 From Midtown Manhattan, it's theCUBE, covering Big Data, New York City 2017, brought to you by SiliconANGLE Media and its ecosystem sponsors. Okay, welcome back everyone. Live here in New York City, it's theCUBE's special presentation of Big Data NYC. This is our fifth year doing our own event here in New York City, our eighth year covering the Hadoop World ecosystem from the beginning through eight years. It's had a lot of evolutions, Hadoop World, Strata, Conference, Strata, Hadoop. Now it's called Strata Data, happening right around the corner. We run our own event here in Manhattan, talking about the thought leaders and the experts, CEOs, entrepreneurs, getting the data for you, sharing that with you. I'm John Furrier, the co-host of theCUBE, with my co-host here, Jim Kobielus, who's the analyst, lead analyst at Wikibon on Big Data, and Chuck Yarbrough, who's the vice president of Penthouse Solutions, part of Hitachi's new Ventara, a new company created, just announced last week, of Hitachi and a variety of their portfolio technologies into a new company, out to bring in a lot of those integrated solutions. Chuck, great to see you again, CUBE alumni. We chatted multiple times at Pentaho World, going back, Tony, 15. Always great to be at theCUBE. What a couple of years it's been. Give us quickly, the hard news is pretty awesome. You guys have a variety of things at Pentaho, with Hitachi, that happened. Now the market's evolving. What's this new entity, this new company? Yeah, so the big news, Hitachi Ventara. So what that is, two years ago, Hitachi Data Systems acquired Pentaho, and so fast forward two years, and a new company gets created from Hitachi Data Systems, Pentaho, and a third organization at Hitachi called The Insight Group, so Hitachi Insight Group. Those three groups come together to form Hitachi Ventara, and... What's the motivation behind that? Obviously, I mean, I can almost connect to dots, but I want to hear your perspective, because it really is about pulling things together, the trend this year at the show is, as Jim calls it, hybrid data, hybrid integrated data. Things seem to be coming together. Is that part of the purpose? What's the reason behind pulling this together? Yeah, I think there's a lot of reasons. One of them is what we're seeing, not just in our own business, but in our customer's business, and that is digital transformation, right? This need to evolve, and so Hitachi Ventara is all about data and analytics, and a big focus of what we do is what Pentaho has been doing for years, which is driving in all kinds of data, big data, all data. I think we're getting on the cusp of closing out the big data term, but it's all data, right? It's data everywhere, every application. And applying analytics across the board, and one of the big initiatives, part of why Pentaho was originally acquired, we were actually, Hitachi Data Systems was a customer of Pentaho when we got acquired, so we knew each other pretty well. And part of the reason for that acquisition was to drive analytics in and around Internet of Things, the IoT space, which is something that Hitachi, being a very large IT and operational technology, OT company probably does as well as anybody, if not better. So going back a couple of years, I'm just looking at my notes here from our video index. You visited theCUBE in 2015, but really the concepts have evolved significantly. I want to just highlight a few of them. What data warehouse optimizations about, we talked about that. Data refinery concept, 360 view, as applied to big data. Again, that was foundational concepts that all are in play right now. Absolutely. What is the update in those years? Because the refinery, everyone talks about data refinery, the oil, they use the oil example, but I mean, come on. I mean, data is everywhere. It is the most important. You can use it multiple times, unlike oil, as you were pointing out. So interesting you bring that up. So to me, data refinery in a digital transformation or really in an IoT world, where lots of data is streaming through. In fact, yesterday I read something by IDC that 95% of all data in the future and the data growth is dramatic. It's 10x what it is today in just a few years. 95% of that growth of data is IoT related. The question is, how are you using most of that, right? And what are you going to do with it? So that data is streaming through. There's a lot happening. We can do things at the edge, right? We can apply analytics and filtering and do things. But ultimately, that data's going to land somewhere. And that's where that refinery, think of it as the big data center refinery, right? Where I'm going to take that large amount of data and do the things that Jim does and apply machine learning and deep algorithms to really predict the future. I like to get your thoughts on the IoT. Jim and I were arguing, not arguing, discussing with others on theCUBE about the role of the edge, because obviously the refinery, the data can come back depending on what kind of data it is. Or you push compute to the edge. Kind of known concepts, people have been discussing that. But the issue has been, how do you view the edge? I'd love to get your reaction to that question, because a lot of people are saying, you have to think of IoT as a completely different category than just cloud, than just data center. Because the way some people are looking at IoT, I know it can be semantics, whether it's industrial or just straight internet of things, device or person. But it's a different animal when it comes to like what you call it and how it gets put into a bucket. I mean, most people put a lot of things in the IT bucket. But some are saying, IoT edge should be a completely different category of how you look at those problems. Your thoughts on how that IoT conversation should shape. The question I always ask when I'm talking to somebody about the edge is, well, what do you mean, right? Because it is something that can be defined a little bit differently. But in an industrial IoT context, I think we look at it as one, you have to know what those things are. You have to really understand them. And part of understanding those things is having a digital representation of what those things are. One of the things that, a digital twin, right? Or asset avatar, as we call it at Hitachi. Oh, I like that. So this idea of really managing those assets, understanding what they are, and then being able to know what the current state, what the previous state, things are like that are. And then that refinery we just talked about is sort of where that information goes to so you can do other kinds of analytics, right? But when you're talking about the edge, typically what we're seeing is the kinds of analytics that might happen at the edge are probably more around filtering, not quite as complex of analytics. That's what we're seeing today. Now, the future? I don't know. You have tiered analytics from the edge on in with more minimal, I mean not minimal, that's the wrong term. With the more narrowly scoped inference, like predictions and so forth being handled at the edge with larger, more complex models being, deep learning, whatever, being processed in the cloud, is that it? Yeah, that's exactly the way that I see it. Now, the other thing about the edge, depends on who you're talking to again, but what is an edge device? Or the gateways or the compute, right? So part of IoT is, in my mind, it's not cloud, it's not on-prem or it's not, I mean it's a little bit of everything, right? It depends on the use case and what you're operating. We have a customer who does trains as a service in England, okay, in Europe. And so they don't sell the trains anymore. They actually manufacture trains and they sell the service of getting a passenger from here to there. So, but for them, edge is everything that happens on those trains and tracking as a digital representation, the train and then being able to drill down deeper and deeper and one of the things that I understand is one of the major delays for train service is doors opening and closing or being delayed. So maybe that comes down to a small part and the vibration of it and tracking that, right? So you've got to be able to track that appropriately. Now, on a train, you might have a lot of extra space so you could put compute devices that have a lot of power. What's interesting, you said that edge in this context is everything that happens on that train, in other words, it sounds like all the real-world outcomes that are enabled, perhaps optimized, by embedding of the analytics in those physical devices or in that entire vehicle, that is essentially one way that you're describing the edge, which is not a single device, but it's a complete assembly of devices that play together amongst themselves and with services in the cloud. Is that a large number of frames here that's why I said I usually ask, what do we mean by edge? But yeah, if you've got millions, thousands, whatever, devices out there, feeding sensors, whatever, feeding this data, collecting, processing, there's some level of edge computing, gateways, processes that are going to happen. Well, my question for you, I'd like to get your thoughts because we, again, we're having, we love the hype of IoT, we think it's completely legit and it's going to continue to be hyped because it's obvious what you see with IoT is anything on the edge. But a lot of customers that we talk to are like, look, I got a lot going on, I got application development, I got breakout my security, I got build that up, I got data governance issues, and now you're throwing IoT over the top, I'm choking in the projects. So they come down to a selection criteria. How do they define a working IoT project? And the trend that we're seeing is that it has to do with their industrial equipment or something related to their business. Call it industrial IoT because if they have something in their business, like say trains, it's a critical part of what they do. That's easy to say, let's justify this. Everything else then tends to go on the back burner if they don't have clear visibility of what they're instrumenting. That's kind of what we're hearing. Do you agree with that? Do you see a pattern as well as what customers are doing by saying, I'm going to bring this project in and we're going to connect our IoT, whatever it is. Yeah, no, that's exactly what I see. Industrial Internet of Things is where I see the biggest value today. When you have trains or mining equipment or whatever you know, manufacturing line, right? And being able to fine tune those lines to either predict failures or maybe improve quality. Those are impactful and they can be done right now today. And that's what we're seeing is kind of the big emerging thing IoT is interesting to talk about. The reality is it's really digital transformation that we're seeing. Companies transforming into new business models, doing things significantly different to grow into the future. And IoT is an enabler of that. So you're not going to see IoT everywhere today, but I- The low hanging fruit is where it connects to the real business. But it's going to go across all verticals, right? No doubt. What solutions does Pentau have for digital twins or managing digital twins, the objects, the data itself, within an IIoT context? Is this something that you're engaged in already? So within the Hitachi, Ventara- Okay, the larger company. Bigger company. We have what we call our Lumata IoT platform. And in that, there is this asset avatar technology that does exactly what you're describing. Now, I'm going to throw a quick plug out if you don't mind, the Tahoe world in a couple of, about a month. The cube will be there. The cube will be there and we're excited to have the cube and we're going to give you complete information about that asset avatar with all the right people. Yes, because there's a movie in there somewhere I could feel it, Avatar 2. There's a lot of great representations of data. I want to give you thoughts on how the new firm is going to solve customer problems. Because now as the customers see this new entity from you guys, Pentau has been doing real well. We covered the acquisition and you were kind of left alone. Pentau was integrating in, but it wasn't like a radical shift. Now there's some movement. What does it mean to customers? What's the story to the customer? You know, I think it's great news for the customer because Pentau has always been very customer focused. But when you look at Hitachi Ventura, the wealth of technology and expertise, everything from all of the great IT oriented stuff that Hitachi Data Systems has done and been well known for in the past, still exists. But this broader focus of taking data and processing it in a variety of ways. To solve real business problems. All the way to orchestrating machine learning and applying algorithms and then with the Hitachi. So Hitachi. So what specifically in Hitachi is coming into this? Because again, this is again a focused solution company now with data, so Hitachi Data Center. Yeah, so Hitachi Data Systems is, think of it as the infrastructure company. Hitachi Insight was the really focused largely on the IOT platform development with some Pentaho assets and then the Pentaho business. But here's the thing about Hitachi. Very large, large company, fills everything, right? Mining equipment and all kinds of stuff. So nobody understands how all those things fit together better, I believe, than Hitachi. So, but some of the things that we have at that organization is this idea of the Hitachi Labs. And data scientists that are really doing interesting things, Jim, you'd love to get more embedded into what some of those things are. And making that available to customers is a huge opportunity for customers to now be able to embrace a lot of the technologies we've been talking about. I said last year that this year was gonna be the year of machine learning and if you look through the Expo Hall, that's what everybody's talking about, right? It's AI or machine learning. I'm wondering if you're commercializing R&D that's coming straight out of Hitachi Labs already or whether the Ventara combination will enable that. In other words, bringing more innovation straight out of the labs into commercial arenas. So that's something that we are absolutely trying to do, right? Because there's great things that these lab organizations and Hitachi, they're big labs. I mean, they're really legit. I kind of joke about that. And the kinds of stuff that they're able to bring about. Now Pentaho is part of the engine to help actually commercialize some of those things. So, it's exciting. Have you on. We're looking forward to Pentaho World. Give you the final word here in the segment. Obviously, the big data worlds evolve. Take your Pentaho hat off and put your industry guru hat on. What's happening? I mean, it's AI washed and that's pretty obvious. Not a lot of blockchain discussion which is going to completely open up some things we've seen in the decentralized application market which is going to complement the distributed nature of how we see data analytics flow and certainly the immutability of it's interesting. But that's kind of down the road. But here, you're starting to see the swim lanes in the industry. You're seeing the people who've been successful and the ones who have fallen by the wayside. But now the customers, they want real solutions. They don't want more hype. They don't want another eighth year of hype. They want, okay, let's get into the real meat potatoes of data impact to my organization. Digital transformation. What's happening? What is going on in the landscape? So, I mentioned it before and to me it's digital transformation which is a big, huge thing. But that's what companies are interested in. That's what they're beginning to think. If they're not thinking about those things they're falling behind. Five or six, seven years ago we talked about the same exact thing with big data. It's like, hey, big data is really, it's a big opportunity and they're like, well, I don't know. Those that didn't adopt it aren't necessarily in a position now to transform digitally and to do some of the things that they're going to need to evolve into new business opportunities. And the big data examples of winners are the ones who actually made it valuable. Whether it's insight that converted to a new customer or changed an outcome in a positive way. They go, that wouldn't have been possible without data. The proof points kind of hit the table. That's right. The other thing is who's going to win, who's going to lose? I think people that are implementing technology for technology's sake are going to lose. People that are focused on the outcomes are going to win. That's what it is. Technology enables all that but you've really got to be focused on. I want to get one more quick thing before we go. I know we're tight on time but I want to get your thoughts. Obviously the open ecosystem of open source is going to a whole other level. The projections are code will be shipping at an exponential rate. It's been going to be a lot of onboarding of new stuff. So open obviously works. Community models work. Partnering is critical. So we're seeing that good partnerships, not fake deals or optical deals or Barney deals, what do you want to call it? Like real partnerships. You start to see technology partnerships. What's your view on that? How is the new event tower going to go forward? You're going to continue to do partnerships and what's the strategy? Yeah, I think the opportunity with Hitachi Vantara is we have a breadth that can touch many different aspects. So as Pentaho, we had great partnerships, very meaningful, but it always comes down to what are we doing for the customer? How are we changing things for customers? So I'm not a believer in those Barney kind of relationships. Those are nice, but let's talk about what we're doing for customers. Yeah, real proof points. Yep, and that's where it comes out to. You guys will continue to partner us. We will continue to do that. Okay, great. Chuck, thanks so much. Cube coverage live in New York City in Manhattan. This is the Cube with big data, NYC are fifth year doing our own event in conjunction with Strata Data. Now that's the new name of the show. I was straddle with Strata Hadoop. Hadoop were before that, but we're still the Cube covering eight years of the action here. We'll be back with more after this short break.