>> Okay. Now we're going to dig deeper into HPE Ezmeral and try to better understand how it's going to impact customers. And with me to do that are Robert Christiansen, who is the Vice President of Strategy in the office of the CTO and Kumar Sreekanti, who is the Chief Technology Officer and Head of Software, both of course, with Hewlett Packard Enterprise. Gentlemen, welcome to the program. Thanks for coming on. >> Good seeing you, Dave. Thanks for having us. >> It's always good to see you guys. >> Thanks for having us. >> So, Ezmeral, kind of an interesting name, catchy name, but Kumar, what exactly is HPE Ezmeral? >> It's indeed a catchy name. Our branding team has done fantastic job. I believe it's actually derived from Esmeralda, is the Spanish for emarald. Often it's supposed some very mythical bars, and they derived Ezmeral from there. And we all initially when we heard, it was interesting. So, Ezmeral was our effort to take all the software, the platform tools that HPE has and provide this modern operating platform to the customers and put it under one brand. So, it has a modern container platform, it does persistent storage with the data fabric and it doesn't include as many of our customers from that. So, think of it as a modern container platform for modernization and digitazation for the customers. >> Yeah, it's an interesting, you talk about platform, so it's not, you know, a lot of times people say product, but you're positioning it as a platform so that has a broader implication. >> That's very true. So, as the customers are thinking of this digitazation, modernization containers and Microsoft, as you know, there is, has become the stable all. So, it's actually a container orchestration platform with golfers open source going into this as well as the persistence already. >> So, by the way, Ezmeral, I think Emerald in Spanish, I think in the culture, it also has immunity powers as well. So immunity from lock-in, (Robert and Kumar laughing) and all those other terrible diseases, maybe it helps us with COVID too. Robert, when you talk to customers, what problems do you probe for that Ezmeral can do a good job solving? >> Yeah, that's a really great question because a lot of times they don't even know what it is that they're trying to solve for other than just a very narrow use case. But the idea here is to give them a platform by which they can bridge both the public and private environment for what they do, the application development, specifically in the data side. So, when yo're looking to bring containerization, which originally got started on the public cloud and it has moved its way, I should say it become popular in the public cloud and it moved its way on premises now, Ezmeral really opens the door to three fundamental things, but, you know, how do I maintain an open architecture like you're referring to, to some low or no lock-in of my applications. Number two, how do I gain a data fabric or a data consistency of accessing the data so I don't have to rewrite those applications when I do move them around. And then lastly, where everybody's heading, the real value is in the AI ML initiatives that companies are really bringing and that value of their data and locking that data at where the data is being generated and stored. And so the Ezmeral platform is those multiple pieces that Kumar was talking about stacked together to deliver the solutions for the client. >> So Kumar, how does it work? What's the sort of IP or the secret source behind it all? What makes HPE different? >> Yeah. Continuing on (indistinct) it's a modern glass form of optimizing the data and workloads. But I think I would say there are three unique characteristics of this platform. Number one is that it actually provides you both an ability to run statefull and stateless as workloads under the same platform. And number two is, as we were thinking about, unlike another Kubernete is open source, it actually add, use you all open-source Kurbenates as well as an orchestration behind them so you can actually, you can provide this hybrid thing that Robert was talking about. And then actually we built the workflows into it, for example, they'll actually announced along with it Ezmeral, ML expert on the customers can actually do the workflow management around specific data woakload. So, the magic is if you want to see the secrets out of all the efforts that has been going into some of the IP acquisitions that HPE has done over the years, we said we BlueData, MAPR, and the Nimble, all these pieces are coming together and providing a modern digitization platform for the customers. >> So these pieces, they all have a little bit of a machine intelligence in them, you have people, who used to think of AI as this sort of separate thing, I mean the same thing with containers, right? But now it's getting embedded into the stack. What is the role of machine intelligence or machine learning in Ezmeral? >> I would take a step back and say, you know, there's very well the customers, the amount of data that is being generated and 95% or 98% of the data is machine generated. And it does a series of a window gravity, and it is sitting at the edge and we were the only one that had edge to the cloud data fabric that's built to it. So, the number one is that we are bringing computer or a cloud to the data that taking the data to the cloud, right, if you will. It's a cloud like experience that provides the customer. AI is not much value to us if we don't harness the data. So, I said this in one of the blog was we have gone from collecting the data, to the finding the insights into the data, right. So, that people have used all sorts of analysis that we are to find data is the new oil. So, the AI and the data. And then now your applications have to be modernized and nobody wants write an application in a non microservices fashion because you wanted to build the modernization. So, if you bring these three things, I want to have a data gravity with lots of data, I have built an AI applications and I want to have those three things I think we bring to the customer. >> So, Robert let's stay on customers for a minute. I mean, I want to understand the business impact, the business case, I mean, why should all the cloud developers have all the fun, you've mentioned it, you're bridging the cloud and on-prem, they talk about when you talk to customers and what they are seeing is the business impact, what's the real drivers for that? >> That's a great question cause at the end of the day, I think the recent survey that was that cost and performance are still the number one requirement for this, just real close second is agility, the speed at which they want to move and so those two are the top of mind every time. But the thing we find Ezmeral, which is so impactful is that nobody brings together the Silicon, the hardware, the platform, and all of that stack together work and combine like Ezmeral does with the platforms that we have and specifically, we start getting 90, 92, 93% utilization out of AI ML workloads on very expensive hardware, it really, really is a competitive advantage over a public cloud offering, which does not offer those kinds of services and the cost models are so significantly different. So, we do that by collapsing the stack, we take out as much intellectual property, excuse me, as much software pieces that are necessary so we are closest to the Silicon, closest to the applications, bring it to the hardware itself, meaning that we can interleave the applications, meaning that you can get to true multitenancy on a particular platform that allows you to deliver a cost optimized solution. So, when you talk about the money side, absolutely, there's just nothing out there and then on the second side, which is agility. One of the things that we know is today is that applications need to be built in pipelines, right, this is something that's been established now for quite some time. Now, that's really making its way on premises and what Kumar was talking about with, how do we modernize? How do we do that? Well, there's going to be some that you want to break into microservices containers, and there's some that you don't. Now, the ones that they're going to do that they're going to get that speed and motion, et cetera, out of the gate and they can put that on premises, which is relatively new these days to the on-premises world. So, we think both won't be the advantage. >> Okay. I want to unpack that a little bit. So, the cost is clearly really 90 plus percent utilization. >> Yes. >> I mean, Kumar, you know, even pre virtualization, we know that it was like, even with virtualization, you never really got that high. I mean, people would talk about it, but are you really able to sustain that in real world workloads? >> Yeah. I think when you make your exchangeable cut up into smaller pieces, you can insert them into many areas. We have one customer was running 18 containers on a single server and each of those containers, as you know, early days of new data, you actually modernize what we consider week run containers or microbiome. So, if you actually build these microservices, and you all and you have versioning all correctly, you can pack these things extremely well. And we have seen this, again, it's not a guarantee, it all depends on your application and your, I mean, as an engineer, we want to always understand all of these caveats work, but it is a very modern utilization of the platform with the data and once you know where the data is, and then it becomes very easy to match those two. >> Now, the other piece of the value proposition that I heard Robert is it's basically an integrated stack. So I don't have to cobble together a bunch of open source components, there's legal implications, there's obviously performance implications. I would imagine that resonates and particularly with the enterprise buyer because they don't have the time to do all this integration. >> That's a very good point. So there is an interesting question that enterprises, they want to have an open source so there is no lock-in, but they also need help to implement and deploy and manage it because they don't have the expertise. And we all know that the IKEA desk has actually brought that API, the past layer standardization. So what we have done is we have given the open source and you arrive to the Kubernetes API, but at the same time orchestration, persistent stories, the data fabric, the AI algorithms, all of them are bolted into it and on the top of that, it's available both as a licensed software on-prem, and the same software runs on the GreenLake. So you can actually pay as you go and then we run it for them in a colo or, or in their own data center. >> Oh, good. That was one of my latter questions. So, I can get this as a service pay by the drink, essentially I don't have to install a bunch of stuff on-prem and pay it perpetualized... >> There is a lot of containers and is the reason and the lapse of service in the last discover and knowledge gone production. So both Ezmeral is available, you can run it on-prem, on the cloud as well, a congenital platform, or you can run instead on GreenLake. >> Robert, are there any specific use case patterns that you see emerging amongst customers? >> Yeah, absolutely. So there's a couple of them. So we have a, a really nice relationship that we see with any of the Splunk operators that were out there today, right? So Splunk containerized, their operator, that operator is the number one operator, for example, for Splunk in the IT operation side or notifications as well as on the security operations side. So we've found that that runs highly effective on top of Ezmeral, on top of our platforms so we just talked about, that Kumar just talked about, but I want to also give a little bit of backgrounds to that same operator platform. The way that the Ezmeral platform has done is that we've been able to make it highly active, active with HA availability at nine, it's going to be at five nines for that same Splunk operator on premises, on the Kubernetes open source, which is as far as I'm concerned, a very, very high end computer science work. You understand how difficult that is, that's number one. Number two is you'll see just a spark workloads as a whole. All right. Nobody handles spark workloads like we do. So we put a container around them and we put them inside the pipeline of moving people through that basic, ML AI pipeline of getting a model through its system, through its trained, and then actually deployed to our ML ops pipeline. This is a key fundamental for delivering value in the data space as well. And then lastly, this is, this is really important when you think about the data fabric that we offer, the data fabric itself doesn't necessarily have to be bolted with the container platform, the container, the actual data fabric itself, can be deployed underneath a number of our, you know, for competitive platforms who don't handle data well. We know that, we know that they don't handle it very well at all. And we get lots and lots of calls for people saying, "Hey, can you take your Ezmeral data fabric "and solve my large scale, "highly challenging data problems?" And we say, "yeah, "and then when you're ready for a real world, "full time enterprise ready container platform, "we'd be happy to prove that too." >> So you're saying you're, if I'm inferring correctly, you're one of the values as you're simplifying that whole data pipeline and the whole data science, science project pun intended, I guess. (Robert and Kumar laughing) >> That's true. >> Absolutely. >> So, where does a customer start? I mean, what, what are the engagements like? What's the starting point? >> It's means we're probably one of the most trusted and robust supplier for many, many years and we have a phenomenal workforce of both the (indistinct), world leading support organization, there are many places to start with. One is obviously all these salaries that are available on the GreenLake, as we just talked about, and they can start on a pay as you go basis. There are many customers that actually some of them are from the early days of BlueData and MAPR, and then already running and they actually improvise on when, as they move into their next version more of a message. You can start with simple as well as container platform or system with the store, a computer's operation and can implement as an analyst to start working. And then finally as a big company like HPE as an everybody's company, that finance it's services, it's very easy for the customers to be able to get that support on day to day operations. >> Thank you for watching everybody. It's Dave Vellante for theCUBE. Keep it right there for more great content from Ezmeral.

>>from around the globe. It's the Cube covering HP. Discover Virtual Experience Brought to you by HP >>Everyone welcome to the Cube studios here in Palo Alto, California We here for remote conversation. Where for HP Discover virtual experience. 2020. We would Kumar, Sri Context, chief technology officer and head of Software Cube alumni. We've been following Kumar since he started Blue Data. Now he's heading up the software team and CTO at HP and Robert Christensen, VP of Strategy of Office of the CTO Robert Both Cube alumni's Robert, formerly with CTP, now part of the team that's bringing the modernization efforts around enterprises in this fast changing world that's impacting the operating models for businesses. We're seeing that playing out in real time with Covert 19 as customers are modernizing the efforts. Guys, thanks for coming on. Taking the time. >>You're welcome, John. Good to be back here, >>Kumar. First I have to ask you, I have to ask you your new role at HP sent it up to CTO but also head of the software. How >>do you >>describe that role Because you're CTO and also heading up? This offers a general manager. Could you take him in to explain this new role and why It's important. >>Thank you. Thank you, John. And so good to be back. You get two for one with me and Robert didn't. Yeah, it's very exciting to be here as the CTO of HB. And as Antonio described in in his announcement, we consider software will be very key, essential part of the our people as a service. And, uh, we want we see that it's an opportunity for not only layer division but help drive the execution of that reason. Both organic them in our. So we we see we want to have a different change of software that helps the customers, too, to get us to the workloads optimized, or are there specific solutions? >>You guys were both on the Cube in November, Pre cove it with the minimum John Troyer talking about the container platform news, leveraging the acquisitions you guys have done at HP Kumar, your company Blue Data map, our CTP, Robert, the group. You're there really talking about the strategies around running these kinds of workloads. And if you think about Cove in 19 this transformation, it's really changing work. Workforces, workplaces, workloads, work flows everything to do with work and people are at home. That's an extension of the on premise environment. VPN provisions were under provisional hearing all these stories, exposing all the things that need to be worked on because no one ever saw this kind of direction. It highlights the modern efforts that a lot of your customers are going through rubber. Can you explain? And Kumar talk about this digital transformation in this cove it and then when we come out of it, the growth strategies that need to be put in place and the projects take a minute to explain. >>Go ahead. Robert Cover has been spending a lot of time with our customers, and I would like to go ahead. >>Yeah, thank you so much. It's Ah, uh, accelerators. What's happened? Many of our clients have been forced into the conversation about how do I engage our customers, and how do we engage our broad constituents, including our employees and colleagues, in a more rapid and easier way? And many of the systems that were targeted to make their way to a public cloud digital transformation process did not get the attention just because of their size and breadth and depth effort. So that's really put an accelerator down on what are we gonna do? So we have to be able to bring a platform into our clients organizations that have the same behavior characteristics or what we call you know, the same cloud experiences that people are expecting public. Bring it close to our client's data and their applications without having that you don't have a platform by which you can have an accelerated digital transformation because it's historically a public cloud. But the only path to get that done, what we're really considering, what we introduced a while ago was platform near our clients applications. That data that gives them that ability to move quicker and respond to these industries, situations and specifically, what's happened with company really pushes it harder for real solutions Now that they can act on >>Kumar, your thoughts on this pre coded >>Yeah, yeah, this is the piece of acceleration for the digital transformation is just is a longer dynamically multiplied the code. But I think as you pointed out, John the remote working and the VPN is the security. We were as an edge to the Cloud platform company we were already in that space, so it's actually very, very. As Robert pointed out, it's actually nice to see that transformation is his transition or rapidly getting into the digitization. But one thing that is very interesting to note here is you can you can lift and shift of data has gravity. And you actually saw we actually see the war. All the distributor cloud. We see that we're glad to see what we've seen we've been talking about prior to the Kool Aid. And recently even the industry analysts are talking about we believe there is a computer can happen where the data is on. But this is actually an interesting point for me to say. This is why we have actually announced our new software platform, which we as well, which is our our key differentiator pillar for our as a service people that companies are facing. >>Could you talk about what this platform is? You guys are announcing the capabilities and what customers can expect from this. Is that a repackaging? Is there something new here? What's is it something different, Making something better? What? Can you just give us a quick taste of what this is and what it means. >>Good love alive. >>Yeah, so yeah, that's a great question. Is it repackage There's actually something. Well, I'm happy to say. It's a combination of a lot of existing assets that come together in the ecosystem, I think a platform that is super unique. You know, you look at what the Blue data container Onda adoption of communities holistically is a control plane as well as our data fabric of motion to the market with Matt Bahr and you combine that with our network experiences and our other platform very specific platform solutions and your clients data that all comes together in intellectual property that we have that we packed together and make it work together. So there's a lot of new stuff in there, But more importantly, we have a number of other close partners that we've brought together to form out our as moral platform. We have a new, really interesting combination of security and authentication. Piece is through our site L organization that came underneath with us a few months back and are aggressive motion towards bringing in strong networking service that complexity as well. So these all come together and I'm sure leaving a few out there are specifically with info site software to continue to build out a Dr solution on premises that provides that world class of services that John >>Sorry, Johnny, was the question at the beginning is, what is that? Why the software role is This is exactly what I was waiting for that that that moment where Robert pointed out, our goal is we have a lots of good assets. In addition to a lot of good partnerships, we believe the market is the customers want outcome based solutions. Best motion not. I want peace meal. So we have an opportunity to provide the customers the solution from the top to the bottom we were announced, or the Discover ML ops as a service which is actually total top to the bottom and grow, and customers can build ml solutions on the top of the Green lake. This is built on HP is moral, so it's not. I wouldn't use the word repackaging, but it is actually a lot of the inorganic organic technologies that have come together that building the solution. >>You know, I don't think it's ah, negative package something up in >>Toto. So I wouldn't >>I didn't think >>negative, but I was just saying that it is. It's Ah, it's a lot of new stuff, but also, as Robert said included, or you built a very powerful container platform. As you said, you just mentioned it that you've gone. We announced the well. >>One of the things I liked about your talk on November was that the company is kind of getting in the weeds, but stateless versus State. Full data's a big part >>of >>it, but you don't get the cloud and public cloud and horizontal scalability. No one wants Peace meal, that word you guys just mentioned or these siloed tools and about the workforce workplace transformation with Cove it it's exposing the edge, everybody. It's not just a nightie conversation. You need to have software that traverses the environment. So you now looking at not so much point solutions best to breed but you guys have had in the past, but saying Okay, I got to look at this holistically and say, How do I make sure I make sure security, which is the new perimeter, is the home right or wherever is no perimeter anymore is everywhere, So >>this is now >>just a architectural concept. Not so much a point solution, right? I mean, is that kind of how you're thinking about it? >>That's correct. In fact, as you said, the data is generated at the edge and you take the compute and it's been edge to the cloud platform. What we have, actually what we are actually demonstrating is we want to give a complete solution no matter where the processing needs are. And with HP, you have no that cloud like experience both as UNP prime as well as what we call a hybrid. I think let's be honest, the world is going to be hybrid and you can actually see the changes that is happening even from the public cloud vendors. They're trying to come on pram. So HP is being established player in this, and with this technology I think provides that solution, you can process where the data is. >>Yeah, I would agree it's hybrid. I would say Multi cloud is also, you know, code word for multi environment, right? And Robert, I want todo as you mentioned in your talk with stew minimum in November, consistency across environments. So when you talk to customers. Robert. What are they saying? Because I can imagine them in zoom meetings right now or teleconferencing saying, Look it, we have to have an operating model that spans public on premise. Multiple environments, whether it's edge or clouds. I don't wanna have different environments and being managed separately and different data modeling. I won't have a control plane, and this is architectural. I mean, it's kind of complex, but customers are dealing with this right now. What are you hearing from customers? How are they handling and they doubling down on certain projects? Are they reshaping some of their investments? I mean, what's the mindset of the customer >>right now? The mindset is that the customers, under extreme pressure to control costs and improve automation and governance across all their platforms, the business, the businesses that we deal with have established themselves in a public cloud, at least to some extent, with what they call their systems of engagement. Those are all the lot of the elastic systems, the hype ones that the hyper scale very well, and then they have all of their existing on premises, stuff that you typically heavily focused on. A VM based mindset which is being more more viewed as legacy, actually, and so they're looking for that next decade of operating. While that spans both the public and the private cloud on Premises World and what's risen up, that operating model is the open source kubernetes orchestration based operating model, where they gives them the potential of walking into another operating model that's holistic across both public and private but more importantly, as a way for their existing platforms to move into this new operating model. That's what you're talking about, using state full applications that are more legacy minded, monolithic but still can run in the container based platform and move to a new ballistic operating model. Nobody's under the impression, by the way, that the existing operating model we have today on premises is compatible with the cloud operating model. Those two are not compatible in any shape. Before we have to get to an operating model that holistic in nature. We see that, >>and that's a great tee up for the software question Robert, I want to go to. Come on, I want to get thoughts because I know you personally and I've been following your career. Certainly you know. Well, well, well, deep in computer science and software. So I think it's a good role for you. But if you look at what the future is, this is the conversation we're having with CIOs and customers on the Cube is when I get back to work postcode. But I've gotta have a growth strategy. I need to reset, reinvent and have growth strategy. And all the conversations come back to the APS that they have to redevelop or modernize, right? So workloads or whatever. So what that means is they really want true agility, not just as a punch line or cliche. They gotta move security into the Dev Ops pipeline ing. They got to make the application environment. Dev Ops and Dev Ops was kind of a fringe industry thing for about a decade. And now that's implement. That's influencing I T ops, security ops and network ops. These are operational systems, not just, you know, Hey, let's sling some kubernetes and service meshes around. This is like really nuts and bolts business operations. So, you know, I t Ops has impacted SEC ops isn't impacted. They're working us not for the faint of Heart Dev Ops I get that now it's coming everywhere. What's your thoughts on that? What's your reaction? >>We see those things coming together, John. So again, going back to the Israel were the world we believe this innovative software is. It can run on any infrastructure to start with, whether it's HP hardware knowledge we are with. It's called Hybrid. And as we said we talked about, it is it is, um it's whether it is an edge already where the processing is. We also committed to providing integrated, optimized, secure, elastic and automate our solutions. Right. This is, I think, your question of are it's not just appealing to the one segment of the organization. I think there's going to be a I cannot just say I'm only giving you the devil ops solution, but it has to have a security built into. This is why we are actually committed to making our solutions more elastic, more scalable. We're investing in building a complete runtime stack and making sure it has the all the fleet compose. It's not only optimized for the work solution which we call the work runtime stack, it's also has this is our Green Lake solution that that brings these two pieces together. Robert? Yeah. Sorry. Go ahead. >>Robert, you mentioned automation earlier. This is where the automation dream comes in. The Mission ml ops service. What you're really getting at is program ability for the developer across the board, right? Is that kind of what you're thinking? Or? >>Well, there's two parts of that. This is really important. The developer community is looking for a set of tools that they could be very creative and movement right. They don't want to have to be worried about provisioning managing, maintaining any kind of infrastructure. And so there's this bridge between that automation and the actual getting things done. So that's number one. But more importantly, I think this is hugely important, as you look about pushing into the on premises world for for H, P E or anybody else to succeed in that space, you have to have a high degree of automation that takes care of potential problems that humans would otherwise have to get involved with. And that's when they cost. So you have to drive in a commercial. I'm gonna fleet controls of Fleet management services that automate their behavior and give them an S L A that are custom to public cloud. So you've got two sets of automation that you really have to be dealing with. Not only are you talking about Dev ops, the second stage you just talked about, but you gotta have a corresponding automation bake back into drive. A higher user experience at both levels >>and Esmeraldas platforms is cool. I get that. I hear that. So the question next question on that Kumar is platforms have to enable value. What are you guys enabling for the value when you talk to customers? Because who everyone sees the platform play as the as the architecture, but it has to create disruptive, enabling value. What do you >>Yeah, that I'll go on as a starter, I think way pointed out to you. This is the when we announced the container platform, it's off, the very unique. It's not only it's open source Cuban it is. It has a persistent one of the best underlying persistent stories integrated the original map or a file system, as I pointed out, drones one of the world's largest databases, and we can actually allow the customers to run both both state full and stateless workloads. And as I said a few minutes ago, we are committed to having the run times off they run and both which we are. We're not a hardware, so the customers have the choice on. In addition to all of that, I think we're in a very unique solutions. We're offering is ML ops as we talked about and this is only beginning, and we have lots of other examples of Robert is working on a solution. Hopefully, we'll announce sometime soon, which is similar to that. Some of the key elements that we're seeing in the marketplace, the various solutions that goes from the top of the bar >>Robert to you on the same question. What's in it for me in the customer? Bottom line. What's the what's in it for me? >>Well, so I think, just the ease of simplicity. What we are ultimately want to provide for a client is one opportunity to solve a bunch of problems that otherwise have to stitch together myself. It's really about value and speed to value. If I have to solve the same computer vision problem in manufacturing facility and I need a solution and I don't have the resource of the wherewithal stacks like that, but I got to bring a bigger solution. I want a company that knows how to deliver a computer vision solution there or within an airport or wherever, where I don't need to build out sophisticated infrastructure or people are technologies necessary, is point on my own or have some third party product that doesn't have a vested interest in the whole stack. H P E is purposely have focused on delivering that experience with one organization from both hardware and software up to the stack, including the applications that we believe with the highest value to the client We want to be. That organization will be an organization on premises. >>I think that's great, consistent with what we're hearing if you can help take the heavy lifting away and have them focus on their business and the creativity. And I think the application renaissance and transformation is going to be a big focus both on the infrastructure side but also just straight up application developers. That's gonna be really critical path for a lot of these companies to come out of this. So congratulations on that love that love the formula final conclusion question for both you guys. This is something that a lot of people might be asking at HP. Discover virtual experience, or in general, as they have to plan and get back to work and reset, reinvent and grow their organizations. Where is HP heading? How do you see HP heading? How would you answer that question? If the customers like Kumar Robert, where's HP heading? How would you answer that? >>Go ahead, Robert. And then I can >>Yeah, yeah. Uh huh, Uh huh. I see us heading into the true distributed hybrid platform play where that they would look to HP of handling and providing all of their resource is and solutions needs as they relate to technology further and further into what their specific edge locations would look like. So edge is different for everybody. And what HP is providing is a holistic view of compute and our storage and our solutions all the way up through whether they be very close to the edge. Locations are all the way through the data center and including the integration with our public cloud partners out there. So I see HP is actually solving real value business problems in a way that's turnkey and define it for our clients. Really value >>John. I think I'll start with the word Antonio shared. We are edge to the cloud, everything as a service company and I think the we're actually sending is HPE is Valley Legend, and it's actually honored to be part of the such a great company. I think what we have to change with the market transformation the customer needs and what we're doing is we're probably in the customers that innovative solution that you don't have to. You don't have to take your data where the computers, as opposed to you, can take the compute where the data is and we provide you the simplified, automated, secure solutions no matter where you very rare execution needs are. And that is through the significant innovation of the software, both for as Model and the Green Lake. >>That's awesome. And, you know, for all of us, have been through multiple ways of innovation. We've seen this movie before. It's essentially distributive computing, re imagine and re architected with capability is the new scale. I mean, it's almost back to the old days of network operating systems and networking and Os is and it's a you know, >>I that's a very, very good point. And I will come through the following way, right? I mean, it is, It's Ah, two plus two is four no matter what university, Gordo. But you have to change with the market forces. I think the market is what is happening in the marketplace. As you pointed out, there was a shadow I t There's a devil Ops and his idea off the network ops and six years. So now I think we see that all coming together I call this kubernetes is the best equalizer of the past platform. The reason why it became popular is because it's provided that abstraction layer on. I think what we're trying to do is okay, if that is where the customers want and we provide a solution that helps you to build that very quickly without having to lock into any specific platform. >>I think you've got a good strategy there. I would agree with you. I would call that I call it the old TCP I p. What that did networking back in the day. Kubernetes is a unifying, disruptive enabler, and I think it enables things like a runtime stack. Things that you're mentioning. These are the new realities. I think Covad 19 has exposed this new architectures of the world. >>Yeah, one last year, we were saying >>once, if not having something in place >>started. So the last thing I would say is it we're not bolting coolness to anything. Old technologies. It's a fresh it's built in. It's an open source. And it is as a salaries, it can run on any platform that you choose to run. Now. >>Well, next time we get together, we'll refund, observe ability and security and all that good stuff, because that's what's coming next. All the basic in guys. Thank you so much, Kumar. Robert. Thanks for spending the time. Really appreciate it here for the HP Discover Virtual Spirits Cube conversation. Thanks for Thanks for joining me today. >>Thank you very much. >>I'm John Furrier with Silicon Angle. The Cube. We're here in our remote studios getting all the top conversations for HP Discover virtual experience. Thanks for watching. Yeah, >>yeah, yeah.

>>Live from San Diego, California. It's the cube covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem Marsh. >>Welcome back. This is the cubes coverage of coupon, cloud-native con 2019 here in San Diego. I'm Stu Miniman co-hosting for three days with John Troyer to my left and happy to welcome back to the program. Two of our cube alumni to my right is Robert Christiansen who is the vice president of strategy and office of the CTO with the IP group to see you. And sitting next to him is Kumar Sri Conti, SVP and CTO of that hybrid it group at HPE Kumar. Great to see you. Thank you very much. Thank you John. Good to be back here. Yes, hot off the presses. HP had a big announcement today. Uh, really unveiling it. Full container platform. Uh, Kumar, maybe it help us frame and understand, uh, what that is and why that wants here at at the show. Thank you. Is too good, too good to see John and it's very nice to be back on the cube. >>Yeah, we are very excited. We made an announcement, a HV container platform as we sat in the presser lays and various conversations. This is built on a proven technologies. HP has acquired a few companies in the past which includes my company blue data map. Our blue data has been in the container technology for more than five years. We have containers running specifically for the spa workloads like big data and AML and we brought those technologies together to give the customers the choice of 100% coupon. It has to run both stateful and stateless workloads under the same pane of glass and we are very excited about this opportunity and we have actually talked to a lot of customers and the most important in addition to all of that is the, we also integrated the map, our technology, which is one of the very so robust and sophisticated data store that gives you a persistency for the containers. >>Kumar, John and I were coming out of the keynote and saying, if you're brand new in this environment, Oh my gosh, there's just so many projects and so many pieces. You know, when I think back, you know, who helped me along the way, uh, one of the pieces you picked up with CTP, cloud technology partner and you're talking about specific applications. So you know, really building those bridges to where customers are and helping them give us, if you could some of those key use cases where you're finding that that cloud native philosophy and where customers are, are looking for HPS help. Robert and I spend a lot of time over the last few months internally and talking to the customers. Our thesis is the, all the low hanging fruit applications have mode. It's actually the most difficult applications, both stateful and stateless applications. So customers are asking and say, we want to standardize, we want to have a abstract platform and Gouverneur does is it? And, but we wanted to have a platform that gives us the board hybrid opportunity. I wanted to be able to run the on prem >>when necessary, also on the public cloud. And I wanted to be able to have a same platform to run both stateful answered as application. Yeah. And that's, that's a really interesting point because what Kumar's really, really looking at is that the only way that an enterprise has been using the path that modernization has been been a public cloud, uh, trajectory. Okay. And they really haven't had anything on premises that gave them the set of services necessary to get parody between the two. And what we're finding and you know, been been involved with public cloud since 2010 right? So hundreds and hundreds of engagements, the portion that they thought they were going to move to cloud is substantially dropped the actual number of applications versus now those are going to stay on prem. And we were looking at each other and we're saying, Hey, this is a trifecta of opportunities with the containers coming in and the normalization of Kubernetes as the unified pass platform, the abstraction of bullying all the way down to bare metal, right? >>And giving those clients that true native architectures where they are not having to pay what we consider excessive prices to be putting in that, that world right there and then allowing that monetization practice to happen. So you've got to start with that platform, that, that container platform, and to do it in the way that the motion is going right now in the world today that's consistent with the public cloud. This is really important that you have to have consistency in your development environments, whether they're public or private. And that's where we believe is important. So Robert, you're seeing enterprises develop that. It sounds like you're seeing enterprises develop that operational experience and operational expertise, process development, independent of where their workloads are running. Well, that's the goal. Okay. Yeah, yeah. Well, right now they're siloed. Right? Okay. You've got a public operating model and you've got a private operating model. >>Right? And there's some people that tried to stitch this stuff together, but it's really difficult. What we're looking to do is given consistent plain across, all right? And when you have a consistent plane, a control point across all, no matter where you put your clusters and a management frame around it, now you have the ability to build an operating model that's consistent to go forward. Okay. So you know, we've been at the show for four years. I interviewed Joe beta, uh, and, and Joe says, he said, look, you know, Kubernetes, it's not a magic layer. It does not all of a sudden say add Coobernetti's in it and everything works every hair there. No, it's a very thin layer. I'm glad he said that. Washing my car from that happened on top. Right. If flip problem just rubbed Coobernetti's on it and get better. So Kumar, help us understand kind of the HPE stack if you work and what you put together and therefore it will be an enabler for customers in your application. Thank you. That's a very, very well said and I joke that Gouverneur does, we'll wash your car and post to read and babysit. And um, so I think he enjoys the ride, a lot of wisdom there. So what we found is, uh, content has an ensemble persistence always problem per se. So if I want, if >>I have a database running and my container goes away, we also notice that you want to make sure your endpoints are well secured and you want to expose only things what you want in the thing. We also found out that customers are more interested in applications and are giving me just the engine and the tires. I need to go from point a to point B. What blue data has done is actually it actually automates all your deployments of applications. We announced that product in September, so what what what this continent platform does is bring all these pieces together so the customers to be able to move to the deploy man and not worry about whether I have tires or I have an engine or not. In addition, I would like to find out that, I think Antonio talked about it the hour Sammo we want to come to the customers and it's the best possible lowest cost workload per application. >>This is why we think better metals are very, very, very important. Running containers on bare metal will Remos techs and and there is an, and we've been running better minerals in on bare metal containers in the blue data for almost five years. One of the things I think I wanted to add to that because I, you, you were guys saying, Hey, deploying Kubernetes and just add a little bit on top of that and it's all fine, right? I thought that was a great comment. Um, a lot of our clients are literally talking about container sprawl, right? It don't take anything to go to cncf.org and pull down could the Kubernetes distro launch it out there? And I've got a bunch of stuff running. They're popping up faster than all the shadow it did when the cloud, the public cloud started coming up, right? So you have this, this, um, motion that's uncontrolled, and if you're an enterprise and you're and governance and you're trying to put your arms around a global infrastructure that you want to be able to put your arms around that, more importantly, you may have one group running 1.15. >>You may have another group 0.1, 1.8. You may have two other groups that have an older version that's into production right now, and you have them all independently running. And then you need to maintain a multitenancy across all of that and then separate those. Okay. You have to have a system that does that. And so the container HP container platform does that. This is a huge differentiating with consistent data layer underneath and that, that abstraction between the two and that governance around it is so much bigger than what we consider just Kubernetes on its own and that world comfort zone. Right, right. >>Well, I, I to play on that, right. Uh, we used to say, talk about paths a lot, right? And then a lot of words were spilled. I, I, what I love about some of the work here is that it comes from actual use, you know, proven in production use cases, years of work, you, the rough edges, the, the, the sharp, the, the cuts on your hands. Um, so that's actually great. All open source also and, and, and contributed back to the community. Also. Interesting. There is a, um, you know, but as so as folks, and there's many ways of getting Kubernetes raw, Kubernetes, Kubernetes with pieces, uh, in this room right here. So, you know, an interesting set of technologies that you've put together that with, for ease of use and for, for governance and you know, at the, from the business, from the ops layer, from the, from the dev layer. >>Um, but there is a difference of speed sometimes of uh, of uh, you know, the, what the enterprise wants to move Kubernetes these releases every quarter. And you know, I and you know, the other projects released at their own pace. So in this open source philosophy, uh, and the HPE as a partner with the, you know, point next and, and you know, support is your middle name kind of, uh, you know, how do you, how do you marry the, the, the speed of the cloud native technologies and all of the open source, uh, collaboration with, with kind of the enterprise on the enterprise side and help them? >>Yeah, very good question. I think Robert Weiner, there's one other focus for us is we didn't want to provide, I think before the injury you are talking about the curator Cuban or that we are supporting a hundred percent covenant is open source. So Robert says, I am a developer. I want 1.19 and Stu says I want do I have a 1.17 because I'm stable on that. You can have both the clusters along with the blue data, Epic controller clusters in the same pane of glass. Now you can run big data applications, you can run your cloud narrative, you can run your cloud narrative because you are on 1.19 so that is our goal. So when the CNCF releases newer versions obviously that we will support it. And then as you pointed out, HP support is the middle lame. We have a point next organization we have a CDP. So we will help the customers and we will obviously support certain versions and make sure when somebody gives a call and help the customers. And so we want to give that flexibility so that the developers can deploy whatever the native new versions that are coming up under the umbrella of HP container. It's this Epic layer that's providing some of the multitenancy and governance and controls. >>Exactly right. So this, you know, if you look at the, the, the CNCF, uh, roadmap, they're their grid, right? And you see where Coobernetti's lands in that one piece. There's all these surrounding pieces like that. There's lots and lots of vendors here that have pieces of it, right? But it takes a system, right? And you know that, and then it takes an operating model around that. Then it takes a deployment and governance model around that, right? And then you have, so there's so much more that the enterprise world acquires to make this a legitimate platform that can be scaled. >>One thing that I would like to add it, I don't want to underplay the, the, the value of a persistent proven data layer that has been there for 10 years with the map, our map around some of the best and largest databases in the world. And we are now bringing those two together. It's a, it's a very, very profound and very, very useful for the, for the enterprises. You know, Robert, you were emphasizing the consistency that needs to happen, uh, explained to how that fits in with your partnerships with all the public clouds. Uh, because you know, you hear a very different Coobernetti's message if you go to the Google show versus the Azure versus AWS. And I see HPE know at all of them. >>That's absolutely true. So, you know, I was the CTO with cloud technology partners, right? So I joined in 2013 and it was, um, our, our whole world was how do we work with the three hyperscalers to bring some consistency across them, right? You know, and you have operating models that are different for all three. I mean, what runs on AWS in a certain way is going to run differently on Azure. What's going to be running differently in GCP, right? So the tooling, all that, all the pieces are different. You go pull that back on prem. Now you have a whole different conversation as well. So what we know is that you have to have a unification of behavioral control systems in place before, wherever you deploy your clusters, wherever those are going to be like that. So what we know is is that the tagging nomenclature, the tagging is key to all of this operational models. >>All your tools are gonna be using tagging. And when you go into existing environments, taggy will be inconsistent between, even with inside AWS will be consistent, inconsistent with an Azure. So you have to have a mapping. So what we have as part of our GreenLake offering that would come in together with this is we have a unification tagging layer that bridges that gap and unifies that into a consistent nomenclature and control plane that gives you a basis to have an operating model. This is a, this only gets exposed until you start having 2050 102 hundred clusters out there. And everybody goes, how do I put my arms around this? So it's very important that that, that's just one piece of it. But operating model, operating model, operating model, I keep going back to this every time. There's a bunch of people here can spin up manage clusters all day long and some of them doing better than others, but unless you surround it and you surround it with the stuff that he's talking about is a consistent data layer, persistent and a consistent management system of all these people's behaviors, you're going to get just an unbelievable out of control platform. >>Yup. Kumar, I'd love your viewpoint as to just the overall maturity of this ecosystem and where does HPE see their role as to, you know, we talked about, you know, data and you know, everything that's changing. I heard a lot in the keynote this morning about, >>uh, some of the progress that's being made, but I'd love your viewpoint there. HP is a legend in the Valley as you know. I mean, they've done every, we, all engineering calculator starts with HV calculator. HP recognize they missed a couple of transitions in the industry. And I think there's a new leadership with, uh, with our, with the Robert and me and other other key leaders recognizes this is a great opportunity for us. We see this window to help the customers. Make the modern digitalization transition the applications, taking the monolithic applications, doing microservices. You can. In fact, Robert and I was talking to a bank and they told us they have 6,000 applications built so far. They have micro service, four of them and, and, and we have actually what, what, what we believe with this application is you can actually run your monolithic applications in a container platform while you are figuring it right. So what we see is helping the customers make the digital transition and making sure that they have, they make, they go down this journey. That's what we see. Kumar, Robert, thank you so much for the updates. Congratulations on the launch. I look forward to seeing your presence. Thanks for having and cube. I allow Q. yeah. Thanks Jeff. Again, look for next time. Okay. All right. Bye. Thanks so much for John Troyer. I'm Stu Miniman. Lots more in our three days wall to wall coverage here at cube colon cloud native con 2019 thanks for watching. Fuck you..

(upbeat trumpet music) >> From our studios in the heart of Silicon Valley, Palo Alto, California. This is a CUBE Conversation. >> Welcome, everybody, I'm Dave Vellante and we're here in our Palo Alto studios and we're going to talk about big data. For the last ten years, we've seen organizations come to the realization that data can be used to drive competitive advantage and so they dramatically lowered the cost of collecting data. We certainly saw this with Hadoop, but you know what data is plentiful, insights aren't. Infrastructure around big data is very challenging. I'm here with Kumar Sreekanti, co-founder and CEO of BlueData, and a long time friend of mine. Kumar, it's great to see you again. Thanks so much for coming to theCUBE. >> Thank you, Dave, thank you. Good to see you as well. >> We've had a number of conversations over the years, the Hadoop days, on theCUBE, you and I go way back, but I said up front, big data sounded so alluring, but it's very, very complex to get started and we're going to get into that. I want to talk about BlueData. Recently sold to company to HPE, congratulations. >> Thank you, thank you. >> It's fantastic. Go back, why did you start BlueData? >> When I started BlueData, prior to that I was at VMware and I had a great opportunity to be in the driving seat, working with many talented individuals, as well as with many customers and CIOs. I saw while VMware solved the problem of single instance of virtual machines and transform the data center, I see the new wave of distributed systems, vis-a-vis first example of that is Hadoop, were quite rigid. They were running on bare metal and they were not flexible. They were having, customers, a lot of issues, the ones that you just talked about. There's a new stack coming up everyday. They're running on bare metal. I can't run the production and the DevOps on the same systems. Whereas the cloud was making progress so we felt that there is an opportunity to build a Vmware-like platform that focuses on big data applications. This was back in 2013, right. That was the early genesis. We saw that data is here and data is the new oil as many people have said and the organizations have to figure out a way to harness the power of that and they need an invisible infrastructure. They need very innovative platforms. >> You know, it's funny. We see data as even more valuable than oil because you can only once. (Kumar laughs) You can use data many, many times. >> That's a very good one. >> Companies are beginning to realize that and so talk about the journey of big data. You're a product guy. You've built a lot of products, highly technical. You know a lot of people in the valley. You've built great teams. What was the journey like with BlueData? >> You know, a lot of people would like it to be a straight line from the starting to that point. (Dave laughs) It is not, it's fascinating. At the same time, a stressful, up and downs journey, but very fulfilling. A, this is probably one of the best products that I've built in my career. B, it actually solves a real problem to the customers and in the process you actually find a lot of satisfaction not only building a great product. It actually building the value for the customers. Journey has been very good. We were very blessed with extremely good advisors from the right beginning. We were really fortunate to have good investors and I was very, as you said, my knowledge and my familiarity in the valley, I was able to build a good team. Overall, an extremely good journey. It's putting a bow on the top, as you pointed out, the exit, but it's a good journey. There's a lot of nuance I learned in the process. I'm happy to share as we go through. >> Let's double-click on the problem. We talked a little bit about it. You referenced it. Everyday there's a new open source project coming out. There's The Scoop and The Hive and a new open open source database coming out. Practitioners are challenged. They don't have the skillsets. The Ubers and the Facebooks, they could probably figure it out and have the engineers to do it, but the average enterprise may not. Clearly complexity is the problem, but double-click on that and talk a little bit about, from your perspective, what that challenge is. >> That's a very good point. I think when we started the company, we exactly noticed that. There are companies that have the muscle to hire the set of engineers and solve the problem, vertically specific to their application or their use case, but the average, which is Fortune 500 companies, do not have that kind of engineering man power. Then I also call this day two operations. When you actually go back to Vmware or Windows, as soon as you buy the piece of software, next day it's operational and you know how to use it, but with these new stacks, by the time stack is installed, you already have a newer version. It's actually solutions-led meaning that you want to have a solution understanding, but you want to make the infrastructure invisible meaning, I want to create a cluster or I want to funnel the data. I don't want to think about those things. I just wanted to directly worry about what is my solution and I want BlueData to worry about creating me a cluster, automating it. It's automation, automation, automation, orchestration, orchestration, orchestration. >> Okay, so that's the general way in which you solve this problem. Automate, you got to take the humans out of the equation. Talk specifically about the BlueData architecture. What's the secret sauce behind it? >> We were very fortunate to see containers as the new lightweight virtual machines. We have taken an approach. There are certain applications, particularly stateful, need a different handling than cloud-native non-stateful applications so what we said was, in fact our architecture predates Kubernetes, so we built a bottoms-up, pure white-paper architecture that is geared towards big data, AIML applications. Now, actually, even HPC is starting to move into that direction. >> Well, tell me actually, talk a little bit about that in terms of the evolution of the types of workloads that we've seen. You know, it started all out, Hadoop was batch, and then very quickly that changed. Talk about that spectrum. >> It's actually when we started, the highest ask from the customers were Hadoop and batch processing, but everybody knew that was the beginning and with the streaming and the new streaming technologies, it's near realtime analytics and moving to now AIML applications like H2O and Cafe and now I'm seeing the customer's asking and say, I would like to have a single platform that actually runs all these applications to me. The way we built it, going back to your previous question, the architecture is, our goal is for you to be able to create these clusters and not worry about the copying the data, single copy of the data. We built a technology called DataTap which we talked about in the past and that allows you to have a single copy of the data and multiple applications to be able to access that. >> Now, HPC, you mentioned HPC. It used to be, maybe still is, this sort of crazy crowd. (laughter) You know, they do things differently and everybody bandwidth, bandwidth, bandwidth and very high-end performance. How do you see that fitting in? Do you see that going mainstream? >> I'm glad you pointed out because I'm not saying everything is moving over, but I am starting to see, in fact, I was in a conversation this morning with an HPC team and an HPC customer. They are seeing the value of the scale of distributed systems. HPC tend to be scale up and single high bandwidth. They are seeing the value of how can I actually bring these two pieces together? I would say it's in infancy. Don't take me to say, look how long Hadoop take, 10 years so it's probably going to take a longer time, but I can see enterprises thinking of a single unified platform that's probably driven by Kubernetes and have these applications instantiated, orchestrated, and automated on that type. >> Now, how about the cloud? Where does that fit? We often say in theCUBE that it's not Moore's Law anymore. The innovation cocktail is data, all this data that we've collected, applying machine intelligence, and then scaling with the cloud. Obviously cloud is hugely important. It gobbled up the whole Hadoop business, but where do you see it fitting? >> Cloud is a big elephant in the room. We all have to acknowledge. I think it provides significant advantages. I always used to say this, and I may have said this in my previous CUBE interviews, cloud is all about the innovation. The reason cloud got so much traction, is because if you compare the amount of innovation to on-prem, they were at least five years ahead of that. Even the BlueData technology that we brought to the barer, EMR on Amazon was in front of the data, but it was only available Amazon. It's what we call an opinionated stack. That means you are forced to use what they give you as opposed to, I want to bring my own piece of software. We see cloud, as well as on-prem pretty much homogenous. In fact, BlueData software runs both on-prem, on the cloud, in a hybrid fashion. Same software and you can bring your stack on the top of the BlueData. >> Okay, so hybrid was the next piece of it. >> What we see is cloud has, at least from the angle from my exposure, cloud is very useful for certain applications, especially what I'm seeing is, if you are collecting the large amounts of data on the cloud, I would rather run a batch processing and curate the data and bring the very important amount of data back into the on-prem and run some realtime. It's just one example. I see a balance between the two. I also see a lot of organizations still collecting terabits of data on-prem and they're not going to take terabits of data overnight to the cloud. We are seeing all the customers asking, we would like to see a hybrid solution. >> The reason I like the acquisition by HPE because not only is it a company started by a friend and someone that I respect and knows how to build solid technology that can last, but it's software. HPE, as a company, my view needs more software content. (Kumar laughs) Software's eating the world as Marc Andressen says. It would be great to see that software live as an independent entity. I'm sure decisions are still being made, but how do you see that playing out? What are the initial discussions like? What can you share with us? >> That's a very, very, well put there. Currently, the goal from my boss and the teams there is, we want to keep the BlueData software independent. It runs on all x86 hardware platforms and we want to drive the roadmap driven by the customer needs on the software like we want to run more HPC applications. Our roadmap will be driven by the customer needs and the change in the stack on the top, not by necessarily the hardware. >> Well, that fits with HPE's culture of always trying to give optionality and we've had this conversation many, many times with senior-level people like Antonio. It's very important that there's no lock-in, open mindset, and certainly HPE lives up to that. Thanks so much for coming-- >> You're welcome. Back into theCUBE. >> I appreciate you having me here as well. >> Your career has been amazing as we go back a long time. Wow. From hardware, software, all these-- >> Great technologies. (laughter) >> Yeah, solving hard problems and we look forward to tracking your career going forward. >> Thank you, thank you. Thanks so much. >> And thank you for watching, everybody. This is Dave Vellante from our Palo Alto Studios. We'll see ya next time. (upbeat trumpet music)

(orchestral music) >> Hi, I'm Peter Burris and welcome to another digital community event. As we do with all digital community events, we're gonna start off by having a series of conversations with real thought leaders about a topic that's pressing to today's enterprises as they try to achieve new classes of business outcomes with technology. At the end of that series of conversations, we're gonna go into a crowd chat and give you an opportunity to voice your opinions and ask your questions. So stay with us throughout. So, what are we going to be talking about today? We're going to be talking about the challenge that businesses face as they try to apply AI, ML, and new classes of analytics to their very challenging, very difficult, but nonetheless very value-producing outcomes associated with data. The challenge that all these businesses have is that often, you spend too much time in the infrastructure and not enough time solving the problem. And so what's required is new classes of technology and new classes of partnerships and business arrangements that allow for us to mask the underlying infrastructure complexity from data science practitioners, so that they can focus more time and attention on building out the outcomes that the business wants and a sustained business capability so that we can continue to do so. Once again, at the end of this series of conversations, stay with us, so that we can have that crowd chat and you can, again, ask your questions, provide your insights, and participate with the community to help all of us move faster in this crucial direction for better AI, better ML and better analytics. So, the first conversation we're going to have is with Anant Chintamaneni. Anant's the Vice President of Products at BlueData. Anant, welcome to theCUBE. >> Hi Peter, it's great to be here. I think the topic that you just outlined is a very fascinating and interesting one. Over the last 10 years, data and analytics have been used to create transformative experiences and drive a lot of business growth. You look at companies like Uber, AirBnB, and you know, Spotify, practically, every industry's being disrupted. And the reason why they're able to do this is because data is in their DNA; it's their key asset and they've leveraged it in every aspect of their product development to deliver amazing experiences and drive business growth. And the reason why they're able to do this is they've been able to leverage open-source technologies, data science techniques, and big data, fast data, all types of data to extract that business value and inject analytics into every part of their business process. Enterprises of all sizes want to take advantage of that same assets that the new digital companies are taking and drive digital transformation and innovation, in their organizations. But there's a number of challenges. First and foremost, if you look at the enterprises where data was not necessarily in their DNA and to inject that into their DNA, it is a big challenge. The executives, the executive branch, definitely wants to understand where they want to apply AI, how to kind of identify which huge cases to go after. There is some recognition coming in. They want faster time-to-value and they're willing to invest in that. >> And they want to focus more on the actual outcomes they seek as opposed to the technology selection that's required to achieve those outcomes. >> Absolutely. I think it's, you know, a boardroom mandate for them to drive new business outcomes, new business models, but I think there is still some level of misalignment between the executive branch and the data worker community which they're trying to upgrade with the new-age data scientists, the AI developer and then you have IT in the middle who has to basically bridge the gap and enable the digital transformation journey and provide the infrastructure, provide the capabilities. >> So we've got a situation where people readily acknowledge the potential of some of these new AI, ML, big data related technologies, but we've got a mismatch between the executives that are trying to do evidence-based management, drive new models, the IT organization who's struggling to deal with data-first technologies, and data scientists who are few and far between, and leave quickly if they don't get the tooling that they need. So, what's the way forward, that's the problem. How do we move forward? >> Yeah, so I think, you know, I think we have to double-click into some of the problems. So the data scientists, they want to build a tool chain that leverages the best in-class, open source technologies to solve the problem at hand and they don't want, they want to be able to compile these tool chains, they want to be able to apply and create new algorithms and operationalize and do it in a very iterative cycle. It's a continuous development, continuous improvement process which is at odds with what IT can deliver, which is they have to deliver data that is dispersed all over the place to these data scientists. They need to be able to provide infrastructure, which today, they're not, there's an impotence mismatch. It takes them months, if not years, to be able to make those available, make that infrastructure available. And last but not the least, security and control. It's just fundamentally not the way they've worked where they can make data and new tool chains available very quickly to the data scientists. And the executives, it's all about faster time-to-value so there's a little bit of an expectation mismatch as well there and so those are some of the fundamental problems. There's also reproducibility, like, once you've created an analytics model, to be able to reproduce that at scale, to be then able to govern that and make sure that it's producing the right results is fundamentally a challenge. >> Audibility of that process. >> Absolutely, audibility. And, in general, being able to apply this sort of model for many different business problems so you can drive outcomes in different parts of your business. So there's a huge number of problems here. And so what I believe, and what we've seen with some of these larger companies, the new digital companies that are driving business valley ways, they have invested in a unified platform where they've made the infrastructure invisible by leveraging cloud technologies or containers and essentially, made it such that the data scientists don't have to worry about the infrastructure, they can be a lot more agile, they can quickly create the tool chains that work for the specific business problem at hand, scale it up and down as needed, be able to access data where it lies, whether it's on-prem, whether it's in the cloud or whether it's a hybrid model. And so that's something that's required from a unified platform where you can do your rapid prototyping, you can do your development and ultimately, the business outcome and the value comes when you operationalize it and inject it into your business processes. So, I think fundamentally, this start, this kind of a unified platform, is critical. Which, I think, a lot of the new age companies have, but is missing with a lot of the enterprises. >> So, a big challenge for the enterprise over the next few years is to bring these three groups together; the business, data science world and infrastructure world or others to help with those problems and apply it successfully to some of the new business challenges that we have. >> Yeah, and I would add one last point is that we are on this continuous journey, as I mentioned, this is a world of open source technologies that are coming out from a lot of the large organizations out there. Whether it's your Googles and your Facebooks. And so there is an evolution in these technologies much like we've evolved from big data and data management to capture the data. The next sort of phase is around data exploitation with artificial intelligence and machine learning type techniques. And so, it's extremely important that this platform enables these organizations to future proof themselves. So as new technologies come in, they can leverage them >> Great point. >> for delivering exponential business value. >> Deliver value now, but show a path to delivery value in the future as all of these technologies and practices evolve. >> Absolutely. >> Excellent, all right, Anant Chintamaneni, thanks very much for giving us some insight into the nature of the problems that enterprises face and some of the way forward. We're gonna be right back, and we're gonna talk about how to actually do this in a second. (light techno music) >> Introducing, BlueData EPIC. The leading container-based software platform for distributed AI, machine learning, deep learning and analytics environments. Whether on-prem, in the cloud or in a hybrid model. Data scientists need to build models utilizing various stacks of AI, ML and DL applications and libraries. However, installing and validating these environments is time consuming and prone to errors. BlueData provides the ability to spin up these environments on demand. The BlueData EPIC app store includes, best of breed, ready to run docker based application images. Like TensorFlow and H2O driverless AI. Teams can also add their own images, to provide the latest tools that data scientists prefer. And ensure compliance with enterprise standards. They can use the quick launch button. which provides pre configured templates with the appropriate application image and resources. For example, they can instantly launch a new Sandbox environment using the template for TensorFlow with a Jupyter Notebook. Within just a few minutes, it'll be automatically configured with GPUs and easy access to their data. Users can launch experiments and make GPUs automatically available for analysis. In this case, the H2O environment was set up with one GPU. With BlueData EPIC, users can also deploy end points with the appropriate run time. And the inference run times can use CPUs or GPUs. With a container based BlueData Platform, you can deploy fully configured distributed environments within a matter of minutes. Whether on-prem, in the public cloud, or in a hybrid a architecture. BlueData was recently acquired by Hewlett Packward Enterprise. And now, HPE and BlueData are joining forces to help you on your AI journey. (light techno music) To learn more, visit www.BlueData.com >> And we're back. I'm Peter Burris and we're continuing to have this conversation about how businesses are turning experience with the problems of advance analytics and the solutions that they seek into actual systems that deliver continuous on going value and achieve the business capabilities required to make possible these advanced outcomes associated with analytics, AI and ML. And to do that, we've got two great guests with us. We've got Kumar Sreekanti, who is the co-founder and CEO of BlueData. Kumar, welcome back to theCUBE. >> Thank you, it is nice to be here, back again. >> And Kumar, you're being joined by a customer. Ramesh Thyagarajan, is the executive director of the Advisory Board Company which is part of Optum now. Ramesh, welcome to theCUBE. >> Great to be here. >> Alright, so Kumar let's start with you. I mentioned up front, this notion of turning technology and understanding into actual business capabilities to deliver outcomes. What has been BlueData's journey along, to make that happen? >> Yeah, it all started six years ago, Peter. It was a bold vision and a big idea and no pun intended on big data which was an emerging market then. And as everybody knows, the data was enormous and there was a lot of innovation around the periphery. but nobody was paying attention to how to make the big data consumable in enterprise. And I saw an enormous opportunity to make this data more consumable in the enterprise and to give a cloud-like experience with the agility and elasticity. So, our vision was to build a software infrastructure platform like VMware, specially focused on data intensity distributed applications and this platform will allow enterprises to build cloud like experiences both on enterprise as well as on hybrid clouds. So that it pays the journey for their cloud experience. So I was very fortunate to put together a team and I found good partners like Intel. So that actually is the genesis for the BlueData. So, if you look back into the last six years, big data itself has went through a lot of evolution and so the marketplace and the enterprises have gone from offline analytics to AI, ML based work loads that are actually giving them predictive and descriptive analytics. What BlueData has done is by making the infrastructure invisible, by making the tool set completely available as the tool set itself is evolving and in the process, we actually created so many game changing software technologies. For example, we are the first end-to-end content-arised enterprise solution that gives you distributed applications. And we built a technology called DataTap, that provides computed data operation so that you don't have to actually copy the data, which is a boom for enterprises. We also actually built multitenancy so those enterprises can run multiple work loads on the same data and Ramesh will tell you in a second here, in the healthcare enterprise, the multitenancy is such a very important element. And finally, we also actually contributed to many open source technologies including, we have a project called KubeDirector which is actually is our own Kubernetes and how to run stateful workloads on Kubernetes. which we have actually very happy to see that people like, customers like Ramesh are using the BlueData. >> Sounds like quite a journey and obviously you've intercepted companies like the advisory board company. So Ramesh, a lot of enterprises have mastered or you know, gotten, understood how to create data lakes with a dupe but then found that they still weren't able to connect to some of the outcomes that they saw. Is that the experience that you had. >> Right, to be precise, that is one of the kind of problems we have. It's not just the data lake that we need to be able to do the workflows or other things, but we also, being a traditional company, being in the business for a long time, we have a lot of data assets that are not part of this data lake. We're finding it hard to, how do we get the data, getting them and putting them in a data lake is a duplication of work. We were looking for some kind of solutions that will help us to gather the benefits of leaving the data alone but still be able to get into it. >> This is where (mumbles). >> This is where we were looking for things and then I was lucky and fortunate to run into Kumar and his crew in one of the Hadoop conferences and then they demonstrated the way it can be done so immediately hit upon, it's a big hit with us and then we went back and then did a POC, very quickly adapt to the technology and that is also one of the benefits of corrupting this technology is the level of contrary memorization they are doing, it is helping me to address many needs. My data analyst, the data engineers and the data scientists so I'm able to serve all of them which otherwise wouldn't be possible for me with just this plain very (mumbles). >> So it sounds as though the partnership with BlueData has allowed you to focus on activities and problems and challenges above the technology so that you can actually start bringing data science, business objectives and infrastructure people together. Have I got that right? >> Absolutely. So BlueData is helping me to tie them all together and provide an excess value to my business. We being in the healthcare, the importance is we need to be able to look at the large data sets for a period of time in order to figure out how a patient's health journey is happening. That is very important so that we can figure out the ways and means in which we can lower the cost of health care and also provide insights to the physician, they can help get people better at health. >> So we're getting great outcomes today especially around, as you said that patient journey where all the constituents can get access to those insights without necessarily having to learn a whole bunch of new infrastructure stuff but presumably you need more. We're talking about a new world that you mentioned before upfront, talking about a new world, AI, ML, a lot of changes. A lot of our enterprise customers are telling us it's especially important that they find companies that not only deliver something today but demonstrate a commitment to sustain that value delivery process especially as the whole analytics world evolves. Are you experiencing that as well? >> Yes, we are experiencing and one of the great advantage of the platform, BlueData platform that gave me this ability to, I had the new functionality, be it the TensorFlow, be it the H2O, be it the heart studio, anything that I needed, I call them, they give me the images that are plug-and-play, just put them and all the prompting is practically transparent to nobody need to know how it is achieved. Now, in order to get to the next level of the predictive and prescriptive analytics, it is not just you having the data, you need to be able to have your curated data asset set process on top of a platform that will help you to get the data scientists to make you. One of the biggest challenges that are scientist is not able to get their hands on data. BlueData platform gives me the ability to do it and ensure all the security meets and all the compliances with the various other regulated compliances we need to make. >> Kamar, congratulations. >> Thank you. >> Sounds like you have a happy customer. >> Thank you. >> One of the challenges that every entrepreneur faces is how did you scale the business. So talk to us about where you are in the decisions that you made recently to achieve that. >> As an entrepreneur, when you start a company, odds are against you, right? You're always worried about it, right. You make so many sacrifices, yourself and your team and all that but the the customer is the king. The most important thing for us to find satisfied customers like Rameshan so we were very happy and BlueData was very successful in finding that customer because i think as you pointed out, as Ramesh pointed out, we provide that clean solution for the customer but as you go through this journey as a co-founder and CEO, you always worry about how do you scale to the next level. So we had partnerships with many companies including HPE and we found when this opportunity came in front of me with myself and my board, we saw this opportunity of combining the forces of BlueData satisfied customers and innovative technology and the team with the HPs brand name, their world-class service, their investment in R&D and they have a very long, large list of enterprise customers. We think putting these two things together provides that next journey in the BlueData's innovation and BlueData's customers. >> Excellent, so once again Kumar Sreekanti, co-founder and CEO of BlueData and Ramesh Thyagarajan who is the executive director of the advisory board company and part of Optum, I want to thank both of you for being on theCUBE. >> Thank you >> Thank you, great to be here. >> Now let's hear a little bit more about how this notion of bringing BlueData and HPE together is generating new classes of value that are making things happen today but are also gonna make things happen for customers in the future and to do that we've got Dave Velante who's with Silicon Angle Wiki Bond joined by Patrick Osbourne who's with HPE in our Marlborough studio so Dave over to you. >> Thanks Peter. We're here with Patrick Osbourne, the vice president and general manager of big data and analytics at Hewlett Packard Enterprise. Patrick, thanks for coming on. >> Thanks for having us. >> So we heard from Kumar, let's hear from you. Why did HPE purchase, acquire BlueData? >> So if you think about it from three angles. Platform, people and customers, right. Great platform, built for scale addressing a number of these new workloads and big data analytics and certainly AI, the people that they have are amazing, right, great engineering team, awesome customer success team, team of data scientists, right. So you know, all the folks that have some really, really great knowledge in this space so they're gonna be a great addition to HPE and also on the customer side, great logos, major fortune five customers in the financial services vertical, healthcare, pharma, manufacturing so a huge opportunity for us to scale that within HP context. >> Okay, so talk about how it fits into your strategy, specifically what are you gonna do with it? What are the priorities, can you share some roadmap? >> Yeah, so you take a look at HPE strategy. We talk about hybrid cloud and specifically edge to core to cloud and the common theme that runs through that is data, data-driven enterprises. So for us we see BlueData, Epic platform as a way to you know, help our customers quickly deploy these new mode to applications that are fueling their digital transformation. So we have some great plans. We're gonna certainly invest in all the functions, right. So we're gonna do a force multiplier on not only on product engineering and product delivery but also go to market and customer success. We're gonna come out in our business day one with some really good reference architectures, with some of our partners like Cloud Era, H2O, we've got some very scalable building block architectures to marry up the BlueData platform with our Apollo systems for those of you have seen that in the market, we've got our Elastic platform for analytics for customers who run these workloads, now you'd be able to virtualize those in containers and we'll have you know, we're gonna be building out a big services practice in this area. So a lot of customers often talk to us about, we don't have the people to do this, right. So we're gonna bring those people to you as HPE through Point Next, advisory services, implementation, ongoing help with customers. So it's going to be a really fantastic start. >> Apollo, as you mentioned Apollo. I think of Apollo sometimes as HPC high performance computing and we've had a lot of discussion about how that's sort of seeping in to mainstream, is that what you're seeing? >> Yeah absolutely, I mean we know that a lot of our customers have traditional workloads, you know, they're on the path to almost completely virtualizing those, right, but where a lot of the innovation is going on right now is in this mode two world, right. So your big data and analytics pipeline is getting longer, you're introducing new experiences on top of your product and that's fueling you know, essentially commercial HPC and now that folks are using techniques like AI and modeling inference to make those services more scalable, more automated, we're starting to bringing these more of these platforms, these scalable architectures like Apollo. >> So it sounds like your roadmap has a lot of integration plans across the HPE portfolio. We certainly saw that with Nimble, but BlueData was working with a lot of different companies, its software, is the plan to remain open or is this an HPE thing? >> Yeah, we absolutely want to be open. So we know that we have lots of customers that choose, so the HP is all about hybrid cloud, right and that has a couple different implications. We want to talk about your choice of on-prem versus off-prem so BlueData has a great capability to run some of these workloads. It essentially allows you to do separation of compute and storage, right in the world of AI and analytics we can run it off-prem as well in the public cloud but then we also have choice for customers, you know, any customer's private cloud. So that means they want to run on other infrastructure besides HPE, we're gonna support that, we have existing customers that do that. We're also gonna provide infrastructure that marries the software and the hardware together with frameworks like Info Site that we feel will be a you know, much better experience for the customers but we'll absolutely be open and absolutely have choice. >> All right, what about the business impact to take the customer perspective, what can they expect? >> So I think from a customer perspective, we're really just looking to accelerate deployment of AI in the enterprise, right and that has a lot of implications for us. We're gonna have very scalable infrastructure for them, we're gonna be really focused on this very dynamic AI and ML application ecosystems through partnerships and support within the BlueData platform. We want to provide a SAS experience, right. So whether that's GPUs or accelerators as a service, analytics as a service, we really want to fuel innovation as a service. We want to empower those data scientists there, those are they're really hard to find you know, they're really hard to retain within your organization so we want to unlock all that capability and really just we want to focus on innovation of the customers. >> Yeah, and they spend a lot of time wrangling data so you're really going to simplify that with the cloud (mumbles). Patrick thank you, I appreciate it. >> Thank you very much. >> Alright Peter, back to you in Palo Alto. >> And welcome back, I'm Peter Burris and we've been talking a lot in the industry about how new tooling, new processes can achieve new classes of analytics, AI and ML outcomes within a business but if you don't get the people side of that right, you're not going to achieve the full range of benefits that you might get out of your investments. Now to talk a little bit about how important the data science practitioner is in this equation, we've got two great guests with us. Nanda Vijaydev is the chief data scientists of BlueData. Welcome to theCUBE. >> Thank you Peter, happy to be here. >> Ingrid Burton is the CMO and business leader at H2O.AI, Ingrid, welcome to the CUBE. >> Thank you so much for having us. >> So Nanda Vijaydev, let's start with you. Again, having a nice platform, very, very important but how does that turn into making the data science practitioner's life easier so they can deliver more business value. >> Yeah thank you, it's a great question. I think end of the day for a data scientist, what's most important is, did you understand the question that somebody asked you and what is expected of you when you deliver something and then you go about finding, what do I need for them, I need data, I need systems and you know, I need to work with people, the experts in the process to make sure that the hypothesis I'm doing is structured in a nice way where it is testable, it's modular and I have you know, a way for them to go back to show my results and keep doing this in an iterative manner. That's the biggest thing because the satisfaction for a data scientist is when you actually take this and make use of it, put it in production, right. To make this whole thing easier, we definitely need some way of bringing it all together. That's really where, especially compared to the traditional data science where everything was monolithic, it was one system, there was a very set way of doing things but now it is not so you know, with the growing types of data, with the growing types of computation algorithms that's available, there's a lot of opportunity and at the same time there is a lot of uncertainty. So it's really about putting that structure and it's really making sure you get the best of everything and still deliver the results, that is the focus that all data scientists strive for. >> And especially you wanted, the data scientists wants to operate in the world of uncertainty related to the business question and reducing uncertainty and not deal with the underlying some uncertainty associated with the infrastructure. >> Absolutely, absolutely you know, as a data scientist a lot of time used to spend in the past about where is the data, then the question was, what data do you want and give it to you because the data always came in a nice structured, row-column format, it had already lost a lot of context of what we had to look for. So it is really not about you know, getting the you know, it's really not about going back to systems that are pre-built or pre-processed, it's getting access to that real, raw data. It's getting access to the information as it came so you can actually make the best judgment of how to go forward with it. >> So you describe the world with business, technology and data science practitioners are working together but let's face it, there's an enormous amount of change in the industry and quite frankly, a deficit of expertise and I think that requires new types of partnerships, new types of collaboration, a real (mumbles) approach and Ingrid, I want to talk about what H2O.AI is doing as a partner of BlueData, HPE to ensure that you're complementing these skills in pursuit or in service to the customer's objectives. >> Absolutely, thank you for that. So as Nanda described, you know, data scientists want to get to answers and what we do at H2O.AI is we provide the algorithms, the platforms for data scientist to be successful. So when they want to try and solve a problem, they need to work with their business leaders, they need to work with IT and they actually don't want to do all the heavy lifting, they want to solve that problem. So what we do is we do automatic machine learning platforms, we do that with optimizing algorithms and doing all the kind of, a lot of the heavy lifting that novice data scientists need and help expert data scientists as well. I talk about it as algorithms to answers and actually solving business problems with predictions and that's what machine learning is really all about but really what we're seeing in the industry right now and BlueData is a great example of kind of taking away some of the hard stuff away from a data scientist and making them successful. So working with BlueData and HPE, making us together really solve the problems that businesses are looking for, it's really transformative and we've been through like the digital transformation journey, all of us have been through that. We are now what I would term an AI transformation of sorts and businesses are going to the next step. They had their data, they got their data, infrastructure is kind of seamlessly working together, the clusters and containerization that's very important. Now what we're trying to do is get to the answers and using automatic machine learning platforms is probably the best way forward. >> That's still hard stuff but we're trying to get rid of data science practitioners, focusing on hard stuff that doesn't directly deliver value. >> It doesn't deliver anything for them, right. They shouldn't have to worry about the infrastructure, they should worry about getting the answers to the business problems they've been asked to solve. >> So let's talk a little bit about some of the new business problems that are going to be able to be solved by these kinds of partnerships between BlueData and H2O.AI. Start, Nanda, what do you, what gets you excited when we think about the new types of business problems that customers are gonna be able to solve. >> Yeah, I think it is really you know, the question that comes to you is not filtered through someone else's lens, right. Someone is trying an optimization problem, someone is trying to do a new product discovery so all this is based on a combination of both data-driven and evidence-based, right. For us as a data scientist, what excites me is that I have the flexibility now that I can choose the best of the breed technologies. I should not be restricted to what is given to me by an IT organization or something like that but at the same time, in an organization, for things to work, there has to be some level of control. So it is really having this type of environments or having some platforms where some, there is a team that can work on the control aspect but as a data scientist, I don't have to worry about it. I have my flexibility of tools of choice that I can use. At the same time, when you talk about data, security is a big deal in companies and a lot of times data scientists don't get access to data because of the layers and layers of security that they have to go through, right. So the excitement of the opportunity for me is if someone else takes care of the problem you know, just tell me where is the source of data that I can go to, don't filter the data for me you know, don't already structure the data for me but just tell me it's an approved source, right then it gives me more flexibility to actually go and take that information and build. So the having those controls taken care of well before I get into the picture as a data scientist, it makes it extremely easy for us to focus on you know, to her point, focus on the problem, right, focus on accessing the best of the breed technology and you know, give back and have that interaction with the business users on an ongoing basis. >> So especially focus on, so speed to value so that you're not messing around with a bunch of underlying infrastructure, governance remaining in place so that you know what are the appropriate limits of using the data with security that is embedded within that entire model without removing fidelity out of the quality of data. >> Absolutely. >> Would you agree with those? >> I totally agree with all the points that she brought up and we have joint customers in the market today, they're solving very complex problems. We have customers in financial services, joint customers there. We have customers in healthcare that are really trying to solve today's business problems and these are everything from, how do I give new credit to somebody? How do I know what next product to give them? How do I know what customer recommendations can I make next? Why did that customer churn? How do I reach new people? How do I do drug discovery? How do I give a patient a better prescription? How do I pinpoint disease than when I couldn't have seen it before? Now we have all that data that's available and it's very rich and data is a team sport. It takes data scientists, it takes business leaders and it takes IT to make it all work together and together the two companies are really working to solve problems that our customers are facing, working with our customers because they have the intellectual knowledge of what their problems are. We are providing the tools to help them solve those problems. >> Fantastic conversation about what is necessary to ensure that the data science practitioner remains at the center and is the ultimate test of whether or not these systems and these capabilities are working for business. Nanda Vijaydev, chief data scientist of BlueData, Ingrid Burton CMO and business leader, H2O.AI, thank you very much for being on theCUBE. >> Thank you. >> Thank you so much. >> So let's now spend some time talking about how ultimately, all of this comes together and what you're going to do as you participate in the crowd chat. To do that let me throw it back to Dave Velante in our Marlborough studios. >> We're back with Patrick Osbourne, alright Patrick, let's wrap up here and summarize. We heard how you're gonna help data science teams, right. >> Yup, speed, agility, time to value. >> Alright and I know a bunch of folks at BlueData, the engineering team is very, very strong so you picked up a good asset there. >> Yeah, it means amazing technology, the founders have a long lineage of software development and adoption in the market so we're just gonna, we're gonna invested them and let them loose. >> And then we heard they're sort of better together story from you, you got a roadmap, you're making some investments here, as I heard. >> Yeah, I mean so if we're really focused on hybrid cloud and we want to have all these as a services experience, whether it's through Green Lake or providing innovation, AI, GPUs as a service is something that we're gonna be you know, continuing to provide our customers as we move along. >> Okay and then we heard the data science angle and the data science community and the partner angle, that's exciting. >> Yeah, I mean, I think it's two approaches as well too. We have data scientists, right. So we're gonna bring that capability to bear whether it's through the product experience or through a professional services organization and then number two, you know, this is a very dynamic ecosystem from an application standpoint. There's commercial applications, there's certainly open source and we're gonna bring a fully vetted, full stack experience for our customers that they can feel confident in this you know, it's a very dynamic space. >> Excellent, well thank you very much. >> Thank you. Alright, now it's your turn. Go into the crowd chat and start talking. Ask questions, we're gonna have polls, we've got experts in there so let's crouch chat.