>>Hello everyone and welcome back to Motor City, Michigan. We're live from the Cube and my name is Savannah Peterson. Joined this afternoon with my co-host John Ferer. John, how you doing? Doing >>Great. This next segment's gonna be awesome about application modernization, scaling pluses. This is what's gonna, how are the next generation software revolution? It's gonna be >>Fun. You know, it's kind of been a theme of our day today is scale. And when we think about the complex orchestration platform that is Kubernetes, everyone wants to scale faster, quicker, more efficiently, and our guests are here to tell us all about that. Please welcome to Char and Andy, thank you so much for being here with us. You were on the Red Hat OpenShift team. Yeah. I suspect most of our audience is familiar, but just in case, let's give 'em a quick one-liner pitch so everyone's on the same page. Tell us about OpenShift. >>I, I'll take that one. OpenShift is our ES platform is our ES distribution. You can consume it as a self-managed platform or you can consume it as a managed service on on public clouds. And so we just call it all OpenShift. So it's basically Kubernetes, but you know, with a CNCF ecosystem around it to make things more easier. So maybe there's two >>Lights. So what does being at coupon mean for you? How does it feel to be here? What's your initial takes? >>Exciting. I'm having a fantastic time. I haven't been to coupon since San Diego, so it's great to be back in person and see old friends, make new friends, have hallway conversations. It's, it's great as an engineer trying to work in this ecosystem, just being able to, to be in the same place with these folks. >>And you gotta ask, before we came on camera, you're like, this is like my sixth co con. We were like, we're seven, you know, But that's a lot of co coupons. It >>Is, yes. I mean, so what, >>Yes. >>Take us status >>For sure. Where we are now. Compare and contrast co. Your first co con, just scope it out. What's the magnitude of change? If you had to put a pin on that, because there's a lot of new people coming in, they might not have seen where it's come from and how we got here is maybe not how we're gonna get to the next >>Level. I've seen it grow tremendously since the first one I went to, which I think was Austin several years ago. And what's great is seeing lots of new people interested in contributing and also seeing end users who are trying to figure out the best way to take advantage of this great ecosystem that we have. >>Awesome. And the project management side, you get the keys to the Kingdom with Red Hat OpenShift, which has been successful. Congratulations by the way. Thank you. We watched that grow and really position right on the wave. It's going great. What's the update on on the product? Kind of, you're in a good, good position right now. Yeah, >>No, we we're feeling good about it. It's all about our customers. Obviously the fact that, you know, we have thousands of customers using OpenShift as the cloud native platform, the container platform. We're very excited. The great thing about them is that, I mean you can go to like OpenShift Commons is kind of a user group that we run on the first day, like on Tuesday we ran. I mean you should see the number of just case studies that our customers went through there, you know? And it is fantastic to see that. I mean it's across so many different industries, across so many different use cases, which is very exciting. >>One of the things we've been reporting here in the Qla scene before, but here more important is just that if you take digital transformation to the, to its conclusion, the IT department and developers, they're not a department to serve the business. They are the business. Yes. That means that the developers are deciding things. Yeah. And running the business. Prove their code. Yeah. Okay. If that's, if that takes place, you gonna have scale. And we also said on many cubes, certainly at Red Hat Summit and other ones, the clouds are distributed computer, it's distributed computing. So you guys are focusing on this project, Andy, that you're working on kcp. >>Yes. >>Which is, I won't platform Kubernetes platform for >>Control >>Planes. Control planes. Yes. Take us through, what's the focus on why is that important and why is that relate to the mission of developers being in charge and large scale? >>Sure. So a lot of times when people are interested in developing on Kubernetes and running workloads, they need a cluster of course. And those are not cheap. It takes time, it takes money, it takes resources to get them. And so we're trying to make that faster and easier for, for end users and everybody involved. So with kcp, we've been able to take what looks like one normal Kubernetes and partition it. And so everybody gets a slice of it. You're an administrator in your little slice and you don't have to ask for permission to install new APIs and they don't conflict with anybody else's APIs. So we're really just trying to make it super fast and make it super flexible. So everybody is their own admin. >>So the developer basically looks at it as a resource blob. They can do whatever they want, but it's shared and provisioned. >>Yes. One option. It's like, it's like they have their own cluster, but you don't have to go through the process of actually provisioning a full >>Cluster. And what's the alternative? What's the what's, what's the, what's the benefit and what was the alternative to >>This? So the alternative, you spin up a full cluster, which you know, maybe that's three control plane nodes, you've got multiple workers, you've got a bunch of virtual machines or bare metal, or maybe you take, >>How much time does that take? Just ballpark. >>Anywhere from five minutes to an hour you can use cloud services. Yeah. Gke, E Ks and so on. >>Keep banging away. You're configuring. Yeah. >>Those are faster. Yeah. But it's still like, you still have to wait for that to happen and it costs money to do all of that too. >>Absolutely. And it's complex. Why do something that's been done, if there's a tool that can get you a couple steps down the path, which makes a ton of sense. Something that we think a lot when we're talking about scale. You mentioned earlier, Tohar, when we were chatting before the cams were alive, scale means a lot of different things. Can you dig in there a little bit? >>Yeah, I >>Mean, so when, when >>We talk about scale, >>We are talking about from a user perspective, we are talking about, you know, there are more users, there are more applications, there are more workloads, there are more services being run on Kubernetes now, right? So, and OpenShift. So, so that's one dimension of this scale. The other dimension of the scale is how do you manage all the underlying infrastructure, the clusters, the name spaces, and all the observability data, et cetera. So that's at least two levels of scale. And then obviously there's a third level of scale, which is, you know, there is scale across not just different clouds, but also from cloud to the edge. So there is that dimension of scale. So there are several dimensions of this scale. And the one that again, we are focused on here really is about, you know, this, the first one that I talk about is a user. And when I say user, it could be a developer, it could be an application architect, or it could be an application owner who wants to develop Kubernetes applications for Kubernetes and wants to publish those APIs, if you will, and make it discoverable and then somebody consumes it. So that's the scale we are talking about >>Here. What are some of the enterprise, you guys have a lot of customers, we've talked to you guys before many, many times and other subjects, Red Hat, I mean you guys have all the customers. Yeah. Enterprise, they've been there, done that. And you know, they're, they're savvy. Yeah. But the cloud is a whole nother ballgame. What are they thinking about? What's the psychology of the customer right now? Because now they have a lot of choices. Okay, we get it, we're gonna re-platform refactor apps, we'll keep some legacy on premises for whatever reasons. But cloud pretty much is gonna be the game. What's the mindset right now of the customer base? Where are they in their, in their psych? Not the executive, but more of the the operators or the developers? >>Yeah, so I mean, first of all, different customers are at different levels of maturity, I would say in this. They're all on a journey how I like to describe it. And in this journey, I mean, I see a customers who are really tip of the sphere. You know, they have containerized everything. They're cloud native, you know, they use best of tools, I mean automation, you know, complete automation, you know, quick deployment of applications and all, and life cycle of applications, et cetera. So that, that's kind of one end of this spectrum >>Advanced. Then >>The advances, you know, and, and I, you know, I don't, I don't have any specific numbers here, but I'd say there are quite a few of them. And we see that. And then there is kind of the middle who are, I would say, who are familiar with containers. They know what app modernization, what a cloud application means. They might have tried a few. So they are in the journey. They are kind of, they want to get there. They have some other kind of other issues, organizational or talent and so, so on and so forth. Kinds of issues to get there. And then there are definitely the quota, what I would call the lag arts still. And there's lots of them. But I think, you know, Covid has certainly accelerated a lot of that. I hear that. And there is definitely, you know, more, the psychology is definitely more towards what I would say public cloud. But I think where we are early also in the other trend that I see is kind of okay, public cloud great, right? So people are going there, but then there is the so-called edge also. Yeah. That is for various regions. You, you gotta have a kind of a regional presence, a edge presence. And that's kind of the next kind of thing taking off here. And we can talk more >>About it. Yeah, let's talk about that a little bit because I, as you know, as we know, we're very excited about Edge here at the Cube. Yeah. What types of trends are you seeing? Is that space emerges a little bit more firmly? >>Yeah, so I mean it's, I mean, so we, when we talk about Edge, you're talking about, you could talk about Edge as a, as a retail, I mean locations, right? >>Could be so many things edges everywhere. Everywhere, right? It's all around us. Quite literally. Even on the >>Scale. Exactly. In space too. You could, I mean, in fact you mentioned space. I was, I was going to >>Kinda, it's this world, >>My space actually Kubernetes and OpenShift running in space, believe it or not, you know, So, so that's the edge, right? So we have Industrial Edge, we have Telco Edge, we have a 5g, then we have, you know, automotive edge now and, and, and retail edge and, and more, right? So, and space, you know, So it's very exciting there. So the reason I tag back to that question that you asked earlier is that that's where customers are. So cloud is one thing, but now they gotta also think about how do I, whatever I do in the cloud, how do I bring it to the edge? Because that's where my end users are, my customers are, and my data is, right? So that's the, >>And I think Kubernetes has brought that attention to the laggards. We had the Laed Martin on yesterday, which is an incredible real example of Kubernetes at the edge. It's just incredible story. We covered it also wrote a story about it. So compelling. Cuz it makes it real. Yes. And Kubernetes is real. So then the question is developer productivity, okay, Things are starting to settle in. We've got KCP scaling clusters, things are happening. What about the tool chains? And how do I develop now I got scale of development, more code coming in. I mean, we are speculating that in the future there's so much code in open source that no one has to write code anymore. Yeah. At some point it's like this gluing things together. So the developers need to be productive. How are we gonna scale the developer equation and eliminate the, the complexity of tool chains and environments. Web assembly is super hyped up at this show. I don't know why, but sounds good. No one, no one can tell me why, but I can kind of connect the dots. But this is a big thing. >>Yeah. And it's fitting that you ask about like no code. So we've been working with our friends at Cross Plain and have integrated with kcp the ability to no code, take a whole bunch of configuration and say, I want a database. I want to be a, a provider of databases. I'm in an IT department, there's a bunch of developers, they don't wanna have to write code to create databases. So I can just take, take my configuration and make it available to them. And through some super cool new easy to use tools that we have as a developer, you can just say, please give me a database and you don't have to write any code. I don't have to write any code to maintain that database. I'm actually using community tooling out there to get that spun up. So there's a lot of opportunities out there. So >>That's ease of use check. What about a large enterprise that's got multiple tool chains and you start having security issues. Does that disrupt the tool chain capability? Like there's all those now weird examples emerging, not weird, but like real plumbing challenges. How do you guys see that evolving with Red >>Hat and Yeah, I mean, I mean, talking about that, right? The software, secure software supply chain is a huge concern for everyone after, especially some of the things that have happened in the past few >>Years. Massive team here at the show. Yeah. And just within the community, we're all a little more aware, I think, even than we were before. >>Before. Yeah. Yeah. And, and I think the, so to step back, I mean from, so, so it's not just even about, you know, run time vulnerability scanning, Oh, that's important, but that's not enough, right? So we are talking about, okay, how did that container, or how did that workload get there? What is that workload? What's the prominence of this workload? How did it get created? What is in it? You know, and what, what are, how do I make, make sure that there are no unsafe attack s there. And so that's the software supply chain. And where Red Hat is very heavily invested. And as you know, with re we kind of have roots in secure operating system. And rel one of the reasons why Rel, which is the foundation of everything we do at Red Hat, is because of security. So an OpenShift has always been secure out of the box with things like scc, rollbacks access control, we, which we added very early in the product. >>And now if you kind of bring that forward, you know, now we are talking about the complete software supply chain security. And this is really about right how from the moment the, the, the developer rights code and checks it into a gateway repository from there on, how do you build it? How do you secure it at each step of the process, how do you sign it? And we are investing and contributing to the community with things like cosign and six store, which is six store project. And so that secures the supply chain. And then you can use things like algo cd and then finally we can do it, deploy it onto the cluster itself. And then we have things like acs, which can do vulnerability scanning, which is a container security platform. >>I wanna thank you guys for coming on. I know Savannah's probably got a last question, but my last question is, could you guys each take a minute to answer why has Kubernetes been so successful today? What, what was the magic of Kubernetes that made it successful? Was it because no one forced it? Yes. Was it lightweight? Was it good timing, right place at the right time community? What's the main reason that Kubernetes is enabling all this, all this shift and goodness that's coming together, kind of defacto unifies people, the stacks, almost middleware markets coming around. Again, not to use that term middleware, but it feels like it's just about to explode. Yeah. Why is this so successful? I, >>I think, I mean, the shortest answer that I can give there really is, you know, as you heard the term, I think Satya Nala from Microsoft has used it. I don't know if he was the original person who pointed, but every company wants to be a software company or is a software company now. And that means that they want to develop stuff fast. They want to develop stuff at scale and develop at, in a cloud native way, right? You know, with the cloud. So that's, and, and Kubernetes came at the right time to address the cloud problem, especially across not just one public cloud or two public clouds, but across a whole bunch of public clouds and infrastructure as, and what we call the hybrid clouds. I think the ES is really exploded because of hybrid cloud, the need for hybrid cloud. >>And what's your take on the, the magic Kubernetes? What made it, what's making it so successful? >>I would agree also that it came about at the right time, but I would add that it has great extensibility and as developers we take it advantage of that every single day. And I think that the, the patterns that we use for developing are very consistent. And I think that consistency that came with Kubernetes, just, you have so many people who are familiar with it and so they can follow the same patterns, implement things similarly, and it's just a good fit for the way that we want to get our software out there and have, and have things operate. >>Keep it simple, stupid almost is that acronym, but the consistency and the de facto alignment Yes. Behind it just created a community. So, so then the question is, are the developers now setting the standards? That seems like that's the new way, right? I mean, >>I'd like to think so. >>So I mean hybrid, you, you're touching everything at scale and you also have mini shift as well, right? Which is taking a super macro micro shift. You ma micro shift. Oh yeah, yeah, exactly. It is a micro shift. That is, that is fantastic. There isn't a base you don't cover. You've spoken a lot about community and both of you have, and serving the community as well as your engagement with them from a, I mean, it's given that you're both leaders stepping back, how, how Community First is Red Hat and OpenShift as an organization when it comes to building the next products and, and developing. >>I'll take and, and I'm sure Andy is actually the community, so I'm sure he'll want to a lot of it. But I mean, right from the start, we have roots in open source. I'll keep it, you know, and, and, and certainly with es we were one of the original contributors to Kubernetes other than Google. So in some ways we think about as co-creators of es, they love that. And then, yeah, then we have added a lot of things in conjunction with the, I I talk about like SCC for Secure, which has become part security right now, which the community, we added things like our back and other what we thought were enterprise features needed because we actually wanted to build a product out of it and sell it to customers where our customers are enterprises. So we have worked with the community. Sometimes we have been ahead of the community and we have convinced the community. Sometimes the community has been ahead of us for other reasons. So it's been a great collaboration, which is I think the right thing to do. But Andy, as I said, >>Is the community well set too? Are well said. >>Yes, I agree with all of that. I spend most of my days thinking about how to interact with the community and engage with them. So the work that we're doing on kcp, we want it to be a community project and we want to involve as many people as we can. So it is a heavy focus for me and my team. And yeah, we we do >>It all the time. How's it going? How's the project going? You feel good >>About it? I do. It is, it started as an experiment or set of prototypes and has grown leaps and bounds from it's roots and it's, it's fantastic. Yeah. >>Controlled planes are hot data planes control planes. >>I >>Know, I love it. Making things work together horizontally scalable. Yeah. Sounds like cloud cloud native. >>Yeah. I mean, just to add to it, there are a couple of talks that on KCP at Con that our colleagues s Stephan Schemanski has, and I, I, I would urge people who have listening, if they have, just Google it, if you will, and you'll get them. And those are really awesome talks to get more about >>It. Oh yeah, no, and you can tell on GitHub that KCP really is a community project and how many people are participating. It's always fun to watch the action live to. Sure. Andy, thank you so much for being here with us, John. Wonderful questions this afternoon. And thank all of you for tuning in and listening to us here on the Cube Live from Detroit. I'm Savannah Peterson. Look forward to seeing you again very soon.

(upbeat music) >> We're back. You're watching theCUBE's coverage of Red Hat Summit 2022 here in the Seaport in Boston. I'm Dave Vellante with my co-host, Paul Gillin. Justin Boitano is here. He's the Vice President of Enterprise and Edge Computing at NVIDIA. Maybe you've heard of him. And Tushar Katarki who's the Director of Product Management at Red Hat. Gentlemen, welcome to theCUBE, good to see you. >> Thank you. >> Great to be here, thanks >> Justin, you are a keynote this morning. You got interviewed and shared your thoughts on AI. You encourage people to got to think bigger on AI. I know it's kind of self-serving but why? Why should we think bigger? >> When you think of AI, I mean, it's a monumental change. It's going to affect every industry. And so when we think of AI, you step back, you're challenging companies to build intelligence and AI factories, and factories that can produce intelligence. And so it, you know, forces you to rethink how you build data centers, how you build applications. It's a very data centric process where you're bringing in, you know, an exponential amount of data. You have to label that data. You got to train a model. You got to test the model to make sure that it's accurate and delivers business value. Then you push it into production, it's going to generate more data, and you kind of work through that cycle over and over and over. So, you know, just as Red Hat talks about, you know, CI/CD of applications, we're talking about CI/CD of the AI model itself, right? So it becomes a continuous improvement of AI models in production which is a big, big business transformation. >> Yeah, Chris Wright was talking about basically take your typical application development, you know, pipeline, and life cycle, and apply that type of thinking to AI. I was saying those two worlds have to come together. Actually, you know, the application stack and the data stack including AI need to come together. What's the role of Red Hat? What's your sort of posture on AI? Where do you fit with OpenShift? >> Yeah, so we're really excited about AI. I mean, a lot of our customers obviously are looking to take that data and make meaning out of it using AI is definitely a big important tool. And OpenShift, and our approach to Open Hybrid Cloud really forms a successful platform to base all your AI journey on with the partners such as NVIDIA whom we are working very closely with. And so the idea really is as Justin was saying, you know, the end to end, when you think about life of a model, you've got data, you mine that data, you create models, you deploy it into production. That whole thing, what we call CI/CD, as he was saying DevOps, DevSecOps, and the hybrid cloud that Red Hat has been talking about, although with OpenShift as the center forms a good basis for that. >> So somebody said the other day, I'm going to ask you, is INVIDIA a hardware company or a software company? >> We are a company that people know for our hardware but, you know, predominantly now we're a software company. And that's what we were on stage talking about. I mean, ultimately, a lot of these customers know that they've got to embark on this journey to apply AI, to transform their business with it. It's such a big competitive advantage going into, you know, the next decade. And so the faster they get ahead of it, the more they're going to win, right? But some of them, they're just not really sure how to get going. And so a lot of this is we want to lower the barrier to entry. We built this program, we call it Launchpad to basically make it so they get instant access to the servers, the AI servers, with OpenShift, with the MLOps tooling, with example applications. And then we walk them through examples like how do you build a chatbot? How do you build a vision system for quality control? How do you build a price recommendation model? And they can do hands on labs and walk out of, you know, Launchpad with all the software they need, I'll say the blueprint for building their application. They've got a way to have the software and containers supported in production, and they know the blueprint for the infrastructure and operating that a scale with OpenShift. So more and more, you know, to come back to your question is we're focused on the software layers and making that easy to help, you know, either enterprises build their apps or work with our ecosystem and developers to buy, you know, solutions off the shelf. >> On the harbor side though, I mean, clearly NVIDIA has prospered on the backs of GPUs, as the engines of AI development. Is that how it's going to be for the foreseeable future? Will GPUs continue to be core to building and training AI models or do you see something more specific to AI workloads? >> Yeah, I mean, it's a good question. So I think for the next decade, well, plus, I mean not forever, we're going to always monetize hardware. It's a big, you know, market opportunity. I mean, Jensen talks about a $100 billion, you know, market opportunity for NVIDIA just on hardware. It's probably another a $100 billion opportunity on the software. So the reality is we're getting going on the software side, so it's still kind of early days, but that's, you know, a big area of growth for us in the future and we're making big investments in that area. On the hardware side, and in the data center, you know, the reality is since Moore's law has ended, acceleration is really the thing that's going to advance all data centers. So I think in the future, every server will have GPUs, every server will have DPUs, and we can talk a bit about what DPUs are. And so there's really kind of three primary processors that have to be there to form the foundation of the enterprise data center in the future. >> Did you bring up an interesting point about DPUs and MPUs, and sort of the variations of GPUs that are coming about? Do you see those different PU types continuing to proliferate? >> Oh, absolutely. I mean, we've done a bunch of work with Red Hat, and we've got a, I'll say a beta of OpenShift 4.10 that now supports DPUs as the, I'll call it the control plane like software defined networking offload in the data center. So it takes all the software defined networking off of CPUs. When everybody talks about, I'll call it software defined, you know, networking and core data centers, you can think of that as just a CPU tax up to this point. So what's nice is it's all moving over to DPU to, you know, offload and isolate it from the x86 cores. It increases security of data center. It improves the throughput of your data center. And so, yeah, DPUs, we see everybody copying that model. And, you know to give credit where credit is due, I think, you know, companies like AWS, you know, they bought Annapurna, they turned it into Nitro which is the foundation of their data centers. And everybody wants the, I'll call it democratized version of that to run their data centers. And so every financial institution and bank around the world sees the value of this technology, but running in their data centers. >> Hey, everybody needs a Nitro. I've written about it. It's Annapurna acquisition, 350 million. I mean, peanuts in the grand scheme of things. It's interesting, you said Moore's law is dead. You know, we have that conversation all the time. Pat Gelsinger promised that Moore's law is alive and well. But the interesting thing is when you look at the numbers, that's, you know, Moore's law, we all know it, doubling of the transistor densities every 18 to 24 months. Let's say that, that promise that he made is true. What I think the industry maybe doesn't appreciate, I'm sure you do, being in NVIDIA, when you combine what you were just saying, the CPU, the GPU, Paul, the MPU, accelerators, all the XPUs, you're talking about, I mean, look at Apple with the M1, I mean 6X in 15 months versus doubling every 18 to 24. The A15 is probably averaging over the last five years, a 110% performance improvement each year versus the historical Moore's law which is 40%. It's probably down to the low 30s now. So it's a completely different world that we're entering now. And the new applications are going to be developed on these capabilities. It's just not your general purpose market anymore. From an application development standpoint, what does that mean to the world? >> Yeah, I mean, yeah, it is a great point. I mean, from an application, I mean first of all, I mean, just talk about AI. I mean, they are all very compute intensive. They're data intensive. And I mean to move data focus so much in to compute and crunch those numbers. I mean, I'd say you need all the PUs that you mentioned in the world. And also there are other concerns that will augment that, right? Like we want to, you know, security is so important so we want to secure everything. Cryptography is going to take off to new levels, you know, that we are talking about, for example, in the case of DPUs, we are talking about, you know, can that be used to offload your encryption and firewalling, and so on and so forth. So I think there are a lot of opportunities even from an application point of view to take of this capacity. So I'd say we've never run out of the need for PUs if you will. >> So is OpenShift the layer that's going to simplify all that for the developer. >> That's right. You know, so one of the things that we worked with NVIDIA, and in fact was we developed this concept of an operator for GPUs, but you can use that pattern for any of the PUs. And so the idea really is that, how do you, yeah-- (all giggle) >> That's a new term. >> Yeah, it's a new term. (all giggle) >> XPUs. >> XPUs, yeah. And so that pattern becomes very easy for GPUs or any other such accelerators to be easily added as a capacity. And for the Kubernetes scaler to understand that there is that capacity so that an application which says that I want to run on a GPU then it becomes very easy for it to run on that GPU. And so that's the abstraction to your point about how we are making that happen. >> And to add to this. So the operator model, it's this, you know, open source model that does the orchestration. So Kubernetes will say, oh, there's a GPU in that node, let me run the operator, and it installs our entire run time. And our run time now, you know, it's got a MIG configuration utility. It's got the driver. It's got, you know, telemetry and metering of the actual GPU and the workload, you know, along with a bunch of other components, right? They get installed in that Kubernetes cluster. So instead of somebody trying to chase down all the little pieces and parts, it just happens automatically in seconds. We've extended the operator model to DPUs and networking cards as well, and we have all of those in the operator hub. So for somebody that's running OpenShift in their data centers, it's really simple to, you know, turn on Node Feature Discovery, you point to the operators. And when you see new accelerated nodes, the entire run time is automatically installed for you. So it really makes, you know, GPUs and our networking, our advanced networking capabilities really first class citizens in the data center. >> So you can kind of connect the dots and see how NVIDIA and the Red Hat partnership are sort of aiming at the enterprise. I mean, NVIDIA, obviously, they got the AI piece. I always thought maybe 25% of the compute cycles in the data center were wasted doing storage offloads or networking offload, security. I think Jensen says it's 30%, probably a better number than I have. But so now you're seeing a lot of new innovation in new hardware devices that are attacking that with alternative processors. And then my question is, what about the edge? Is that a blue field out at the edge? What does that look like to NVIDIA and where does OpenShift play? >> Yeah, so when we talk about the edge, we always going to start talking about like which edge are we talking about 'cause it's everything outside the core data center. I mean, some of the trends that we see with regard to the edges is, you know, when you get to the far edge, it's single nodes. You don't have the guards, gates, and guns protection of the data center. So you start having to worry about physical security of the hardware. So you can imagine there's really stringent requirements on protecting the intellectual property of the AI model itself. You spend millions of dollars to build it. If I push that out to an edge data center, how do I make sure that that's fully protected? And that's the area that we just announced a new processor that we call Hopper H100. It supports confidential computing so that you can basically ensure that model is always encrypted in system memory across the bus, of the PCI bus to the GPU, and it's run in a confidential way on the GPU. So you're protecting your data which is your model plus the data flowing through it, you know, in transit, wallet stored, and then in use. So that really adds to that edge security model. >> I wanted to ask you about the cloud, correct me if I'm wrong. But it seems to me that that AI workloads have been slower than most to make their way to the cloud. There are a lot of concerns about data transfer capacity and even cost. Do you see that? First of all, do you agree with that? And secondly, is that going to change in the short-term? >> Yeah, so I think there's different classes of problems. So we'll take, there's some companies where their data's generated in the cloud and we see a ton of, I'll say, adoption of AI by cloud service providers, right? Recommendation engines, translation engines, conversational AI services, that all the clouds are building. That's all, you know, our processors. There's also problems that enterprises have where now I'm trying to take some of these automation capabilities but I'm trying to create an intelligent factory where I want to, you know, merge kind of AI with the physical world. And that really has to run at the edge 'cause there's too much data being generated by cameras to bring that all the way back into the cloud. So, you know, I think we're seeing mass adoption in the cloud today. I think at the edge a lot of businesses are trying to understand how do I deploy that reliably and securely and scale it. So I do think, you know, there's different problems that are going to run in different places, and ultimately we want to help anybody apply AI where the business is generating the data. >> So obviously very memory intensive applications as well. We've seen you, NVIDIA, architecturally kind of move away from the traditional, you know, x86 approach, take better advantage of memories where obviously you have relationships with Arm. So you've got a very diverse set of capabilities. And then all these other components that come into use, to just be a kind of x86 centric world. And now it's all these other supporting components to support these new applications and it's... How should we think about the future? >> Yeah, I mean, it's very exciting for sure, right? Like, you know, the future, the data is out there at the edge, the data can be in the data center. And so we are trying to weave a hybrid cloud footprint that spans that. I mean, you heard Paul come here, talk about it. But, you know, we've talked about it for some time now. And so the paradigm really that is, that be it an application, and when I say application, it could be even an AI model as a service. It can think about that as an application. How does an application span that entire paradigm from the core to the edge and beyond is where the future is. And, of course, there's a lot of technical challenges, you know, for us to get there. And I think partnerships like this are going to help us and our customers to get there. So the world is very exciting. You know, I'm very bullish on how this will play out, right? >> Justin, we'll give you the last word, closing thoughts. >> Well, you know, I think a lot of this is like I said, it's how do we reduce the complexity for enterprises to get started which is why Launchpad is so fundamental. It gives, you know, access to the entire stack instantly with like hands on curated labs for both IT and data scientists. So they can, again, walk out with the blueprints they need to set this up and, you know, start on a successful AI journey. >> Just a position, is Launchpad more of a Sandbox, more of a school, or more of an actual development environment. >> Yeah, think of it as it's, again, it's really for trial, like hands on labs to help people learn all the foundational skills they need to like build an AI practice and get it into production. And again, it's like, you don't need to go champion to your executive team that you need access to expensive infrastructure and, you know, and bring in Red Hat to set up OpenShift. Everything's there for you so you can instantly get started. Do kind of a pilot project and then use that to explain to your executive team everything that you need to then go do to get this into production and drive business value for the company. >> All right, great stuff, guys. Thanks so much for coming to theCUBE. >> Yeah, thanks. >> Thank you for having us. >> All right, thank you for watching. Keep it right there, Dave Vellante and Paul Gillin. We'll be back right after this short break at the Red Hat Summit 2022. (upbeat music)

>> Announcer: From around the globe, it's theCUBE with coverage of KubeCon + CloudNativeCon Europe 2020 Virtual brought to you by Red Hat, the Cloud Native Computing Foundation and Ecosystem partners. >> Welcome back I'm Stu Miniman, this is theCUBE's coverage of KubeCon + CloudNativeCon Europe 2020, the virtual event. Of course, when we talk about Cloud Native we talk about Kubernetes there's a lot that's happening to modernize the infrastructure but a very important thing that we're going to talk about today is also what's happening up the stack, what sits on top of it and some of the new use cases and applications that are enabled by all of this modern environment and for that we're going to talk about artificial intelligence and machine learning or AI and ML as we tend to talk in the industry, so happy to welcome to the program. We have two first time guests joining us from Red Hat. First of all, we have Abhinav Joshi and Tushar Katarki they are both senior managers, part of the OpenShift group. Abhinav is in the product marketing and Tushar is in product management. Abhinav and Tushar thank you so much for joining us. >> Thanks a lot, Stu, we're glad to be here. >> Thanks Stu and glad to be here at KubeCon. >> All right, so Abhinav I mentioned in the intro here, modernization of the infrastructure is awesome but really it's an enabler. We know... I'm an infrastructure person the whole reason we have infrastructure is to be able to drive those applications, interact with my data and the like and of course, AI and ML are exciting a lot going on there but can also be challenging. So, Abhinav if I could start with you bring us inside your customers that you're talking to, what are the challenges, the opportunities? What are they seeing in this space? Maybe what's been holding them back from really unlocking the value that is expected? >> Yup, that's a very good question to kick off the conversation. So what we are seeing as an organization they typically face a lot of challenges when they're trying to build an AI/ML environment, right? And the first one is like a talent shortage. There is a limited amount of the AI, ML expertise in the market and especially the data scientists that are responsible for building out the machine learning and the deep learning models. So yeah, it's hard to find them and to be able to retain them and also other talents like a data engineer or app DevOps folks as well and the lack of talent can actually stall the project. And the second key challenge that we see is the lack of the readily usable data. So the businesses collect a lot of data but they must find the right data and make it ready for the data scientists to be able to build out, to be able to test and train the machine learning models. If you don't have the right kind of data to the predictions that your model is going to do in the real world is only going to be so good. So that becomes a challenge as well, to be able to find and be able to wrangle the right kind of data. And the third key challenge that we see is the lack of the rapid availability of the compute infrastructure, the data and machine learning, and the app dev tools for the various personas like a data scientist or data engineer, the software developers and so on that can also slow down the project, right? Because if all your teams are waiting on the infrastructure and the tooling of their choice to be provisioned on a recurring basis and they don't get it in a timely manner, it can stall the projects. And then the next one is the lack of collaboration. So you have all these kinds of teams that are involved in the AI project, and they have to collaborate with each other because the work one of the team does has a dependency on a different team like say for example, the data scientists are responsible for building the machine learning models and then what they have to do is they have to work with the app dev teams to make sure the models get integrated as part of the app dev processes and ultimately rolled out into the production. So if all these teams are operating in say silos and there is lack of collaboration between the teams, so this can stall the projects as well. And finally, what we see is the data scientists they typically start the machine learning modeling on their individual PCs or laptops and they don't focus on the operational aspects of the solution. So what this means is when the IT teams have to roll all this out into a production kind of deployment, so they get challenged to take all the work that has been done by the individuals and then be able to make sense out of it, be able to make sure that it can be seamlessly brought up in a production environment in a consistent way, be it on-premises, be it in the cloud or be it say at the edge. So these are some of the key challenges that we see that the organizations are facing, as they say try to take the AI projects from pilot to production. >> Well, some of those things seem like repetition of what we've had in the past. Obviously silos have been the bane of IT moving forward and of course, for many years we've been talking about that gap between developers and what's happening in the operation side. So Tushar, help us connect the dots, containers, Kubernetes, the whole DevOps movement. How is this setting us up to actually be successful for solutions like AI and ML? >> Sure Stu I mean, in fact you said it right like in the world of software, in the world of microservices, in the world of app modernization, in the world of DevOps in the past 10, 15 years, but we have seen this evolution revolution happen with containers and Kubernetes driving more DevOps behavior, driving more agile behavior so this in fact is what we are trying to say here can ease up the cable to EIML also. So the various containers, Kubernetes, DevOps and OpenShift for software development is directly applicable for AI projects to make them move agile, to get them into production, to make them more valuable to organization so that they can realize the full potential of AI. We already touched upon a few personas so it's useful to think about who the users are, who the personas are. Abhinav I talked about data scientists these are the people who obviously do the machine learning itself, do the modeling. Then there are data engineers who do the plumbing who provide the essential data. Data is so essential to machine learning and deep learning and so there are data engineers that are app developers who in some ways will then use the output of what the data scientists have produced in terms of models and then incorporate them into services and of course, none of these things are purely cast in stone there's a lot of overlap you could find that data scientists are app developers as well, you'll see some of app developers being data scientist later data engineer. So it's a continuum rather than strict boundaries, but regardless what all of these personas groups of people need or experts need is self service to that preferred tools and compute and storage resources to be productive and then let's not forget the IT, engineering and operations teams that need to make all this happen in an easy, reliable, available manner and something that is really safe and secure. So containers help you, they help you quickly and easily deploy a broad set of machine learning tools, data tools across the cloud, the hybrid cloud from data center to public cloud to the edge in a very consistent way. Teams can therefore alternatively modify, change a shared container images, machine learning models with (indistinct) and track changes. And this could be applicable to both containers as well as to the data by the way and be transparent and transparency helps in collaboration but also it could help with the regulatory reasons later on in the process. And then with containers because of the inherent processes solution, resource control and protection from threat they can also be very secure. Now, Kubernetes takes it to the next level first of all, it forms a cluster of all your compute and data resources, and it helps you to run your containerized tools and whatever you develop on them in a consistent way with access to these shared compute and centralized compute and storage and networking resources from the data center, the edge or the public cloud. They provide things like resource management, workload scheduling, multi-tendency controls so that you can be a proper neighbors if you will, and quota enforcement right? Now that's Kubernetes now if you want to up level it further if you want to enhance what Kubernetes offers then you go into how do you write applications? How do you actually make those models into services? And that's where... and how do you lifecycle them? And that's sort of the power of Helm and for the more Kubernetes operators really comes into the picture and while Helm helps in installing some of this for a complete life cycle experience. A kubernetes operator is the way to go and they simplify the acceleration and deployment and life cycle management from end-to-end of your entire AI, ML tool chain. So all in all organizations therefore you'll see that they need to dial up and define models rapidly just like applications that's how they get ready out of it quickly. There is a lack of collaboration across teams as Abhinav pointed out earlier, as you noticed that has happened still in the world of software also. So we're talking about how do you bring those best practices here to AI, ML. DevOps approaches for machine learning operations or many analysts and others have started calling as MLOps. So how do you kind of bring DevOps to machine learning, and fosters better collaboration between teams, application developers and IT operations and create this feedback loop so that the time to production and the ability to take more machine learning into production and ML-powered applications into production increase is significant. So that's kind of the, where I wanted shine the light on what you were referring to earlier, Stu. >> All right, Abhinav of course one of the good things about OpenShift is you have quite a lot of customers that have deployed the solution over the years, bring us inside some of your customers what are they doing for AI, ML and help us understand really what differentiates OpenShift in the marketplace for this solution set. >> Yeah, absolutely that's a very good question as well and we're seeing a lot of traction in terms of all kinds of industries, right? Be it the financial services like healthcare, automotive, insurance, oil and gas, manufacturing and so on. For a wide variety of use cases and what we are seeing is at the end of the day like all these deployments are focused on helping improve the customer experience, be able to automate the business processes and then be able to help them increase the revenue, serve their customers better, and also be able to save costs. If you go to openshift.com/ai-ml it's got like a lot of customer stories in there but today I will not touch on three of the customers we have in terms of the different industries. The first one is like Royal Bank of Canada. So they are a top global financial institution based out of Canada and they have more than 17 million clients globally. So they recently announced that they build out an AI-powered private cloud platform that was based on OpenShift as well as the NVIDIA DGX AI compute system and this whole solution is actually helping them to transform the customer banking experience by being able to deliver an AI-powered intelligent apps and also at the same time being able to improve the operational efficiency of their organization. And now with this kind of a solution, what they're able to do is they're able to run thousands of simulations and be able to analyze millions of data points in a fraction of time as compared to the solution that they had before. Yeah, so like a lot of great work going on there but now the next one is the ETCA healthcare. So like ETCA is one of the leading healthcare providers in the country and they're based out of the Nashville, Tennessee. And they have more than 184 hospitals as well as more than 2,000 sites of care in the U.S. as well as in the UK. So what they did was they developed a very innovative machine learning power data platform on top of our OpenShift to help save lives. The first use case was to help with the early detection of sepsis like it's a life-threatening condition and then more recently they've been able to use OpenShift in the same kind of stack to be able to roll out the new applications that are powered by machine learning and deep learning let say to help them fight COVID-19. And recently they did a webinar as well that had all the details on the challenges they had like how did they go about it? Like the people, process and technology and then what the outcomes are. And we are proud to be a partner in the solution to help with such a noble cause. And the third example I want to share here is the BMW group and our partner DXC Technology what they've done is they've actually developed a very high performing data-driven data platform, a development platform based on OpenShift to be able to analyze the massive amount of data from the test fleet, the data and the speed of the say to help speed up the autonomous driving initiatives. And what they've also done is they've redesigned the connected drive capability that they have on top of OpenShift that's actually helping them provide various use cases to help improve the customer experience. With the customers and all of the customers are able to leverage a lot of different value-add services directly from within the car, their own cars. And then like last year at the Red Hat Summit they had a keynote as well and then this year at Summit, they were one of the Innovation Award winners. And we have a lot more stories but these are the three that I thought are actually compelling that I should talk about here on theCUBE. >> Yeah Abhinav just a quick follow up for you. One of the things of course we're looking at in 2020 is how has the COVID-19 pandemic, people working from home how has that impacted projects? I have to think that AI and ML are one of those projects that take a little bit longer to deploy, is it something that you see are they accelerating it? Are they putting on pause or are new project kicking off? Anything you can share from customers you're hearing right now as to the impact that they're seeing this year? >> Yeah what we are seeing is that the customers are now even more keen to be able to roll out the digital (indistinct) but we see a lot of customers are now on the accelerated timeline to be able to say complete the AI, ML project. So yeah, it's picking up a lot of momentum and we talk to a lot of analyst as well and they are reporting the same thing as well. But there is the interest that is actually like ramping up on the AI, ML projects like across their customer base. So yeah it's the right time to be looking at the innovation services that it can help improve the customer experience in the new virtual world that we live in now about COVID-19. >> All right, Tushar you mentioned that there's a few projects involved and of course we know at this conference there's a very large ecosystem. Red Hat is a strong contributor to many, many open source projects. Give us a little bit of a view as to in the AI, ML space who's involved, which pieces are important and how Red Hat looks at this entire ecosystem? >> Thank you, Stu so as you know technology partnerships and the power of open is really what is driving the technology world these days in any ways and particularly in the AI ecosystem. And that is mainly because one of the machine learning is in a bootstrap in the past 10 years or so and a lot of that emerging technology to take advantage of the emerging data as well as compute power has been built on the kind of the Linux ecosystem with openness and languages like popular languages like Python, et cetera. And so what you... and of course tons of technology based in Java but the point really here is that the ecosystem plays a big role and open plays a big role and that's kind of Red Hat's best cup of tea, if you will. And that really has plays a leadership role in the open ecosystem so if we take your question and kind of put it into two parts, what is the... what we are doing in the community and then what we are doing in terms of partnerships themselves, commercial partnerships, technology partnerships we'll take it one step at a time. In terms of the community itself, if you step back to the three years, we worked with other vendors and users, including Google and NVIDIA and H2O and other Seldon, et cetera, and both startups and big companies to develop this Kubeflow ecosystem. The Kubeflow is upstream community that is focused on developing MLOps as we talked about earlier end-to-end machine learning on top of Kubernetes. So Kubeflow right now is in 1.0 it happened a few months ago now it's actually at 1.1 you'll see that coupon here and then so that's the Kubeflow community in addition to that we are augmenting that with the Open Data Hub community which is something that extends the capabilities of the Kubeflow community to also add some of the data pipelining stuff and some of the data stuff that I talked about and forms a reference architecture on how to run some of this on top of OpenShift. So the Open Data Hub community also has a great way of including partners from a technology partnership perspective and then tie that with something that I mentioned earlier, which is the idea of Kubernetes operators. Now, if you take a step back as I mentioned earlier, Kubernetes operators help manage the life cycle of the entire application or containerized application including not only the configuration on day one but also day two activities like update and backups, restore et cetera whatever the application needs. Afford proper functioning that a "operator" needs for it to make sure so anyways, the Kubernetes operators ecosystem is also flourishing and we haven't faced that with the OperatorHub.io which is a community marketplace if you will, I don't call it marketplace a community hub because it's just comprised of community operators. So the Open Data Hub actually can take community operators and can show you how to run that on top of OpenShift and manage the life cycle. Now that's the reference architecture. Now, the other aspect of it really is as I mentioned earlier is the commercial aspect of it. It is from a customer point of view, how do I get certified, supported software? And to that extent, what we have is at the top of the... from a user experience point of view, we have certified operators and certified applications from the AI, ML, ISV community in the Red Hat marketplace. And from the Red Hat marketplace is where it becomes easy for end users to easily deploy these ISVs and manage the complete life cycle as I said. Some of the examples of these kinds of ISVs include startups like H2O although H2O is kind of well known in certain sectors PerceptiLabs, Cnvrg, Seldon, Starburst et cetera and then on the other side, we do have other big giants also in this which includes partnerships with NVIDIA, Cloudera et cetera that we have announced, including our also SaaS I got to mention. So anyways these provide... create that rich ecosystem for data scientists to take advantage of. A TEDx Summit back in April, we along with Cloudera, SaaS Anaconda showcased a live demo that shows all these things to working together on top of OpenShift with this operator kind of idea that I talked about. So I welcome people to go and take a look the openshift.com/ai-ml that Abhinav already referenced should have a link to that it take a simple Google search might download if you need some of that, but anyways and the other part of it is really our work with the hardware OEMs right? And so obviously NVIDIA GPUs is obviously hardware, and that accelerations is really important in this world but we are also working with other OEM partners like HP and Dell to produce this accelerated AI platform that turnkey solutions to run your data-- to create this open AI platform for "private cloud" or the data center. The other thing obviously is IBM, IBM Cloud Pak for Data is based on OpenShift that has been around for some time and is seeing very good traction, if you think about a very turnkey solution, IBM Cloud Pak is definitely kind of well ahead in that and then finally Red Hat is about driving innovation in the open-source community. So, as I said earlier, we are doing the Open Data Hub which that reference architecture that showcases a combination of upstream open source projects and all these ISV ecosystems coming together. So I welcome you to take a look at that at opendatahub.io So I think that would be kind of the some total of how we are not only doing open and community building but also doing certifications and providing to our customers that assurance that they can run these tools in production with the help of a rich certified ecosystem. >> And customer is always key to us so that's the other thing that the goal here is to provide our customers with a choice, right? They can go with open source or they can go with a commercial solution as well. So you want to make sure that they get the best in cloud experience on top of our OpenShift and our broader portfolio as well. >> All right great, great note to end on, Abhinav thank you so much and Tushar great to see the maturation in this space, such an important use case. Really appreciate you sharing this with theCUBE and Kubecon community. >> Thank you, Stu. >> Thank you, Stu. >> Okay thank you and thanks a lot and have a great rest of the show. Thanks everyone, stay safe. >> Thanks you and stay with us for a lot more coverage from KubeCon + CloudNativeCon Europe 2020, the virtual edition I'm Stu Miniman and thank you as always for watching theCUBE. (soft upbeat music plays)

(inspirational music) >> Hi everybody, this is, Dave Vellante, from our Palo Alto Cube studios. Welcome to this Cube conversation with two gentlemen from Datrium. Tushar Agarwal is the Director of Product Management, and Sazzala Reddy is the CTO and co-founder of Datrium. We're going to talk about disaster recovery. Disaster recovery has been a nagging problem for organizations and IT organizations for years. It's complex, it's expensive, it's not necessarily reliable, it's very risky to test, and Datrium has announced a product called CloudShift. Now, Datrium is a company who creates sets of data services, particularly for any cloud, and last year introduced a backup in archiving on AWS. We've written about that, we've profiled that. Gentlemen welcome to the Cube, >> Good to see you. >> Thank you. >> Good to be here. >> Thank you (mumbles). >> So tell us about, CloudShift. >> Yeah, sure, great. So if you kind of step back and look at our journey starting with Cloud DVX, which was what we announced last year, our end goal has been to simplify infrastructure for customers and eliminate any access infrastructure that they need, starting with Cloud DVX, which addressed the backup part of it Where the customers do not need to keep a dedicated off-site backup anymore and extending that with CloudShift, which now brings it to a DR context and makes the economics so phenomenal that they don't need to keep a DR site anymore just waiting for a disaster to happen. So, CloudShift, at very beginnings, is a sort of a multi-year journey where we bring the ability to do workload mobility orchestration across an on-premises DVX system to a DVX running in the cloud, leveraging Cloud DVX backups, so that customers can do just-in-time DR. >> Sazzala, I talked earlier about some of problems with DR, and let's talk about what you see. I mean, I've talked to customers who've set up three sites, put in a fireproof box, I mean all kinds of just really difficult challenges and solutions. What are you seeing in, terms of some of the problems and challenges that customers are facing, and how are you addressing this? >> Yeah, so like you said, I don't think I've heard anybody saying my DR plan is awesome. (Laughing) or it works, or I'm enjoying this thing. It's a very fearful situation because when things go down, that's when everyone is watching you, and then that's when the fear comes in, right? So, we built kind of a. We built our service, CloudShift service. It's very easy to use, firstly, step one. And the reason, the other goals, kind of, so, if you click a button, you want to just (mumbles) to some new place, right? But to make that really work well, what are the customers, I mean, if I was a customer, what would I think about? I want the same experience no matter where I moved, right? But it has to be seamlessly like, you know, I don't have to change my tool sets, I have the same operational consistency, that's got number one, and number two is that, does it really work when I click the button, is it going to work? So if you go to Amazon, it'll convert VMs. That's a different experience completely, right? So how do you make that experience be likely foolproof? It will work fundamentally. So we've done lot of things like no conversion of VMs. And the second one is that we have built-in compliance checks. Every half an hour it checks itself to see that the whole plan is compliant. You know that when actually there is a problem it'll actually, the compliance actually has caught the issues before hand. And the third one is that, you can do schedule testing. That you can set up schedules and say know what test it every month for me. So that you know. And test it, give a report to you saying okay it's all this, all looking good for you. So that's kind of things you maybe do to make sure that it's going to be foolproof, guaranteed DR success when you initially have to hit the button. >> Yeah and just to add to that. I think, if you look at a DR equation for a customer it's really two things. I'm paying a lot for it. What can I do to address that problem? And will it work when I need it to work, right? I think it's really fundamentally those two problems. And cloud gives us a great way to address the cost equation because now you got an infrastructure that is truly on tank, can be truly on demand. And so you don't really keep those resources running unless you have to, unless you have a test event or you have the actual DR event. On the will it work when I want it to work, Cloud has typically had a lot of challenges that's a lot of outline, right? You have VMs that are going from a VMware infrastructure to an Amazon infrastructure which means those washing machines now need to be running in a different format. You don't have a simple, single-user interface to manage those two environments where you have an Amazon console at one end and a VMware recenter on the other. And then thirdly, you have this data mobility problem where you don't have the data going across a consistent, common architecture. And so we sort of solve all these problems collectively by making DR just in time because we only spin up those resources when they need to be there in the cloud. There is no VM conversion because we are building this, leveraging the benefits of VMware Cloud in AWS. There is a common single pane of glass to manage this infrastructure. And there is a tremendous amount of speed in data mobility and a tremendous amount of economics in the way that we store that data in a de-duplicated compressed way all the time so it kind of checks off the cost equation and it checks out the fact that it actually works when it needs to work. >> So, let's unpack that a little bit. So normally what I would have is a remote site and that site has resources there. It's got hardware and software and building and infrastructure hopefully far enough away from whether it's an earthquake zone or a hurricane or whatever it is and it sits there as an underutilized asset. Now maybe there's some other things that I can do with it, but if it's my DR site, it's just sitting there as insurance. >> Right. >> That's one problem. >> The other problem is testing, DR testing is oftentimes very risky. A lot of customers we talk to don't want to test because they might fail over and then they go to fail back and oops, there's a problem. And what am I going to do? Am I going to stop running my business? So maybe talk about how you address some of those challenges. >> So I think yes, that's true. We heard people like spent half a million dollars in testing DR and never be able to come back from it. Like that's a lot of money and a lot of (mumbles) and then you can't come back is a completely different business problem. So you know, more than just having the DR site, there's like expanse and maintenance, but the other problem is that when you add something, new workloads, you have to add more work. It would kind of change. It would kind of beget new licenses, get new new other, like you know more and more things. So all of this actually is a fundamental problem but if you go to the cloud, just-in-time on-demand thing is amazing because you are only paying for the backups which is you need to do. If you cannot lose it, there are backups. You need backups fundamentally to be on another site because if ransomware hits you, you need to be able to go back in time so you need copies of deep copies to be in another place. And so the thing about just-in-time DR is that you pay for the backups, sure. It's very cost-effective with us, but you only pay for the services for running your applications for the two weeks you have a problem and then when you're done with it, you're done with paying that. So it's a difference with paying everyday versus paying for insurance. Sometimes insurance pays for those kind of things. It's very cost effective. >> Okay, so I'm paying Datrium for the service. Okay, I get that. And I'm paying a little bit, let's say, for instance it's running on Amazon, a little bit for S3, got to pay for S3 and I'm only paying for the EC2 resource when I'm using that resource. (crosstalk) It's like serverless for DR. >> It actually goes beyond that, Dave, right? >> Actually I like that word that you used. You should probably use that. >> Absolutely because I think it's not just the EC2 part but if you look at a total cost of ownership equation of a data center, right, you're looking at networking, you're looking at software, you're looking at compute, you're looking at people managing that infrastructure all the time, you're looking at power cooling and so I think by having this just-in-time data center that gets spun up and you have to do nothing, literally, you just have to click a button. That saves you know a tremendous amount. That's a transformational economics situation right there where you can simply go ahead and eliminate a lot of time, a lot of energy, a lot of costs that customers pay and have to deal with to just keep that DR site running across the board. >> Mm hm. >> Let me give one more savings note. So let's say you had 100 terabytes and you failed over, so when you're done with two weeks' testing, only one terabyte changed. Are you going to bring back everything or are you going to bring only one terabyte? It's a fundamental underlying technology thing. If you don't have dedupe over the wire, you'll bring back everything 100 terabytes. You're going to pay for the digress cost and ultimately it'll be too slow for you to bring it all back. So what you really want is underlying technology which has dedupe over the wire. We call it global dedupe that you can only move back what's changed and it's fast. One terabyte moving there is not that bad, right? Otherwise you'd end up moving everything back which is kind of untenable again. So you have to make all these things happen to make DR really successful in the cloud. >> So you're attacking the latency issues. >> Latency and bestly 100 terabyte moving from one place to the other, it'll take a long time because the vanpipe is only that much and you're paying for the egress cost. >> We always joke the smartest people in Silicon Valley are working on solving the speed of light problem. >> That's right so if you look at data, if you're going to move from one place to the other. First of all, data has gravity, it doesn't want to move, right? So that's one fundamental problem. So how do you build a antigravity device to actually fix that problem, right? So if you leap forward, global dedupe is here where you can transfer only what's changed to the other side. That really defeats light speed, right? And then, both ways, moving it here and moving it there. Without having this van deduplication technology, I think you will be paying a significant amount of time and money, so then it becomes untenable. If you can't really move it fast, then it's like people don't do it anymore. >> And in the typical Datrium fashion, it's just there. It just works. (crosstalk) >> I think that's such a good point, Dave, because if you look at traditional DR solutions today, the challenge is that there are a collection of software and services and hardware from multiple vendors. And that's not such a bad thing. I think the challenge that that causes is the fact that you don't have the ability to do an end-to-end, closed loop verification of your DR plan. You know the DR orchestration software does not know whether the VM that I'm supposed to protect actually has a snapshot on the storage array on which its protecting it, right, and so that, in many ways, leads to a lot of risk to customers and it makes the DR plans very fragile because you know, you set a plan on day one and then let's say three months down the line, you know, something got changed in the system and that wasn't caught by the DR orchestration software because it's unlinked. It doesn't have the same visibility into the actual storage system. The advantage we get with the integrated, built-in backup in DR system is that we can actually verify that the virtual machine that you're supposed to protect actually has all the key ingredients that are needed for a successful DR across the stack as well as in target fader ware site. >> It's kind of the perfect use case, a perfect use case for the cloud and I think, you know, there's something even more here is that because of the complexity of the IT infrastructure around DR and the change management challenges that you talked about, the facilities management challenges that all of the sudden an organization becomes, they're in the DR business and they don't want to be in the DR business. (crosstalk) >> Show no value, I mean, really it's not really adding significantly. It's not improving organization. >> That's actually true and I think the way we have tried to tackle that problem, Dave, is kind of going back to the whole premises of this multi-cloud data services. We will make DR, you know, as simple as possible and what we really enable for them to do is to not have to worry about installing any software, not have to worry about upgrading any software, managing any software. It's a, you know, service that they can just enter their DR plans into. It's very intelligent because it's integrated very well with the DVX system. And they can schedule testing. They don't even have to click a button to actually do a plan failover and in case of an actual event, it's just a single click. It's conveniently checked all the time so you kind of take away a lot of the hassles and a lot of the worry and a lot of the risks and make it truly simple, give them a (mumbles) software as a service experience. >> So I'm kind of racking my brain here. Is there anything out there like this that provides an on-demand DR SaaS? >> I don't know of any actually. >> Yeah, I think, so if you you kind of look at the landscape, Sazzala is right, actually there is none and there a few solutions from leading providers that focus on instantiation of a virtual machine on native AWS, but they don't enter the challenge that they have to convert a virtual machine from a VMware virtual machine to an Amazon AMI and that doesn't always work. Secondly, you know, if you run into that kind of a problem, can you really call it true DR because in case of a DR, you want that virtual machine to come up and run and be a valid environment as against just a test-of-use case. >> So the other one is that backup vendors can't do this. Generally, they traditionally can probably, but I think because they are one day behind, they backup once a day, so you can't do DR if you are one day behind. DR wants to be like, okay, I am five minutes behind, I can recover my stuff, right? And then primary vendors like Pure, for example, like whole flash vendors, they focused on just running it, not about backup, but you need the backups to actually make it successful so that you can go back in time if you have ransomware. So you need a combination of both primary and backup and the ability to have it running in the service in the cloud. That's why you need all these pieces to work together. >> So you talked about ransomware a couple of times. Obviously, DR, ransomware, maybe talk a little bit more about some of the other use cases beyond DR. >> So I think that kind of goes back to why we decided to name this feature CloudShift, right? If you think about a traditional DR solution, you would call it something like DR Orchestrator, right, but that's not really the full vision for this product. DR is one of the very important use cases and we talked about how we do that phenomenally well than other solutions out there but what this solution really enables customers to do is actually look at true workload mobility between on-prem and cloud and look at interesting use cases such as ransomware protection. And the reason why we are so great at ransomware protection is because we are an indicated primary and backup from a restart points perspective and in a ransomware situation, you can't really go back to a restart point that's, you know, a day before or two days before. You really want to go down to as many points as you want and because we have this very efficient way of storing these restart points or snapshots in Cloud DVX, you have the ability to instantiate or run a backup which is from sufficiently long time ago, which gives you a great amount of ransomware protection and it's completely isolated from your on-prem copy of that data. >> Let me add one more point to that. So if you just go beyond the DR case, from a developer perspective, right, from a company perspective, developers want a flexible infrastructure to like try new stuff and try new experiments in terms of building new applications for the business, they can try it in the cloud with our platform. And when they're done, for three months, they'll like, you know, have the, because they figured out okay this is how it's going to work, this is how much (mumbles) I need, it's more elastic there. When they're done testing it, whatever they built it, they can click a button with our CloudShift and move it all back on-prem and then now you kind of have it more secure and in an environment you want to. >> Alright, guys, love to see the evolution of your data services, you know, from backup, now DR, other use cases. Congratulations on CloudShift and thanks for explaining it to us. >> Thank you very much. >> Pleasure being here. >> Okay, thanks for watching, everybody. This is Dave Vellante from our Palo Alto Cube studios. We'll see you next time. (inspiring music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering HPE Discover 2017. Brought to you by Hewlett Packard Enterprise. (upbeat techno music) >> Welcome back everyone, we're here live in Las Vegas for theCUBE's exclusive three days of coverage for Hewlett Packard Enterprise's Discover 2017, also known as HPE Discover, I'm Jeff Furrier siliconANGLE, here is my co-host, David Villante with Wikibon.org Our next guests are Jeff Carlat, Senior Director, Solutions Good Market for HPE, Internet of Things and Tushar Halgali, who's the IoT Senior Manager at Deloitte these guys putting together all the solutions. Welcome back to theCube, great to meet you, thanks for joining us. >> Jeff: You bet, it's great to be here. Great to see you guys again. >> So one of the things, actually, digital transformation which is really overblown we all know we are in this digital transformation wave. But the thing that we've been hearing on the queue over the past, I'd say 6 months of event coverage, the consistent theme with digital transformation is business transformation, and really people putting it into action. And that really is whether it's a service provider we've heard from earlier, and also just businesses trying to get their value chains and reconstruct their architectures at a business level but then having their infrastructure be responsive to that. And that's cool, but really IoT has kind of changed the equation, right, that's what you guys are doing so I want just dig right into it, IoT wave that's hitting here. >> Jeff: Right >> John: Your thoughts on the impact to customers in real time to their world. I mean obviously they have refresh cycles they're going through all kinds of infrastructure they had apps, Cloud-native on the horizon, Hybrid Cloud what's the impact to their business? How has IoT changed the game for the customers? >> Jeff: Well I'll start it, you can add on. First off, IoT brings the promise of changing the game, but not everyone is really realizing that yet, first-off because right now there's still many, many business challenges for companies of all sizes. Ya know the lack of internal corporate sponsorship to do a massive transformation and change, or the organization and the culture within. Cause you're talkin' a full life cycle digitization rather than, ya know investing or dropping new applications technology wise we've got problems, IoT represents IT merging with OT, so you've got this partnership and your solutions and offerings need to transcend your core data center and your IT technologies with the traditional operational technologies. You're talking companies that have been, Bosch and National Instruments, and folks that have been in the marketplace for some time so it's harder, it's heavy lifting, and there're limitations in the customer environment around the current IT architecture, so first and foremost to get the benefit, you've got to get them across the chasm to be able to deliver that new transformation. >> John: Tushar, I want you to weigh-in on this because the question also kind of digging in here a bit kind of subtext of the original question, where's the mindset of the customer? Are they having a wake-up call moment, are they beyond that? Where are they in the progress bar, if you will, on the IoT? Yeah, they've had some pre-existing infrastructure, operational technologies, sensors. Is it a wake-up call? Where are they? >> Tushar: Yeah, so I mean I think what is happening really is that a lot of organizations are now beginning to look into business outcomes and what technology does for them, right. A lot of them are saying "Well why should we invest on anything else?" So, companies are becoming really focused on top line growth ya know, bottom line cost optimization, and ultimately margin improvement for their shareholders. So, as industry lines are blurring, as new entrants are coming into markets, and new threats are being created there is more pressure from shareholders to come up with new growth opportunities. IoT as a field is sort of encapsulates, and takes all these different technology domains and puts it all together. Case and example, I mean, since 1970 to 2010 the worldwide productivity for manufacturing was about four percent every year, and then it just dropped to one percent. Now that's a really big deal, right. Manufacturing costs are about 18-20 percent of the costs of goods sold for a manufacturing client, so how do you increase the productivity because any impact on the productivity, or reduced down time for a manufacturing client, not only has cause on revenue but also a lot on the profit margin, right. The same thing around retailers. Because of the online presence, and because of the sales are increasing over there retail margins have reduced from 10.5 percent to about 9 percent. So retailers are asking "Well, how do we increase our sales in the in-store channel?" Where 85 percent of their sales are coming. So IoT is a huge component in delivering that. >> John: You bring up a good point. What I love about the IoT, and some of the stuff you guys are doing, is that it's the confluence of big data meets real infrastructure, and what you're referring to we hear this in the ad business all the time. "I don't know where my spins going." It's an instrumentation game, right. So talk about that impact because now actually not an art it's actually science as well. You can actually instrument it and focus on those areas. >> Tushar: I mean absolutely, just to build on the marketing story that you just talked about, that's a huge piece in retail, right. So if you have a multi-brand retailer you want to be able to not only see what your customers are doing, but also try and monetize the data. So one channel is to look into who your marketers are, advertisers are, and then be able to place the ad at the right place, in the right context with the consumer that you might have in your store. And a lot of this is about in-store data attribution right. What is the ROI that marketers and the advertisers are getting back for the spin that they have. And so ROI with the help of, Beacons and Colts and wifi all these technologies, is able to sort of capture all of that location data the contextual data, the behavioral data of the clients along with wireless infrastructure data. Put it all together and create that picture. >> Jeff: And what I'm seeing customers are kind of one of two camps. Those that understand a grocket, but they don't know where to start. How do I truly start digitizing? Then the other ones are they don't fully realize the value and the necessity to start transforming, or their going to be out of business. >> Tushar: Yep. >> Jeff: I mean go look at a lot of examples, your brick and mortars >> Tushar: Yep. >> Jeff: talk about your retails. I think this is where we're coming together to really deliver and make it easier for those clients... >> John: It's the classic case of early adopters. Believers and non-believers, and the believers kind of go jump in the deep end, waffle around, learn how to swim. And then the non-believers become believers cause they get bitten in the butt with cost >> Jeff: Yeah. >> John: or some sort of impact. >> Jeff: Exactly right. >> Tushar: Or their out of business. >> David: But it's a really hard problem for organizations. So and you mentioned it before, is that companies have to go through their digital transformation, but they have to fund it. And it's hard to fund it if your having to grow your top-line, and cut the cost of your legacy systems. Okay so part of the problem is you talk about digital transformation, it's all about technology, it's all about data certainly IoT plays into that as John pointed out but people really don't understand the value of their data. The accounting industry doesn't recognize value of data on the balance sheet. There's really no standards. People don't know how to monetize data. So how can you guys help customers through those really gnarly problems? Where do you start? >> Tushar: Well I mean what we started with was an industry focused view, right. So Deloitte goes to the market by industry, so let's take retail and manufacturing, whichever the case might be, and what we really are looking into is an industrial digital value chain transformation story. So we'll take the value chain off an industry, break it down into processes, and then break that down further into use cases. We'll look at a use case, look at the value drivers of the use case. See what economic impact, or the business outcomes that might be derived of those use cases. And then when you aggregate all of them it starts creating a shareholder value impact, and that becomes really interesting. So case and example, for a retailer you can look at improving the basket size, or in-store conversion improving the the foot fall traffic. All of that improves the growth, increases the revenue. You talk about asset efficiency or improving the resources or the associates, their utilization to store the supply chain operations improvement. All of that improves the cost optimization and together impacts the margin. So we put that picture together for our clients to see in real economic terms. >> David: And data sits at the center of that analysis, right? >> Tushar: Well, correct. So the enablement of the use cases happen through technology and as the various facets of technology, the ERB system, the CRM the point-of-sales, the Beacons, the wifi all work together. The data generated will create 360 degree views of the customer, which then leads to all of these outcomes. >> John: Tushar talk about the value chain piece on that. Because I think that's indicative of IoT's impact as well as other things that are digitally connected. What is the difference between the digital value chain, in terms of its configuration its value, verus non-digital? How they used to approach it from a management perspective, and obviously digital is a little bit different. Is there any characteristics you can point out that you've seen in your observations, and with your engagement with customers, that jump out? >> Tushar: Sure, I mean the traditional value chain I think is very linear, right. If you take a manufacturing value chain for example a lot of it was let's do R&D, come up with a product, then let's go procure the product, the raw materials. Then make the product, then you ship it, logistics, and then you do after sale services. It's very linear one after the other. With the admit of data and the way you capture at every stage of the value chain. Well different stages now talk to one another. So as a machine is about to break you can create a new order, and then it improves the production. So it's less linear and more interrelated, and so the value chain is no longer very simple it's very complex, but by showing visibility into each stage of the value chain, that's where value created and captured from. >> David: And the data model is very complex, >> Tushar: Absolutely. >> David: Before you've got external data and now you've got a whole new data quality challenge >> Tushar: That's right. >> David: and data access challenge. Okay so back to John's question about where are we on the maturity meter? Is it sort of second inning here or the game is just starting, national anthem? >> Jeff: Well, hey for certain industries I think we're on second inning. You go look at areas like oil and gas, I mean there is a lot of historical work going on around machine learning, AI. Go and look at automotive, autonomous vehicles semi-autonomous vehicles, I think that's advancing and advancing rapidly. But I'll guarantee there are many, many industries that they don't even realize how much data they have. And yes there may be tag in two to three percent of that. This is a new wave. This is a really, really exciting time. >> John: So Jeff, on that point are you finding that, that makes a lot of sense actually if people have existing operational technologies, they have some legacy experience in some systems. It may not be connected to IT so they have some legacy with respect to that piece. >> Jeff: Perfect, perfect example. Part of our joint partnership and the announcement that we're making together around IoT is not only deliver the consulting the advisory services, but we're delivering prepackaged offerings specifically for vertical use margins. Asset maintenance and monitoring, we're coming together, bringing together our edge line capabilities we're bringing together PTC and National Instruments from the center. Bringing all this together in consortium, building an appliance and its going through consulting of nature of proof of concept to show and prove through proof of concepts the value that a customer can achieve by harnessing all that data, and being able to actually drive predictive analytics and then well once they see the benefits of that the value, the proof in the pudding, they will expand that across their entire production line, then its just going to go skyrocket. >> John: Alright talk about the relationship with Deloitte. I'd like you guys to just take us through a day in the life of a use case and how someone would envision and engage with you guys. Obviously Deloitte well known on the services side you guys got great credibility and track record, also with you guys IoT new market, how do you guys engage? What does a joint relationship look like? Take us through an example. >> Jeff: Well I'll start. First off we're building off of twenty years of joint partnership together, and a day in the life is we strategically sit down and we take the assets we can bring to the table as the new HPE, and that spans heavily the infrastructure and some of the support, point next services capability and we bring that in with the capabilities of Deloitte and we build these offerings, and we build a comprehensive program to take it to market, and have those discussions at the right level of the organization and hold their hand through this whole transformation process. Don't worry we got ya covered. We can help you get through this, and we can demonstrate the value on the returns. >> Tushar: So yeah, I'll just build on this. Some of the offerings that we have built together now, so as we get a client who's let's say interested in IoT what we'd actually do is sort of work with them and say let's do an IoT workshop, right. It might be a one day workshop, we might get our industry experts that are very focused on the vertical. We might get our technology experts. We might get our ecosystem partners who are doing startups and things of that sort, so they kind of know what is going on in the marketplace. We're together then we'll sit down we'll figure out what's a value chain transformation story. What are the things, let's say a manufacturing client just take for example, needs to do to go from a modern factory to connected factory to a smart factory to do that manufacturing transformation story. What are those 50 60 use cases that they need to go through. And out of that what are the one or two use cases that they need to do today that'll deliver near term tangible value. So for those 50-60 let's create the business case that delivers the enterprise shareholder return. Today what do they need to do to get that quick win. Take those two-three use cases, the offerings that Jeff spoke about, let's take those offerings and within 8 weeks let's deliver a proof of concept that shows the client I can take one of your assets, connect them, get the data out, show the inside, and then create the roadmap for scaling it out to make it a reality. >> Jeff: Start small, think big, and scale fast. That's what we say. >> John: Alright that's a great point I'm glad you brought that up because I want to ask the tough question. Cause this is the bottom line, we hear a lot of customers through our research Wikibon team, and we get a lot of "There's tons of barriers in front of me." So I want to ask you what are the barriers and how do they get over those obstacles, but also privately a lot of CXOs say to us, "Look it, this is like a four year sports contract, if I'm not up and running in four years, I'm out of job." So the notion of bringing the consultant, and HP, and we're going to do a focus group, and we're going to lay this out. The old days, back in the early ERP days, those time cycles were 18 months just to get going, and do the organizational transformation. They need proof on the table immediately. >> Tushar: That's right. >> John: So the Ford CEO was replaced, not sayin that was because of this, but people have short tenure, they need to see results immediately. >> Tushar: That's right. >> John: So the psychology of the pressure, with the work that needs to get done are two huge issues. What are the obstacles? And then the psychology of showing the results immediately. >> Tushar: I think in terms of the sort of business challenges we have a lot of centers around leadership and sponsorship. Do you have a tech focused culture in the company? Right. Is there collaboration between business and IT? Do you have expertise for IoT within the business, or within the enterprise and outside? Right. Those are some very basic, it's people, people, people all the time. From a technology stand point a lot of this is around the whole IT OT convergence piece of things. Right, it's this very complex domain. Nobody has all the knowledge base, so how do you get that to work? And traditionally IT hasn't played well with OT and vice versa. So how do you get that? Standards are evolving around security, privacy things of that sort, so how do you keep up with that? And finally, there are so many different solutions. How you do make sense out of that? Procurement is painful, right. And that's where some of the solutions like Jeff talked about were made. The solutions were at the procurement cycle becomes really simple. >> John: So tons of choices out there, >> Tushar: Right, >> John: That's an obstacle init of itself. >> Tushar: Exactly >> Jeff: Yeah >> Tushar: So how do we deal with these challenges, and how do we jumpstart the story. If you take the principle of agile and software development that's what we have pulled into our offerings, right. Instead of spending three, four, six months in trying to figure out what the universe is going to look like, and how things will change, it's not like that. We've taken sprint approaches to our delivery, like I shared earlier it's about that one day IoT journey workshop, quickly get that done, get it out of the way. >> John: Not a lot of waterfall, which that prolongs that organizational transformation piece >> Tushar: Correct. And then its constant recalibration, that's what we want to focus on. Let's show some quick wins in eight week increments. >> Jeff: And I'll guarantee as we are showing the quick wins in certain verticals, their dropping like dominoes because when they see their competition all of sudden gain efficiencies and providing greater experience for their clients or their customers, believe me everyone wants a piece of that. >> John: Bottom line there's obstacles to point. Move fast, start small, think big, move fast, I love that. And again there's a psychology out there it's real, and being agile, the waterfall takes too long. Alright guys thanks so much for sharing the inside of IoT, congratulations. Event here, what do you think, what's going on for you guys real quick we'll end the segment, final words. >> Jeff: Final words? >> John: 2017 Discover, what's your take away so far? >> Tushar: Well my take away is we are just at the cusp here. In IoT we are still in the, I'd call it the crawl stages of this. IoT's going to be huge, very exciting times coming, and it's going to impact every industry. >> Jeff: Yeah my parting word, I love to see the partner first mentality we have in here. The fact that we are here with all SIs our OT partners. I also love to see we are now building and designing innovations, such as the HP Edgeline Conversion systems from the ground up, specifically for IoT, same thing with Aruba Portfolios. We got a great set of tools and a great set of partners to work with. >> John: We didn't bring up Aruba, we had a big conversation on that earlier. Tushar, Jeff thanks so much for sharing the insight. Internet of Things, Industrial of Things. This theCube, the video of things here at HPE Discover 2017 I'm John Furrier, Dave Villante. We'll be back with more coverage after this short break. Stay with us. (upbeat techno music)