 You are CUBE alumni from Munich, Germany. It's the CUBE coverage. DataWorks Summit Europe 2017, brought to you by Hortonworks. Hello everyone, welcome back to live coverage at DataWorks 2017. I'm John Furrier with my co-host, Dave Vellante. Two days of coverage here in Munich, Germany, covering Hortonworks and Yahoo! Presenting Hadoop Summit now called DataWorks 2017. Our next guest is Carlo Vaiti, who's the HPE Chief Technology Strategist, EMEA Digital Solutions, Europe, Middle East, and Africa. Welcome to the CUBE. Thank you, John. So we were just chatting before we came on of your historic background, IBM, Oracle, now HPE, and back into the saddle there. Don't forget Sun Microsystems. Sun Microsystems, sorry, Sun, yeah. I mean, great, great run. You've seen the computer revolution happen. I worked at HPE for nine years from 88 to 97. Again, Dave was a premier analyst during that run of client server. We've seen the computing revolution happen. Now we're seeing the digital revolution where the iPhone is now 10 years old, cloud is booming. Data's at the center of the value proposition, so a completely new disruptive capability. So what are you doing at the CTO, Chief Technology for HPE? How are you guys bringing this story together? Because there's so much going on at HPE. You've got the services split. You've got the software split, and HPE's focusing on the new style of IT as Meg Whitman calls it. So yeah, my role in HPE is actually all about having basically a visionary kind of strategy role for what's going to be HPE in the future in terms of IT. And one of the things that we are looking at is specifically to have, we split our strategy in three different aspects or three transformation areas. The first one we usually talk is what I call hybrid IT, right? Which is making, making services around either on-premise on cloud for our customer base. The second one is actually power the intelligent edge. So it's actually looking after our collaboration and where we acquire a RUBA components. And the third one, which is in the middle, and that's why I'm here at the Data Work Summit, is actually the data analytics aspects. And we have a couple of solutions in there. One is the Enterprise Grader Doop, which is part of this. So this is actually how we generalize all the figure and the strategy. It's interesting, Dave and I were talking yesterday, being in Europe, it's obviously a different size show. It's different, smaller than the Data Works or Hadoop Summit in North America and San Jose. But there's a ton of internet of things, IOT or IIOT, because here in Germany I'll see a lot of the industrial nations. But in Europe in general, a lot of smart cities initiatives, a lot of mobility, a ton of internet of things opportunity more than in the US. Can you comment on how you guys are tackling the IOT because it's an intelligent edge, certainly, but it's also data, it's in your wheelhouse. Yes, sure. So I'm actually working, it's a good question because I actually work in a couple of projects in Eastern Europe, where it's all about industrial IOT analytics, I-I-O-T-A, that's the new terminology we use. So what we do is actually we analyze from a business perspective, what are the business pain points in an oil and gas company, for example, we understand, for example, what kind of things they need and must have. So, and what I'm saying here is, one of the aspects, for example, is the drilling opportunity. So how much oil you can extract from a specific rig in the middle of the North Sea, for example. This is one of the key question because the customers want to understand in the future how much oil they can extract. The other one is, for example, the upstream business. So on doing on the retail side, and having, say, when my customer is stopping in the gas station, I want to go in the shop and immediately giving, I don't know, my daughter are a kind of campaign for the Barbie because they like the Barbie. So IOT, industrial IOT help us in actually making a much better customer experience, that's the case of the upstream business, but it's also helping us in actually a much faster business outcomes. And that's what the customer wants, right? Because, and I was talking with your colleague before, I'm talking to the business guy. I'm not talking to the IT anymore in this kind of place. Industrial IOT allow us actually to change the conversation at the industry level. Well, these are first time conversations too. I mean, you're getting at, you know, the kinds of business conversations that weren't possible five years ago. Yeah, sure. I mean, 10 years ago they would have seen fantasy. Now they're reality. The role of analytics in my opinion is becoming extremely key. And I said this morning for me, my best sentence is that the data is the stone foundation of the digital economy. I continue to repeat this terminology because it's actually where everything is starting from. So what I mean is let's take a look at the analytic aspect. So if I'm able to analyze the data close to the shop floor, okay? Close to the shop manufacturing floor. If I'm able to analyze my data on the rig in the oil and gas industry, if I'm able to analyze doing preprocessing analytics with Kafka, Druid, this kind of open source software where close to the intelligent edge, then my customer is going to be happy because I give them very fast response and the decision maker can get the decision in a faster time. Today it takes a long time to take this type of decision. So that's why we want to move into the power intelligence. So you're saying data's foundational, but if you get to the intelligent edge, it's dynamic. So you have a dynamic reactive real-time time series or presence of data, but you need the foundational pre-data. Is that kind of what you're getting at? That's the first step. Pre-processing analytics is what we do. In the next generation of what we think is going to be industrial IoT analytics, we're going to actually put massive amount of compute close to the shop manufacturing floor. We call it internally, actually externally, convergent plan infrastructure. And that's the key point. Convergent plan infrastructure, CPI. If you look at the Google, you will find it's a solution we bring in the market a few months ago. We announced it in December last year. And Jonio Spar, he also had a Converge systems as well. Yeah, that's Converge compute at the edge, basically. Converge compute at the edge. Very powerful and we run analytics on the edge. Which we love because that means you don't have to send everything back to the cloud because it's too expensive, it can take too long, and it's not powerful. And the bandwidth on the network is much less. There's no way that's going to be successful unless you go to the edge. Well, the cost. Now, the other thing is, of course, you've got the Aruba asset to be able to, I always say, connect the windmill. But, Carla, can we go back to the IOTA example? And I want to help our audience understand sort of the new HP post these spin merges. So previously, you would say, okay, we have Vertica, you still have partnership, or you still own Vertica, but after September 1st, you know, it goes right. Yes, absolutely. But the new strategy is to be, you know, more of a platform for a variety of technology. So how, for instance, would you solve or did you solve that problem that you described? What did you actually deliver? So again, as I said, we are, especially in the industrial IOT, we are in ecosystem, okay? So we are one element of the ecosystem solution. For the oil and gas specifically, we're working with other system integrators. We're working with oil and industry gas expertise, like DXC company, right? The company that we just split a few days ago. And we're working with them. They're providing the industry expertise. We are an infrastructure provider around that and the services around that for the infrastructure element. But for the industry expertise, we try to have a kind of middle group of knowledge to start the conversation with the customer. But again, my role and the strategy is actually to be an ecosystem digital integrator. That's the new terminology I would like to bring on the market because we really believe that's the way HP role is going to be. And the relevance of HP is totally depending if you're going to be successful in these type of things. Okay, now a couple of other things you talked about in your keynote. I'm just going to list them and then we can go wherever we want. There was data lake 3.0, storage disaggregation, which is kind of interesting, because it's been a problem, a Hadoop as a service, real time everywhere, and then analytics at the edge, which we kind of just talked about. Let's pick one. Let's start with data lake 3.0, what is that? John doesn't like the term data lake. He likes data ocean. I like data ocean. Is data lake 3.0 becoming an ocean? It's becoming an ocean. So data lake 3.0 for us is actually following what is going to be the future for HDFS 3.0. So we have three elements. The erasure coding feature, which is coming on HDFS. The second element is around having HDFS data tier, multi data tier. So we're going to have faster SSD drives. We're going to have big memory nodes. We're going to have GPU nodes. The reason why I say it is aggregation is because some of the worker will be only compute, some of the worker will be only storage. So we're going to bring, and the customer require this because it's getting more data. And they need to have, for example, yarn application running on compute nodes. At the same level, they want to have storage compute block, storage components running on the storage model. Like HBase, for example, like HDFS 3.0 with the multi tier option. So that's why the data desegregation, desegregation between compute and storage is the key point. We call this asymmetric, right? Adupe is becoming asymmetric. That's what it means. And the problem you're solving there is when I add a node to a cluster, I don't have to add compute and storage together. I can disaggregate and choose whatever I need based on the workload. Multi-terrency kind of workload and they are independent and they scale out. Of course, it's much more complex, but we have actually proved that this is the way to go because that's what the customer is demanding. So 3.0 is actually functional. It's a ratio coding, you said. There's a data tier, you've got different memory levels. I forgot to mention the containerization of the application, having dockerized the application, for example, using Mesosphere, for example, right? So having the containerization of the application is one other element. Because what we do in Adupe, we actually build the different clusters that need to talk to each other and exchange data in a faster way. And a solution like a product-like cycle manager from Hortonworks is actually helping us to get this connection between the cluster, cluster, and cluster. And that's what the customer wants. And then Hadoop as a service, is that an on-prem solution? Is that a hybrid solution? Is a cloud solution, all three? I can offer all of them. Hadoop as a service could be run on premise, could be run on public cloud, could be run on Azure, or could be mixed of them partially on premise and partially on... And what are you seeing with regard to customer adoption of cloud and specifically around Hadoop and big data? I think the way I see the adoption is all the customer want to start very small. The maturity is actually better from the technology standpoint. If you ask me the same question maybe a year ago, I would say, that's difficult. Now, I think they got the point. Every large customer, they want to build this big data auction, not data lake or ocean, whatever you want to call it. Love that. All right. They want to build this data auction and the point I want to make is they want to start small, but they want to think very high, very big, right, from that perspective. And the way they approach us is we have a kind of methodology. We establish the maturity assessment. We do a kind of capability maturity assessment. We define that the customer is actually a pioneer or is actually a very traditional one. So it's very slow growing. Once we determine where is the stage of the customer is, we propose some specific proof of concept. Okay. And in three months, usually they were putting this in place. You also talked about real time everywhere. We, in our research, we talked about the historically batch, you had batch, you have interactive and now you have what we call continuous or real time streaming workloads. How prevalent is that? Where do you see it going in the future? So I think it's another trend for the future. As I mentioned this morning in my presentation. So Spark is actually doing an open source memory engine process. It's actually the core of this stuff. We see 60 to 70 times faster analytics compared to not to use Spark. So many customers are implementing Spark because of this. The requirement are that the customer needs immediate response time, okay? For a specific decision making that they have to do in order to improve their business, in order to improve their life. But this requires a different architecture. I have a question, because you've lived in the United States, you're obviously global and spent a lot of time in Europe as well. And a lot of times people want to discuss the differences between, let's make it specific here, the European continent in North America and from a sophistication standpoint, same, we can agree on that. But there are still differences. Maybe more greater privacy concerns, the whole thing with the cloud and the NSA in the United States created some concerns. What do you see is the differences today between North America and Europe? So from my perspective, I think we are much more, for example, takes IoT, industrial IoT. I think in Europe we are much more advanced. I think in the manufacturing and the automotive space, the connected car kind of things, autonomous driving, this is something that we know already how to manage how to do it. I mean, Tesla in the US is a good example that what I'm saying is not true. But if I look at, for example, a large German manufacturing car, they always implemented these type of things already today. For years. So that's the difference, right? I think the second step is about the faster analytic approach. So what I mentioned before, the power of the intelligent edge, in my opinion at the moment, is much more advanced in the US compared to Europe. But I think Europe is trying to run back and going on the same route. Because we believe that putting compute capacity on the edge is what actually the customer wants. But that's a too big difference. The other two big external factors that we like to look at are Brexit and Trump. So, how about Brexit? Is it now that it's starting to sort of actually become, you know, be in the process, is it, how should we think about it? Is it overblown? Is it critical, what do you take? Well, I think it's too early to say. UK just split a few days ago, right? Officially, it's going to take another 18 months before it's going to be completed. From a commercial standpoint, we don't see any difference so far. We are actually working in the same way. For me, it's too early to say there's going to be any implication on that. And we don't know about Trump. We don't have to talk about it. But I saw some data recently that European sentiment, business sentiment is trending stronger than the US, which is different than it's been for the last many years. What do you see in terms of just sentiment, business conditions in Europe? Do you see it pick up? I think it's getting better. It's getting better. I mean, if I look at the major countries, the PNL is going positive, 1.5%. So, I think from that perspective, we are getting better. Of course, we're still suffering from the Chinese and Japanese markets sometimes, especially in some of the big, large deals. The inclusion of the Japanese market, I feel it, and the Chinese market, I feel it there. But I think the economy is going to be okay. So, it's going to be good. Carlo, I want to thank you for coming on and sharing your insight. Final question for you. Your new to HPE, okay? We have a lot of history. Obviously, I spent a long part of my career there, early in my career. Dave and I have covered the transformation of HPE for many, many years with theCUBE, certainly. What attracted you to HPE and what would you say is going on at HPE from your standpoint that people should know about? So, I think the number one thing is that for us, the world is going to be hybrid. It means that some of the services that you can implement, either on premise or on cloud, could be done very well by the new Pointnext organization. I'm not part of the Pointnext, I'm in the EG Enterprise Group division, but I'm fun for Pointnext because I believe this is the future of our company, is on the services side. And I was just pointing out, Dave and I, our commentary on it, the spin merge, has been create these highly cohesive entities, very focused on Antonio now running EG, big fans of work. It's actually an efficient business model. Absolutely. And Chris Hsu's running the micro focus, keep it one night. It's a very efficient model, yeah. Well, congratulations and thanks for coming on and sharing your insights here in Europe. Certainly, it is an IOT world, I-I-O-T, I love the analytics story, foundational services. It's going to be great, open source powering it, and this is theCUBE, opening up our content, sharing that with you. I'm John Furrier, Dave Wants. Stay with us for more great coverage here from Munich after this short break.