 From Palo Alto, California, it's Cube Conversations with John Furrier. Welcome to our special Cube Conversation here in Palo Alto, California. I'm John Furrier, co-founder of SiliconANGLE Media and co-host of theCUBE. I'm here with Fo Wong, who's the co-founder and Chief Strategy Officer of Data Tourant. Great to see you again. Welcome back. Thank you so much, John. It's a Cube Conversation. So you're now the Chief Strategy Officer, which is Codes Words 4. You were the CEO and co-founder of the company. You bring in a pro guide. Churchwood, we know very well, former EMC or Real Pro. Gives you a chance to kind of like get down and dirty into the organization and get back to your roots and kind of look at the big picture. Great management team. Talk about what your background is, because I think I want to start there because you have an interesting background, former Yahoo executive we talked before. Take a minute to talk about your background. Yeah, sure. You know, I think I'm just one of those super lucky engineer I got involved with Yahoo way early in 1996. I think I was a fifth engineer or so, and I stayed there for 12 years, ended up running about close to 3,000 engineers, and had the chance to really experience the whole growth of the internet. We built out hundreds of sites worldwide, and so all of our engineering team developed all of those websites throughout the world. You must have a cheer in your eye on how Yahoo ended up, but we don't want to go there. But folks that don't remember the Yahoo during the Web 1.0 days, it was the beginning of a revolution, and I kind of see the same thing happening with Blockchain and what's going on now, a whole new Wild West is happening, but back then you couldn't buy off the shelves. You had to certainly buy servers, but the software, you guys were handling kind of a first generation use case, and folks may or may not know, but Yahoo really was the inventor of Hadoop. Doing Hadoop at large scale, obviously MapReduce is written by Google, but the rest is you guys were deploying a lot of that stuff, you had to deal with scale and write your own software for big data before it was called big data. That's exactly right, it's interesting because originally we thought that our job was really customer facing website and all of the data crunching and massaging that we would actually be able to use enterprise software to do that, and very quickly we learned at the pace of scale of data that we were generating that we really couldn't use that software and we were kind of on our own, and so we had to invent approaches to do that, and the thing we knew a lot was commodity servers on racks, and so we ended up saying, how do I solve this big data processing problem using that hardware? And so it didn't happen overnight, it took many years of doing it right, doing it wrong and fixing it, but you start to iterate around how to do distributed processing across many hundreds of servers to solve the problem. It's interesting, Yahoo had the same situation, and ultimately Amazon ended up having it because they were a pioneer, and people dismissed Amazon web services like, oh, it's just hosting and bare metal on the cloud, but really what's interesting is that you guys were hardening and operationalizing big data. So I got to ask you the question, and because this is kind of more of a geeky computer science concept, but batch processing has been around since the mainframe and that's become normal, databases, et cetera, software, but now over the past eight years in particular, as big data and unstructured data is proliferated in massive scale, certainly now with internet of things you see it booming. This notion of real time data in motion, so you have two paradigms out there, batch processing, which is well known, and data in motion, which is essentially real time. It sets self-driving cars, I mean the evidence is everywhere where this is going, real time is not near real time. In nanoseconds people want results. This is a fundamental data challenge. What's your thoughts on this and how does this relate to how big data will evolve for customers? I think you're exactly right. I think as big data came and people were able to process data and understand it better and derive insights from it, very quickly for competitive reason they find out that they want those insights sooner and sooner and they couldn't get it soon enough. And so you have this opposing trends of more and more data, but yet at the same time faster and faster insight and where does that go? And I think when you really come down to it, people don't really want to do batch processing. They do batch processing because that was the technology that they have. If they have their way, they don't want to just, information is coming into their business, customers are interacting with their products constantly 24 by seven. So those events, if you will, that are giving them insights are happening all the time, except for a long time they store it into a file, they wait till midnight and then they process it overnight. More and more there are now capabilities in memory distributed to do that processing as it comes in. And that's one of the big motivation for forming data torrent. And I want to get the data torrent a minute but I want to get some of these trends because I think they're important to kind of put together the two big pieces of the puzzle if you will. One is you mentioned batch processing in real time. The companies historically have built their infrastructure and their operations, IT and whatever around that. How storage was procured and deployed. And now with IoT and the edge of the network becoming huge, it's a big deal. So data in motion is pretty much well agreed upon amongst most of the smart people. This is a big issue. But let me throw a little wrench in the equation. Cloud computing kind of changes the security paradigm. There's no perimeter anymore, so there's no door you can secure, no firewall model. Once you get in, you're in and that's where we've seen a lot of attacks on ransomware and a lot of cyber attacks. This, the penetration is everywhere. Now there's APIs and everything. So when you bring cloud into it and you bring in the fact that you got data in motion, what is the challenge for the customer? How do top architects get their arms around this? What's the solution? What's your vision on that? Well, I'll stop by saying it's a hard problem. You know, and I think you're absolutely right. I think we're still in the phase where the problems are very visible about how do you solve this? I think we're still as an industry still figuring out how to solve it. Because you're right, the security issues. You know, security is not this one point tool. It's an entire ecosystem process for doing that. And the cloud opens up all of those opportunities for fraud and so on. So it's still an ongoing challenge. I think, you know, the trend of memory becoming cheaper and cheaper so that things are done more in memory and less in storage could actually help a bit on that. But overall, security, internal, external processes are- It's a movement train. Yeah, it's a movement. So let me ask you about the other trend to throw on top of this. This is really kind of where you see a lot of the activity, although some will claim that the app stores not seeing as many apps now as they used to be, but certainly in the enterprise is massive growth and application development. So ready-made apps with DevOps and cloud have built a whole culture of infrastructure as code, which is essentially saying that I'm going to build apps and make the infrastructure kind of invisible. You're seeing a lot of apps like that called ready-made apps. How are we going to call it? Those are the things. So how are you guys at data turn handling and supporting that trend? We're right smack in the middle of exactly that trend. One of the thesis that we had was that big data's hard. Hadoop is hard, Hadoop is now 12 years old and lots of people are using Hadoop, trying Hadoop, but yet it's still not something that is fully operationalized and easy for everybody. And I think that part of that is big data's hard, distributed processing is hard, how to get all that working. So there were two things we were focusing on. So one was the real-time thing, but the other one was how do we make this stuff a lot easier to use? So we focused a lot on building tools on top of the open source engine, if you will, to kind of make it easy to use. But the other one is exactly that, ready-made apps. As we continue to learn in working with our customers and starting to see the patterns, putting kind of bigger functional block together so that it's easier to kind of build these big data applications at this next layer. You know, machine learning, rule engines, whatever not, but how do you piece that together in a way that is 80% done so that the customer only has a little bit the last mile. So you want to be the tooling for that? Yeah, I think so, and I think you have to. I mean, this stuff, you know, if you have to kind of go through the whole six layer of what it takes to get the final business value out, you're not going to have the skill set to do it, you know? So the more we can abstract and get it to the top, the better. For every company that's got their own DNA, Intel has Moore's Law, you're the co-founder of DataTurrent. What's the DNA of your company as the founder? Intel, I'll talk about what's the, what do employees, you try to instill into your culture that is the DNA that you want to be known for? Interesting, so I start out sort of on the technical or, you know, product side. Actually, our DNA is all about ops. We think that, especially in big data, there's lots of ways to do prototypes and get some proof of concept going. But getting that to production, to run it 24 by seven, never lose data, that's really, has been hard. And so our entire existence is around how to truly build 24 by seven, no data, fast applications. So all of our engineers live and breathe how to do that well. You know, ops is consistent with stability. And it's interesting, you know, Silicon Valley's going through its own transformation around programmers and the role of entrepreneurship. It's interesting in the enterprise. They were always kind of like, oh, no big deal. Because at the end of the day, they need stuff to run at five, nine. These are networks. And the old saying that Mark Zuckerberg used to have is, you know, move fast and break stuff. They've changed their tune to move fast and be 100% reliable. So this is the trend that the enterprise has always put out there. How do companies stay and maintain that ops integrity? As, and still be innovative without a lot of command and control and compliance restrictions, how do they experiment with this to data tsunami that's happening and maintain that integrity? My answer to that is, I think as an industry, you know, we have to build products and tools to allow for that. I mean, some of that is processes inside a company, but I think a lot of that can be productized. You know, the advances in that big data processing layer and how to recover, you know, get new containers and do all the right things, allow for the application developer not to have to worry about many of those segments. So it's like, I think technology exists out there for tools to be developed to deal with a lot of that. I love talking with the entrepreneurs and you're the co-founder of DataTurrent. Talk about the journey you've been on from the beginning. You have a new CEO, which, you know, as the CEO, you want to lighten the load up a little bit, it gets bigger, you got to have HR issues, things are happening, you're putting culture in place and trying to scale out and get a group swing and certainly Uber could have taken a few tips from your playbook and has bring it in senior management. You did it at the right time. Talk about your journey, the company and what people should know about DataTurrent? Well, I think, you know, we were just a bunch of guys that are just still trying to make a contribution to the industry. I think we saw an opportunity to really help people move towards big data, move towards real-time analytics and really help them solve some really hairy problems that they have coming up with data. From a skill set and personally, you know, I think, you know, kind of my particular strength was really about that initial vision, be able to kind of build out a set of capabilities and maybe get a first set of, you know, have a dozen wins and really prove point, but to sort of make it into a, you know, a machine that has all the right marketing tools and business development tools and so on, it will be great to be able to bring in someone like Guy who has done that many, many times over and has been super successful at that to take us to the next level. It takes a lot of self-awareness too. I mean, you probably had your moments where should you stay on, be the CEO and, but what are you doing now? I mean, because you get down and you can get into the products, are you doing a lot more product tinkering, are you involved in obviously the roadmap? What's your involvement day to day now? Oh, I love it because it's exactly what I enjoy most, which is really interacting with customers and users and really continue to hone in on the product market fit and continue to understand what are the pain points, what are the issues and how can we solve it? All coming from not so much a services mentality, but a product mentality. How do we really? At the cloud ops too, that's a big area. So what's the big problem that you solve for the customers? What's the big, big airy problem? Really easy, how to productize, how to operationalize this data pipeline that they have so that it can truly be accepting real life business data that they are getting in and giving them the insight. Bit a lot of talk about automation and AI lately. Obviously it's Buzzword, you know, Wikibon just put out a report called True Private Cloud that shows all the automation is actually going at and replacing non-differentiated labor, which actually like racking the stack and gear. Moving to values actually has to be more employment on that side, but talk about the role of automation in the data world because if you just think about the amount of data coming like Facebook and Yahoo take in, you need machine learning, you need automation. What is the key to automation in a lot of these new emerging years around large data sets? It's so funny, yesterday I was driving, I was listening to a KQED segment and they were talking about in its next phase, AI and machine learning is going to do sort of the first layer of all the reporting. So you actually have reporters now doing much more sophisticated reporting because there's an AI layer that has a template of what are the questions to answer and they can just spill out all the news for you. Pay by cryptocurrency. Yeah. I think machine learning and AI will be everywhere and we will continue to learn and it will continue to get better at doing more and more things for us so that we get to kind of play at that creative, disruptive layer while it does all the menial task and I think it will touch every part of our civilization. The technology is getting incredible, the algorithms are incredible, the power, the computing power to allow for that is getting exponential. So, and I think it's super interesting that the engineers are super interested in it. Everything we do now revolves around, when we talk about the analytics layer at real time, it's all about machine learning scoring and how to rule rules and all that. Great to have you here in the CUBE Conference. I'll give you the last word. Talk, give a quick plug about data turn. What should your customers know about you guys? Why should they call you? Well, we're a company solely focused on bringing big data applications to production. We focus on making sure that as long as you understand what you want to do with data, we can make it super fast, super reliable, super scalable, all that stuff. Co-founder, data turn here in the CUBE Conference here in Palo Alto. I'm John Furrier, thanks for watching.