>> Announcer: You are CUBE Alumni. Live from Munich, Germany, it's theCUBE. Covering, 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 cohost, 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 theCUBE. >> Thank you, John. >> So we were just chatting before we came on, of your historic background at IBM, Oracle, and now HPE, and now back into the saddle there. >> Don't forget Sun Microsystems. >> Sun Microsystems, sorry, Sun, yeah. I mean, great, great run. >> It was a long run. >> You've seen the computer revolution happen. I worked at HP for nine years, from '88 to '97. Again, Dave was a premier analyst during that run of client-server. We've seen the computer 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. >> Carlo: Sure, yes. >> So what are you doing as the CTO, chief technologist for HPE, how are you guys bringing this story together? 'Cause there's so much going on at HPE. You got the services spit, you got the software split, and HP's focusing on the new style of IT, as Meg Whitman calls it. >> So, yeah. My role in EMEA is actually all about having basically a visionary kind of strategy role for what's going to be HP in the future, in terms of IT. And one of the things that we are looking at is, is specifically to have, we split our strategy in three different aspects, so three transformation areas. The first one which we usually talk is what I call hybrid IT, right, which is basically making services around either On-Premise or on Cloud for our customer base. The second one is actually power the Intelligent Edge, so is actually looking after our collaboration and when we acquire Aruba components. And the third one, which is in the middle, and that's why I'm here at the DataWorks Summit, is actually the data-analytics aspects. And we have a couple of solution in there. One is the Enterprise great Hadoop, which is part of this. This is actually how we generalize all the figure and the strategy for HP. >> It's interesting, Dave and I were talking yesterday, being in Europe, it's obviously a different sideshow, it's smaller than the DataWorks or Hadoop Summit in North America in San Jose, but there's a ton of Internet of things, IoT or IIoT, 'cause here in Germany, obviously, a lot of 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. >> Absolutely. >> 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'm actually working a couple of projects in Eastern Europe, where it's all about Industrial IoT Analytics, IIoTA. 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. And we understand for example, what kind of things that they need and must have. 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 customer want to understand, in the future, how much oil they can extract. The other one is for example, the upstream business. So doing on the retail side and having, say, when my customer is stopping in a gas station, I want go in the shop, immediately giving, I dunno, my daughter, 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, and that's the case of the upstream business, but is also helping us in actually much faster business outcomes. And that's what the customer wants, right? 'Cause, and was talking with your colleague before, I'm talking to the business guy. I'm not talking to the IT anymore in these kind of place, and that's how IoT allow us a chance to change the conversation at the industry level. >> These are first-time conversations too. You're getting at the kinds of business conversations that weren't possible five years ago. >> Carlo: Yes, sure. >> I mean and 10 years ago, they would have seemed 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 center is that the detail, 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, these kind of open-source software, where close to the Intelligent Edge, then my customers going to be happy, because I give them very fast response, and the decision-maker can get to decision in a faster time. Today, it takes a long time to take these type of decision. So that's why we want to move into the power Intelligent Edge. >> So you're saying, data's foundational, but if you get to the Intelligent Edge, it's dynamic. So you have a dynamic reactive, realtime time series, or presences of data, but you need the foundational pre-data. >> Perfect. >> Is that kind of what you're getting at? >> Yes, that's the first step. Preprocessing analytics is what we do. In the next generation of, 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 internally or actually externally, convergent planned infrastructure. And that's the key point, right? >> John: Convergent plan? >> Convergent planned infrastructure, CPI. If you look at in Google, you will find. It's a solution we bring in the market a few months ago. We announce it in December last year. >> Yeah, Antonio's smart. He also had a converged systems as well. One of the first ones. >> Yeah, so that's converge compute at the edge basically. >> Correct, converge compute-- >> Very powerful. >> Very powerful, and we run analytics on the edge. That's the key point. >> Which we love, because that means you don't have to send everything back to the Cloud because it's too expensive, it's going to take too long, it's not going to work. >> Carlo: The bandwidth on the network is much less. >> There's no way that's going to be successful, unless you go to the edge and-- >> It takes time. >> With a cost. >> Now the other thing is, of course, you've got the Aruba asset, to be able to, I always say, joke, connect the windmill. But, Carlo, can we go back to the IoTA example? >> Carlo: Correct, yeah. >> I want to help, help our audience understand, sort of, the new HP, post these spin merges. So perviously you would say, okay, we have Vertica. You still have partnership, or you still own Vertica, but after September 1st-- >> Absolutely, absolutely. It's part of the columnar side-- >> Right, yes, absolutely, but, so. But the new strategy is to be 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're, especially in the Industrial IoT, we are an ecosystem, okay? So we're one element of the ecosystem solution. For the oil and gas specifically, we're working with other system integrator. We're working with oil and the 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 a infrastructure provided around that, and the services around that for the infrastructure element. But for the industry expertise, we try to have a kind of little bit of knowledge, to start the conversation with the customer. But again, my role in the strategy is actually to be a ecosystem digital integrator. That's the new terminology we like to bring in 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 we are going to be successful in these type of things. >> Okay, now a couple 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 Link 3.0, Storage Disaggregation, which is kind of interesting, 'cause it's been a problem. Hadoop as a service, Realtime Everywhere, and then Analytics at the Edge, which we kind of just talked about. Let's pick one. Let's start with Data Link 3.0. What is that? John doesn't like the term data link. He likes data ocean. >> I like data ocean. >> Is Data Link 3.0 becoming an ocean? >> It's becoming an ocean. So, Data Link 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. And the reason why I say disaggregation is because some of the workload will be only compute, and some of the workload will be only storage, okay? 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, and the same level, they want to have storage compute block, sorry, 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 disaggregation, or disaggregation between compute and storage, is the key point. We call this asymmetric, right? Hadoop is becoming asymmetric. That's what it mean. >> 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, >> Everyone that we did. >> based on the workload. >> They are all multitenancy 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 erasure coding, you said. There's a data tier. You've got different memory levels. >> And 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 what all of that means, because what we do in Hadoop, we actually build the different clusters, they need to talk to each other, and change data in a faster way. And a solution like, a product like SQL Manager, from Hortonworks, is actually helping us to get this connection between the cluster faster and faster. And that's what the customer wants. >> And then Hadoop as a service, is that an on-premise solution, is that a hybrid solution, is it a Cloud solution, all three? >> I can offer all of them. Hadoop is a service could be run on-premise, could be run on a public Cloud, could be run on Azure, or could be mix of them, partially on-premise, and partially on public. >> 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 that option is all the customer want to start very small. The maturity is actually better from a technology standpoint. If you're asking me the same question maybe a year ago, I would say, it's difficult. Now I think they've got the point. Every large customer, they want to build this big data ocean, note the delay, ocean, whatever you want to call it. >> John: Love that. (laughs) >> All right. They want to build this data ocean, and the point I want to make is, they want to start small, but they want to think very high. Very big, right, from their 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, where we find that if the customer is actually a pioneer, or is actually a very traditional one, so it's very slow-going. Once we determine where is the stage of the customer is, we propose some specific proof of concept. And in three months usually, we're putting this in place. >> You also talked about realtime everywhere. We in our research, we talk about the, historically, you had batchy of interactive, and now you have what we call continuous, or realtime streaming workloads. How prevalent is that? Where do you see it going in the future? >> So I think is another train for the future, as I mentioned this morning in my presentation. So and Spark is actually doing the open-source memory engine process, is actually the core of this stuff. We see 60 to 70 time faster analytics, compared to not to use Spark. So many customer implemented Spark because of this. The requirement are that the customer needs an 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 require a different architecture. >> I have a question, 'cause you, 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 and 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 as the differences today between North America and Europe? >> From my perspective, I think we are much more for example take 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, large German manufacturing car, they always implemented these type of things already today. >> Dave: For years, yeah. >> That's the difference, right? I think the second step is about the faster analytic approach. So what I mentioned before. The Power the Intelligent Edge, in my opinion at the moment, is much more advanced in the US compared to Europe. But I think Europe is starting 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 the two big differences I see. >> The other two big external factors that we like to look at, are Brexit and Trump. So (laughs) how 'about Brexit? Now that it's starting to sort of actually become, begin the process, how should we think about it? Is it overblown? It is critical? What's your 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're actually working the same way. For me it's too early to say if 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 the, but I saw some data recently that's, 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 a pick up? >> It's getting better, it is getting better. I mean, if I look at the major countries, the P&L is going positive, 1.5%. So I think from that perspective, we are getting better. Of course we are still suffering from the Chinese, and Japanese market sometimes. Especially in some of the big large deals. The inclusion of the Japanese market, I feel it, and the Chinese market, I feel that. 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. You're new to HPE, okay. We have a lot of history, obviously I was, spent a long part of my career there, early in my career. Dave and I have covered the transformation of HP for many, many years, with theCUBE certainly. What attracted you to HP and what would you say is going on at HP from your standpoint, that people should know about? >> So I think the number one thing is that for us the word 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 Pointnext. I'm in the EG, Enterprise Group division. But I am fan for Pointnext because I believe this is the future of our company, is on the services side, that's where it's going. >> I would just point out, Dave and I, our commentary on the spin merge has been, create these highly cohesive entities, very focused. Antonio now running EG, big fans, of where it's actually an efficient business model. >> Carlo: Absolutely. >> And Chris Hsu is running the Micro Focus, CUBE Alumni. >> Carlo: It's a very efficient model, yes. >> Well, congratulations and thanks for coming on and sharing your insights here in Europe. And certainly it is an IoT world, IIoT. 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, and sharing that with you. I'm John Furrier, Dave Vellante. Stay with us for more great coverage, here from Munich after the short break.