoT is going to happen, right? If earlier when I say a billion people, that's just human beings. Once you now talk about sensor and 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 remember, we're consulting, we're going to sit around and say, hey, you know, we have, what do we have? We have relationship with the consumer. That's right. We have big marketing budgets. We can talk to them directly. We have access to their device. But you'll bifurcate the business. I mean, and again, we're in the boardroom here. Yes, there's nothing more than that. But I would look at it and say, look, you've got a consumer business. The same is 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 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 Wi-Fi. 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, you know, the little cameras, and they've got one now where I can basically float it with this own cellular signal. So it's its own cell phone. That's a great use of IoT for that. You know, and then you step to the consumer side of I've got a cell phone and then what I'd do is literally, in essence, rip off what Yahoo did in the early days and say, I'm now the new browser. But the person's a browser. So in other words, follow the location, follow where it is, and then basically do the locational base. By the way, you have to license the patent from our earlier guest yesterday, Willie Leek, because he's got the patent on personal firewall for personal server. No, he's built a mobile personal server. But this is the opportunity around wireless. I love the confusion, but the opportunity around wireless right now is you can get bandwidth at high capacity. You have millimeter wave four, but 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 can maybe go somewhere. So I think this is an interesting opportunity. And they're going to end up watching the data centers as well. I mean, there is a distilled gamut of saying, are customers going to continue to support their own data centers or are they going to be one to 100 data centers out there? And then how does a cellular manufacturer or a telco play into that? And do they want to be that guy or not? Right. Guy, thanks for coming in. I want to give you guys a chance to put a plug in for data turn. And thanks for sharing some great commentary on the industry. So what's up with you guys? Give us the update you're hiring, you're 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 Apache Apex. So it's open source. So you can use our product as 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. That's right. 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 operation side, you can then license our products to enable you to manage and ease it. 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. I mean, it's just, you know, so you've got all the tools to do it, but we focus from a products group of time to value, total cost of ownership. We're trying to bring Hadoop and real-scale, real-time streaming to the masses. So what's the technology innovation? What's the disruptive enabler for you guys? You know, 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. And 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. Great to meet you. I'd love to talk about the old Yahoo days, total throwback, Web 1.0, 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 the new CEO opportunity. Thank you. And great to have you on the queue. 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 getting down to the end of our day here in the afternoon in California. We'll be right back with more after this short break.