>>from around the globe. It's the Cube with digital coverage of workplace next made possible by Hewlett Packard Enterprise. >>Hello, everyone. We're here covering workplace next on the Cube For years, you know, we've talked about new ways to work, and it was great thought exercise. And then overnight the pandemic heightened the challenges of creating an effective work force. Most of the executives that we talked to in our survey say that productivity actually has improved since the work from Home Mandate was initiative. But, you know, we're talking not just about productivity, but the well being of our associates and managing the unknown. We're going to shift gears a little bit now. We've heard some interesting real world examples of how organizations are dealing with the rapid change in workplace, and we've heard about some lessons to take into the future. But now we're going to get more practical and look at some of the tools that are available to help you navigate. The changes that we've been discussing and with me to talk about these trends related to the future of work are are are Qadoura, who's the vice president of worldwide sales and go to market for Green Lake at HP Sadat Malik is the VP of I O t and Intelligent Edge at HP and Satish Yarra Valley is the global cloud and infrastructure practice Head at Whip Probe guys welcomes. Good to see you. Thanks for coming on. >>Thanks for having us. >>You're very welcome. Let me start with Sadat. You're coming from Austin, Texas here. So thank you. Stay crazy. As they say in Austin, for the uninitiated, maybe you could talk a little bit about h p E point. Next. It's a strategic component of H p. E. And maybe tell us a little bit about those services. >>Thank you so much for taking the time today. Appreciate everybody's participation here. So absolutely so point Next is HP Services on. This is the 23,000 strong organization globally spread out, and we have a very strong ecosystem of partners that be leveraged to deliver services to our customers. Um, our organization differentiates itself in the market by focusing on digital digital transformation journeys for our customers. For customers looking toe move to a different way off, engaging with its customers, transforming the way its employees work, figuring out a different way off producing the products that it sells to. His customers are changing the way it operationalize these things. For example, moving to the cloud going to a hybrid model, we help them achieve any of these four transformation outcomes. So point next job is toe point. What is next in this digital transformation journey and then partner with our customers to make that happen? So that's what we do. >>Thank you for that. I mean, obviously, you're gonna be seeing a lot of activity around workplace with shift from work from home, changes in the network changes in security. I mean the whole deal. What are some of your top takeaways that you can share with our audience? >>Yeah, they're >>so a lot has been happening in the workplace arena lately. So this is not new, right? This is not something that all of a sudden side happening when Kobe 19 hit, uh, the digital workplace was already transforming before over 19 happened. What over 19 has done is that it has massively accelerated the pace at which this change was happening. So, for example, right remote work was already there before over 19. But now everybody is working remotely so, in many ways, the solution that we have for remote work. They have been strained to appoint, never seen before. Networks that support these remote work environments have been pushed to their limits. Security was already there, right? So security was a critical piece off any off the thinking, any of the frameworks that we had. But now security is pivotal and central. Any discussion that we're having about the workplace environment data is being generated all across the all across the environment that we operated, right? So it's no longer being generated. One place being stored. Another. It's all over the place now. So what Kobe, 19 has done is that the transformation that was already underway in the digital workplace, it has taken that and accelerated it massive. The key take away for me is right that we have to make sure that when we're working with our customers, our clients, we don't just look at the technology aspect of things. We have to look at all the other aspect as well the people in the process aspect off this environment. It is critical that we don't assume that just because the technology is there to address these challenges that I just mentioned. Our people and our processes would be able to handle that as well. We need to bring everybody along. Everybody has different needs, and we need to be able to cater to those needs effectively. So that's my biggest take away. Make sure that the process and the people aspect of things was hand in glove with the technology that we were able to bring to bear here. >>Got it. Thank you. So, ah, let's go to San Francisco, bringing our war to the conversation. You're one of your areas of focus is is HP Green Lake. You guys were early on with the as a service model. Clearly, we've seen Mawr interest in cloud and cloud like models. I wonder if you could just start by sharing. What's Green Lake all about? Where does it fit into this whole workplace? Next, Uh, conversation that we're having? >>Yeah, absolutely. Um HP Green lake effectively is the cloud that comes to your data center to your Coehlo or to your edge, right? We saw with Public Cloud. The public cloud brought a ton of innovations, um, into the sort of hyper scale model. Now, with HP. What we've done is we've said, Look, customers need this level of innovation and this level of, you know, pay as you go economics the, you know, management layer the automation layer not just in a public cloud environment, but also in our customers data center or to the other potential edges or Coehlo scenarios. And what we've done is we've brought together Asada just mentioned the best of our point next services our software management layer as well as H. P. E s rich portfolio of hardware to come together to create that cloud experience. Um, of course, we can't do this without the rich ecosystem around us as well. And so everything from you know, some of our big S I partners like we bro, who also have the virtual desktop expertise or virtual desk that then come together to start helping us launch some of these new workloads supported cloud services such as D. D i eso for my perspective, v. D. I is the most important topic for a lot of our customers right now, especially in sectors like financial services, um, advanced engineering scenarios and health care where they need access to those, uh to their data centers in a very secure way and in a highly cost optimized way as well. >>Well, okay. Thank you. And then let's let's bring in, uh, petition talk a little bit about the ecosystem. I mean, we're pro. That's really kind of your wheelhouse. We've been talking a lot on the cube about moving from an industry of point products to platforms and now ecosystem innovation, Uh, are are mentioned VD I we saw that exploding eso teach. Maybe you could weigh in here and and share with us what you're seeing in the market and specifically around ecosystem. >>As we all know, the pandemic has redefined the way we collaborate to support this collaboration. We have set up huge campuses and office infrastructure In summary, our industry has centralized approach. Now, the very premise of the centralization bringing people together for work has changed. This evolving workspace dynamics have triggered the agency to reimagine the workspace strategy. CEO, CEO S and C H R ose are all coming together to redefine the business process and find new ways off engaging with customers and employees as organizations embrace work from home for the foreseeable future. Customer need to create secure by design workspaces for remote working environments. With the pro virtual disk platform, we can help create such seamless distal workspaces and enable customers to connect, collaborate and communicate with ease from anywhere securely. They're consistent user experience. Through this platform led approach, we are able to utter the market demands which are focused on business outcomes. >>Okay, and this is the specifics of this hard news that you're talking about Video on demand and Citrix coming together with your ecosystem. H p E were pro and again, the many partners that you work with is that correct? >>Well, actually, Dave, we see a strong playoff ecosystem partners coming together to achieve transformative business outcomes. As Arbor said earlier, HP and Wipro have long standing partnership, and today's announcement around HP Green Lake is an extension off this collaboration, where we provide leverage HP Green Leg Andre Pro, which elders platform to offer video as a service in a paper user model. Our aim is to enable customers fast track there. It is still works based transformation efforts by eliminating the need to support upfront capital investments and old provisioning costs while allowing customers to enjoy the benefit off compromise, control, security and compliance. Together, we have implemented our solution across various industry segments and deliver exceptional customer experiences by helping customer businesses in their workspace. Transformation journeys by defining their workspace strategy with an intelligent, platform led approach that enables responsiveness, scalability and resilience. It's known that Wipro is recognized as a global leader in the distal workspace and video I, with HP being a technology leader, enabling us with high level of program ability on integration capabilities. We see tremendous potential to jointly address the industry challenges as we move forward. >>Excellent. Uh, sad. I wanna come back to you. We talk a lot about the digital business, the mandate for digital business, especially with the pandemic. Let's talk about data. Earlier this year, HP announced the number of solutions that used data to help organizations work more productively safely. You know, the gamut talk about data and the importance of data and what you guys were doing there specifically, >>Yeah, that's a great question. So that is fundamental to everything that we're doing in the workplace arena, right? So from a technology perspective that provides us with the wherewithal to be able to make all the changes that we want to make happen for the people in the process side of things. So the journey that we've been on this past year is a very interesting one. Let me share with the audience a little bit of what's been going on on the ground with our customers. Um, what's what's been happening in the field? So when the when Kobe 19 hit right, a lot of our customers were subjected to these shutdown, which were very pervasive, and they had to stop their operations. In many cases, they had to send their employees home. So at that point, HB stepped in the point. Next organization stepped in and helped these customers set up remote work out options, which allowed them to keep their businesses going while they handle these shutdowns. Fast forward. Six months and the shutdown. We're starting to get lifted and our customers were coming back to us and saying to us that Hey, we would now like to get a least a portion off our workforce back to the normal place of work. But we're concerned that if we do that, it's gonna jeopardize their safety because off the infection concerned that were there. So what we did was that we built a cities or five solutions using various types of video analytics and data analysis analysis technologies that allowed these customers to make that move. So these five solutions, uh, let me walk, walk our customers and our clients and audience through those. The first two of these solutions are touchless entry and fever detection. So this is the access control off your premise, right? So to make sure that whoever is entering the building that's in a safe manner and any infection concerned, we stop it at the very get go once the employees inside the workplace, the next thing that we have is a set of two solutions. What one is social distance tracing and tracking, and the other one is workplace alerting. What these two solutions do is that they use video analytics and data technology is to figure out if there is a concern with employees adhering to the various guidelines that are in place on alerting the employees and the employers if there is any infringement happening which could risk overall environment. Finally, we realized right that irrespective off how much technology and process we put in place. Not everybody will be able to come into the normal place of work. So what we have done is that the first solution that we have is augmented reality and visual remote guidance. This solution uses a our technologies allow. People were on site to take advantage of the expertise that resides offsite to undertake complex task task, which could be as complex as overhauling a machine on ah factory floor using augmented reality where somebody off site who's an expert in that machine is helping somebody on site data has become central to a lot of the things that we do. But as I said, technology is one aspect of things. So ultimately the people process technology continuum has to come together to make these solutions real for our customers. >>Thank you, Arwa. We just have just about 30 seconds left and I wonder if you could close on. We're talking about cloud hybrid. Uh, everybody's talking about hybrid. We're talking about the hybrid workplace. What do you see for the for the future over the next 2345 years? >>Absolutely. And I think you're right, Dave. It is, ah, hybrid world. It's a multi cloud world. Ultimately, what our customers want is the choice and the flexibility to bring in the capabilities that drive the business outcomes that they need to support. And that has multiple dimensions, right? It's making sure that they are minimizing their egress costs, right. And many of our on Prem solutions do give them that flexibility. It is the paper use economics that we talked about. It is about our collective capability as an ecosystem to come together. You know, with Citrix and NVIDIA with R s I partner we pro and the rich heritage of HP es services as well as hardware to bring together these solutions that are fully managed on behalf of our customers so that they can focus their staff their i t capabilities on the products and services they need to deliver to their customers. >>Awesome. Guys, I wish we had more time. We got to go day volonte for the cube. Keep it right there. Lots of great more content coming your way. >>Yeah,

(orchestral music playing) >> Hi. I'm Peter Burris. Welcome to another Cube Conversation. Brought to you by theCUBE from our beautiful studios in Palo Alto, California. Great conversation today. We're going to be speaking with Datera about some of the new trends and how we're going to utilize data within the business, with greater success, generating more value to superior customer objectives. To do that, we've got Marc Fleischmann, who's the CEO and Founder of Datera. Marc, welcome to theCUBE. >> Thank you. >> And Guy Churchward, who's the Executive Chairman of Datera. >> Yeah, thank you Peter. >> So guys, this is a great topic, great conversation, very very timely industry. One of the reasons is we've heard a lot about the Cloud-native stack. Now the Cloud-native stack is increasingly going to reach into the enterprise and not just demand that everything come back to the cloud, but bring the cloud more to the enterprise. Well one of the things that's still something of a challenge is and how do we bring data given it's native attributes into that model more successfully. Marc, what are the issues? So look, ultimately we believe it's all about data freedom, the capability to extract the value of data across the enterprise. However, as long as we continue to think about proprietary systems silos, where data is trapped, where it can't move freely across the enterprise, we're not going to be able to get there. So ultimately what it requires is changing our thinking of infrastructure from a hard for centric prospective to a service centric prospective. Ready applications drive the needs from the data, where it's an application centric perspective that automatically drives how data is actually consumed across the enterprise. >> But the, we've been thinking about that through software defined, ECI, and other, you know, hyperconversion infrastructure in other things. But at the end of the day, we really have to make sure that we're doing so in a way that marries to the realities of data. >> Absolutely. >> Talk to us a little bit about how Datera is providing that substrate that is native to data, but also native to the cloud. >> Absolutely. So I would describe Datera as Datera is to data what Kubernetes is to compute. What do I mean by that? First of all, it's all about data orchestration. We orchestrate the data just like Kubernetes would orchestrate compute. That's the foundation of our platform. Now if we don't deliver enterprise performance, so that we can actually, you know, replace existing storage, we wouldn't be able to actually broadly deploy. So we have enterprise performance as well. And lastly, to get away from a hard for centric model, we offer wide variety, wide choice, future ready choice of Harver. Those are the three key tenants that we actually see as getting to that vision. >> So Guy, you've been in this business a long time. You've looked at a lot of changes in technology, for rays where we were mainly focused on persisting data to now some of the new technologies, we were more focusing on delivering data to new classes of applications. From your perspective, how does this message Marc's bringing line up with customer needs? >> Yeah, I know, appreciate it. I mean that was one of the reasons that when I had the opportunity to work closely with Datera, I kind of jumped into it. You know, because part of this is, as Marc said, data freedom. Unlocking, in other words, unlocking from the boundaries of basically a physical location. I think, you know, we always aspire and believe that we want to move towards a cloud, a pure cloud model. But we're going to be in this transition for five, six, seven years where we have on premise a bit of hybrid and a bit of distributed and things like Intelligent Edge. So in other words, the whole concept is to say how do I utilize data no matter where it is into a fabric or a mesh. And I think that the industry that we all live in sort of, by accident, tries to own the data, you know. It doesn't matter whether you own it in a physical construct of a data center or we own it in a physical construct of a piece of hardware or a proprietary format. But in essence you have these data silos absolutely everywhere. And so for me to move to a cloud, you've got the simplicity you need. You've got the orchestration that you actually need. But you need this freedom outside of the bounds of a physical location or a piece of tent. >> I want to return back to the issues of performance >> Yeah. >> and the need for performance because the world that you just laid out guys, makes an enormous amount of sense to me and the Wikibon community. But it does mean that this data generated by that application in this location may have value to some other applications somewhere else that may have completely different performance action. >> Absolutely. >> So let's talk about that need for ensuring, that again, this notion of a native data approach to incorporating data into the cloud. How does the performance angle really work? >> I would argue where traditional self defined storage, SDS, fell short was exactly on the promise of performance. We saw that we contributed a significant part of the Linux data path itself. The way we architected the system, we delivered true, primary application performance. So that in combination with the ability to orchestrate data across the data center, across multiple data centers, and ultimately across the data center and the cloud gives you the best of both worlds. It gives you primary workloads, the ability to actually serve primary workloads across multiple protocols, but to serve them location dependent, wherever you like, because we orchestrate the data through those places. >> And- >> So- >> Oops! Go ahead. >> Sorry. It's the coffee. It's going to kick in. (Peter laughs) So I mean part of it is not just that, but it's also the life cycle. >> Ah, very true. Right, I mean and, you know, this is the thing that kind of attracts me is, and you mentioned, you know, what you learn with the amount of hair I don't have now and the gray beard I've got is, you know, there's one thing about this sort of data boundaries and things getting locked in. The other one is the speed of which people want to build an application. They need it to be have the enterprisilities, and then they'll take the application down. You know, if you kind of think when we started in the industry and it would last 20 years. And then 10 years. And then five years. And now you look at it saying somebody wants an enterprisility application up and running within two or three months, which is preposterous, but needs to be done. And then it might be down within a month. Because- >> Oh 15 years ago it took us two or three months to create the test data required for the application to follow up. >> Right, and how many people would ever used to tell you never use an application if it's a window zero. But we're talking about, in a window zero period, they're actually going to serve their communities, the most critical thing. Data is it for a company. If you're analytics don't run as fast as your company's competitive space, you're behind. So if you're going to analyze something that application that you bring up to analyze has to be critical to your business. And that's going to go up and it's going to go down. So in other words, it's going to go from test and dev, up into production, tier zero, then tier one, tier two, tier three, and then out into an archive in a period of time that normally a window zero would gestate. And so you need a platform that has that ultimate agility and again it can't be bound by anything. And this is something that, you know, Datera has as unique. This was why I like software defined and why I believe that this market's place is now for this space. Everything prior to SDS is basically what I call new legacy. You know, it doesn't matter whether it's a ray or it's hyperconversion, and they're great and they've got their place. But each one of them has this fixed boundary that allows you to flex but inside of its own control. Businesses aren't like that. They can't be done like that and applications can't be done like that now. So it's all multi-cloud, it's all going to be versed. >> Well let's build on that. So the Kubernetes describes, as you said, a cluster of compute. When you pull away the- It's really a network of compute. >> That's right. >> It's a network of compute resources that Kubernetes has visibility into so we can move resources >> That's right. >> Or move elements where they need to be to be optimally utilized. Let's build on that. So what where is Datera in this relationship between resources as it starts to build a an orchestrator, a manager, a network of data elements, and pull that into something that makes it easier for developers to do what they need to do, operators to do what they need to do, and the business to do what it needs to do? >> Yeah, so you can call Kubernetes the network of compute or a swarm of compute, right? So the power of Kubernetes is that it abstracts the infrastructure to a level where it gets delivered continuously to the application on demand. We do exactly the same thing for data, for the ability to store, manage, and ultimately life cycle data. So simply label based, like Kubernetes is, you specify the service level objectives for every individual application, and Kubernetes pretty much does all the rest of the job, completely independent of the hardware underneath. Again, we do that for data. You have certain access requirements, protocols, authentications, security. You have certain performance requirements. You have certain reliability requirements. You articulate them simply in similar SLO, service level objectives. Datera does all the actual implementation automatically across the data center. So now you get to a point where in the modern data center and the soft defined data center, I would argue we are the data foundation in those kinds of scenarios, we can co-orchestrate data along, since you said Kubernetes specifically with Kubernetes, with its compute. Obviously we work in other environments as well. We work equally well for Enver. We work for some other, a number of other cloud orchestration frameworks. But Kubernetes is a really good example here. >> So who's going to buy it? I mean cause going back to this issue of the orchestrator, the developers clearly need this because they want access to real data, but they typically don't think in terms of underlying data structures. If it's available that's all they care about. Data administrators, business people. Who do you find your customers today are really making that, not the initial contact, but actually driving the adoption of this new data fabric? >> So Marc, I mean I know you'll answer it more accurately than I will. But just from a higher level to step down, there seems to be two types of people inside of large companies. One is a project owner. So for instance, you know, I've been blessed with a job inside of BMW that I have to do, autonomic cars. And I'm tying together a very complicated pipeline that has to be extremely agile. So that's the type of person that would basically look to buy and move us forward. And the other one is an internal service provider to the enterprise. So in other words, instead of being a group that has a physical job, what I'm actually doing is I'm saying I'm now going to be a service provider, or a cloud provider, or a resource provider to an organization that now has complexity that's moving into and embracing the digital economy or digital transformation. So if those are the two types of person inside of an organization, I think if you get a tie kicker, you know, there are places that we struggle with, I think it would be fair to say, is there's always going to be a geek somewhere that wants to kick the latest, cool technology, so we get involved with that. And then by the time you go all the way through it, there's no project there. They just really enjoyed themselves and so have we. But in essence there's enough people now who recognize my business is going through this transformation, I need to get out of my technical debt, I'm throwing business into, you know, this economy. It's normally around machine learning applications, Kubernetes, things that are fast moving, you know. And they need that level of ility that they're used to getting through fixed bounded technology, you know. And so we're actually seeing that as a service provider, both external and internal. But internal, inside the enterprises, is something which we're very key on. >> And let me give you perhaps a few examples. We're looking at Fortune 2000 companies. A good example, for instance, would be one of the top airlines in the world that is replatforming from a more rigid siloed IT to really deliver all their applications to internal and external customers as a service. It would also be digital businesses where there currency really is speed, agility, and obviously data is their currency. So if you're looking here at one of the top travel fare aggregators, that's one of the customers, actually interestingly we are in their tier zero at Storch. That's quite an endorsement of the performance aspect. We are also in one of, I would say, the leading service providers outside of the typical crowd you think, those are one of the up and coming guys. So those are typical markets and customers we're looking at. Really Fortune 2000 companies that are replatforming to cloud, hybrid cloud, and digital service businesses. Digital businesses. >> But it is most people who are basically going from, they're transforming their data center into a metadata center. They're embracing the distribution and then cloud. But they're not going wholesale and just saying (claps hands) we're over. They have this practicality of first thing I need to do is to free up my data, make my data center agile, and then decide how I want to distribute it across. >> Marc Fleischmann. Guy Churchward. Datera. Thank you very much for being on theCUBE. >> Thank you very much Peter. >> A pleasure. Thank you. >> And once again, this is Peter Burris from our CUBE studios in Palo Alto, California. Thanks very much for participating in this CUBE conversation with Datera. (orchestral music plays)

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE! Now, here's your host, Dave Vellante. >> Hi, everybody, welcome to this special CUBE Conversation. You know, big data workloads have evolved, and the infrastructure that runs big data workloads is also evolving. Big data, AI, other emerging workloads need infrastructure that can keep up. Welcome to this special CUBE Conversation with Patrick Osborne, who's the vice president and GM of big data and secondary storage at Hewlett Packard Enterprise, @patrick_osborne. Great to see you again, thanks for coming on. >> Great, love to be back here. >> As I said up front, big data's changing. It's evolving, and the infrastructure has to also evolve. What are you seeing, Patrick, and what's HPE seeing in terms of the market forces right now driving big data and analytics? >> Well, some of the things that we see in the data center, there is a continuous move to move from bare metal to virtualized. Everyone's on that train. To containerization of existing apps, your apps of record, business, mission-critical apps. But really, what a lot of folks are doing right now is adding additional services to those applications, those data sets, so, new ways to interact, new apps. A lot of those are being developed with a lot of techniques that revolve around big data and analytics. We're definitely seeing the pressure to modernize what you have on-prem today, but you know, you can't sit there and be static. You gotta provide new services around what you're doing for your customers. A lot of those are coming in the form of this Mode 2 type of application development. >> One of the things that we're seeing, everybody talks about digital transformation. It's the hot buzzword of the day. To us, digital means data first. Presumably, you're seeing that. Are organizations organizing around their data, and what does that mean for infrastructure? >> Yeah, absolutely. We see a lot of folks employing not only technology to do that. They're doing organizational techniques, so, peak teams. You know, bringing together a lot of different functions. Also, too, organizing around the data has become very different right now, that you've got data out on the edge, right? It's coming into the core. A lot of folks are moving some of their edge to the cloud, or even their core to the cloud. You gotta make a lot of decisions and be able to organize around a pretty complex set of places, physical and virtual, where your data's gonna lie. >> There's a lot of talk, too, about the data pipeline. The data pipeline used to be, you had an enterprise data warehouse, and the pipeline was, you'd go through a few people that would build some cubes and then they'd hand off a bunch of reports. The data pipeline, it's getting much more complex. You've got the edge coming in, you've got, you know, core. You've got the cloud, which can be on-prem or public cloud. Talk about the evolution of the data pipeline and what that means for infrastructure and big data workloads. >> For a lot of our customers, and we've got a pretty interesting business here at HPE. We do a lot with the Intelligent Edge, so, our Edgeline servers in Aruba, where a a lot of the data is sitting outside of the traditional data center. Then we have what's going on in the core, which, for a lot of customers, they are moving from either traditional EDW, right, or even Hadoop 1.0 if they started that transformation five to seven years ago, to, a lot of things are happening now in real time, or a combination thereof. The data types are pretty dynamic. Some of that is always getting processed out on the edge. Results are getting sent back to the core. We're also seeing a lot of folks move to real-time data analytics, or some people call it fast data. That sits in your core data center, so utilizing things like Kafka and Spark. A lot of the techniques for persistent storage are brand new. What it boils down to is, it's an opportunity, but it's also very complex for our customers. >> What about some of the technical trends behind what's going on with big data? I mean, you've got sprawl, with both data sprawl, you've got workload sprawl. You got developers that are dealing with a lot of complex tooling. What are you guys seeing there, in terms of the big mega-trends? >> We have, as you know, HPE has quite a few customers in the mid-range in enterprise segments. We have some customers that are very tech-forward. A lot of those customers are moving from this, you know, Hadoop 1.0, Hadoop 2.0 system to a set of essentially mixed workloads that are very multi-tenant. We see customers that have, essentially, a mix of batch-oriented workloads. Now they're introducing these streaming type of workloads to folks who are bringing in things like TensorFlow and GPGPUs, and they're trying to apply some of the techniques of AI and ML into those clusters. What we're seeing right now is that that is causing a lot of complexity, not only in the way you do your apps, but the number of applications and the number of tenants who use that data. It's getting used all day long for various different, so now what we're seeing is it's grown up. It started as an opportunity, a science project, the POC. Now it's business-critical. Becoming, now, it's very mission-critical for a lot of the services that drives. >> Am I correct that those diverse workloads used to require a bespoke set of infrastructure that was very siloed? I'm inferring that technology today will allow you to bring those workloads together on a single platform. Is that correct? >> A couple of things that we offer, and we've been helping customers to get off the complexity train, but provide them flexibility and elasticity is, a lot of the workloads that we did in the past were either very vertically-focused and integrated. One app server, networking, storage, to, you know, the beginning of the analytics phase was really around symmetrical clusters and scaling them out. Now we've got a very rich and diverse set of components and infrastructure that can essentially allow a customer to make a data lake that's very scalable. Compute, storage-oriented nodes, GPU-oriented nodes, so it's very flexible and helps us, helps the customers take complexity out of their environment. >> In thinking about, when you talk to customers, what are they struggling with, specifically as it relates to infrastructure? Again, we talked about tooling. I mean, Hadoop is well-known for the complexity of the tooling. But specifically from an infrastructure standpoint, what are the big complaints that you hear? >> A couple things that we hear is that my budget's flat for the next year or couple years, right? We talked earlier in the conversation about, I have to modernize, virtualize, containerizing my existing apps, that means I have to introduce new services as well with a very different type of DevOps, you know, mode of operations. That's all with the existing staff, right? That's the number one issue that we hear from the customers. Anything that we can do to help increase the velocity of deployment through automation. We hear now, frankly, the battle is for whether I'm gonna run these type of workloads on-prem versus off-prem. We have a set of technology as well as services, enabling services with Pointnext. You remember the acquisition we made around cloud technology partners to right-place where those workloads are gonna go and become like a broker in that conversation and assist customers to make that transition and then, ultimately, give them an elastic platform that's gonna scale for the diverse set of workloads that's well-known, sized, easy to deploy. >> As you get all this data, and the data's, you know, Hadoop, it sorta blew up the data model. Said, "Okay, we'll leave the data where it is, "we'll bring the compute there." You had a lot of skunk works projects growing. What about governance, security, compliance? As you have data sprawl, how are customers handling that challenge? Is it a challenge? >> Yeah, it certainly is a challenge. I mean, we've gone through it just recently with, you know, GDPR is implemented. You gotta think about how that's gonna fit into your workflow, and certainly security. The big thing that we see, certainly, is around if the data's residing outside of your traditional data center, that's a big issue. For us, when we have Edgeline servers, certainly a lot of things are coming in over wireless, there's a big buildout in advent of 5G coming out. That certainly is an area that customers are very concerned about in terms of who has their data, who has access to it, how can you tag it, how can you make sure it's secure. That's a big part of what we're trying to provide here at HPE. >> What specifically is HPE doing to address these problems? Products, services, partnerships, maybe you could talk about that a little bit. Maybe even start with, you know, what's your philosophy on infrastructure for big data and AI workloads? >> I mean, for us, we've over the last two years have really concentrated on essentially two areas. We have the Intelligent Edge, which is, certainly, it's been enabled by fantastic growth with our Aruba products in the networks in space and our Edgeline systems, so, being able to take that type of compute and get it as far out to the edge as possible. The other piece of it is around making hybrid IT simple, right? In that area, we wanna provide a very flexible, yet easy-to-deploy set of infrastructure for big data and AI workloads. We have this concept of the Elastic Platform for Analytics. It helps customers deploy that for a whole myriad of requirements. Very compute-oriented, storage-oriented, GPUs, cold and warm data lakes, for that matter. And the third area, what we've really focused on is the ecosystem that we bring to our customers as a portfolio company is evolving rapidly. As you know, in this big data and analytics workload space, the software development portion of it is super dynamic. If we can bring a vetted, well-known ecosystem to our customers as part of a solution with advisory services, that's definitely one of the key pieces that our customers love to come to HP for. >> What about partnerships around things like containers and simplifying the developer experience? >> I mean, we've been pretty public about some of our efforts in this area around OneSphere, and some of these, the models around, certainly, advisory services in this area with some recent acquisitions. For us, it's all about automation, and then we wanna be able to provide that experience to the customers, whether they want to develop those apps and deploy on-prem. You know, we love that. I think you guys tag it as true private cloud. But we know that the reality is, most people are embracing very quickly a hybrid cloud model. Given the ability to take those apps, develop them, put them on-prem, run them off-prem is pretty key for OneSphere. >> I remember Antonio Neri, when you guys announced Apollo, and you had the astronaut there. Antonio was just a lowly GM and VP at the time, and now he's, of course, CEO. Who knows what's in the future? But Apollo, generally at the time, it was like, okay, this is a high-performance computing system. We've talked about those worlds, HPC and big data coming together. Where does a system like Apollo fit in this world of big data workloads? >> Yeah, so we have a very wide product line for Apollo that helps, you know, some of them are very tailored to specific workloads. If you take a look at the way that people are deploying these infrastructures now, multi-tenant with many different workloads. We allow for some compute-focused systems, like the Apollo 2000. We have very balanced systems, the Apollo 4200, that allow a very good mix of CPU, memory, and now customers are certainly moving to flash and storage-class memory for these type of workloads. And then, Apollo 6500 were some of the newer systems that we have. Big memory footprint, NVIDIA GPUs allowing you to do very high calculations rates for AI and ML workloads. We take that and we aggregate that together. We've made some recent acquisitions, like Plexxi, for example. A big part of this is around simplification of the networking experience. You can probably see into the future of automation of the networking level, automation of the compute and storage level, and then having a very large and scalable data lake for customers' data repositories. Object, file, HTFS, some pretty interesting trends in that space. >> Yeah, I'm actually really super excited about the Plexxi acquisition. I think it's because flash, it used to be the bottleneck was the spinning disk, flash pushes the bottleneck largely to the network. Plexxi gonna allow you guys to scale, and I think actually leapfrog some of the other hyperconverged players that are out there. So, super excited to see what you guys do with that acquisition. It sounds like your focus is on optimizing the design for I/O. I'm sure flash fits in there as well. >> And that's a huge accelerator for, even when you take a look at our storage business, right? So, 3PAR, Nimble, All-Flash, certainly moving to NVMe and storage-class memory for acceleration of other types of big data databases. Even though we're talking about Hadoop today, right now, certainly SAP HANA, scale-out databases, Oracle, SQL, all these things play a part in the customer's infrastructure. >> Okay, so you were talking before about, a little bit about GPUs. What is this HPE Elastic Platform for big data analytics? What's that all about? >> I mean, we have a lot of the sizing and scalability falls on the shoulders of our customers in this space, especially in some of these new areas. What we've done is, we have, it's a product/a concept, and what we do is we have this, it's called the Elastic Platform for Analytics. It allows, with all those different components that I rattled off, all great systems in of their own, but when it comes to very complex multi-tenant workloads, what we do is try to take the mystery out of that for our customers, to be able to deploy that cookie-cutter module. We're even gonna get to a place pretty soon where we're able to offer that as a consumption-based service so you don't have to choose for an elastic type of acquisition experience between on-prem and off-prem. We're gonna provide that as well. It's not only a set of products. It's reference architectures. We do a lot of sizing with our partners. The Hortonworks, CloudEra's, MapR's, and a lot of the things that are out in the open source world. It's pretty good. >> We've been covering big data, as you know, for a long, long time. The early days of big data was like, "Oh, this is great, "we're just gonna put white boxes out there "and off the shelf storage!" Well, that changed as big data got, workloads became more enterprise, mainstream, they needed to be enterprise-ready. But my question to you is, okay, I hear you. You got products, you got services, you got perspectives, a philosophy. Obviously, you wanna sell some stuff. What has HPE done internally with regard to big data? How have you transformed your own business? >> For us, we wanna provide a really rich experience, not just products. To do that, you need to provide a set of services and automation, and what we've done is, with products and solutions like InfoSight, we've been able to, we call it AI for the Data Center, or certainly, the tagline of predictive analytics is something that Nimble's brought to the table for a long time. To provide that level of services, InfoSight, predictive analytics, AI for the Data Center, we're running our own big data infrastructure. It started a number of years ago even on our 3PAR platforms and other products, where we had scale-up databases. We moved and transitioned to batch-oriented Hadoop. Now we're fully embedded with real-time streaming analytics that come in every day, all day long, from our customers and telemetry. We're using AI and ML techniques to not only improve on what we've done that's certainly automating for the support experience, and making it easy to manage the platforms, but now introducing things like learning, automation engines, the recommendation engines for various things for our customers to take, essentially, the hands-on approach of managing the products and automate it and put into the products. So, for us, we've gone through a multi-phase, multi-year transition that's brought in things like Kafka and Spark and Elasticsearch. We're using all these techniques in our system to provide new services for our customers as well. >> Okay, great. You're practitioners, you got some street cred. >> Absolutely. >> Can I come back on InfoSight for a minute? It came through an acquisition of Nimble. It seems to us that you're a little bit ahead, and maybe you say a lot a bit ahead of the competition with regard to that capability. How do you see it? Where do you see InfoSight being applied across the portfolio, and how much of a lead do you think you have on competitors? >> I'm paranoid, so I don't think we ever have a good enough lead, right? You always gotta stay grinding on that front. But we think we have a really good product. You know, it speaks for itself. A lot of the customers love it. We've applied it to 3PAR, for example, so we came out with some, we have VMVision for a 3PAR that's based on InfoSight. We've got some things in the works for other product lines that are imminent pretty soon. You can think about what we've done for Nimble and 3PAR, we can apply similar type of logic to Elastic Platform for Analytics, like running at that type of cluster scale to automate a number of items that are pretty pedantic for the customers to manage. There's a lot of work going on within HPE to scale that as a service that we provide with most of our products. >> Okay, so where can I get more information on your big data offerings and what you guys are doing in that space? >> Yeah, so, we have, you can always go to hp.com/bigdata. We've got some really great information out there. We're in our run-up to our big end user event that we do every June in Las Vegas. It's HPE Discover. We have about 15,000 of our customers and trusted partners there, and we'll be doing a number of talks. I'm doing some work there with a British telecom. We'll give some great talks. Those'll be available online virtually, so you'll hear about not only what we're doing with our own InfoSight and big data services, but how other customers like BTE and 21st Century Fox and other folks are applying some of these techniques and making a big difference for their business as well. >> That's June 19th to the 21st. It's at the Sands Convention Center in between the Palazzo and the Venetian, so it's a good conference. Definitely check that out live if you can, or if not, you can all watch online. Excellent, Patrick, thanks so much for coming on and sharing with us this big data evolution. We'll be watching. >> Yeah, absolutely. >> And thank you for watcihing, everybody. We'll see you next time. This is Dave Vellante for theCUBE. (fast techno music)

>> Announcer: Live from Washington D.C., it's theCUBE covering AWS Public Sector Summit 2017. Brought to you by Amazon Web Services and its partner, Ecosystem. >> Welcome back to our nation's capitol where we continue our coverage here on theCUBE of the AWS Public Sector Summit 2017. Some 10,000 strong in attendance this week here in the Walter Washington Convention Center. It's just about a mile from the U.S. Capitol. John Walsh, this is John Furrier. John, do you feel the energy of the centerpiece of the political universe. >> It's hot here in D.C. >> It is hot. >> It's a pressure cooker, the humidity. >> But, it's not global warming we know that because, ya know, climate change is >> Climate change is not real. That's from what I heard. >> That's what we've been told. >> The problem with D.C. is it's a data lake that's turned into a data swamp. So, someone really needs to drain that data swamp. >> Well, ya know, to help us do that. You know who's going to help us do that? >> Amazon Web Services. >> Marlin McFate's going to help us do that. He is the technical leader of the Advanced Technology Group in the office of the CTO and Riverbed. And Marlin, thank you for being with us here on theCUBE. Your first time, I believe. >> Yes, it is my first time on theCUBE. >> So, you're a Cube rookie? >> Yes, Cube rookie. >> Good to have you aboard. >> I appreciate it, thanks. >> Tell us a little bit first about Riverbed, about what you do there specifically, what you do there and what the company's mission is overall. >> Absolutely, so I work for the Advanced Technology Group, the Advanced Technology Group works underneath the office of the CTO. There's actually two groups that work under the office of the CTO, my group, the Advanced Technology Group and another one called the Strategic Technology Group. The ATT Group, the one that I belong to, we focus on being the subject matter experts of our products. I think there's about nine of us now and we all focus on different products. Riverbed's grown from a company of being just the WAN Optimization Company to really being the performance company, right, whether that be visibility, whether it be optimization, whether it be network optimization. Each one of us focuses on a different piece. I, predominantly focus on our WAN optimization, our SteelConnect product and at times our SteelFusion project, which is the combined Edge product. >> SteelConnect, yeah, tell us what that's all about. >> SteelConnect, SteelConnect is not actually our most recent product to come to market. We have a couple of visibility products that have come out recently, but SteelConnect addresses the idea that we have been doing networking for the same way say, you know, 1993 beyond, right. We are still doing it the same way. Everything within our industry, whether you take a look at virtualization, whether you take a look at Cloud, whether or not you take a look at storage, everything has changed substantially in how we do it and this brings that change to networking. The idea is that when you think about servers you say, I no longer want to think about you know, hardware. I never want to think about that. I never want to think about resources. Maybe I don't even want to worry about operating systems. I only want to worry about containers, right. Now, when it comes to networking I don't necessarily want to have to worry about each individual piece within my network. I want it to be orchestrated and controlled centrally and what I tell it to do, I want it to do. I shouldn't have to do that. >> You missed a challenged. We heard Vernon Vogel on stage here at Amazon a couple of seconds ago say, I'm here in D.C. say hey, it's a new normal. We had another entrepreneur on just before you from FUGE who said, hey, it's inevitably the world of the future and it's inherently different, or intrinsically different in Cloud than it is on premise with enterprises, so the question for you is, what is the use case that you guys are winning at because the Cloud is impacting federal government and public sector, but a lot of times they have old, antiquated systems like back in 1993, '94. So, they're moving fast to commercialize, to modernize, that's the focus. How do you guys help them? What's the big lynch pen for you guys and that goal mission to the customer? >> Alright, so you're absolutely right. The government has been here, or the government or public sector as a whole has been moving to the Cloud quite quickly here recently, right. We've seen this move more on the commercial side first, obviously, and now in the public sector. One of the very large use cases that we address is the ability to provision for your applications, right. Some of the characteristics that you find in commercial world, such as, I want to use internet as transport. You don't see as much in public sector. But, you do see, I can spin up an application in the Cloud. If you go to your Cloud person and say, how would it take me to get application B, they could possibly come back to you and say, well, would this afternoon be okay, right. Can you provision in hours like that? Can you get the policy in place for users? Could you get the connectivity? Could you get any of that in place in the same amount of time? That is a use case that SD WAN addresses without having to rip up, take out the network that you already have, which is the physical network, or what we refer to as the underlay. Being able to give you that flexibility on top of that network. >> The big thing that customers have a challenge on is that other focus it's DebOps trend programmable infrastructure is another one, so that they want to make it programmable. >> Right. >> So, how do you guys fit into that? Because one of the things that we hear is, could I have develop 'cause all I want to do is have infrastructure just works as code. That's all I need for whatever use case. >> Yeah, we usually see that DebOps is actually one that'll probably be the first movers to the Cloud for the public sector, right. With our, really it's every single one of our products, whether or not we're talking about SteelConnect, SteelHead, SteelEssential, any one of them, there's a RESTful API for every single one of them. So, you can actually go in and utilizing a very easy scripting a RESTful API directly itself and spin up whole environments and then spin them down if you wanted to do that. So, it fits very, very nicely into that DebOps world. >> Do you have SteelEdge yet? >> SteelEdge? >> Copyright on theCUBE. >> It might be a razor company that might have that. I don't know. >> Well, the Edge in the network is huge and this is where we're talking about as you guys do it, you know SD WAN, I mean, come on, why the area networks? You don't beat, you can't get any more edgier than that. You guys have a core competency in this. How do you guys look at the Edge and IOT and all these use cases popping around? >> Well, we do actually have a product that has Edge in it, it was SteelFusion Edge. We could address that in a couple of different ways. I want to make sure that I understood your question, though. Your question was around IOT, specifically? >> Well, how do you guys look at the Edge? The trends right now are super hyped up right now, Intelligent Edge is a big message we're hearing from others. IOT is an Edge application with its Industrial Edge with Genery Censor networks, help with safety, surveillance, all this is Edge devices. >> It still ends up in the end being you know, and that has, we've heard the change from people calling it Branch to calling it Edge, which is probably pretty appropo, right. But, really in the end, what it comes down to is connectivity, right. So, if I have IOT sensors in a warehouse, whether or not I have an application, whether or not I have a group of users, whether or not I have mobile users, in the end what it really comes down to is connectivity. And, we all especially with our cell phones, right, we have come pretty much to the point where we expect our data and our connectivity to be there at all times, right. That's one of the things SD WAN addresses. Whether it be our direct, our SD WAN products, SteelConnect, or whether or not it has works with some of the pieces that move further into the LAN architectural, like our wireless access points, our switching, right. So, you can imagine here, right, I can provide policy for my IOT devices. I can provide that policy one time at an organizational or agency level. I can have that policy filter down, all the way down to the axis point and now the axis point might be my axis point to my IOT or to my user. So, in the end, it still comes to connectivity. >> Marlin, what's some of the use cases or scenarios you've been involved with customers where it's been super exciting from an architectural standpoint, where you guys are doing some cutting edge things. Like, is it more the network size? Is it software? Is it Edge. I mean, I'm tryin' to get a sense of, could you share a personal perspective? >> Absolutely so. One of the ones that we're working on right now I think is probably the most exciting. It is combining some aspects, you could call it an FE. You could call it SD WAN. You could call it Grey Box. What I like to call it is just a combined Edge piece, right, which encompasses both the SteelConnect piece which handles your firewall characteristics, your identity management characteristics, built into that some switching, virtualization, so you can run other products on there. What the customer really wanted to end up doing was they had school systems that, a school system that was in a very far away place and that school system, they were putting in a router, a switch, an access point, you know, all these different little pieces and devices, right. What we did was we were able to take that design and crunch it down into basically one box, right. They have enough switchboards. They have the ability to run virtual machines 'cause they said that they had a server here or there. They have their virtualized SteelConnect gateway which gives them the firewall capability, gives them the routing capability and this is all combined in a box that already has the WAN Optimization built in. So, they get everything that they would have had onsite in one box. >> Is there something to working, you bring up education as an example, but in that space overall in the .gov, the .edu space that's separate and aside from commercial partners or commercial relationships like different concerns, different priorities and yet they're using the same technologies. >> Most certainly. The only thing that I could really say from a using technology, right, I mean there are some pockets where different technology, far off weird technologies is utilized. But, I would say that they are the public sector, schools, federal government, intel, they're all using a lot of the same technology, right. It's when they adopt it. When did they bring it into their environment? And then, what are the special characteristics of their environment? So for example, what I said earlier, right, your commercial customers are looking at utilizing SD WAN to move maybe completely off of MPLS. It's probably not something that we're going to see within the public sector, right. They're want to still use some sort of private networking. I do have some customers that are utilizing public internet, but then, they are tunneling an overlay back to an MPLS entry point to get back into their Cloud. We just have interesting requirements. Whether that be a trusted internet connection, whether or not that'd be JRSS, we have different security requirements in the public sector. >> Well, I love some of what you're doin'. Did you get all of that MPLS stuff there? >> Yeah, I got the first four. >> I want to jump in and double down on that. This is interesting conversation because the whole trend right now is hybrid Cloud on the Enterprise side which is a leading indicator to the government, a little bit lagging on that, so whatever that translates to in terms of Hybrid or Legacy, it's going to be somewhat similar, I believe. But, really multi-Cloud is a trend that people are talking about. It's super hyped up but it's not yet real. The thing that's holding multi-Cloud back not multi-Cloud in the sense I got to workload over hear and a workload over there, I'm talking about moving resources around the network, data, compute, what not, is latency, huge problem. You mentioned MPLS and all this tunneling, there's still the latency problem of how do you get the laws of physics down to the point where you can actually have those kinds of latencies? What is Riverbed doing? Can you share some insights to that direction 'cause that's the holy grail right now. That's the last hurdle. Then, well getting all the silicons is still the final hurdle, but latency's critical. >> So, problem number one there, right. Even if it is Cloud to Cloud in that example, right, is first how do I get a WAN Optimization device, something that can optimize that traffic for me. Something that can affect my latency for me into that environment. Riverbed has worked tirelessly to get that in there right. But, to your point, you can't change how an electron flies, right. The speed of light is the speed of light. You're not going to get an electron to move any faster. So, what Riverbed developed that's still very relevant today is the ability to, instead of change your latency, mitigate the negative affects of your latency, right. So, if I. >> Or work around it. >> Absolutely, and you can do that at the application level, absolutely, program around it, but there are a lot of protocols out there that aren't necessarily optimized for that longer latency environment, right. So, what we do is, or the adage is, the trip never taken, right, the shortest trip. So, if I have to, not to get into the weeds or anything like that, but if I have to make a thousand round trips to accomplish something, right, and I could put something in there that understands what I was getting, right, that data that I was getting each one of those times and I can take less trips, well then, that just made that faster. So, if I have a thousand round trips and it takes a minute to do, and now I can do ten round trips and it only took ten seconds, or six seconds if we're doing the math right. >> It's kind of like here in D.C., you're local. I noticed that coming from Dulles Airport they have Sirius pricing on the toll roads. That's basically private networking right there. >> That's right. >> These cost path routing opposed to the other side. I was in the, you know. >> Marlin was more describing my trips to the hardware store on the weekends, a thousand round trips, be a lot more economical. But you're right, it is private networking. >> If you're off the road, you're off the packets aren't on the network it saves some room for someone else. >> More traffic, you hear more traffic at the higher speeds. >> You actually could. So, you get two benefits. One is the increase of speed, but the other is the perceived capacity increase of your network. And, we accomplish these things through compression which is really, really simple. I think compression is a must, right. But, through our data duplication. Data duplication is I've seen these patterns before and it's a byte level. We're not talking about an object. I haven't seen a file. No, I've seen these byte level patterns before, I don't need to resend them. And, in traditional network or traditional applications you see pretty much in any organization, right, you typically can get somewhere between 50 and 80, if not sometimes 90% reduction total in traffic. >> My final question before we wrap up this segment here is, Share with the folks, take a minute to talk to the audience about what you're doing with Riverbed at the show and what they should know about the current Riverbed. I know you've guys trying a transformation of yourselves, give a quick plug. Go ahead. >> Absolutely. So, what we're specifically doing here or one of the pieces that is a differentiator for us and our SD WAN is, we went ahead and we thought why couldn't we make that an AWSPPC or a Cloud instance one of my Edge sites, right, connecting into the Cloud, there's many different ways to do it, but why couldn't we make a very simple way of doing that? Why couldn't I take the technology that I'm already putting in place at my data centers, I'm already putting in place at my branch offices, why can't I utilize that to create a secure connection into my BBCs. And, to your point, actually earlier one of the things that's also interesting was Cloud to Cloud. Why couldn't I take that same technology and connect multiple Clouds? Whether they be private Cloud or two public Clouds or connect them all together and take the best of all worlds, right, the best from each and make the best infrastructure that I possibly can. So, what we're showing off here from a SteelConnect perspective is our ability to do that. I can take an AWSVPC, actually I can take all, I think there's 16 regions within AWS and I can interconnect them in less than 10 minutes with the click of a button. And, then back into my infrastructure. So, that and then we also have brought Eternity, which is one of our visibility products that is basically rounded out on our visibility play within the market. We have the network. We have the app. We have the database. Now, we have the end users computer. >> Alright, well, if you could interconnect me to my home in 10 minutes I'm a client. I'd be sold, I'd be all over it. >> I'm going to be in the same traffic as you later. >> I'm not that far from here, but it might as well be another day. Marlin, thanks for the time. >> Absolutely, my pleasure. >> Good to have you on theCUBE, alright. >> Thanks, hope we get to do it again. >> Riverbed has joined us here on theCUBE. We'll be live with more from Washington D.C. right after this.

>> 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.