>>The cube presents HPE discover 2022 brought to you by HPE. >>Hey everyone. Welcome back to the Cube's coverage of HPE. Discover 22 live from the Sans expo center in Las Vegas. Lisa Martin, here with Dave Velante. We've an alumni back joining us to talk about high performance computing and AI, Justin ARD, EVP, and general manager of HPC and AI at HPE. That's a mouthful. Welcome back. >>It is no, it's great to be back and wow, it's great to be back in person as well. >>It's it's life changing to be back in person. The keynote this morning was great. The Dave was saying the energy that he's seen is probably the most out of, of any discover that you've been at and we've been feeling that and it's only day one. >>Yeah, I, I, I agree. And I think it's a Testament to the places in the market that we're leading the innovation we're driving. I mean, obviously the leadership in HPE GreenLake and, and enabling as a service for, for every customer, not just those in the public cloud, providing that, that capability. And then obviously what we're doing at HPC and AI breaking, uh, you know, breaking records and, uh, advancing the industry. So >>I just saw the Q2 numbers, nice revenue growth there for HPC and AI. Talk to us about the lay of the land what's going on, what are customers excited about? >>Yeah. You know, it's, it's a, it's a really fascinating time in this, in this business because we're, you know, we just, we just delivered the first, the world's first exo scale system. Right. And that's, uh, you know, that's a huge milestone for our industry, a breakthrough, you know, 13 years ago, we did the first Petta scale system. Now we're doing the first exo scale system, huge advance forward. But what's exciting too, is these systems are enabling new applications, new workloads, breakthroughs in AI, the beginning of being able to do proper quantum simulations, which will lead us to a much, you know, brighter future with quantum and then actually better and more granular models, which have the ability to really change the world. >>I was telling Lisa that during the pandemic we did, uh, exo scale day, it was like this co yep. You know, produce event. And we weren't quite at exo scale yet, but we could see it coming. And so it was great to see in frontier and, and the keynote you guys broke through that, is that a natural evolution of HPC or is this we entering a new era? >>Yeah, I, I think it's a new era and I think it's a new era for a few reasons because that, that breakthrough really, it starts to enable a different class of use cases. And it's combined with the fact that I think, you know, you look at where the rest of the enterprises data set has gone, right? We've got a lot more data, a lot more visibility to data. Um, but we don't know how to use it. And now with this computing power, we can start to create new insights and new applications. And so I think this is gonna be a path to making HPC more broadly available. And of course it introduces AI models at scale. And that's, that's really critical cause AI is a buzzword. I mean, lots of people say they're doing AI, but when you know, to, to build true intelligence, not, not effectively, you know, a machine that learns data and then can only handle that data, but to build true intelligence where you've got something that can continue to learn and understand and grow and evolve, you need this class of system. And so I think we're at, we're at the forefront of a lot of exciting innovation. H how, >>In terms of innovation, how important is it that you're able to combine as a service and HPC? Uh, what does that mean for, for customers for experimentation and innovation? >>You know, a couple things I've been, I've actually been talking to customers about that over the last day and a half. And, you know, one is, um, you think about these, these systems are, they're very large and, and they're, they're pretty, you know, pretty big bets if you're a customer. So getting early access to them right, is, is really key, making sure that they're, they can migrate their software, their applications, again, in our space, most of our applications are custom built, whether you're a, you know, a government or a private sector company, that's using these systems, you're, you're doing these are pretty specialized. So getting that early access is important. And then actually what we're seeing is, uh, with the growth and explosion of insight that we can enable. And some of the diversity of, you know, new, um, accelerator partners and new processors that are on the market is actually the attraction of diversity. And so making things available where customers can use multimodal systems. And we've seen that in this era, like our customer Lumi and Finland number, the number three fastest system in the world actually has two sides to their system. So there's a compute side, dense compute side and a dense accelerator side. >>So Oak Ridge national labs was on stage with Antonio this morning, the, the talking about frontier, the frontier system, I thought what a great name, very apropo, but it was also just named the number one to the super computing, top 500. That's a pretty big accomplishment. Talk about the impact of what that really means. >>Yeah. I, I think a couple things, first of all, uh, anytime you have this breakthrough of number one, you see a massive acceleration of applications. And if you really, if you look at the applications that were built, because when the us department of energy funded these Exoscale products or platforms, they also funded app a set of applications. And so it's the ability to get more accurate earth models for long term climate science. It's the ability to model the electrical grid and understand better how to build resiliency into that grid. His ability is, um, Dr. Te Rossi talked about a progressing, you know, cancer research and cancer breakthroughs. I mean, there's so many benefits to the world that we can bring with these systems. That's one element. The other big part of this breakthrough is actually a list, a lesser known list from the top 500 called the green 500. >>And that's where we measure performance over power consumption. And what's a huge breakthrough in this system. Is that not only to frontier debut at number one on the top 500, it's actually got the top two spots, uh, because it's got a small test system that also is up there, but it's got the top two spots on the green 500 and that's actually a real huge breakthrough because now we're doing a ton more computation at far lesser power. And that's really important cuz you think about these systems, ultimately you can, you can't, you know, continue to consume power linearly with scaling up performance. There's I mean, there's a huge issue on our impact on our environment, but it's the impact to the power grid. It's the impact to heat dissipation. There's a lot of complexities. So this breakthrough with frontier also enables us no pun intended to really accelerate, you know, the, the capacity and scale of these systems and what we can deliver. >>It feels like we're entering a new Renaissance of HPC. I mean, I'm old enough to remember. I, it was, it wasn't until recently my wife, not recently, maybe five, six years ago, my wife threw out my, my green thinking machines. T-shirt that Danny Hillis gave you guys probably both too young to remember, but you had thinking machines, Ken to square research convex tried to mini build a mini computer HPC. Okay. And there was a lot of innovation going on around that time and then it just became too expensive and, and, and other things X 86 happened. And, and, but it feels like now we're entering a, a new era of, of HPC. Is that valid or is it true? What's that mean for HPC as an industry and for industry? >>Yeah, I think, I think it's a BR I think it's a breadth. Um, it's a market that's opening and getting much more broader the number of applications you can run, you know, and we've traditionally had, you know, scientific applications, obviously there's a ton in energy and, and you know, physics and some of the traditional areas that obviously the department of energy sponsor, but, you know, we saw this with, with even the COVID pandemic, right? Our, our supercomputers were used to identify the spike protein to, to help and validate and test these vaccines and bring them to market and record time. We saw some of the benefits of these breakthroughs. And I think it's this combination of that, that we actually have the data, you know, it's, it's digital, it's captured, um, we're capturing it at, you know, at the edge, we're capturing it and, and storing it obviously more broadly. So we have the access to the data and now we have the compute power to run it. And the other big thing is the techniques around artificial intelligence. I mean, what we're able to do with neural networks, computer vision, large language models, natural language processing. These are breakthroughs that, um, one require these large systems, but two, as you give them a large systems, you can actually really enable acceleration of how sophisticated these, these applications can get. >>Let's talk about the impact of the convergence of HPC and AI. What are some of the things that you're seeing now and what are some of the things that we're gonna see? >>Yeah. So, so I, one thing I like to talk about is it's, it's really, it's not a convergence. I think it's it. Sometimes it gets a little bit oversimplified. It's actually, it's traditional modeling and simulation leveraging machine learning to, to refine the simulation. And this is a, is one of the things we talk about a lot in AI, right? It's using machine learning to actually create code in real time, rather than humans doing it, that ability to refine the model as you're running. So we have an example. We did a, uh, we, we actually launched an open source solution called smart SIM. And the first application of that was climate science. And it's what it's doing is it's actually learning the data from the model as the simulation is running to provide more accurate climate prediction. But you think about that, that could be run for, you know, anything that has a complex model. >>You could run that for financial modeling, you can use AI. And so we're seeing things like that. And I think we'll continue to see that the other side of that is using modeling and simulation to actually represent what you see in AI. So we were talking about the grid. This is one of the Exoscale compute projects you could actually use once you actually get, get the data and you can start modeling the behavior of every electrical endpoint in a city. You know, the, the meter in your house, the substation, the, the transformers, you can start measuring the FX of that. You can then build equations. Well, once you build those equations, you can then take a model, cuz you've learned what actually happens in the real world, build the equation. And then you can provide that to someone who doesn't need a extra scale supercomputer to run it, but that, you know, your local energy company can better understand what's happening and they'll know, oh, there's a problem here. We need to shift the grid or respond more, more dynamically. And hopefully that avoids brownouts or, you know, some of the catastrophic outages we've >>Seen so they can deploy that model, which, which inherently has that intelligence on sort of more cost effective systems and then apply it to a much broader range. Do any of those, um, smart simulations on, on climate suggest that it's, it's all a hoax. You don't have to answer that question. <laugh> um, what, uh, >>The temperature outside Dave might, might give you a little bit of an argument to that. >>Tell us about quantum, what's your point of view there? Is it becoming more stable? What's H HPE doing there? >>Yeah. So, so look, I think there's, there's two things to understand with quantum there's quantum hardware, right? Fundamentally, um, how, um, how that runs very differently than, than how we run traditional computers. And then there's the applications. And ultimately a quantum application on quantum hardware will be far more efficient in the future than, than anything else. We, we see the opportunity for, uh, much like we see with, you know, with HPC and AI, we just talked about for quantum to be complimentary. It runs really well with certain applications that fabricate themselves as quantum problems and some great examples are, you know, the, the life sciences, obviously quantum chemistry, uh, you see some, actually some opportunities in, in, uh, in AI and in other areas where, uh, quantum has a very, very, it, it just lends itself more naturally to the behavior of the problem. And what we believe is that in the short term, we can actually model quantum effectively on these, on these super computers, because there's not a perfect quantum hardware replacement over time. You know, we would anticipate that will evolve and we'll see quantum accelerators much. Like we see, you know, AI accelerators today in this space. So we think it's gonna be a natural evolution in progression, but there's certain applications that are just gonna be solved better by quantum. And that's the, that's the future we'll we'll run into. And >>You're suggesting if I understood it correctly, you can start building those applications and, and at least modeling what those applications look like today with today's technology. That's interesting because I mean, I, I think it's something rudimentary compared to quantum as flash storage, right? When you got rid of the spinning disc, it changed the way in which people thought about writing applications. So if I understand it, new applications that can take advantage of quantum are gonna change the way in which developers write, not one or a zero it's one and virtually infinite <laugh> combinations. >>Yeah. And I actually, I think that's, what's compelling about the opportunity is that you can, if you think about a lot of traditional the traditional computing industry, you always had to kind of wait for the hardware to be there, to really write, write, and test the application. And we, you know, we even see that with our customers and HPC and, and AI, right? They, they build a model and then they, they actually have to optimize it across the hardware once they deploy it at scale. And with quantum what's interesting is you can actually, uh, you can actually model and, and, and make progress on the software. And then, and then as the hardware becomes available, optimize it. And that's, you know, that's why we see this. We talk about this concept of quantum accelerators as, as really interesting, >>What are the customer conversations these days as there's been so much evolution in HPC and AI and the technology so much change in the world in the last couple of years, is it elevating up the CS stack in terms of your conversations with customers wanting to become familiar with Exoscale computing? For example? >>Yeah. I, I think two things, uh, one, one is we see a real rise in digital sovereignty and Exoscale and HPC as a core fund, you know, fundamental foundation. So you see what, um, you know, what Europe is doing with the, the, the Euro HPC initiative, as one example, you know, we see the same kind of leadership coming out of the UK with the system. We deployed with them in Archer two, you know, we've got many customers across the globe deploying next generation weather forecasting systems, but everybody feels, they, they understand the foundation of having a strong supercomputing and HPC capability and competence and not just the hardware, the software development, the scientific research, the, the computational scientists to enable them to remain competitive economically. It's important for defense purposes. It's important for, you know, for helping their citizens, right. And providing, you know, providing services and, and betterment. >>So that's one, I'd say that's one big theme. The other one is something Dave touched on before around, you know, as a service and why we think HP GreenLake will be, uh, a beautiful marriage with our, with our HPC and AI systems over time, which is customers also, um, are going to scale up and build really complex models. And then they'll simplify them and deploy them in other places. And so there's a number of examples. We see them, you know, we see them in places like oil and gas. We see them in manufacturing where I've gotta build a really complex model, figure out what it looks like. Then I can reduce it to a, you know, to a, uh, certain equation or application that I can then deploy. So I understand what's happening and running because you, of course, as much as I would love it, you're not gonna have, uh, every enterprise around the world or every endpoint have an exit scale system. Right. So, so that ability to, to, to really provide an as a service element with HP GreenLake, we think is really compelling. >>HP's move into HPC, the acquisitions you've made it really have become a differentiator for the company. Hasn't it? >>Yeah. And I, and I think what's unique about us today. If you look at the landscape is we're, we're really the only system provider globally. Yeah. You know, there are, there are local players that we compete with. Um, but we are the one true global system provider. And we're also the only, I would say the only holistic innovator at the system level to, to, you know, to credit my team on the work they're doing. But, you know, we're, we're also very committed to open standards. We're investing in, um, you know, in a number of places where we contribute the dev the software assets to open source, we're doing work with standards bodies to progress and accelerate the industry and enable the ecosystem. And, uh, and I think that, you know, ultimately the, the, the last thing I'd say is we, we are so connected in, um, with, through our, through the legacy or the, the legend of H Hewlett Packard labs, which now also reports into me that we have these really tight ties into advanced research and that some of that advanced research, which isn't just, um, around kind of core processing Silicon is really critical to enabling better applications, better use cases and accelerating the outcomes we see in these systems going forward. >>Can >>You double click on that? I, I, I wasn't aware that kind of reported into your group. Yeah. So, you know, the roots of HP are invent, right? Yeah. HP labs are, are renowned. It kinda lost that formula for a while. And now it's sounds like it's coming back. What, what, what are some of the cool things that you guys are working on? Well, >>You know, let me, let me start with a little bit of recent history. So we just talked about the exo scale program. I mean, that was a, that's a great example of where we had a public private partnership with the department of energy and it, and it wasn't just that we, um, you know, we built a system and delivered it, but if you go back a decade ago, or five years ago, there were, there were innovations that were built, you know, to accelerate that system. One is our Slingshot fabric as an example, which is a core enable of, of acceler, you know, of, of this accelerated computing environment, but others in software applications and services that allowed us to, you know, to really deliver a, a complete solution into the market. Um, today we're looking at things around trustworthy and ethical AI, so trustworthy AI in the sense that, you know, the models are accurate, you know, and that's, that's a challenge on two dimensions, cuz one is the, model's only as good as the data it's studying. >>So you need to validate that the data's accurate and then you need to really study how, you know, how do I make sure that even if the data is accurate, I've got a model that then, you know, is gonna predict the right things and not call a, a dog, a cat, or a, you know, a, a cat, a mouse or whatever that is. But so that's important. And, uh, so that's one area. The other is future system architectures because, um, as we've talked about before, Dave, you have this constant tension between the fabric, uh, you know, the interconnect, the compute and the, and the storage and, you know, constant, constantly balancing it. And so we're really looking at that, how do we do more, you know, shared memory access? How do we, you know, how do we do more direct rights? Like, you know, looking at some future system architectures and thinking about that. And we, you know, we think that's really, really critical in this part of the business because these heterogeneous systems, and not saying I'm gonna have one monolithic application, but I'm gonna have applications that need to take advantage of different code, different technologies at different times. And being able to move that seamlessly across the architecture, uh, we think is gonna be the, you know, a part of the, the hallmark of the Exoscale era, including >>Edge, which is a completely different animal. I think that's where some disruption is gonna gonna bubble up here in the next decade. >>So, yeah know, and, and that's, you know, that's the last thing I'd say is, is we look at AI at scale, which is another core part of the business that can run on these large clusters. That means getting all the way down to the edge and doing inference at scale, right. And, and inference at scale is, you know, I, I was, um, about a month ago, I was at the world economic forum. We were talking about the space economy and it's a great, you know, to me, it's the perfect example of inference, because if you get a set of data that you know, is, is out at Mars, it doesn't matter whether, you know, whether you wanna push all that data back to, uh, to earth for processing or not. You don't really have a choice, cuz it's just gonna take too long. >>Don't have that time. Justin, thank you so much for spending some of your time with Dave and me talking about what's going on with HBC and AI. The frontier just seems endless and very exciting. We appreciate your time on your insights. >>Great. Thanks so much. Thanks. >>Yes. And don't call a dog, a cat that I thought I learned from you. A dog at no, Nope. <laugh> Nope. <laugh> for Justin and Dave ante. I'm Lisa Martin. You're watching the Cube's coverage of day one from HPE. Discover 22. The cube is, guess what? The leader, the leader in live tech coverage will be right back with our next guest.

(upbeat music) >> We're back at VeamON 2022, at the Aria in Las Vegas. You're watching The Cube. My name is Dave Vellante, and I'm here with my co-host, David Nicholson. Danny Allan here is the Chief Technical Officer at Veeam. And he's joined by Ope Bakare who's the Chief Technical Officer at HBC Dave. One of the few companies that's older than my home. >> Unbelievable. >> Ope. >> That's right. >> Danny, great to see you. Thanks for coming on. It's true by the way. 1670, we're going to learn more about HBC. But I wonder, Danny, if you could set it up. The kind of topic of this discussion here is hybrid cloud, we've got a pretty interesting use case, give us the high level, what should we be focused on here? >> So lots of customers today focused on digital transformation and moving into the cloud, everyone talks about that, I can take my workload and move into the cloud. And one of the interesting things that we saw originally was, you know, I'll just lift it and move it over there. That's not necessarily the best model for the cloud. So you see people doing that. What I actually think is really interesting, and I know Ope has been very focused on is actually transforming the application so that works most effectively in the cloud model. >> So Ope, maybe give us the background on HBC, for folks who aren't familiar with the company and your role there. >> Sure, so HBC is 350, somewhat years old. It's the oldest corporation that's continually existed in North America. I have the privilege to serve as the chief technology officer there. And, you know, HBC is a company that has innovation kind of baked into its core DNA. We have to keep reinventing ourselves, otherwise, we get stagnant and we get left behind. Clearly, we're still around so--. >> So far so good. >> We must be doing something right. But kind of pivoting to what you were saying earlier, you know, our journey to the cloud was multifaceted. Some of it was to improve the pace of innovation, some of it was to improve on quality. So you know, we have typical data center technologies, and, you know, we had some of the typical issues you would have, right, so some older equipment, you know, failures, etc, etc. When you're in the cloud, a lot of that is just managed for you. Again, it's about what I talked about this morning, it's about moving your team up the value chain, towards creating value, right? So you start with the managing of core basic infrastructure, and you start consuming them as services. The interesting thing is, as you mentioned, for the vast majority of people, your first foray into the cloud, is pick up all those virtual machines that you had on-prem, and put them in the cloud. And that's great, you get, immediately you get a better, possibly a better or more available under a cloud platform there. But you're just barely scratching the surface. You don't really get into cloud until you start consuming cloud native services, until you go serverless, you go stateless with containers in Kubernetes, you can use platforms like, you know, Kafka for streaming your data, as opposed to, you know, constructing cumbersome, easy to break data pipelines and all that. So it's a very interesting pivot. And I think a lot of people sometimes struggle with going past that first step, they have the VMs, it's familiar to what they're used to. But for us, we had a digital transformation in the works. We were replatforming from a legacy platform, some of you may know, Blue Martini. But we were moving to a more modern, more flexible platform that was really suited to accelerate our omni channel strategy. Thank goodness we did because the pandemic came around and proved it exactly correct. >> Good timing. >> Yeah, so that's really what happened for us, that actually forced us forward in the cloud journey. >> So Alan Nance, who was at the time, he was like a CIO slash CTO at Philips. And he said to me, if you just lift and shift to the cloud, this is early days of cloud, he said, he's not going to change your operational model. The company, if you want to save billions, you got to change that operational model. But listening to what Ope just said, Danny, what does that mean, from your perspective, I mean, cloud native, and what does that do for your business? >> Well, cloud native,. The benefit of the cloud, of course, makes completely portable, and it's elastic, you can scale almost infinitely, and you don't have to build it. However, the hard part is not the technology. I always say the hard part is the process, you actually have to rewrite your applications to take advantage of all the things in the cloud. And that is not an easy thing. So what we're seeing a lot in the industry across our customer base, is when they have a greenfield opportunity, a new project, they always start in the cloud. We're not seeing a lot of, hey, am going to completely modernize my applications, because that's expensive. It's already built. And so customers will sometimes pick that up and move it to the cloud. And sometimes they'll actually move it back on premises, because the cost model isn't there. But I do think in the long term, if you're looking at four or five years, all the new applications will be designed for a cloud native experience. What that means is written in containers, with container orchestration, you know, seamlessly orchestrating the entire portfolio and data lifecycle. >> So Ope. >> Spot on. >> Translate that into what actually happened at HBC. So as Danny said, we're not going to just going to move everything into the cloud, we've got a hybrid setup, maybe some of the new stuff. What did you do? You have the, your back end systems, your database kind of protected that? How did you go about this omni channel journey? >> So, you know, for us, you know, by the way, that was completely spot on. You know, it's not a fallacy to really examine some cost, because we all have to, we'll have to live in the real world, right? We understand that there are budgets, and there are limits to what we can accomplish within a fiscal year. So you look at an application that's already built, that's already fulfilling the business purpose for which for which it was built. What's the value in immediately going and taking it all apart and containerizing it? If there is a small or easy lift, sure, it might be worth it. But if it's a major system that you have to rewrite, the ROI is just not there, right? So a lift and shift model in that scenario, kind of makes sense. But what you said earlier is exactly what we did. When we had an opportunity again, with the omni channel strategy, we're looking to strengthen our digital arm. And so we were moving from our legacy platform to this new one. And that required us to do a bunch of work. So we had to modernize some of our services, we had to change some of our data, our data process, how we stream data into and out of the e-commerce platform. And all of that actually provided sort of almost a groundswell of support for all of this transformative works. Apologies, for all this transformative work we had to do. So it totally made sense in that case, we actually were able to kill two birds with one stone, really transform and go cloud native, at the same time as deprecating a bunch of legacy technologies that to be perfectly frank didn't really have much of a place in the cloud. >> So many questions. I hope, go. >> Yeah, so it's interesting, because when you talk about that sort of journey to cloud that you're on, sometimes people will ask the question, well, how long before everything is in the cloud? And often the answer is, if you look at what's called the vanishing point, where the two sides of the highway come together, off in the distance, it's like, that's, that's when it'll happen. But as you get closer to that point, it gets further away. So if you had to categorize it in terms of a percentage of where you are now, and then an aspiration over time, how would you categorize that? >> So I have the pleasure of telling you that we are probably at about, I'd say 90% in the cloud? >> Oh, wow, okay. >> We were very aggressive about it. And frankly, I think, you know, first of all, I have the privilege to lead an amazing team. And they did everything possible to make this real. We had a goal, and it was focusing on our customers, being customer obsessed, really. And for us, data centers just didn't make sense in that world. So all we did was work towards how do we deprecate these legacy technologies? How do we consolidate and then move them to the cloud as quickly as possible? So for us 90%, and we're going even even further. Is that last 10% worth it, to go for that? I mean, you know, what's the, you know, you get to that marginal return? >> I really think the next 5% will be worth it, the last five we're not going to pursue and here's why. So think about, you know, we talked about really low latency things that need to be physically in the building. So we have a bunch of, we have a whole lot of fulfillment and distribution centers, right? Those, in some cases, we have automation equipment that really requires low latency connectivity to physical equipment. Moving that to the cloud, is not really a high value proposition. If you think about, you know, large corporate presences, there are some pieces of technology that you could move to the cloud. But again, latency in the customer, the users experience might be compromised as a result. If there's no value, really, to moving that into the cloud, why would you do it? >> And wouldn't you have to freeze the application in order to move it into the cloud or not for these 10% or 5%, or not necessarily? >> Not necessarily. In many cases, we have applications that are built in a distributed fashion so that you can take, you know, some percentage of it, move it to the cloud, validate it over there, and then move the rest of it-- >> You could build some kind of abstraction layer, okay. So the million dollar question is, what does Veeam have to do with all this? >> Well, so Veeam has been for quite some time now, our data protection engine. You know, when I talk about moving people up the value stack, I don't take that lightly. For me, you know, having engineers do things like and please forgive me for a second here, but do things like backups, to me that's, it's a hard requirement, but it's not really high value for me. So if I can get a platform that can use policies, can use tags can operate natively in the cloud. And once you have it running, you can set it and forget it, other than your periodic, you know, business continuity to DR Tests. You know, that's the dream scenario. And we've achieved that largely. We still have some legacy systems that are not on vignette. But that's something that's going to change over the next, let's call it 18 or so months. >> So did you evolve as Veeam evolved? How long have you been in this role? I apologize-- >> I've been with HBC for three years now. >> Okay, so now, Veeam goes, well, I remember I first saw Veeam at a VMUG. I'm like VMware, I was just brilliant, right? Of course, we all say that. Now, but you saw Veeam's ascendancy through virtualization, and then it took a while, but then all of a sudden, bare metal, the first in SAS, great cloud strategy. Now the first in I don't know if I can say that. Scratch that. We will talk to you about that tomorrow. Someone will come here. >> Someone else will come here. At VeeamON. So, from what you know, about HBC, did you kind of follow that Veeam strategy, they were just sort of there as you migrate it to the cloud, SAS, you know, Microsoft 365, etc? >> Yeah, so we actually started using Veeam in a very limited capacity quite some time ago, mostly to protect on-prem virtualized workloads. And that was, you know, that was really the limit. And, you know, my team had been used Veeam, in my previous role when I worked for a large healthcare provider, health care company in the states. So I was pretty familiar with Veeam as a platform, I was very familiar with the journey. I think that you know, more than many other, most of their competition, they've made the transition into the cloud first world, far more successfully. If you think about the policy engine, the automatic tearing, by age, as well as some of the cloud tagging, and the full integration with the native capabilities in AWS and Azure, it's been a dream scenario for us. >> You and I have talked about this Danny, and a lot of your competitors, especially early on the cloud, they wrap their stack in, you know, to container, or Kubernetes, it's shoved it in the cloud, which is really hosted on prem app. You guys didn't do that. I mean, I pushed you on this a number of times. What did you do? >> Every time there's a modern infrastructure, we say, how can we actually apply data protection, modern data protection to that infrastructure, specifically. We don't try and take what already exists. And Veeam started at this. If you think back when we first started, everyone was doing agents. And if you took an agent, put it on a hypervisor, and you'd 100 of them running at the same time, you would kill your production system. So we said, we'll take a snapshot at the hypervisor level. And then when storage arrays came up with snapshots, let's take advantage of that. When we went to the cloud, we said let's take advantage of the API's rather than trying to put an agent in there. And so every time we encounter a new infrastructure, we say, how do we take advantage of what that infrastructure is bringing? >> We're going to dig into more of this tomorrow. But I don't want to steal from the HBC story. Let me ask you about, you talked about, we talk a lot about digital transformation and modernization. And, of course, COVID was like a force march to digital, we all sort of realize this. What do you see Ope, that's now permanent? Whether it's, you know, security, data protection, and how you're thinking about modernization? What are those practices that are now best practices that will become permanent? >> Well, the obvious one that kind of hits up hits us all in the face is remote work. For the past, let's call it two ish years, my team has been almost completely remote. And as a result, you know, we've been able to show that, for us, it worked just fine. There were some teething pains as we all did >> It was like Y2K. Wasn't it? Hey, the world didn't end. >> It became a non factor very quickly, why? Because for most technology organizations were too used to working outside of normal hours. So it wasn't a stretch really to extend a logic to just working, you know, working remotely permanently. That said, you know, one of the things that for us, and I'm going to deviate away from the technology side for a second, one of the things that is really critical for us is we're trying to make sure that we respect people's work-life balance. As we have colleagues who work from home, you know, today, it's very easy to roll out of bed in the morning, you know, put your zoom suit on, and you know, where you're wearing your shorts, and all that and just work the whole day and then around like five to 7 P.M. or whatever, you sign off and you just realized, I just spent way more time working than I probably would have if I were going to the office. That's you know, it's a great productivity-- >> With no breaks. >> With no breaks, right? And there's no button, no water cooler moments or whatever. But, you know, we're trying to, we're trying to come up with various ways to respect people's, you know, work-life balance. Interestingly enough, we actually have a law that is going to effect in early June, in Ontario, where there will be a right to disconnect. So outside of normal working hours, you will be required to disconnect from your employees unless it is an operational issue, or some other pertinent emergency that requires them to engage. So, I think that's going to become the new norm as we go forward. Coming back to technology, I think just looking at the last two years, I don't know if you've noticed the same thing, but the pace of innovation seems to have picked up a tick. And I think that is going to become the new normal. You're going to see a lot of people challenging status quo a lot of sacred, a lot of sacred cows are going to get, you know, get, you know put out to pasture. And I think that's a good thing for our industry, it's going to quicken the pace of innovation. And it's also going to make people more thoughtful about where they place their bets, I think. You know, the other thing, this is the last one, dollars and cents. If you think about the pandemic, when it first started, we all had to take a breath, because instantly, a whole lot of industries just paused, right? And when that happened, you know, you had no revenue coming in. You had, it was whoa, what are we doing here? And I think that also sharpened our focus, when it came to making some some decisions. You know, we all had to deal with, you know, in some cases, furloughs and some cases reductions. Thankfully, we're all back to back to normal now. But where you place your bets financially, it's going to drive a lot of technology decision in investing, right? So I think that's going to be a larger part of our kind of landscape going forward. >> So that last point about innovation, Danny, it's got to be music to your ears, because your, the premise, you're saying, behind Veeam, is you look at the next trend and then modernize, you put meaning behind modern data protection. It's not just a tagline. You gave a couple of good examples. But talk a little bit more about, you know, what Ope just said and what that means to you guys? >> Well, at a technology level, I always talk about three things being part of modern data protection. One is, around the security, everyone working from home, there's intellectual property going into the home on the endpoint in Microsoft Teams, in all the collaboration tools, that needs to be protected. And actually, we're seeing because of the rise in ransomware, cyber insurance is actually requiring data protection for that. So a big part of modern data protection is all about the security of the environment. The second is cloud acceleration. We want customers to move to the cloud. I love sitting here quietly listening to him tell the story of what they're doing, because it's perfect. That is the story that we want from our customers moving to the cloud. And we don't want to stop that in any way. In fact, all of our licensing models go to market, support set cloud acceleration. And then the last thing is, of course, data protection. If they're going to do that, you own that data, you need to protect it on any cloud and on every cloud. And so our focus around modern data protection is those three things. Ransomware protection, cloud acceleration and modern data protection >> In an environment that is not bespoke, I presume, we're going to talk about Supercloud tomorrow. But right, but this idea that instead of going to, I don't know, if you run on Google, AWS, Azure, whatever, but instead of going there and doing your thing, and going over here and doing your on-prem, but you want a consistent experience across all your estates, whether it's on-prem and the cloud, eventually out to the edge, we're going to talk about that tomorrow, too. Is that a fair premise? >> It is. I mean, operational consistency is absolutely crucial for my team to succeed. I mean, think about running multiple different tools for data protection, it just creates a whole lot of interaction, let's call it that has friction. And ultimately, with anything and technology, wherever there's friction, you're going to have problems eventually, and you're going to have varying levels of skill in the team. Suppose you have part of your data protection team, you lose one or two people to COVID for a week, right? And you have a DR test. And it's so happens that these are the experts at FUBAR software, that is your data protection platform. The people that you may have on-prem, available may not have the right skills. I mean, unifying that stuff and actually running them out of the same ethos, really. I think that creates operational consistency that is so valuable for us to be successful. There was one thing I wanted to bring up, just hearing what you said earlier. Zero trust, I think is going to become part of our industry baseline as well. Zero trust approaches to network connectivity to tooling so that you stop dealing with traditional VPN. >> Tho nication >> Tho nication It just, that's where we're going as well. So apologies but-- >> No, not at all, it was a buzzword before the pandemic. >> It was but it's actually-- >> Now, it's a mandate. >> It's kind of, it's come back and become actually useful. >> If people are trying to, okay, what does this really mean? What does this mean to our organization? Exciting times, you know, the thing is, there's a lot of unknowns, right? And we certainly saw that with COVID. So how do you as a technologist deal with, you know, it used to be we would automate the known. This industry is built on that, right? How are you approaching what you don't know, from a technology, infrastructure and process standpoint? >> So I'm going to, everyone watching, everyone turn their videos off, when it's, I'm going to give them a secret, it's the people. The people are the secret sauce. If you surround yourself with amazing people, curious people, you can solve any problem. I again, like I said, I have the privilege of leading this team. And we have some amazing thinkers and problem solvers. If you set them to task and give them the right support as a leader, they will accomplish anything. And so for me, having a robust and just really diversely skilled team allows us to attack any problem, I have zero, I have zero worries about the future of state of technology, I have absolute confidence, we'll be able to engage, master and exploit whatever technologies come our way or any other challenges that actually happened to you know, be in our path as well. >> We hear this a lot in The Cube people process technology. Technology, figure itself out and get the good people you can get the right process and win. >> Absolutely. >> Ope, Danny, thanks so much for coming on The Cube. Danny, we'll see you tomorrow. Tomorrow afternoon Danny's coming back and we're going to dig into a lot of this stuff and double click on it. Appreciate your time. >> Absolutely. >> Thank you. >> This is Dave Vellante, for David Nicholson. You're watching The Cube's coverage VeamON 2022. From the Aria, in Las Vegas. This is day one. Keep it right there. (enchanting music)

>>The cube presents, Dell technologies world brought to you by Dell. >>Welcome back to Dell tech world 2022. This is the cube alive. My name is Dave Volante. We're here with our wall to wall coverage. This is day two. We actually started last night. Uh, the, the cube after dark John furry is here. Lisa Martin, Dave Nicholson. We're gonna talk about apex. The business value of apex flex on demand. Darren fedora is here. He's the senior vice president of Dell financial services, and we're joined by a customer and a partner Jud Barron is R and D infrastructure architect at Silicon labs. And Steve end is the regional VP of copy center comp computer center. I say that like I'm from Boston guys. Welcome to the queue. >>Thank you, >>Darren, take us through what's going on with, with apex, you got custom solutions, you know, people are gonna ask, is this just a financial gimick? What is this? >>No gimmicks, no gimmicks, Dave. So I think when we think about technology, historically customers purchased, they bought and they owned and they may have financed it and paid over time, but it was really an ownership model, especially in infrastructure and apex is about subscription. So think about Dell apex, as you can either buy, or you can subscribe to your technology and under apex subscription, we have options for custom based solutions or an outcome base. And I know today we're gonna talk about flex on demand and, and custom based solutions. So it's a high level pay for what you use when you use it with a high level of choice and flexibility. All >>Right, Steve, I'm gonna ask you to play little >>Co-host all right. I like >>This. Okay. So add some color color commentary, Jud, tell us a little bit about, uh, Silicon labs. I'm really interested in what your requirements were, your challenges and kinda why you landed on, on apex. Sure. >>Uh, Silicon labs is a semiconductor company were headquartered in Austin, 10 Xs, uh, just under a billion dollars a year right now. And, uh, at any ed shop or, uh, that, that people who are doing electronic design automation, that's not just in the semiconductor industry, but we have these HPC farms who are running, you know, millions of jobs a day. And the, a balance that you have to strike when you're doing capacity planning in one of these environments is we have these things called tape outs, and that's where we're finishing a project and there's a much higher volume of jobs that we have to run and you have to decide, do we buy for peak or do we, you know, come under that some amount and say, oh, we're gonna buy 80% of what we think >>As an over, over, over under, right. Do we over buy for peak normally, right, correct. Right >>Hard. One is geo Overy the under buy. It's always a hard decision. >>There's a tradeoff. Right? And, and so the, the challenge there is that you'll end up kind of linking the time and potentially miss a tape out window. And there's costs associated with that because you work with the Foundry and you kind of schedule based off that tape out when you're gonna deliver the photo mask to them. So anyway, the point is we in the past using a traditional like camp X, we're gonna buy a bunch of servers. We, we tend to undershoot whatever our peaks are. Cause we may have a peak every couple of months during, you know, these tape outs. Uh, but you know, sometimes tape outs, slip. And so one slips two months, another one comes in a little bit early and now you have multiple tape outs in the same months. And what was gonna be a, a small, uh, difference in from peak to what you actually purchased ends up being a big peak. And, uh, the thing that was interesting to us about flex on demand is the ability to have a commit rate that, you know, the customer can work with Dell financial services to figure out is that 80% is at 60% whatever. And they give us additional servers that we pay just when we're using them. Now I'm somewhat oversimplifying the process. Um, but we're, we gotta talk about that, >>But, but the point is, if I understand it correctly, that infrastructure was dominoing the, the time to tape out in a negative way, and you you've been able to address that more cost effectively. >>It, it can, it, it has on occasion. And so this, this basically gives us a way to lever to pull, to say, well, we can spend some additional OPEX this month and open up this additional capacity. So it's not like bursting to the cloud. Exactly. Uh, because I mean, you have to have the equipment in your data center already for you to be able to use it. But, um, it's under a traditional acquisition model. It's, it's just not a, a, a thing that was available to us before and looking at leasing or other types of, uh, you know, financing was wasn't really attractive previously, but the flex on demand model, when we first heard about it, we're like, that's very interesting. Tell me more. And we ended up using it in, in Austin, and then we built a whole data center in Asia and did the whole thing on flex on demand and >>Got it. Okay, Steve, uh, talk a little bit about your role what's going on at, at computer center and you know, why apex give us the background? Yeah. >>Um, computer center is a, one of the largest global VAs on the planet, right? Um, we, we have a lot of global and international reach, but at the end of the day, it's about one on one customer of relationships. Um, talking to them, understanding what their challenges are. And we've had a multiyear relationship with Jud. I've known you for a long time. And, and, um, typically that relationship, or initially that relationship was about collaborating, working hand in hand, kind of figure out what the solutions were that best fit their environment to solve their issues they need. And it was typically a procurement, a, a purchase based relationship and, and it worked well for a long time, but it, when Jud posed the challenge to us about kind of more pay as you go, uh, uh, subscription based modeling for, for how he want to do acquire in the future. >>Um, we just, we huddle with the Dell team collectively, um, and, and talked about what we could offer and how we could solve the problem. Uh, apex is a really nice brand today, but this was two and a half years ago, Uhhuh. Okay. So it was a little, we were a little early on on putting it together. I feel good that we were able to, to put that type of solution together for Jud and it's, and it's working today, working wonderful today. And it was good for it's good for the whenever it's good for the customer, the manufacturer and the partner altogether. It's a wonderful solution. >>So you took a little risk, but it worked out and you helped. >>Yeah, that was probably the infancy as we were growing our, as a service, think of this, you know, there's a, a lot of big words out there, Dave, right? As a service utility cloud, it doesn't matter what it is super cloud it's super cloud. It doesn't really matter. Super. This is really Jud was talking about a really important element, which is around flexibility choice. There's uncertainty oftentimes in a, in an environment, but they want to control. They still want have a level of control and leveraging partnerships, being able to deliver flexibility and choice. Don't worry about the words. Don't worry about cloud utility as a service we end up solving the customer need, right? And when we talk about flex on demand, I'll give you a little bit deeper into flex on demand. So when we think about flex on demand, it really is about understanding the customer needs and our capability and Jed reference this, determining what a baseline is. So if you think about your own utility bill, right, you, you go home and even if you're on vacation for a month, I'm sure you went on vacation for a month right. Month at a time. If I ever. >>Yeah, >>I know, but if you leave you your utility bill, even if you don't turn on a light, you still get a utility bill, it's your baseline. So we, we determine a baseline with our customers, with computer center, to understand in your environment, you're gonna use this minimum amount and that becomes your baseline. And that baseline can go as low as 25%. And up to 80% in a environment, it usually is typically in this 70, 80%. And then we determine what is gonna be optimal based on that 25 or above we charge based on the usage on a day to day basis, average by a month. And if you go up one month during your peak, you get charged at that peak. If you then a couple months are lower, then you're gonna pay only for the usage. And so for a customer that's growing has variability or seasonality. >>Um, this is a great model cuz they can still control their environment either within their own domain or um, in a colo. They also have the capability to pick anything within the Dell ISG catalog, any product, configure it to meet their environment, be able to work with a trusted partner like computer center. That it's a solution based on a partner relationship and delivers choice and flexibility on the catalog of anything Dell sells within your control of how you can configure it. So it gives this ability to say, instead of buying and instead of paying a predictable payment, a I E a financing I'm gonna pay for use. Yeah. If I turn on my light switch more or if it's during the summer in Texas where I am the ACS a lot higher. So your utilities go up and if you are a much lower because you're on vacation in Hawaii, maybe you've been in vacation in Hawaii for a month, you're gonna have a much lower and you're gonna hit your baseline. Right. So it gives flexibility choice and it gives the control back to the customer. >>Okay. So the whole ISD portfolio. So you're like the tip of the spear for future apex, right? >>We, we, we absolutely are the tip and that's why, you know, Steve referenced a couple years ago as we were still in our infancy, growing, listening to our customers, listening to our partners, we've evolved to become a more robust program, um, 35 countries today. So we can cover 35 countries over the globe, all ISG you products that are sold with a high level of flexibility and it, and it's Jud and feedback over time that we've continued to evolve this program. Mm-hmm >>So Jud you, if I understood correctly, the business impact to you was gonna better predict predictability. You didn't have to over buy or undery and take all that risk. Is that right? You maybe could quantify. Did you ever quantify that? What can you tell us about the, the business impact? Yeah, >>Sure. So, I mean, traditionally we will, uh, base our capacity demands on, uh, complex calculation that effectively just boils down to number of engineers, like head count, uh, and you know, kind of personas within that. And we figure out, okay, well how many compute do we need? And then we say, okay, well how many tape outs are we doing? And when are those tape outs gonna land? And try to figure out which months are gonna be the hot months and the design teams have to kind of vary their tape out schedules so that they don't pile up all into like July or something. And then there's not enough compute capacity. So with, with something like flex on and where I can turn additional capacity on in our HBC farm, it, you know, we just go in and make some changes to the LSF configuration and say, Hey, you know, now you've got these extra nodes available. >>We don't really have to worry about that as much. Uh, in fact, last year we, we ended up with one month where for us, it was unusual. We had five tape outs, uh, at all land within two weeks of one and a other. And they all finished, which in previous years before we had deployed that that would not have been the outcome things we would've had multiple, uh, tape outs delayed. And you know, that that's a seven figure impact for each one of those commits that we miss with the foundries. So it it's a big deal. >>Yeah. That's real dollars. And >>It is. And you know what else, this, as, as Joe's going through this, we all know their supply chain chain constraints, right? And this solves a lot of supply constraints because Joe, if you would be purchasing today, you'd be buying, you're looking at had, and you're actually having to purchase today where if you go into an apex flex on demand, you don't have that full commitment of having to purchase, but you can get ahead of the supply chain. So you can be looking six months in advance, you can be doing capacity planning and I'm Jed. I'm sure you're doing that leveraging. Like what's my future and not be worried about, I have this huge burden upfront. >>Yeah. And I mean, we have two levers right now. One is we have this extra capacity there. I can, you know, pick up the phone and, and call our Dell rep and say, Hey, I'm gonna modify my commit rate. And so now that's, you know, the new baseline I can use all day every day. Uh, and, and, you know, we still have some burstability and then separately, we can say, we want to expand the contract or, or, or, you know, basically acquire more hardware for additional burst or additional commit. Both of those things are, are options. We only had the, we had to go buy it and we need to know when we have to have it available. So you kind of back into this ordering schedule for, uh, you know, like a traditional CapEx purchase. >>So Steve, obviously Silicon labs is, is leaning again. Are you seeing any other patterns in your customer base, uh, where this is being applied? What can you share >>With us there? Yeah, it's it, I believe this is a fairly horizontal solution. Any customer can really utilize it. I mean, traditionally people would buy for two and three years worth of capacity and slowly consume it over time, but you paid up front. Right. That's how it, that's kind of how it worked. Cause I didn't want to go back to the well year after year after year. Right. So, um, you know, and I, and I think, I think if anything, the, the, the cloud, the hyperscalers has, uh, taught the world, some things taught the industry. Some things, you know, in a, in a perfect world customers like to consume and pay for what they use, you know, and in the increments that they use it as much as possible as closely aligned to that as they could get. And what I see, what I see in this, you know, cuz I, I kind of put solu in my role, I'm putting solutions and customers and bringing those together other right. And, and complimenting that with services of our own. Right. But, but what I see over time that, that almost all the manufacturers and Dells does a wonderful job, but almost all the manufacturers will be delivering technology on a subscription basis. So the more I learn, the more I know, the more I understand about how to deliver those and provide those to customers is better off we are >>Because it aligns with business value. And that's what you're seeing Jud correct. >>Steve made an interesting comment in there. Uh, you know, he was talking about the cloud and for us, there's always pressure to say, Hey, you know, can we burst in the cloud? And for Edda workloads, every time we look at this, it's a data problem. It, it, it's not a computing problem for us. EA workloads tend to generate a lot of data and you know, there's a, there are a lot of tools, uh, you know, there's just a bunch of stuff that you have to have available to run those jobs. And so you have to look at that very carefully. The company that I work for Silicon labs has been around for a long time and we have a lot of development effort. That's been put into automating and simplifying things for our design engineering and trying to, you know, manipulate that and make it to where we can burst just certain jobs out to the cloud efficiently and cost effectively. Hasn't really resonated for us. But the flex on demand thing gave a us the ability to kind of achieve some of that burst ability. I mean, not to the same level of scale of course, but you know, we, we can do that at, you know, our own speed in our own data centers with our own data. And we don't have to worry about trying to, you know, peel an onion and put something new together, make it cloud friendly. It's >>Substantially similar. We gotta go. But to Aaron bring us home. >>Yeah. Hey, I think when we think about Dell, it's about listening to our customers and our partners. Mm-hmm <affirmative>, which we continue to do. We continue to evolve our products and, and apex is around choice and flexibility in delivering to customers an option to pay for what they use. It's a great solution. Appreciate the time guys. >>Great conversation. Thanks so much for coming on the cube. All right. Thank you. Good luck. All right. And thank you for watching. This is Dave VoLTE for the cube. We've been back with more wall to wall coverage. John furry, you'll be back Lisa Martin and Dave Nicholson. You're watching the queue >>And.

(gentle music) >> Hello and welcome today's session theCUBE presentation of AWS Startup Showcase powered by theCUBE, I'm John Furrier, for your host of theCUBE. This is a session on breaking through with DevOps data analytics tools, cloud management tools with MontyCloud and cloud management migration, I'm your host. Thanks for joining me, I've got two great guests. Venkat Krishnamachari who's the co-founder and CEO of MontyCloud and Shruthi Sreenivasa Murthy, solution architect research computing group St. Louis University. Thanks for coming on to talk about transforming IT, day one day two operations. Venkat, great to see you. >> Great to see you again, John. So in this session, I really want to get into this cloud powerhouse theme you guys were talking about before on our previous Cube Conversations and what it means for customers, because there is a real market shift happening here. And I want to get your thoughts on what solution to the problem is basically, that you guys are targeting. >> Yeah, John, cloud migration is happening rapidly. Not an option. It is the current and the immediate future of many IT departments and any type of computing workloads. And applications and services these days are better served by cloud adoption. This rapid acceleration is where we are seeing a lot of challenges and we've been helping customers with our platform so they can go focus on their business. So happy to talk more about this. >> Yeah and Shruthi if you can just explain your relationship with these guys, because you're a cloud architect, you can try to put this together. MontyCloud is your customer, talk about your solution. >> Yeah I work at the St. Louis University as the solutions architect for the office of Vice President of Research. We can address St. Louis University as SLU, just to keep it easy. SLU is a 200-year-old university with more focus on research. And our goal at the Research Computing Group is to help researchers by providing the right infrastructure and computing capabilities that help them to advance their research. So here in SLU research portfolio, it's quite diverse, right? So we do research on vaccines, economics, geospatial intelligence, and many other really interesting areas, and you know, it involves really large data sets. So one of the research computing groups' ambitious plan is to move as many high-end computation applications from on-prem to the AWS. And I lead all the cloud initiatives for the St. Louis university. >> Yeah Venkat and I, we've been talking, many times on theCUBE, previous interviews about, you know, the rapid agility that's happening with serverless and functions, and, you know, microservices start to see massive acceleration of how fast cloud apps are being built. It's put a lot of pressure on companies to hang on and manage all this. And whether you're a security group was trying to lock down something, or it's just, it's so fast, the cloud development scene is really fun and you're implementing it at a large scale. What's it like these days from a development standpoint? You've got all this greatness in the cloud. What's the DevOps mindset right now? >> SLU is slowly evolving itself as the AWS Center of Excellence here in St. Louis. And most of the workflows that we are trying to implement on AWS and DevOps and, you know, CICD Pipelines. And basically we want it ready and updated for the researchers where they can use it and not have to wait on any of the resources. So it has a lot of importance. >> Research as code, it's like the internet, infrastructure as code is DevOps' ethos. Venkat, let's get into where this all leads to because you're seeing a culture shift in companies as they start to realize if they don't move fast, and the blockers that get in the way of the innovation, you really can't get your arms around this growth as an opportunity to operationalize all the new technology, could you talk about the transformation goals that are going on with your customer base. What's going on in the market? Can you explain and unpack the high level market around what you guys are doing? >> Sure thing, John. Let's bring up the slide one. So they have some content that Act-On tabs. John, every legal application, commercial application, even internal IT departments, they're all transforming fast. Speed has never been more important in the era we are today. For example, COVID research, you know, analyzing massive data sets to come up with some recommendations. They don't demand a lot from the IT departments so that researchers and developers can move fast. And I need departments that are not only moving current workloads to the cloud they're also ensuring the cloud is being consumed the right way. So researchers can focus on what they do best, what we win, learning and working closely with customers and gathering is that there are three steps or three major, you know, milestone that we like to achieve. I would start the outcome, right? That the important milestone IT departments are trying to get to is transforming such that they're directly tied to the key business objectives. Everything they do has to be connected to the business objective, which means the time and you know, budget and everything's aligned towards what they want to deliver. IT departments we talk with have one common goal. They want to be experts in cloud operations. They want to deliver cloud operations excellence so that researchers and developers can move fast. But they're almost always under the, you know, they're time poor, right? And there is budget gaps and that is talent and tooling gap. A lot of that is what's causing the, you know, challenges on their path to journey. And we have taken a methodical and deliberate position in helping them get there. >> Shruthi hows your reaction to that? Because, I mean, you want it faster, cheaper, better than before. You don't want to have all the operational management hassles. You mentioned that you guys want to do this turnkey. Is that the use case that you're going after? Just research kind of being researchers having the access at their fingertips, all these resources? What's the mindset there, what's your expectation? >> Well, one of the main expectations is to be able to deliver it to the researchers as demand and need and, you know, moving from a traditional on-prem HBC to cloud would definitely help because, you know, we are able to give the right resources to the researchers and able to deliver projects in a timely manner, and, you know, with some additional help from MontyCloud data platform, we are able to do it even better. >> Yeah I like the onboarding thing and to get an easy and you get value quickly, that's the cloud business model. Let's unpack the platform, let's go into the hood. Venkat let's, if you can take us through the, some of the moving parts under the platform, then as you guys have it's up at the high level, the market's obvious for everyone out there watching Cloud ops, speed, stablism. But let's go look at the platform. Let's unpack that, do you mind pick up on slide two and let's go look at the what's going on in the platform. >> Sure. Let's talk about what comes out of the platform, right? They are directly tied to what the customers would like to have, right? Customers would like to fast track their day one activities. Solution architects, such as Shruthi, their role is to try and help get out of the way of the researchers, but we ubiquitous around delegating cloud solutions, right? Our platform acts like a seasoned cloud architect. It's as if you've instantly turned on a cloud solution architect that should, they can bring online and say, Hey, I want help here to go faster. Our lab then has capabilities that help customers provision a set of governance contracts, drive consumption in the right way. One of the key things about driving consumption the right way is to ensure that we prevent a security cost or compliance issues from happening in the first place, which means you're shifting a lot of the operational burden to left and make sure that when provisioning happens, you have a guard rails in place, we help with that, the platform solves a problem without writing code. And an important takeaway here, John is that a was built for architects and administrators who want to move fast without having to write a ton of code. And it is also a platform that they can bring online, autonomous bots that can solve problems. For example, when it comes to post provisioning, everybody is in the business of ensuring security because it's a shared model. Everybody has to keep an eye on compliance, that is also a shared responsibility, so is cost optimization. So we thought wouldn't it be awesome to have architects such as Shruthi turn on a compliance bot on the platform that gives them the peace of mind that somebody else and an autonomous bot is watching our 24 by 7 and make sure that these day two operations don't throw curve balls at them, right? That's important for agility. So platform solves that problem with an automation approach. Going forward on an ongoing basis, right, the operation burden is what gets IT departments. We've seen that happen repeatedly. Like IT department, you know, you know this, John, maybe you have some thoughts on this. You know, you know, if you have some comments on how IT can face this, then maybe that's better to hear from you. >> No, well first I want to unpack that platform because I think one of the advantages I see here and that people are talking about in the industry is not only is the technology's collision colliding between the security postures and rapid cloud development, because DevOps and cloud, folks, are moving super fast. They want things done at the point of coding and CICB pipeline, as well as any kind of changes, they want it fast, not weeks. They don't want to have someone blocking it like a security team, so automation with the compliance is beautiful because now the security teams can provide policies. Those policies can then go right into your platform. And then everyone's got the rules of the road and then anything that comes up gets managed through the policy. So I think this is a big trend that nobody's talking about because this allows the cloud to go faster. What's your reaction to that? Do you agree? >> No, precisely right. I'll let Shurthi jump on that, yeah. >> Yeah, you know, I just wanted to bring up one of the case studies that we read on cloud and use their compliance bot. So REDCap, the Research Electronic Data Capture also known as REDCap is a web application. It's a HIPAA web application. And while the flagship projects for the research group at SLU. REDCap was running on traditional on-prem infrastructure, so maintaining the servers and updating the application to its latest version was definitely a challenge. And also granting access to the researchers had long lead times because of the rules and security protocols in place. So we wanted to be able to build a secure and reliable enrollment on the cloud where we could just provision on demand and in turn ease the job of updating the application to its latest version without disturbing the production environment. Because this is a really important application, most of the doctors and researchers at St. Louis University and the School of Medicine and St. Louis University Hospital users. So given this challenge, we wanted to bring in MontyCloud's cloud ops and, you know, security expertise to simplify the provisioning. And that's when we implemented this compliance bot. Once it is implemented, it's pretty easy to understand, you know, what is compliant, what is noncompliant with the HIPAA standards and where it needs an remediation efforts and what we need to do. And again, that can also be automated. It's nice and simple, and you don't need a lot of cloud expertise to go through the compliance bot and come up with your remediation plan. >> What's the change in the outcome in terms of the speed turnaround time, the before and after? So before you're dealing with obviously provisioning stuff and lead time, but just a compliance closed loop, just to ask a question, do we have, you know, just, I mean, there's a lot of manual and also some, maybe some workflows in there, but not as not as cool as an instant bot that solve yes or no decision. And after MontyCloud, what are some of the times, can you share any data there just doing an order of magnitude. >> Yeah, definitely. So the provisioning was never simpler, I mean, we are able to provision with just one or two clicks, and then we have a better governance guardrail, like Venkat says, and I think, you know, to give you a specific data, it, the compliance bot does about more than 160 checks and it's all automated, so when it comes to security, definitely we have been able to save a lot of effort on that. And I can tell you that our researchers are able to be 40% more productive with the infrastructure. And our research computing group is able to kind of save the time and, you know, the security measures and the remediation efforts, because we get customized alerts and notifications and you just need to go in and, you know. >> So people are happier, right? People are getting along at the office or virtually, you know, no one is yelling at each other on Slack, hey, where's? Cause that's really the harmony here then, okay. This is like a, I'm joking aside. This is a real cultural issue between speed of innovation and the, what could be viewed as a block, or just the time that say security teams or other teams might want to get back to you, make sure things are compliant. So that could slow things down, that tension is real and there's some disconnects within companies. >> Yeah John, that's spot on, and that means we have to do a better job, not only solving the traditional problems and make them simple, but for the modern work culture of integrations. You know, it's not uncommon like you cut out for researchers and architects to talk in a Slack channel often. You say, Hey, I need this resource, or I want to reconfigure this. How do we make that collaboration better? How do you make the platform intelligent so that the platform can take off some of the burden off of people so that the platform can monitor, react, notify in a Slack channel, or if you should, the administrator say, Hey, next time, this happens automatically go create a ticket for me. If it happens next time in this environment automatically go run a playbook, that remediates it. That gives a lot of time back that puts a peace of mind and the process that an operating model that you have inherited and you're trying to deliver excellence and has more help, particularly because it is very dynamic footprint. >> Yeah, I think this whole guard rail thing is a really big deal, I think it's like a feature, but it's a super important outcome because if you can have policies that map into these bots that can check rules really fast, then developers will have the freedom to drive as fast as they want, and literally go hard and then shift left and do the coding and do all their stuff on the hygiene side from the day, one on security is really a big deal. Can we go back to this slide again for the other project? There's another project on that slide. You talked about RED, was it REDCap, was that one? >> Yeah. Yeah, so REDCap, what's the other project. >> So SCAER, the Sinfield Center for Applied Economic Research at SLU is also known as SCAER. They're pretty data intensive, and they're into some really sophisticated research. The Center gets daily dumps of sensitive geo data sensitive de-identified geo data from various sources, and it's a terabyte so every day, becomes petabytes. So you know, we don't get the data in workable formats for the researchers to analyze. So the first process is to convert this data into a workable format and keep an analysis ready and doing this at a large scale has many challenges. So we had to make this data available to a group of users too, and some external collaborators with ads, you know, more challenges again, because we also have to do this without compromising on the security. So to handle these large size data, we had to deploy compute heavy instances, such as, you know, R5, 12xLarge, multiple 12xLarge instances, and optimizing the cost and the resources deployed on the cloud again was a huge challenge. So that's when we had to take MontyCloud help in automating the whole process of ingesting the data into the infrastructure and then converting them into a workable format. And this was all automated. And after automating most of the efforts, we were able to bring down the data processing time from two weeks or more to three days, which really helped the researchers. So MontyCloud's data platform also helped us with automating the risk, you know, the resource optimization process and that in turn helped bring the costs down, so it's been pretty helpful then. >> That's impressive weeks to days, I mean, this is the theme Venkat speed, speed, speed, hybrid, hybrid. A lot of stuff happening. I mean, this is the new normal, this is going to make companies more productive if they can get the apps built faster. What do you see as the CEO and founder of the company you're out there, you know, you're forging new ground with this great product. What do you see as the blockers from customers? Is it cultural, is it lack of awareness? Why aren't people jumping all over this? >> Only people aren't, right. They go at it in so many different ways that, you know, ultimately be the one person IT team or massively well-funded IT team. Everybody wants to Excel at what they're delivering in cloud operations, the path to that as what, the challenging part, right? What are you seeing as customers are trying to build their own operating model and they're writing custom code, then that's a lot of need for provisioning, governance, security, compliance, and monitoring. So they start integrating point tools, then suddenly IT department is now having a, what they call a tax, right? They have to maintain the technical debt while cloud service moving fast. It's not uncommon for one of the developers or one of the projects to suddenly consume a brand new resource. And as you know, AWS throws up a lot more services every month, right? So suddenly you're not keeping up with that service. So what we've been able to look at this from a point of view of how do we get customers to focus on what they want to do and automate things that we can help them with? >> Let me, let me rephrase the question if you don't mind. Cause I I didn't want to give the impression that you guys aren't, you guys have a great solution, but I think when I see enterprises, you know, they're transforming, right? So it's not so much the cloud innovators, like you guys, it's really that it's like the mainstream enterprise, so I have to ask you from a customer standpoint, what's some of the cultural things are technical reasons why they're not going faster? Cause everyone's, maybe it's the pandemic's forcing projects to be double down on, or some are going to be cut, this common theme of making things available faster, cheaper, stronger, more secure is what cloud does. What are some of the enterprise challenges that they have? >> Yeah, you know, it might be money for right, there's some cultural challenges like Andy Jassy or sometimes it's leadership, right? You want top down leadership that takes a deterministic step towards transformation, then adequately funding the team with the right skills and the tools, a lot of that plays into it. And there's inertia typically in an existing process. And when you go to cloud, you can do 10X better, people see that it doesn't always percolate down to how you get there. So those challenges are compounded and digital transformation leaders have to, you know, make that deliberate back there, be more KPI-driven. One of the things we are seeing in companies that do well is that the leadership decides that here are our top business objectives and KPIs. Now if we want the software and the services and the cloud division to support those objectives when they take that approach, transformation happens. But that is a lot more easier said than done. >> Well you're making it really easy with your solution. And we've done multiple interviews. I've got to say you're really onto something really with this provisioning and the compliance bots. That's really strong, that the only goes stronger from there, with the trends with security being built in. Shruthi, got to ask you since you're the customer, what's it like working with MontyCloud? It sounds so awesome, you're customer, you're using it. What's your review, what's your- What's your, what's your take on them? >> Yeah they are doing a pretty good job in helping us automate most of our workflows. And when it comes to keeping a tab on the resources, the utilization of the resources, so we can keep a tab on the cost in turn, you know, their compliance bots, their cost optimization tab. It's pretty helpful. >> Yeah well you're knocking projects down from three weeks to days, looking good, I mean, looking real strong. Venkat this is the track record you want to see with successful projects. Take a minute to explain what else is going on with MontyCloud. Other use cases that you see that are really primed for MontyCloud's platform. >> Yeah, John, quick minute there. Autonomous cloud operations is the goal. It's never done, right? It there's always some work that you hands-on do. But if you set a goal such that customers need to have a solution that automates most of the routine operations, then they can focus on the business. So we are going to relentlessly focused on the fact that autonomous operations will have the digital transformation happen faster, and we can create a lot more value for customers if they deliver to their KPIs and objectives. So our investments in the platform are going more towards that. Today we already have a fully automated compliance bot, a security bot, a cost optimization recommendation engine, a provisioning and governance engine, where we're going is we are enhancing all of this and providing customers lot more fluidity in how they can use our platform Click to perform your routine operations, Click to set up rules based automatic escalation or remediation. Cut down the number of hops a particular process will take and foster collaboration. All of this is what our platform is going and enhancing more and more. We intend to learn more from our customers and deliver better for them as we move forward. >> That's a good business model, make things easier, reduce the steps it takes to do something, and save money. And you're doing all those things with the cloud and awesome stuff. It's really great to hear your success stories and the work you're doing over there. Great to see resources getting and doing their job faster. And it's good and tons of data. You've got petabytes of that's coming in. It's it's pretty impressive, thanks for sharing your story. >> Sounds good, and you know, one quick call out is customers can go to MontyCloud.com today. Within 10 minutes, they can get an account. They get a very actionable and valuable recommendations on where they can save costs, what is the security compliance issues they can fix. There's a ton of out-of-the-box reports. One click to find out whether you are having some data that is not encrypted, or if any of your servers are open to the world. A lot of value that customers can get in under 10 minutes. And we believe in that model, give the value to customers. They know what to do with that, right? So customers can go sign up for a free trial at MontyCloud.com today and get the value. >> Congratulations on your success and great innovation. A startup showcase here with theCUBE coverage of AWS Startup Showcase breakthrough in DevOps, Data Analytics and Cloud Management with MontyCloud. I'm John Furrier, thanks for watching. (gentle music)

>> Narrator: From theCUBE studios in Palo Alto in Boston connecting with our leaders all around the world. This is theCUBE conversation. >> Welcome to theCUBE and the special IBM Brocade panel. I'm Lisa Martin. And I'm having a great opportunity here to sit down for the next 20 minutes with three gentlemen please welcome Brian Sherman a distinguished engineer from IBM, Brian, great to have you joining us. >> Thanks for having me. >> And Matt key here. Flash systems SME from IBM, Matt, happy Friday. >> Happy Friday, Lisa. Thanks for having us. >> Our pleasure. And AIG Customer solution here from Brocade is here. AJ welcome. >> Thanks for having me along. >> AJ we're going to stick with you, IBM and Brocade have had a very long you said about 22 year strategic partnership. There's some new news. And in terms of the evolution of that talk to us about what's going on with with Brocade IBM and what is new in the storage industry? >> Yeah, so the the newest thing for us at the moment is that IBM just in mid-October launched our Gen seven platforms. So this is think about the stresses that are going on in the IT environments. This is our attempt to keep pace with with the performance levels that the IBM teams are now putting into their storage environments the All-Flash Data Centers and the new technologies around non-volatile memory express. So that's really, what's driving this along with the desire to say, "You know what people aren't allowed "to be in the data center." And so if they can't be in the data center then the fabrics actually have to be able to figure out what's going on and basically provide a lot of the automation pieces. So something we're referring to as the autonomous SAM. >> And we're going to dig into NBME of our fabrics in a second but I do want to AJ continue with you in terms of industries, financial services, healthcare airlines there's the biggest users, biggest need. >> Pretty much across the board. So if you look at the global 2000 as an example, something on the order of about 96, 97% of the global 2000 make use of fiber channel environments and in portions of their world generally tends to be a lot of the high end financial guys, a lot of the pharmaceutical guys, the automotive, the telcos, pretty much if the data matters, and it's something that's critical whether we talk about payment card information or healthcare environments, data that absolutely has to be retained, has to get there, has to perform then it's this combination that we're bringing together today around the new storage elements and the functionalities they have there. And then our ability in the fabric. So the concept of a 64 gig environment to help basically not be the bottleneck in the application demands, 'cause one thing I can promise you after 40 years in this industry is the software guys always figure out how to all the performance that the hardware guys put on the shelf, right? Every single time. >> Well there's gauntlet thrown down there. Matt, let's go to you. I want to get IBM's perspective on this. Again, as we said, a 22 year strategic partnership, as we look at things like not being able to get into the data center during these unprecedented times and also the need to be able to remove some of those bottlenecks how does IBM view this? >> Yeah, totally. It's certainly a case of raising the bar, right? So we have to as a vendor continue to evolve in terms of performance, in terms of capacity, cost density, escalating simplicity, because it's not just a case of not be able to touch the rates, but there's fewer people not being able to adjust the rates, right? It's a case where our operational density continues to have to evolve being able to raise the bar on the network and be able to still saturate those line rates and be able to provide that simply a cost efficiency that gets us to a utilization that raises the bar from our per capita ratio from not just talking about 200, 300 terabytes per admin but going beyond the petabyte scale per admin. And we can't do that unless people have access to the data. And we have to provide the resiliency. We have to provide the simplicity of presentation and automation from our side. And then this collaboration that we do with our network brother like Brocade here continued to stay out of the discussion when it comes to talking about networks and who threw the ball next. So we truly appreciate this Gen seven launch that they're doing we're happy to come in and fill that pipe on the flash side for them. >> Excellent and Brian as a distinguished engineer and let me get your perspectives on the evolution of the technology over this 22 year partnership. >> Thanks Lisa. It certainly has been a longstanding, a great relationship, great partnership all the way from inventing joint things, to developing, to testing and deploying to different technologies through the course of time. And it's been one of those that where we are today, like AJ had talked about being able to sustain what the applications require today in this always on time type of environment. And as Matt said, bringing together the density and operational simplicity to make that happen 'cause we have to make it easier from the storage side for operations to be able to manage this volume of data that we have coming out and our due diligence is to be able to serve the data up as fast as we can and as resilient as we can. >> And so sticking with you, Brian that simplicity is key because as we know as we get more and more advances in technology the IT environment is only becoming more complex. So really truly enabling organizations in any industry to simplify is absolute table stakes. >> Yeah, it definitely is. And that's core to what we're focused on and how do we make the storage environment simple. It's been one those through the years and historically, we've had entry-level us and the industry as a whole, is that an entry-level product mid range level products, high-end level products. And earlier this year, we said enough, enough of that it's one product portfolio. So it's the same software stack it's just, okay. Small, medium and large in terms of the appliances that get delivered. Again, building on what Matt said, from a density perspective where we can have a petabyte of uncompressed and data reduced storage in a two Enclosure. So it becomes from a overall administration perspective, again, one software stake, one automation stack, one way to do point in time copies, replication. So in focusing on how to make that as simple for the operations as we possibly can. >> I think we'd all take a little bit of that right now. Matt, let's go to you and then AJ view, let's talk a little bit more, dig into the IBM storage arrays. I mean, we're talking about advances in flash, we're talking about NBME as a forcing function for applications to change and evolve with the storage. Matt, give us your thoughts on that. >> We saw a monumental leap in where we take some simplicity pieces from how we deliver our arrays but also the technology within the arrays. About nine months ago, in February we launched into the latest generation of non technology and with that born the story of simplicity one of the pieces that we've been happily essentially negating of value prop is storage level tiering and be able to say, "Hey, well we still support the idea of going down "to near line SaaS and enterprise disc in different flavors "of solid state whether it's tier one short usage "the tier zero high performance, high usage, "all the way up to storage class memory." While we support those technologies and the automated tiering, this elegance of what we've done as latest generation technology that we launched nine months ago has been able to essentially homogenize the environments to we're able to deliver that petabyte per rack unit ratio that Brian was mentioning be able to deliver over into all tier zero solution that doesn't have to go through woes of software managed data reduction or any kind of software managed hearing just to be always fast, always essentially available from a 100% data availability guaranteed that we offer through a technology called hyper swap, but it's really kind of highlighting what we take in from that simplicity story, by going into that extra mile and meeting the market in technology refresh. I mean, if you say the words IBM over the Thanksgiving table, you're kind of thinking, how big blue, big mainframe, old iron stuff but it's very happy to say over in distributed systems that we are in fact leading this pack by multiple months not just the fact that, "Hey, we announced sooner." But actually coming to delivering on-prem the actual solution itself nine, 10 months prior to anybody else and when that gets us into new density flavors gets us into new efficiency offerings. Not just talk about, "Hey, I can do this petabyte scale "a couple of rack units but with the likes of Brocade." That actually equates to a terabyte per second and a floor tile, what's that do for your analytics story? And the fact that we're now leveraging NBME to undercut the value prop of spinning disc in your HBC analytics environments by five X, that's huge. So now let's take near line SaaS off the table for anything that's actually per data of an angle of value to us. So in simplicity elements, what we're doing now will be able to make our own flash we've been deriving from the tech memory systems acquisition eight years ago and then integrating that into some essentially industry proven software solutions that we do with the bird flies. That appliance form factor has been absolutely monumental for us in the distributed systems. >> And thanks for giving us a topic to discuss at our socially distant Thanksgiving table. We'll talk about IBM. I know now I have great, great conversation. AJ over to you lot of advances here also in such a dynamic times, I want to get Brocade's perspective on how you're taking advantage of these latest technologies with IBM and also from a customer's perspective, what are they feeling and really being able to embrace and utilize that simplicity that Matt talked about. >> So there's a couple of things that fall into that to be honest, one of which is that similar to what you heard Brian described across the IBM portfolio for storage in our SaaS infrastructure. It's a single operating system up and down the line. So from the most entry-level platform we have to the largest platform we have it's a single software up and down. It's a single management environment up and down and it's also intended to be extremely reliable and extremely performance because here's part of the challenge when Matt's talking about multiple petabytes in a two U rack height, but the conversation you want to flip on its head there a little bit is "Okay exactly how many virtual machines "and how many applications are you going to be driving "out of that?" Because it's going to be thousands like between six and 10,000 potentially out of that, right? So imagine then if you have some sort of little hiccup in the connectivity to the data store for 6,000 to 10,000 applications, that's not the kind of thing that people get forgiving about. When we're all home like this. When your healthcare, when your finance, when your entertainment, when everything is coming to you across the network and remotely in this version and it's all application driven, the one thing that you want to make sure of is that network doesn't hiccup because humans have a lot of really good characteristics. Patience would not be one of those. And so you want to make sure that everything is in fact in play and running. And that's as one of the things that we work very hard with our friends at IBM to make sure of is that the kinds of analytics that Matt was just describing are things that you can readily get done. Speed is the new currency of business is a phrase you hear from... A quote you hear from Marc Benioff at Salesforce, right. And he's right if you can get data out of intelligence out of the data you've been collecting, that's really cool. But one of the other sort of flip sides on the people not being able to be in the data center and then to Matt's point, not as many people around either is how are humans fast enough when you look... Honestly when you look at the performance of the platforms, these folks are putting up how is human response time going to be good enough? And we all sort of have this headset of a network operations center where you've got a couple dozen people in a half lit room staring at massive screens on the thing to pop. Okay, if the first time a red light pops the human begins the investigation at what point is that going to be good enough? And so our argument for the autonomy piece of of what we're doing in the fabrics is you can't wait on the humans. You need to augment it. I get that people still want to be in charge and that's good. Humans are still smarter than the Silicon. We're not as repeatable, but we're still so far smarter about it. And so we needed to be able to do that measurement. We need to be able to figure out what normal looks like. We need to be able to highlight to the storage platform and to the application admins, when things go sideways because the demand from the applications isn't going to slow down. The demands from your environment whether you want to think about take the next steps with not just your home entertainment home entertainment systems but learning augmented reality, right. Virtual reality environments for kids, right? How do you make them feel like they're part and parcel of the classroom, for as long as we have to continue living a modified world and perhaps past it, right? If you can take a grade school from your local area and give them a virtual walkthrough of the loop where everybody's got a perfect view and it all looks incredibly real to them those are cool things, right? Those are cool applications, right? If you can figure out a new vaccine faster, right. Not a bad thing, right. If we can model better, not a bad thing. So we need to enable those things we need to not be the bottleneck, which is you get Matt and Brian over an adult beverage at some point and ask them about the cycle time for the Silicon they're playing with. We've never had Moore's law applied to external storage before never in the history of external storage. Has that been true until now. And so their cycle times, Matt, right? >> Yeah you struck a nerve there AJ, cause it's pretty simple for us to follow the linear increase in capacity and computational horsepower, right. We just ride the X86 bandwagon, ride the Silicon bandwagon. But what we have to do in order to maintain But what we have to do in order to maintain the simplicity story is followed more important one is the resiliency factor, right? 'Cause as we increased the capacity as we increased the essentially the amount of data responsible for each admin we have to literally log rhythmically increase the resiliency of these boxes because we're going to talk about petabyte scale systems and hosting them really 10,000 virtual machines in the two U form factor. I need to be able to accommodate that to make sure things don't blip. I need resilient networks, right. Have redundancy and access. I need to have protection schemes at every single layer of the stack. And so we're quite happy to be able to provide that as we leapfrog the industry and go in literally situations that are three times the competitive density that we you see out there and other distributed systems that are still bound by the commercial offerings, then, hey we also have to own that risk from a vendor side we have to make these things is actually rate six protection scheme equivalent from a drive standpoint and act back from controllers everywhere. Be able to supply the performance and consistency of that service throughout even the bad situations. >> And to that point, one of the things that you talked about, that's interesting to me that I'd kind of like you to highlight is your recovery times, because bad things will happen. And so you guys do something very, very different about that. That's critical to a lot of my customers because they know that Murphy will show up one day. So, I mean 'cause it happens, so then what. >> Well, speaking of that, then what Brian I want to go over to you. You mentioned Matt mentioned resiliency. And if we think of the situation that we're in in 2020 many companies are used to DR and BC plans for natural disasters, pandemics. So as we look at the shift and then the the volume of ransomware, that's going up one ransomware attack every 11 seconds this year, right now. How Brian what's that change that businesses need to make from from cyber security to cyber resiliency? >> Yeah, it's a good point in, and I try to hammer that home with our clients that, you're used to having your business continuity disaster recovery this whole cyber resiliency thing is a completely separate practice that we have to set up and think about and go through the same thought process that you did for your DR What are you going to do? What are you going to pretest? How are you going to test it? How are you going to detect whether or not you've got ransomware? So I spent a lot of time with our clients on that theme of you have to think about and build your cyber resiliency plan 'cause it's going to happen. It's not like a DR plan where it's a pure insurance policy and went and like you said, every 11 seconds there's an event that takes place. It's going to be a win not then. Yeah and then we have to work with our customers to put in a place for cyber resiliency and then we spent a lot of discussion on, okay what does that mean for my critical applications, from a restore time of backup and mutability. What do we need for those types of services, right? In terms of quick restore, which are my tier zero applications that I need to get back as fast as possible, what other ones can I they'll stick out on tape or virtual tape in and do things like that. So again, there's a wide range of technology that we have available in the in the portfolio for helping our clients from cyber resiliency. And then we try to distinguish that cyber resiliency versus cyber security. So how do we help to keep every, everybody out from a cybersecurity view? And then what can we do from the cyber resiliency, from a storage perspective to help them once once it gets to us, that's a bad thing. So how can we help? How help our folks recover? Well, and that's the point that you're making Brian is that now it's not a matter of, could this happen to us? It's going to, how much can we tolerate? But ultimately we have to be able to recover. We can't restore that data and one of those things when you talk about ransomware and things, we go to that people as the weakest link insecurity AJ talked about that, there's the people. Yeah there's probably quite a bit of lack of patients going on right now. But as we look as I want to go back over to you to kind of look at, from a data center perspective and these storage solutions, being able to utilize things to help the people, AI and Machine Learning. You talked about AR VR. Talk to me a little bit more about that as you see, say in the next 12 months or so as moving forward, these trends these new solutions that are simplified. >> Yeah, so a couple of things around that one of which is iteration of technology the storage platforms the Silicon they're making use of Matt I think you told me 14 months is the roughly the Silicon cycle that you guys are seeing, right? So performance levels are going to continue to go up the speeds. The speeds are going to continue to go up. The scale is going to is going to continue to shift. And one of the things that does for a lot of the application owners is it lets them think broader. It lets them think bigger. And I wish I could tell you that I knew what the next big application was going to be but then we'd be having a conversation about which Island in the Pacific I was going to be retiring too. But they're going to come and they're going to consume this performance because if you look at the applications that you're dealing with in your everyday life, right. They continue to get broader. The scope of them continues to scale out, right. There's things that we do. I saw I think it was an MIT development recently where they're talking about being able to and they were originally doing it for Alzheimer's and dementia, but they're talking about being able to use the microphones in your smartphone to listen to the way you cough and use that as a predictor for people who have COVID that are not symptomatic yet. So asymptomatic COVID people, right? So when we start talking about where this, where this kind of technology can go and where it can lead us, right. There's sort of this unending possibility for it. But what that on, in part is that the infrastructure has to be extremely sound, right? The foundation has to be there. We have to have the resilience, the reliability and one of the points that Brian was just making is extremely key. We talk about disaster tolerance business continuous, so business continuance is how do you recover? Cyber resilience is the same conversation, right? So you have the protection side of it. Here's my defenses. Now what happens when they actually get in. And let's be honest, right? Humans are frequently that weak link, right. For a variety of behaviors that the humans that humans have. And so when that happens, where's the software in the storage that tells you, "Hey, wait there's an odd traffic behavior here "where data is being copied "at rates and to locations that that are not normal." And so that's part of when we talk about what we're doing in our side of the automation is how do you know what normal looks like? And once you know what normal looks like you can figure out where the outliers are. And that's one of the things that people use a lot for trying to determine whether or not ransomware is going on is, "Hey, this is a traffic pattern, that's new. "This is a traffic pattern. "That's different." Are they doing this because they're copying the dataset from here to here and encrypting it as they go, right? 'Cause that's one of the challenges you got to, you got to watch for. So I think you're going to see a lot of advancement in the application space. And not just the MIT stuff, which is great. The fact that people are actually able to or I may have misspoken, maybe Johns Hopkins. And I apologize to the Johns Hopkins folks that kind of scenario, right. There's no knowing what they can make use of here in terms of the data sets, right. Because we're gathering so much data, the internet of things is an overused phrase but the sheer volume of data that's being generated outside of the data center, but manipulated analyzed and stored internally. 'Cause you got to have it someplace secure. Right and that's one of the things that we look at from our side is we've got to be that as close to unbreakable as we can be. And then when things do break able to figure out exactly what happened as rapidly as possible and then the recovery cycle as well. >> Excellent and I want to finish with you. We just have a few seconds left, but as AJ was talking about this massive evolution and applications, for example when we talk about simplicity and we talk about resiliency and being able to recover when something happens, how did these new technologies that we've been unpacking today? How did these help the admin folks deal with all of the dynamics that are happening today? >> Yeah so I think the biggest the drop, the mic thing we can say right now is that we're delivering 100% tier zero in Vme without data reduction value props on top of it at a cost that undercuts off-prem S3 storage. So if you look at what you can do from an off-prem solution for air gap and from cyber resiliency you can put your data somewhere else. And it's going to take whatever long time to transfer that data back on prem, to read get back to your recover point. But when you work at economics that we're doing right now in the distributed systems, hey, you're DR side, your copies of data do not have to wait for that. Off-prem bandwidth to restore. You can actually literally restore it in place. And you couple that with all of the the technology on the software side that integrates with it I get incremental point in time. Recovery is either it's on the primary side of DRS side, wherever, but the fact that we get to approach this thing from a cost value then by all means I can naturally absorb a lot of the cyber resiliency value in that too. And because it's all getting all the same orchestrated capabilities, regardless of the big, small, medium, all that stuff, it's the same skillsets. And so I don't need to really learn new platforms or new solutions to providing cyber resiliency. It's just part of my day-to-day activity because fundamentally all of us have to wear that cyber resiliency hat. But as, as our job, as a vendor is to make that simple make it cost elegance, and be able to provide a essentially a homogenous solutions overall. So, hey, as your business grows, your risk gets averted on your recovery means also get the thwarted essentially by your incumbent solutions and architecture. So it's pretty cool stuff that we're doing, right. >> It is pretty cool. And I'd say a lot of folks would say, that's the Nirvana but I think the message that the three of you have given in the last 20 minutes or so is that IBM and Brocade together. This is a reality. You guys are a cornucopia of knowledge. Brian, Matt, AJ, thank you so much for joining me on this panel I really enjoyed our conversation. >> Thank you. >> Thank you again Lisa. >> My pleasure. From my guests I'm Lisa Martin. You've been watching this IBM Brocade panel on theCUBE.

>>from around the globe. It's the Cube with digital coverage of HP Green Lake day made possible by Hewlett Packard Enterprise. Okay, we're not gonna dive right into some of the news and get into the Green Lake Announcement details. And with me to do that is Keith White is the senior vice president and general manager for Green Lake Cloud Services and Hewlett Packard Enterprise. Keith, thanks for your time. Great to see you. >>Hey, thanks so much for having me. I'm really excited to be here. >>You're welcome. And so listen, before we get into the hard news, can you give us an update on just Green Lake and the business? How's it going? >>You bet. No, it's fantastic. And thanks, you know, for the opportunity again. And hey, I hope everyone's at home staying safe and healthy. It's been a great year for HP Green Lake. There's a ton of momentum that we're seeing in the market place. Uh, we've booked over $4 billion of total contract value to date, and that's over 1000 customers worldwide, and frankly, it's worldwide. It's in 50 50 different countries, and this is a variety of solutions. Variety of workloads. So really just tons of momentum. But it's not just about accelerating the current momentum. It's really about listening to our customers, staying ahead of their demands, delivering more value to them and really executing on the HB Green Lake. Promise. >>Great. Thanks for that and really great detail. Congratulations on the progress, but I know you're not done. So let's let's get to the news. What do people need to know? >>Awesome. Yeah, you know, there's three things that we want to share with you today. So first is all about it's computing. So I could go into some details on that were actually delivering new industry work clothes, which I think will be exciting for a lot of the major industries that are out there. And then we're expanding RHP capabilities just to make things easier and more effective. So first off, you know, we're excited to announce today, um, acceleration of mainstream as adoption for high performance computing through HP Green Lake. And you know, in essence, what we're really excited about is this whole idea of it's a. It's a unique opportunity to write customers with the power of an agile, elastic paper use cloud experience with H. P s market. See systems. So pretty soon any enterprise will be able to tackle their most demanding compute and did intensive workloads, power, artificial intelligence and machine learning initiatives toe provide better business insights and outcomes and again providing things like faster time to incite and accelerated innovation. So today's news is really, really gonna help speed up deployment of HPC projects by 75% and reduced TCO by upto 40% for customers. >>That's awesome. Excited to learn more about the HPC piece, especially. So tell us what's really different about the news today From your perspective. >>No, that's that's a great thing. And the idea is to really help customers with their business outcomes, from building safer cars to improving their manufacturing lines with sustainable materials. Advancing discovery for drug treatment, especially in this time of co vid or making critical millisecond decisions for those finance markets. So you'll see a lot of benefits and a lot of differentiation for customers in a variety of different scenarios and industries. >>Yeah, so I wonder if you could talk a little bit mawr about specifically, you know exactly what's new. Can you unpack some of that for us? >>You bet. Well, what's key is that any enterprise will be able to run their modeling and simulation work clothes in a fully managed because we manage everything for them pre bundled. So we'll give folks this idea of small, medium and large H p e c h piece services to operate in any data center or in a cold a location. These were close air, almost impossible to move to the public cloud because the data so large or it needs to be close by for Leighton see issues. Oftentimes, people have concerns about I p protection or applications and how they run within that that local environment. So if customers are betting their business on this insight and analytics, which many of them are, they need business, critical performance and experts to help them with implementation and migration as well as they want to see resiliency. >>So is this a do it yourself model? In other words, you know the customers have toe manage it on their own. Or how are you helping there? >>No, it's a great question. So the fantastic thing about HP Green Lake is that we manage it all for the customer. And so, in essence, they don't have to worry about anything on the back end, we can flow that we manage capacity. We manage performance, we manage updates and all of those types of things. So we really make it. Make it super simple. And, you know, we're offering these bundled solutions featuring RHP Apollo systems that are purpose built for running things like modeling and simulation workloads. Um, and again, because it's it's Green Lake. And because it's cloud services, this provides itself. Service provides automation. And, you know, customers can actually, um, manage however they want to. We can do it all for them. They could do some on their own. It's really super easy, and it's really up to them on how they want to manage that system. >>What about analytics? You know, you had a lot of people want to dig deeper into the data. How are you supporting that? >>Yeah, Analytics is key. And so one of the best things about this HPC implementation is that we provide unopened platform so customers have the ability to leverage whatever tools they want to do for analytics. They can manage whatever systems they want. Want to pull data from so they really have a ton of flexibility. But the key is because it's HP Green Lake, and because it's HP es market leading HPC systems, they get the fastest they get the it all managed for them. They only pay for what they use, so they don't need to write a huge check for a large up front. And frankly, they get the best of all those worlds together in order to come up with things that matter to them, which is that true business outcome, True Analytics s so that they could make the decisions they need to run their business. >>Yeah, that's awesome. You guys clearly making some good progress here? Actually, I see it really is a game changer for the types of customers that you described. I mean, particularly those folks that you like. You said You think they can't move stuff into the cloud. They've got to stay on Prem. But they want that cloud experience. I mean, that's that's really exciting. We're gonna have you back in a few minutes to talk about the Green Lake Cloud services and in some of the new industry platforms that you see evolving >>awesome. Thanks so much. I look forward to it. >>Yeah, us too. So Okay, right now we're gonna check out the conversation that I had earlier with Pete Ungaro and Addison Snell on HPC. Let's watch welcome everybody to the spotlight session here green. Late day, We're gonna dig into high performance computing. Let me first bring in Pete Ungaro, Who's the GM for HPC and Mission Critical solutions, that Hewlett Packard Enterprise. And then we're gonna pivot Addison Snell, who is the CEO of research firm Intersect 3. 60. So, Pete, starting with you Welcome. And really a pleasure to have you here. I want to first start off by asking you what is the key trends that you see in the HPC and supercomputing space? And I really appreciate if you could talk about how customer consumption patterns are changing. >>Yeah, I appreciate that, David, and thanks for having me. You know, I think the biggest thing that we're seeing is just the massive growth of data. And as we get larger and larger data sets larger and larger models happen, and we're having more and more new ways to compute on that data. So new algorithms like A. I would be a great example of that. And as people are starting to see this, especially they're going through a digital transformations. You know, more and more people I believe can take advantage of HPC but maybe don't know how and don't know how to get started on DSO. They're looking for how to get going into this environment and many customers that are longtime HBC customers, you know, just consume it on their own data centers. They have that capability, but many don't and so they're looking at. How can I do this? Do I need to build up that capability myself? Do I go to the cloud? What about my data and where that resides. So there's a lot of things that are going into thinking through How do I start to take advantage of this new infrastructure? >>Excellent. I mean, we all know HPC workloads. You're talking about supporting research and discovery for some of the toughest and most complex problems, particularly those that affecting society. So I'm interested in your thoughts on how you see Green Lake helping in these endeavors specifically, >>Yeah, One of the most exciting things about HPC is just the impact that it has, you know, everywhere from, you know, building safer cars and airplanes. Thio looking at climate change, uh, to, you know, finding new vaccines for things like Covic that we're all dealing with right now. So one of the biggest things is how do we take advantage event and use that to, you know, benefit society overall. And as we think about implementing HPC, you know, how do we get started? And then how do we grow and scale as we get more and more capability? So that's the biggest things that we're seeing on that front. >>Yes. Okay, So just about a year ago, you guys launched the Green Lake Initiative and the whole, you know, complete focus on as a service. So I'm curious as to how the new Green Lake services the HPC services specifically as it relates to Greenlee. How do they fit in the H. P s overall high performance computing portfolio and the strategy? >>Yeah, great question. You know, Green Lake is a new consumption model for eso. It's a very exciting We keep our entire HPC portfolio that we have today, but extend it with Green Lake and offer customers you know, expanded consumption choices. So, you know, customers that potentially are dealing with the growth of their data or they're moving toe digital transformation applications they can use green light just easily scale up from workstations toe, you know, manage their system costs or operational costs, or or if they don't have staff to expand their environment. Green Light provides all of that in a manage infrastructure for them. So if they're going from like a pilot environment up into a production environment over time, Green Lake enables them to do that very simply and easily without having toe have all that internal infrastructure people, computer data centers, etcetera. Green Lake provides all that for them so they can have a turnkey solution for HBC. >>So a lot easier entry strategies. A key key word that you use. There was choice, though. So basically you're providing optionality. You're not necessarily forcing them into a particular model. Is that correct? >>Yeah, 100%. Dave. What we want to do is just expand the choices so customers can buy a new choir and use that technology to their advantage is whether they're large or small. Whether they're you know, a startup or Fortune 500 company, whether they have their own data centers or they wanna, you know, use a Coehlo facility whether they have their own staff or not, we want to just provide them the opportunity to take advantage of this leading edge resource. >>Very interesting, Pete. It really appreciate the perspective that you guys have bring into the market. I mean, it seems to me it's gonna really accelerate broader adoption of high performance computing, toe the masses, really giving them an easier entry point I want to bring in now. Addison Snell to the discussion. Addison. He's the CEO is, I said of Intersect 3 60 which, in my view, is the world's leading market research company focused on HPC. Addison, you've been following the space for a while. You're an expert. You've seen a lot of changes over the years. What do you see is the critical aspect in the market, specifically as it relates toward this as a service delivery that we were just discussing with Pete and I wonder if you could sort of work in their the benefits in terms of, in your view, how it's gonna affect HPC usage broadly. Yeah, Good morning, David. Thanks very much for having me, Pete. It's great to see you again. So we've been tracking ah lot of these utility computing models in high performance computing for years, particularly as most of the usage by revenue is actually by commercial endeavors. Using high performance computing for their R and D and engineering projects and the like. And cloud computing has been a major portion of that and has the highest growth rate in the market right now, where we're seeing this double digit growth that accounted for about $1.4 billion of the high performance computing industry last year. But the bigger trend on which makes Green like really interesting is that we saw an additional about a billion dollars worth of spending outside what was directly measured in the cloud portion of the market in in areas that we deemed to be cloud like, which were as a service types of contracts that were still utility computing. But they might be under a software as a service portion of the budget under software or some other managed services type of contract that the user wasn't reported directly is cloud, but it was certainly influenced by utility computing, and I think that's gonna be a really dominant portion of the market going forward. And when we look at growth rate and where the market's been evolving, so that's interesting. I mean, basically, you're saying this, you know, the utility model is not brand new. We've seen that for years. Cloud was obviously a catalyst that gave that a boost. What is new, you're saying is and I'll say it this way. I'd love to get your independent perspective on this is so The definition of cloud is expanding where it's you know, people always say it's not a place, it's an experience and I couldn't agree more. But I wonder if you could give us your independent perspective on that, both on the thoughts of what I just said. But also, how would you rate H. P. E s position in this market? Well, you're right, absolutely, that the definition of cloud is expanding, and that's a challenge when we run our surveys that we try to be pedantic in a sense and define exactly what we're talking about. And that's how we're able to measure both the direct usage of ah, typical public cloud, but also ah more flexible notion off as a service. Now you asked about H P E. In particular, And that's extremely relevant not only with Green Lake but with their broader presence in high performance computing. H P E is the number one provider of systems for high performance computing worldwide, and that's largely based on the breath of H. P s offerings, in addition to their performance in various segments. So picking up a lot of the commercial market with their HP apology and 10 plus, they hit a lot of big memory configurations with Superdome flex and scale up to some of the most powerful supercomputers in the world with the HP Cray X platforms that go into some of the leading national labs. Now, Green Light gives them an opportunity to offer this kind of flexibility to customers rather than committing all it wants to a particular purchase price. But if you want to do position those on a utility computing basis pay for them as a service without committing to ah, particular public cloud. I think that's an interesting role for Green Lake to play in the market. Yeah, it's interesting. I mean earlier this year, we celebrated Exa scale Day with support from HP, and it really is all about a community and an ecosystem is a lot of camaraderie going on in the space that you guys are deep into, Addison says. We could wrap. What should observers expect in this HPC market in this space over the next a few years? Yeah, that's a great question. What to expect because of 2020 has taught us anything. It's the hazards of forecasting where we think the market is going. When we put out a market forecast, we tend not to look at huge things like unexpected pandemics or wars. But it's relevant to the topic here because, as I said, we were already forecasting Cloud and as a service, models growing. Any time you get into uncertainty, where it becomes less easy to plan for where you want to be in two years, three years, five years, that model speaks well to things that are cloud or as a service to do very well, flexibly, and therefore, when we look at the market and plan out where we think it is in 2020 2021 anything that accelerates uncertainty actually is going. Thio increase the need for something like Green Lake or and as a service or cloud type of environment. So we're expecting those sorts of deployments to come in over and above where we were already previously expected them in 2020 2021. Because as a service deals well with uncertainty. And that's just the world we've been in recently. I think there's a great comments and in a really good framework. And we've seen this with the pandemic, the pace at which the technology industry in particular, of course, HP specifically have responded to support that your point about agility and flexibility being crucial. And I'll go back toe something earlier that Pete said around the data, the sooner we can get to the data to analyze things, whether it's compressing the time to a vaccine or pivoting our business is the better off we are. So I wanna thank Pete and Addison for your perspectives today. Really great stuff, guys. Thank you. >>Yeah, Thank you. >>Alright, keep it right there from, or great insights and content you're watching green leg day. Alright, Great discussion on HPC. Now we're gonna get into some of the new industry examples and some of the case studies and new platforms. Keith HP, Green Lake It's moving forward. That's clear. You're picking up momentum with customers, but can you give us some examples of platforms for industry use cases and some specifics around that? >>You know, you bet, and actually you'll hear more details from Arwa Qadoura she leads are green like the market efforts in just a little bit. But specifically, I want to highlight some examples where we provide cloud services to help solve some of the most demanding workloads on the planet. So, first off in financial services, for example, traditional banks are facing increased competition and evolving customer expectations they need to transform so that they can reduce risk, manage cop and provided differentiated customer experience. We'll talk about a platform for Splunk that does just that. Second, in health care institutions, they face the growing list of challenges, some due to the cove in 19 Pandemic and others. Years in the making, like our aging population and rise in chronic disease, is really driving up demands, and it's straining capital budgets. These global trance create a critical need for transformation. Thio improve that patient experience and their business outcomes. Another example is in manufacturing. They're facing many challenges in order to remain competitive, right, they need to be able to identify new revenue streams run more efficiently from an operation standpoint and scale. Their resource is so you'll hear more about how we're optimizing and delivery for manufacturing with S. A P Hana and always gonna highlight a little more detail on today's news how we're delivering supercomputing through HP Green Lake It's scale and finally, how we have a robust ecosystem of partners to help enterprises easily deploy these solutions. For example, I think today you're gonna be talking to Skip Bacon from Splunk. >>Yeah, absolutely. We sure are. And some really great examples there, especially a couple industries that that stood out. I mean, financial services and health care. They're ripe for transformation and maybe disruption if if they don't move fast enough. So Keith will be coming back to you a little later today to wrap things up. So So thank you. Now, now we're gonna take a look at how HP is partnering with Splunk and how Green Lake compliments, data rich workloads. Let's watch. We're not going to dig deeper into a data oriented workload. How HP Green Lake fits into this use case and with me, a Skip Bacon vice president, product management at Splunk Skip. Good to see >>you. Good to see you as well there. >>So let's talk a little bit about Splunk. I mean, you guys are a dominant player and security and analytics and you know, it's funny, Skip, I used to comment that during the big data, the rise of big data Splunk really never positioned themselves is this big data player, and you know all that hype. But But you became kind of the leader in big data without really, even, you know, promoting it. It just happened overnight, and you're really now rapidly moving toward a subscription model. You're making some strategic moves in the M and a front. Give us your perspective on what's happening at the company and why customers are so passionate about your software. >>Sure, a great, great set up, Dave. Thanks. So, yeah, let's start with the data that's underneath big data, right? I think I think it is usual. The industry sort of seasons on a term and never stops toe. Think about what it really means. Sure, one big part of big data is your transaction and stuff, right? The things that catch generated by all of your Oracle's USC Cheops that reflect how the business actually occurred. But a much bigger part is all of your digital artifacts, all of the machine generated data that tells you the whole story about what led up to the things that actually happened right within the systems within the interactions within those systems. That's where Splunk is focused. And I think what the market is the whole is really validating is that that machine generated data those digital artifacts are a tely least is important, if not more so, than the transactional artifacts to this whole digital transformation problem right there. Critical to showing I t. How to get better developing and deploying and operating software, how to get better securing these systems, and then how to take this real time view of what the business looks like as it's executing in the software right now. And hold that up to and inform the business and close that feedback loop, right? So what is it we want to do differently digitally in order to do different better on the transformation side of the house. So I think a lot of splints. General growth is proof of the value crop and the need here for sure, as we're seeing play out specifically in the domains of ICTs he operations Dev, ops, Cyber Security, right? As well as more broadly in that in that cloak closing the business loop Splunk spin on its hair and growing our footprint overall with our customers and across many new customers, we've been on its hair with moving parts of that footprints who and as a service offering and spawn cloud. But a lot of that overall growth is really fueled by just making it simpler. Quicker, faster, cheaper, easier toe operates Plunkett scale because the data is certainly not slowing down right. There's more and more and more of it every day, more late, their potential value locked up in it. So anything that we can do and that our partners conducive to improve the cost economics to prove the agility to improve the responsiveness of these systems is huge. That that customer value crop and that's where we get so excited about what's going on with green life >>Yeah, so that makes sense. I mean, the digital businesses, a data business. And that means putting data at the core. And Splunk is obviously you keep part of that. So, as I said earlier, spunk your leader in this space, what's the deal with your HP relationship? You touched on that? What should we know about your your partnership? And what's that solution with H h p E? What's that customer Sweet spot. >>Yep. Good. All good questions. So we've been working with HP for quite a while on on a number of different fronts. This Green lake peace is the most interesting and sort of the intersection of, you know, purist intersection of both of these threads of these factories, if you will. So we've been working to take our core data platform deployed on an enterprise operator for kubernetes. Stick that a top H P s green like which is really kubernetes is a service platform and go prove performance, scalability, agility, flexibility, cost economics, starting with some of slugs, biggest customers. And we've proven, you know, alot of those things In great measure, I think the opportunity you know, the ability to vertically scale Splunk in containers that taught beefy boxes and really streamline the automation, the orchestration, the operations, all of that yields what, in the words of one of our mutual customers, literally put it as This is a transformational platform for deploying and operating spot for us so hard at work on the engineering side, hard at work on the architectural referencing, sizing, you know, capacity planning sides, and then increasing really rolling up our sleeves and taking the stuff the market together. >>Yeah, I mean, we're seeing the just the idea of cloud. The definition of cloud expanding hybrid brings in on Prem. We talked about the edge and and I really We've seen Splunk rapidly transitioning its pricing model to a subscription, you know, platform, if you will. And of course, that's what Green Lakes all about. What makes Splunk a good fit for Green Lake and vice versa? What does it mean for customers? >>Sure, So a couple different parts, I think, make make this a perfect marriage. Splunk at its core, if you're using it well, you're using it in a very iterative discovery driven kind of follow you the path to value basis that makes it a little hard to plan the infrastructure and decides these things right. We really want customers to be focused on how to get more data in how to get more value out. And if you're doing it well, those things, they're going to go up and up and up over time. You don't wanna be constrained by size and capacity planning, procurement cycles for infrastructure. So the Green Lake model, you know, customers got already deployed systems already deployed, capacity available in and as the service basis, very fast, very agile. If they need a next traunch of capacity to bring in that next data set or run, that next set of analytics right it's available immediately is a service, not hey, we've got to kick off the procurement cycle for a whole bunch more hardware boxes. So that flexibility, that agility or key to the general pattern for using Splunk and again that ability to vertically scale stick multiple Splunk instances into containers and load more and more those up on these physical boxes right gives you great cost economics. You know, Splunk has a voracious appetite for data for doing analytics against that data less expensive, we can make that processing the better and the ability to really fully sweat, you know, sweat the assets fully utilize those assets. That kind of vertical scale is the other great element of the Green Lake solution. >>Yes. I mean, when you think about the value prop for for customers with Splunk and HP green, that gets a lot of what you would expect from what we used to talk about with the early days of cloud. Uh, that that flexibility, uh, it takes it away. A lot of the sort of mundane capacity planning you can shift. Resource is you talked about, you know, scale in a in a number of of use cases. So that's sort of another interesting angle, isn't it? >>Yeah. Faster. It's the classic text story. Faster, quicker, cheaper, easier, right? Just take in the whole whole new holy levels and hold the extremes with these technologies. >>What do you see? Is the differentiators with Splunk in HP, Maybe what's different from sort of the way we used to do things, but also sort of, you know, modern day competition. >>Yeah. Good. All good. All good questions. So I think the general attributes of splinter differentiated green Laker differentiated. I think when you put them together, you get this classic one plus one equals three story. So what? I hear from a lot of our target customers, big enterprises, big public sector customers. They can see the path to these benefits. They understand in theory how these different technologies would work together. But they're concerned about their own skills and abilities to go building. Run those and the rial beauty of Green Lake and Splunk is this. All comes sort of pre design, pre integrated right pre built HP is then they're providing these running containers as a service. So it's taking a lot of the skills and the concerns off the customers plate right, allowing them to fast board to, you know, cutting edge technology without any of the wrist. And then, most importantly, allowing customers to focus their very finite resource is their peoples their time, their money, their cycles on the things that are going to drive differentiated value back to the business. You know, let's face facts. Buying and provisioning Hardware is not a differentiating activity, running containers successfully, not differentiating running the core of Splunk. Not that differentiating. He can take all of those cycles and focus them instead on in the simple mechanics. How do we get more data in? Run more analytics on it and get more value out? Right then you're on the path to really delivering differentiated, you know, sustainable competitive basis type stuff back to the business, back to that digital transformation effort. So taking the skills out, taking the worries out, taking the concerns about new tech, out taking the procurement cycles, that improving scalability again quicker, faster, cheaper. Better for sure. >>It's kind of interesting when you when you look at the how the parlance has evolved from cloud and then you had Private Cloud. We talk a lot about hybrid, but I'm interested in your thoughts on why Splunk and HP Green Light green like now I mean, what's happening in the market that makes this the right place and in the right time, so to speak. >>Yeah, again, I put cloud right up there with big data is one of those really overloaded terms. Everything we keep keep redefining as we go if we define it. One way is as an experience instead of outcomes that customers looking for right, what does anyone of our mutual customers really want Well, they want capabilities that air quick to get up and running that air fast, to get the value that are aligned with how the price wise, with how they deliver value to the business and that they can quickly change right as the needs of the business and the operation shift. I think that's the outcome set that people are looking thio. Certainly the early days of cloud we thought were synonymous with public cloud. And hey, the way that you get those outcomes is you push things out. The public cloud providers, you know, what we saw is a lot of that motion in cases where there wasn't the best of alignment, right? You didn't get all those outcomes that you were hoping for. The cost savings weren't there or again. These big enterprises, these big organizations have a whole bunch of other work clothes that aren't necessarily public cloud amenable. But what they want is that same cloud experience. And this is where you see the evolution in the hybrid clouds and into private clouds. Yeah, any one of our customers is looking across the entirety of this landscape, things that are on Prem that they're probably gonna be on Prem forever. Things that they're moving into private cloud environments, things that they're moving into our growing or expanding or landing net new public cloud. They want those same outcomes, the same characteristics across all of that. That's a lot of Splunk value. Crop is a provider, right? Is we can go monitor and help you operate and developed and secure exactly all of that, no matter where it's located. Splunk on Green Lake is all about that stack, you know, working in that very cloud native way even where it made sense for customers to deploy and operate their own software. Even if this want, they're running over here themselves is hoping the modern, secure other work clothes that they put into their public cloud environments. >>Well, it Z another key proof point that we're seeing throughout the day here. Your software leader, you know, HP bring it together. It's ecosystem partners toe actually deliver tangible value. The customers skip. Great to hear your perspective today. Really appreciate you coming on the program. >>My pleasure. And thanks so much for having us take care. Stay well, >>Yeah, Cheers. You too. Okay, keep it right there. We're gonna go back to Keith now. Have him on a close out this segment of the program. You're watching HP Green Lake Day on the Cube. All right, We're So we're seeing some great examples of how Green Lake is supporting a lot of different industries. A lot of different workloads we just heard from Splunk really is part of the ecosystem. Really? A data heavy workload. And we're seeing the progress. HPC example Manufacturing. We talked about healthcare financial services, critical industries that are really driving towards the subscription model. So, Keith, thanks again for joining us. Is there anything else that we haven't hit that you feel are audience should should know about? >>Yeah, you bet. You know, we didn't cover some of the new capabilities that are really providing customers with the holistic experience to address their most demanding workloads with HP Green Lake. So first is our Green Lake managed security services. So this provides customers with an enterprise grade manage security solution that delivers lower costs and frees up a lot of their resource is the second is RHP advisory and Professional Services Group. So they help provide customers with tools and resource is to explore their needs for their digital transformation. Think about workshops and trials and proof of concepts and all of that implementation. Eso You get the strategy piece, you get the advisory piece, and then you get the implementation piece that's required to help them get started really quickly. And then third would be our H. P s moral software portfolio. So this provides customers with the ability to modernize their absent data unify, hybrid cloud and edge computing and operationalized artificial intelligence and machine learning and analytics. >>You know, I'm glad that you brought in the sort of machine intelligence piece in the machine learning because that's, ah, lot of times. That's the reason why people want to go to the cloud at the same time you bring in the security piece a lot of reasons why people want to keep things on Prem. And, of course, the use cases here. We're talking about it, really bringing that cloud experience that consumption model on Prem. I think it's critical critical for companies because they're expanding their notion of cloud computing really extending into hybrid and and the edge with that similar experience or substantially the same experience. So I think folks are gonna look at today's news as real progress. We're pushing you guys on some milestones and some proof points towards this vision is a critical juncture for organizations, especially those look, they're looking for comprehensive offerings to drive their digital transformations. Your thoughts keep >>Yeah, I know you. You know, we know as many as 70% of current and future APS and data are going to remain on Prem. They're gonna be in data centers, they're gonna be in Colo's, they're gonna be at the edge and, you know, really, for critical reasons. And so hybrid is key. As you mentioned, the number of times we wanna help customers transform their businesses and really drive business outcomes in this hybrid, multi cloud world with HP Green Lake and are targeted solutions. >>Excellent. Keith, Thanks again for coming on the program. Really appreciate your time. >>Always. Always. Thanks so much for having me and and take Take care of. Stay healthy, please. >>Alright. Keep it right there. Everybody, you're watching HP Green Lake day on the Cube

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Everyone welcome back to the cubes. Coverage of AWS Reinvent Amazon Web services Annual conference theme. Cuba's normally there in person. This year we can't be. It's a virtual event. This is the Cube virtual. I'm your host for the Cube. John Ferrier Tresh Damn Kroger, VP of G M and G m of the high performance computing team at Intel is here in the Cube until a big part of the cube every year. Trish, thank you for coming on Were remote. We can't be in person. Um, good to see you. >>Good to see you. >>I'm really impressed with Reinvent Has grown from kind of small show eight years ago to now kind of a bellwether. And and every year it's the same story. More scale, more performance, lower prices. This is kind of the intel cadence that we've seen of Intel over the years. But high performance computing, which >>has been >>around for a while, has gotten much more mainstream thinking because it's applying now to scale. So I want to get your thoughts and and just set the table real quick. What is high performance computing mean these days from Intel? And has that relate to what people are experiencing >>e high performance computing? Um, yes, it's been traditionally known as something that's, you know, in the in the labs and the government, you know, not used widely. But high performance computing is truly just changing the world is what you can dio Cove. It is a great example of where they've taken high performance computing to speed up the discovery of drugs and vaccines for or cova 19. They use it every day. You know, whether it's making Pampers or Clorox boxes. So they are those bottles so that they, when you drop them, they don't break, um, to designing airplanes and designing, um, Caterpillar tractors. So it is pervasive throughout. And, um, sometimes people don't realize that high performance computing infrastructure is kind of that basics that you use whenever you need to do something with dense compute. >>So what some examples of workloads can you just share? I mean, obviously Xeon processor. We've covered that many times, but I mean from a workload standpoint, what kind of workloads are high performance computing kind of related or unable or ideal for that's out there, >>right? Z on scalable processors are the foundation for high performance computing. If you look at what most people run high performance computing on its see on, and I think that it's so broad. So if you look at seismic processing or molecular dynamics for the drug discovery type work or if you think about, um, open foam for fluid dynamics or, um, you know, different financial trade service, you know, frequency, fats, frequency trading or low. I can't even think of that word. But anyway, trading is very common using high performance computing. I mean, it's just used pervasively throughout. >>Yeah, and you're seeing you're seeing the cloud of clarification of that. I want to get your thoughts. The next question is, you know it's not just Intel hardware. You mentioned Zeon, but HBC in AWS were here. It reinvent. Can you share how that plays out? What's your what's your What's your take on that? Because it's not just hard work and you just take them into explain relationship, >>right? So we definitely have seen the growth of high performance computing in the cloud over the last couple of years. We've talked about this for, you know, probably a decade, and we've definitely seen that shift. And with AWS, we have this wonderful partnership where Intel is not only bringing the hardware like you say, the Z on scalable processors, but we're also having accelerators and then on that whole software ecosystem where we work closely with our I s V and O S v partners. And when we bring, um, not only compilers but also analyzers in our full to tool suite so people can move between an on Prem situation Thio Public cloud like aws. Um, seamlessly. >>So talk about the developer impact. As I say, it's that learning show reinvent. There's a lot of developers here. I'll see mainstream you're seeing, you know, obviously the born in the cloud. But now you're seeing large scale enterprises and big businesses. You mentioned financial services from high frequency trading to oil and gas. Every vertical has a need for cloud and and what, you should be traditionally on premises compute. So you have. You're kind of connecting those dots here with AWS. Um, what is some of the developer angle here? Because they're in the cloud to they want to develop. How does how does the developer, um, engage with you guys on HPC in Amazon, >>Right? Well, there's there's a couple ways. I mean, so we do work with some of our partners eso that they could help move those workloads to the cloud. So an example is 69 which recently helped a customer successfully port a customized version of the in car models for prediction across scales. So they chose the C 59 18 x large instance type because this is what really deliver the highest performance and the lowest price for compute ratio. Another great example is P. K. I, which is a partner out of the UK, has worked with our customers to implement AI in retail and other segments running on Intel Instances of the EEC too. So I think these air just so you could have people help you migrate your workloads into the cloud. But then also, one of the great things I would like to talk about is, um a ws has come out with the parallel cluster, which is an Intel select solution, which really helps, um, ease that transition from on Prem to cloud. >>That's awesome. Um, let's get into that parallel cluster and you mentioned Intel Select Solution program. There's been some buzz on that. Can you take a minute to explain what that is? I >>mean, the HBC has, AH reputation of being hard, and the whole philosophy between behind the Intel Select solution is to make it easier for our customers to run HBC workloads in the cloud or on Prem and with E Intel Select Solution. It's also about scaling your job across a large number of notes, so we've made it a significant investment into the full stack. So this is from the silicon level all the way up to the application level so that we ensure that your application runs best on Intel and we bring together all the everything that you need into. Basically, it's a reference design. So it's a recipe where we jointly created it with our I, C, P and O S V partners and our open source environment for all the different relevant workloads. And so Amazon Web Services is the first cloud service provider to actually verify a service such as Intel Select Solution and this is this is amazing because this truly means that somebody can say it works today on Prem, and I know it will work exactly the same in AWS Cloud. >>That's huge. And I wanna just call that out because I think it's worth noting. You guys just don't throw this around like in the industry like doing these kind of partnerships. Intel's been pretty hard core on the quality, and so having a cloud service provider kind of go through the thing, it's really notable you mentioned parallel cluster um, deal. What is Can you just tie that together? Because if I get this right, the Intel, uh, select solution with the cloud service provider Amazon is a reference designed for how to go on premise or edge or revenue. It is to cloud in and out of cloud. How does this parallel cluster project fit into all this? Can you just unpack that a little bit? >>Right. So the parallel cluster basically, um, it's a parallel cluster until select solution. And there's three instances that we're featuring with the Intel Xeon Scalable processor, which gives you a variety of compute characteristics. So the select solution gives you the compute, the storage, the memory the networking that you need. You know, it says the specifications for what you need to run a non optimal way. And then a WS has allowed us to take some of the C five or some of the instances, and we are on. Three different instances were on the C five, in instance. But that's for your compute optimize work clothes. We're on the in five instance and that's really for a balanced between higher memory per core ratio. And then you have your are five and instance at a W s that's really targeted for that memory intensive workloads. And so all of these are accessible within the single A. W s parallel cholesterol environment on bits at scale. And it's really you're choosing of what you want to take and do. And then on top of that, the they're enabled with the next generation AWS Nitro system, which delivers 100 gigabits of networking for the HBC workloads. So that is huge for HPC. >>I was gonna get to the Nitro is my one of my top questions. Thanks >>for >>thanks for clarifying that. You know, I'm old enough to remember the old days when you have the intel inside the PC a shell of, ah box and create all that great productivity value. But with cloud, it's almost like we're seeing that again. You just hit on some key points you have. Yeah, this is HPC is like memory storage. You've got networking a compute. All these things kind of all kind of working together. If I get that right, you just kind of laid that out there. And it's not an intel Has to be intel. Everything. Your intel inside the cloud now and on premise, which is the There is no on premise anymore. It's cloud operations. If I get this right because you're essentially bridging the two worlds with the chips, you bring on premise which could be edge a big edge or small legend in cloud. Is that right? I mean, this is kind of where this is >>going. Yeah, so I mean, what I think about so a lot of them. The usages for HBC in the cloud is burst capacity. Most HBC centers are 100% not 100% because they have to do maintenance, but 95% utilized, so there is no more space. And so when you have a need to do a larger run or you need thio, you know, have something done quickly you burst to the cloud. That's just what you need to do now. I mean, or you want to try out different instances. So you want to see whether maybe that memory intensive workload would work better? Maybe in kind of that are five in instance, and that gives you that opportunity to see and also, you know, maybe what you want to purchase. So truly, we're entering this hybrid cloud bottle where you can't, um the demand for high performance computing is so large that you've got to be able to burst to the cloud. >>I think you guys got it right. I'm really impressed. And I like what I'm seeing. And I think you talked about earlier the top of the interview, government labs and whatnot. I think those are the early adopters because when they need more power and they usually don't have a lot of big budgets, a little max out and then go to the cloud Whether it's, you know, computing, you know what's going on in the ocean and climate change are all these things that they work on that need massive compute and power. That's a a pretext to enterprise. So if you can't connect the dots, you're kind of right in line with what we're seeing. So super impressive. Thanks for sharing that. Final thoughts on this is that performance. So Okay, the next question is, OK, all great. You're looking good off the tee or looking down the road. Clear path to success in the future. How does the performance compare in the cloud versus on premise? >>It could be well, and that's one of the great things about the Intel select solution because we have optimized that reference designed so that you can get the performance you're used to on Prem in the AWS Cloud. And so that is what's so cool honestly, about this opportunity So we can help you know, that small and medium business that doesn't maybe have this resource is or even those industries that do. And they know they're already a reference using that modeling SIM reference design, and they can now just burst to the cloud and it will work. But the performance they expect >>Trish, great to have you on great insight. Thanks for sharing all the great goodness from Intel and the A W s final thoughts on the on the partnership. We're not in person. And by the way, Intel usually has a huge presence. The booth is usually right behind the cube stage, which you guys sponsor. Thank you very much greater. Always partner with you. Great party. You sponsor the replay, which is always great, and it's always great party and great partnership. Good content. We're not there this year. What's the relationship like? And you take a minute to explain your final thoughts on a Amazon Web services and intel. >>Yeah, I know we have, Ah, Long term partnership 14 plus year partnership with AWS. And I mean, I think it's with the your, um taking Intel Select solution. It's going to be even a richer partnership we're gonna have in the future. So I'm thrilled that I have the opportunity to talk about it and really talk about how excited I am to be able Thio bring Mawr HBC into the world. It's all about the democratization of HBC because HBC changes the world >>well. Tricia, congratulations on the select program with AWS and the first cloud service provider really is a nice directional indicator of what's gonna happen. Futures laid out. Of course. Intel's in front. Thank you for coming. I appreciate it. >>Oh, thank you, John. >>Okay, that's the cubes. Virtual coverage Cube. Virtual. We're not in person. Aws reinvent 2020 is virtual. Three weeks were over the next three weeks, we're gonna bring you coverage. Of course. Cube Live in studio in Palo Alto will be covering a lot of the news. Stay with us from or coverage after this short break. Thank you.

>>from around the globe. It's the Q with digital coverage of exa scale day made possible by Hewlett Packard Enterprise. Welcome, everyone to the Cube celebration of Exa Scale Day. Shaheen Khan is here. He's the founding partner, an analyst at Orion X And, among other things, he is the co host of Radio free HPC Shaheen. Welcome. Thanks for coming on. >>Thanks for being here, Dave. Great to be here. How are you >>doing? Well, thanks. Crazy with doing these things, Cove in remote interviews. I wish we were face to face at us at a supercomputer show, but, hey, this thing is working. We can still have great conversations. And And I love talking to analysts like you because you bring an independent perspective. You're very wide observation space. So So let me, Like many analysts, you probably have sort of a mental model or a market model that you look at. So maybe talk about your your work, how you look at the market, and we could get into some of the mega trends that you see >>very well. Very well. Let me just quickly set the scene. We fundamentally track the megatrends of the Information Age And, of course, because we're in the information age, digital transformation falls out of that. And the megatrends that drive that in our mind is Ayotte, because that's the fountain of data five G. Because that's how it's gonna get communicated ai and HBC because that's how we're gonna make sense of it Blockchain and Cryptocurrencies because that's how it's gonna get transacted on. That's how value is going to get transferred from the place took place and then finally, quantum computing, because that exemplifies how things are gonna get accelerated. >>So let me ask you So I spent a lot of time, but I D. C and I had the pleasure of of the High Performance computing group reported into me. I wasn't an HPC analyst, but over time you listen to those guys, you learning. And as I recall, it was HPC was everywhere, and it sounds like we're still seeing that trend where, whether it was, you know, the Internet itself were certainly big data, you know, coming into play. Uh, you know, defense, obviously. But is your background mawr HPC or so that these other technologies that you're talking about it sounds like it's your high performance computing expert market watcher. And then you see it permeating into all these trends. Is that a fair statement? >>That's a fair statement. I did grow up in HPC. My first job out of school was working for an IBM fellow doing payroll processing in the old days on and and And it went from there, I worked for Cray Research. I worked for floating point systems, so I grew up in HPC. But then, over time, uh, we had experiences outside of HPC. So for a number of years, I had to go do commercial enterprise computing and learn about transaction processing and business intelligence and, you know, data warehousing and things like that, and then e commerce and then Web technology. So over time it's sort of expanded. But HPC is a like a bug. You get it and you can't get rid of because it's just so inspiring. So supercomputing has always been my home, so to say >>well and so the reason I ask is I wanted to touch on a little history of the industry is there was kind of a renaissance in many, many years ago, and you had all these startups you had Kendall Square Research Danny Hillis thinking machines. You had convex trying to make many supercomputers. And it was just this This is, you know, tons of money flowing in and and then, you know, things kind of consolidate a little bit and, uh, things got very, very specialized. And then with the big data craze, you know, we've seen HPC really at the heart of all that. So what's your take on on the ebb and flow of the HPC business and how it's evolved? >>Well, HBC was always trying to make sense of the world, was trying to make sense of nature. And of course, as much as we do know about nature, there's a lot we don't know about nature and problems in nature are you can classify those problems into basically linear and nonlinear problems. The linear ones are easy. They've already been solved. The nonlinear wants. Some of them are easy. Many of them are hard, the nonlinear, hard, chaotic. All of those problems are the ones that you really need to solve. The closer you get. So HBC was basically marching along trying to solve these things. It had a whole process, you know, with the scientific method going way back to Galileo, the experimentation that was part of it. And then between theory, you got to look at the experiment and the data. You kind of theorize things. And then you experimented to prove the theories and then simulation and using the computers to validate some things eventually became a third pillar of off science. On you had theory, experiment and simulation. So all of that was going on until the rest of the world, thanks to digitization, started needing some of those same techniques. Why? Because you've got too much data. Simply, there's too much data to ship to the cloud. There's too much data to, uh, make sense of without math and science. So now enterprise computing problems are starting to look like scientific problems. Enterprise data centers are starting to look like national lab data centers, and there is that sort of a convergence that has been taking place gradually, really over the past 34 decades. And it's starting to look really, really now >>interesting, I want I want to ask you about. I was like to talk to analysts about, you know, competition. The competitive landscape is the competition in HPC. Is it between vendors or countries? >>Well, this is a very interesting thing you're saying, because our other thesis is that we are moving a little bit beyond geopolitics to techno politics. And there are now, uh, imperatives at the political level that are driving some of these decisions. Obviously, five G is very visible as as as a piece of technology that is now in the middle of political discussions. Covert 19 as you mentioned itself, is a challenge that is a global challenge that needs to be solved at that level. Ai, who has access to how much data and what sort of algorithms. And it turns out as we all know that for a I, you need a lot more data than you thought. You do so suddenly. Data superiority is more important perhaps than even. It can lead to information superiority. So, yeah, that's really all happening. But the actors, of course, continue to be the vendors that are the embodiment of the algorithms and the data and the systems and infrastructure that feed the applications. So to say >>so let's get into some of these mega trends, and maybe I'll ask you some Colombo questions and weaken geek out a little bit. Let's start with a you know, again, it was one of this when I started the industry. It's all it was a i expert systems. It was all the rage. And then we should have had this long ai winter, even though, you know, the technology never went away. But But there were at least two things that happened. You had all this data on then the cost of computing. You know, declines came down so so rapidly over the years. So now a eyes back, we're seeing all kinds of applications getting infused into virtually every part of our lives. People trying to advertise to us, etcetera. Eso So talk about the intersection of AI and HPC. What are you seeing there? >>Yeah, definitely. Like you said, I has a long history. I mean, you know, it came out of MIT Media Lab and the AI Lab that they had back then and it was really, as you mentioned, all focused on expert systems. It was about logical processing. It was a lot of if then else. And then it morphed into search. How do I search for the right answer, you know, needle in the haystack. But then, at some point, it became computational. Neural nets are not a new idea. I remember you know, we had we had a We had a researcher in our lab who was doing neural networks, you know, years ago. And he was just saying how he was running out of computational power and we couldn't. We were wondering, you know what? What's taking all this difficult, You know, time. And it turns out that it is computational. So when deep neural nets showed up about a decade ago, arm or it finally started working and it was a confluence of a few things. Thalib rhythms were there, the data sets were there, and the technology was there in the form of GPS and accelerators that finally made distractible. So you really could say, as in I do say that a I was kind of languishing for decades before HPC Technologies reignited it. And when you look at deep learning, which is really the only part of a I that has been prominent and has made all this stuff work, it's all HPC. It's all matrix algebra. It's all signal processing algorithms. are computational. The infrastructure is similar to H B. C. The skill set that you need is the skill set of HPC. I see a lot of interest in HBC talent right now in part motivated by a I >>mhm awesome. Thank you on. Then I wanna talk about Blockchain and I can't talk about Blockchain without talking about crypto you've written. You've written about that? I think, you know, obviously supercomputers play a role. I think you had written that 50 of the top crypto supercomputers actually reside in in China A lot of times the vendor community doesn't like to talk about crypto because you know that you know the fraud and everything else. But it's one of the more interesting use cases is actually the primary use case for Blockchain even though Blockchain has so much other potential. But what do you see in Blockchain? The potential of that technology And maybe we can work in a little crypto talk as well. >>Yeah, I think 11 simple way to think of Blockchain is in terms off so called permission and permission less the permission block chains or when everybody kind of knows everybody and you don't really get to participate without people knowing who you are and as a result, have some basis to trust your behavior and your transactions. So things are a lot calmer. It's a lot easier. You don't really need all the supercomputing activity. Whereas for AI the assertion was that intelligence is computer herbal. And with some of these exa scale technologies, we're trying to, you know, we're getting to that point for permission. Less Blockchain. The assertion is that trust is computer ble and, it turns out for trust to be computer ble. It's really computational intensive because you want to provide an incentive based such that good actors are rewarded and back actors. Bad actors are punished, and it is worth their while to actually put all their effort towards good behavior. And that's really what you see, embodied in like a Bitcoin system where the chain has been safe over the many years. It's been no attacks, no breeches. Now people have lost money because they forgot the password or some other. You know, custody of the accounts have not been trustable, but the chain itself has managed to produce that, So that's an example of computational intensity yielding trust. So that suddenly becomes really interesting intelligence trust. What else is computer ble that we could do if we if we had enough power? >>Well, that's really interesting the way you described it, essentially the the confluence of crypto graphics software engineering and, uh, game theory, Really? Where the bad actors air Incentive Thio mined Bitcoin versus rip people off because it's because because there are lives better eso eso so that so So Okay, so make it make the connection. I mean, you sort of did. But But I want to better understand the connection between, you know, supercomputing and HPC and Blockchain. We know we get a crypto for sure, like in mind a Bitcoin which gets harder and harder and harder. Um and you mentioned there's other things that we can potentially compute on trust. Like what? What else? What do you thinking there? >>Well, I think that, you know, the next big thing that we are really seeing is in communication. And it turns out, as I was saying earlier, that these highly computational intensive algorithms and models show up in all sorts of places like, you know, in five g communication, there's something called the memo multi and multi out and to optimally manage that traffic such that you know exactly what beam it's going to and worth Antenna is coming from that turns out to be a non trivial, you know, partial differential equation. So next thing you know, you've got HPC in there as and he didn't expect it because there's so much data to be sent, you really have to do some data reduction and data processing almost at the point of inception, if not at the point of aggregation. So that has led to edge computing and edge data centers. And that, too, is now. People want some level of computational capability at that place like you're building a microcontroller, which traditionally would just be a, you know, small, low power, low cost thing. And people want victor instructions. There. People want matrix algebra there because it makes sense to process the data before you have to ship it. So HPCs cropping up really everywhere. And then finally, when you're trying to accelerate things that obviously GP use have been a great example of that mixed signal technologies air coming to do analog and digital at the same time, quantum technologies coming so you could do the you know, the usual analysts to buy to where you have analog, digital, classical quantum and then see which, you know, with what lies where all of that is coming. And all of that is essentially resting on HBC. >>That's interesting. I didn't realize that HBC had that position in five G with multi and multi out. That's great example and then I o t. I want to ask you about that because there's a lot of discussion about real time influencing AI influencing at the edge on you're seeing sort of new computing architectures, potentially emerging, uh, video. The acquisition of arm Perhaps, you know, amore efficient way, maybe a lower cost way of doing specialized computing at the edge it, But it sounds like you're envisioning, actually, supercomputing at the edge. Of course, we've talked to Dr Mark Fernandez about space born computers. That's like the ultimate edge you got. You have supercomputers hanging on the ceiling of the International space station, but But how far away are we from this sort of edge? Maybe not. Space is an extreme example, but you think factories and windmills and all kinds of edge examples where supercomputing is is playing a local role. >>Well, I think initially you're going to see it on base stations, Antenna towers, where you're aggregating data from a large number of endpoints and sensors that are gathering the data, maybe do some level of local processing and then ship it to the local antenna because it's no more than 100 m away sort of a thing. But there is enough there that that thing can now do the processing and do some level of learning and decide what data to ship back to the cloud and what data to get rid of and what data to just hold. Or now those edge data centers sitting on top of an antenna. They could have a half a dozen GPS in them. They're pretty powerful things. They could have, you know, one they could have to, but but it could be depending on what you do. A good a good case study. There is like surveillance cameras. You don't really need to ship every image back to the cloud. And if you ever need it, the guy who needs it is gonna be on the scene, not back at the cloud. So there is really no sense in sending it, Not certainly not every frame. So maybe you can do some processing and send an image every five seconds or every 10 seconds, and that way you can have a record of it. But you've reduced your bandwidth by orders of magnitude. So things like that are happening. And toe make sense of all of that is to recognize when things changed. Did somebody come into the scene or is it just you know that you know, they became night, So that's sort of a decision. Cannot be automated and fundamentally what is making it happen? It may not be supercomputing exa scale class, but it's definitely HPCs, definitely numerically oriented technologies. >>Shane, what do you see happening in chip architectures? Because, you see, you know the classical intel they're trying to put as much function on the real estate as possible. We've seen the emergence of alternative processors, particularly, uh, GP use. But even if f b g A s, I mentioned the arm acquisition, so you're seeing these alternative processors really gain momentum and you're seeing data processing units emerge and kind of interesting trends going on there. What do you see? And what's the relationship to HPC? >>Well, I think a few things are going on there. Of course, one is, uh, essentially the end of Moore's law, where you cannot make the cycle time be any faster, so you have to do architectural adjustments. And then if you have a killer app that lends itself to large volume, you can build silicon. That is especially good for that now. Graphics and gaming was an example of that, and people said, Oh my God, I've got all these cores in there. Why can't I use it for computation? So everybody got busy making it 64 bit capable and some grass capability, And then people say, Oh, I know I can use that for a I And you know, now you move it to a I say, Well, I don't really need 64 but maybe I can do it in 32 or 16. So now you do it for that, and then tens, of course, come about. And so there's that sort of a progression of architecture, er trumping, basically cycle time. That's one thing. The second thing is scale out and decentralization and distributed computing. And that means that the inter communication and intra communication among all these notes now becomes an issue big enough issue that maybe it makes sense to go to a DPU. Maybe it makes sense to go do some level of, you know, edge data centers like we were talking about on then. The third thing, really is that in many of these cases you have data streaming. What is really coming from I o t, especially an edge, is that data is streaming and when data streaming suddenly new architectures like F B G. A s become really interesting and and and hold promise. So I do see, I do see FPG's becoming more prominent just for that reason, but then finally got a program all of these things on. That's really a difficulty, because what happens now is that you need to get three different ecosystems together mobile programming, embedded programming and cloud programming. And those are really three different developer types. You can't hire somebody who's good at all three. I mean, maybe you can, but not many. So all of that is challenges that are driving this this this this industry, >>you kind of referred to this distributed network and a lot of people you know, they refer to this. The next generation cloud is this hyper distributed system. When you include the edge and multiple clouds that etcetera space, maybe that's too extreme. But to your point, at least I inferred there's a There's an issue of Leighton. See, there's the speed of light s So what? What? What is the implication then for HBC? Does that mean I have tow Have all the data in one place? Can I move the compute to the data architecturally, What are you seeing there? >>Well, you fundamentally want to optimize when to move data and when to move, Compute. Right. So is it better to move data to compute? Or is it better to bring compute to data and under what conditions? And the dancer is gonna be different for different use cases. It's like, really, is it worth my while to make the trip, get my processing done and then come back? Or should I just developed processing capability right here? Moving data is really expensive and relatively speaking. It has become even more expensive, while the price of everything has dropped down its price has dropped less than than than like processing. So it is now starting to make sense to do a lot of local processing because processing is cheap and moving data is expensive Deep Use an example of that, Uh, you know, we call this in C two processing like, you know, let's not move data. If you don't have to accept that we live in the age of big data, so data is huge and wants to be moved. And that optimization, I think, is part of what you're what you're referring to. >>Yeah, So a couple examples might be autonomous vehicles. You gotta have to make decisions in real time. You can't send data back to the cloud flip side of that is we talk about space borne computers. You're collecting all this data You can at some point. You know, maybe it's a year or two after the lived out its purpose. You ship that data back and a bunch of disk drives or flash drives, and then load it up into some kind of HPC system and then have at it and then you doom or modeling and learn from that data corpus, right? I mean those air, >>right? Exactly. Exactly. Yeah. I mean, you know, driverless vehicles is a great example, because it is obviously coming fast and furious, no pun intended. And also, it dovetails nicely with the smart city, which dovetails nicely with I o. T. Because it is in an urban area. Mostly, you can afford to have a lot of antenna, so you can give it the five g density that you want. And it requires the Layton sees. There's a notion of how about if my fleet could communicate with each other. What if the car in front of me could let me know what it sees, That sort of a thing. So, you know, vehicle fleets is going to be in a non opportunity. All of that can bring all of what we talked about. 21 place. >>Well, that's interesting. Okay, so yeah, the fleets talking to each other. So kind of a Byzantine fault. Tolerance. That problem that you talk about that z kind of cool. I wanna I wanna sort of clothes on quantum. It's hard to get your head around. Sometimes You see the demonstrations of quantum. It's not a one or zero. It could be both. And you go, What? How did come that being so? And And of course, there it's not stable. Uh, looks like it's quite a ways off, but the potential is enormous. It's of course, it's scary because we think all of our, you know, passwords are already, you know, not secure. And every password we know it's gonna get broken. But give us the give us the quantum 101 And let's talk about what the implications. >>All right, very well. So first off, we don't need to worry about our passwords quite yet. That that that's that's still ways off. It is true that analgesic DM came up that showed how quantum computers can fact arise numbers relatively fast and prime factory ization is at the core of a lot of cryptology algorithms. So if you can fact arise, you know, if you get you know, number 21 you say, Well, that's three times seven, and those three, you know, three and seven or prime numbers. Uh, that's an example of a problem that has been solved with quantum computing, but if you have an actual number, would like, you know, 2000 digits in it. That's really harder to do. It's impossible to do for existing computers and even for quantum computers. Ways off, however. So as you mentioned, cubits can be somewhere between zero and one, and you're trying to create cubits Now there are many different ways of building cubits. You can do trapped ions, trapped ion trapped atoms, photons, uh, sometimes with super cool, sometimes not super cool. But fundamentally, you're trying to get these quantum level elements or particles into a superimposed entanglement state. And there are different ways of doing that, which is why quantum computers out there are pursuing a lot of different ways. The whole somebody said it's really nice that quantum computing is simultaneously overhyped and underestimated on. And that is that is true because there's a lot of effort that is like ways off. On the other hand, it is so exciting that you don't want to miss out if it's going to get somewhere. So it is rapidly progressing, and it has now morphed into three different segments. Quantum computing, quantum communication and quantum sensing. Quantum sensing is when you can measure really precise my new things because when you perturb them the quantum effects can allow you to measure them. Quantum communication is working its way, especially in financial services, initially with quantum key distribution, where the key to your cryptography is sent in a quantum way. And the data sent a traditional way that our efforts to do quantum Internet, where you actually have a quantum photon going down the fiber optic lines and Brookhaven National Labs just now demonstrated a couple of weeks ago going pretty much across the, you know, Long Island and, like 87 miles or something. So it's really coming, and and fundamentally, it's going to be brand new algorithms. >>So these examples that you're giving these air all in the lab right there lab projects are actually >>some of them are in the lab projects. Some of them are out there. Of course, even traditional WiFi has benefited from quantum computing or quantum analysis and, you know, algorithms. But some of them are really like quantum key distribution. If you're a bank in New York City, you very well could go to a company and by quantum key distribution services and ship it across the you know, the waters to New Jersey on that is happening right now. Some researchers in China and Austria showed a quantum connection from, like somewhere in China, to Vienna, even as far away as that. When you then put the satellite and the nano satellites and you know, the bent pipe networks that are being talked about out there, that brings another flavor to it. So, yes, some of it is like real. Some of it is still kind of in the last. >>How about I said I would end the quantum? I just e wanna ask you mentioned earlier that sort of the geopolitical battles that are going on, who's who are the ones to watch in the Who? The horses on the track, obviously United States, China, Japan. Still pretty prominent. How is that shaping up in your >>view? Well, without a doubt, it's the US is to lose because it's got the density and the breadth and depth of all the technologies across the board. On the other hand, information age is a new eyes. Their revolution information revolution is is not trivial. And when revolutions happen, unpredictable things happen, so you gotta get it right and and one of the things that these technologies enforce one of these. These revolutions enforce is not just kind of technological and social and governance, but also culture, right? The example I give is that if you're a farmer, it takes you maybe a couple of seasons before you realize that you better get up at the crack of dawn and you better do it in this particular season. You're gonna starve six months later. So you do that to three years in a row. A culture has now been enforced on you because that's how it needs. And then when you go to industrialization, you realize that Gosh, I need these factories. And then, you know I need workers. And then next thing you know, you got 9 to 5 jobs and you didn't have that before. You don't have a command and control system. You had it in military, but not in business. And and some of those cultural shifts take place on and change. So I think the winner is going to be whoever shows the most agility in terms off cultural norms and governance and and and pursuit of actual knowledge and not being distracted by what you think. But what actually happens and Gosh, I think these exa scale technologies can make the difference. >>Shaheen Khan. Great cast. Thank you so much for joining us to celebrate the extra scale day, which is, uh, on 10. 18 on dso. Really? Appreciate your insights. >>Likewise. Thank you so much. >>All right. Thank you for watching. Keep it right there. We'll be back with our next guest right here in the Cube. We're celebrating Exa scale day right back.

>>long from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in Came along with its ecosystem partners. >>Hey, welcome back to the Cube. Lisa Martin at AWS Reinvent in Vegas. Very busy. Sands Expo Center. Pleased to be joined by my co host this afternoon. Justin Warren, founder and chief analyst at Pivot nine. Justin, we're hosting together again. We are. >>It's great to be >>here. It's great to have you that. So. Justin Meyer, please welcome a couple of our cue ball. Um, back to the program. A couple guys from nut up. We have Anthony Lie, the S B, P and G m of the Cloud business unit. Welcome back at the >>very much great to be here >>and color coordinating with Anthony's Jandi Stephenson, Chief Technology officer and GPS Cloud. Welcome back. >>Thank you. Thank you >>very shortly. Dress, guys and very >>thank you. Thank you. It's, uh, the good news Is that their suits anymore. So we're not going to have to wear ties >>comfortable guys net up a w s this event even bigger than last year, which I can't even believe that 65,000 or so thugs. But, Anthony, let's start with you. Talk to us about what's new with the net up AWS partnership a little bit about the evolution of it. >>Yeah. I mean, you know, we started on AWS. Oh, my gosh. Must be almost five or six years ago now and we made a conscious effort to port are operating system to AWS, which was no small task on dhe. It's taken us a few years, but we're really starting to hit our stride Now. We've been very successful, were on boarding customers on an ever increasing rate. We've added more. Service is on. We just continue to love the cloud as a platform for development. We can go so fast, and we can do things in in an environment like aws that, frankly, you just couldn't do on premise, you know, they're they're complexity and EJ ineighty of on premise was always a challenge. The cloud for us is an amazing platform where we can go very, very fast >>and from a customer demand standpoint. Don't talk to me about that, Chief technologist. One of the thing interesting things that that Andy Jassy shared yesterday was that surprised me. 97% of I t spend is still on from So we know that regardless of the M word, multi cloud work customers are living in that multi cloud world. Whether it's by strategy, a lot of it's not. A lot of it's inherited right, but they have to have that choice, right? It's gonna depend on the data, the workload, etcetera. What can you tell us about when you're talking with customers? What what? How are they driving NetApp evolution of its partnership with public provider AWS? >>So actually, I don't know if it's the desired state to be running in a hybrid, mostly cloud fashion, but it's it's It's driven by strategy, and it's usually driven by specific workloads and on the finding the best home for your application or for your workers at any given time. Because it's it's ultimately unrealistic for on premise customers to try to compete with like a machine and keep learning algorithms and the rate of development and rate off basically evolution in the cloud. So you always have to be there to be able to stay competitive, so it's becoming a part of the strategy even though it was probably asked that developers that drove a lot off cloud adoption to begin with. Maybe, maybe not. Not in favor of the c i o r. You have, like a lot of Cloud Cloud sprawling, but there's no longer sprawling it. It's part of the strategy before every company in my way >>heard from any Jesse in the keynote yesterday about the transformation being an important thing. And he also highlighted a lot of enterprise. Nedda has a long history with enterprise, Yes, very solid reputation with enterprise. So it feels to me like this This is an enterprise show. Now that the enterprise has really arrived at with the cloud, what are you seeing from the customers that you've already had for a long time? No, no, no, I'm familiar with it. Trust Net up. We're now exploring the Clouded and doing more than just dipping their toe in the water. What are they actually doing with the cloud and and we'll get up together, you know, >>we see and no one ever growing list of workload. I think when people make decisions in the cloud, they're not making those traditional horizontal decisions anymore. They're making workload by workload by workload decisions and Internet EPPS history and I think, uh, performance on premises, given customers peace of mind now in the cloud, they sort of know that what's been highly reliable, highly scaleable for them on premise, they can now have that same confidence in the cloud. So way started. Like just like Amazon. We started off seeing secondary workloads like D r Back Up Dev ops, but now is seeing big primaries go A s, a p big database workloads, e commerce. Ah, lot of HBC high forming compute. We're doing very well in oil and gas in the pharmaceutical industries where file has been really lacking on the public cloud. I think we leaned in as a company years ago and put put, put a concerted effort to make it there. And I think now the workloads a confident that were there and we can give them the throughput. We give them the performance on the protocols and now we're seeing big, big workloads come over to the public clouds. >>And he did make a big deal about transformation being important. And a lot of that was around the operational model. Let's let's just the pure technology. But what about the operating model? How are you seeing Enterprises Transformer? There's a lot of traditionally just taken a workload, do a bit of lift and shift and put it to the cloud. Where are they now transforming the way they actually operate? Things because of >>cloud? Absolutely. I mean, they have to They have to adopt the new technologies and new ways of doing business. So I mean, I think they are actually celebrating that to answer point. I think this is not a partnership and we're partnering with. We have a very unique story. We're partnering with all of them and have really deep engineering relationship with all of them. And they are now able to go after enterprise type workloads that they haven't been gone. I've been able to go after before, so that's why it's such a strategic strategic relationship that we have with all of them. That sort of brings in in the freedom of choice. You can basically go everywhere anywhere. That, in my opinion, is that true hyper cloud story lot has always been really difficult. But with the data management capabilities of not top, it's really easy to move my greater replicate across on premise toe are hyper scaler off choice. >>I mean, I think you know, if you're in enterprise right now, you know you're a CEO. You're probably scared to death of, like, being uber, you know exactly on. Uh, you know, if you're you know, So speed has now become what we say. The new scale they used to be scaled is your advantage. And now, if you're not fast, you could be killed any day by some of these startups who just build a mobile app. And all of a sudden they've gotten between you and the customer and you've lost. And I think CEOs are now. How fast are we going? How many application developers do we have? And did a scientist do we have? And because of that, that they're seeing Amazon as a platform for speed on. So that's just that paranoia. I think digital transformation is driving everybody to the cloud. >>You're right. If we look at transformation if a business and Andy Jassy and John for your talked about this and that exclusive interview that they did the other day. And Andy, if you're and a legacy enterprise and you're looking at your existing market share segment exactly, and you're not thinking there's somebody else. What assisting on there on the side mirror? Objects in mirror are closer. Not getting ready for that. You're on the wrong. You're going to be on the wrong side of that equation. But if we look at cloud, it has had an impact on traditional story one of naps. Taglines is data driven. If we look at transformation and if we'll even look at the translation of cloud in and of itself, data is at the heart of everything. Yes, and they talk to us about net APS transformation as cloud is something that you're enabling on prime hybrid multi cloud as you talked about. But how is your advantage allowing customers to not only be data driven, but to find value in that data that gives them that differentiation that they need for the guy or a girl that's right behind them. I already did take over. >>Well, I think if you're you know, if you're an enterprise, you know, the one asset you have is data. You have history now >>a liability Now with an asset. >>Can they can they do anything with it. Do they know where it is? Do they know how to use it where it should be, you know, Is it secured? Is it protected all of those things? It's very hard for enterprise to answer those questions. What one end up, I think it's done incredibly well, is by leaning in as much as we did onto AWS way. Give our customers the absolute choice to leave our on premise business and a lot of people, I think years ago thought we were crazy. But because now we've expanded our footprint to allow customers to run anywhere without any fear of lock in, people will start to see us now not as a storage vendor but as a strategic partner, and that that that strategic partnership is really has really come about because of our willingness to let people move the data and manage the data wherever they needed to be. On that something our customers have said, you know, used to be a storage vendor on along with the other storage vendors and now all of a sudden that we're having conversations with you about strategy where the data should be, you know who's using it is. It's secured all of those kinds of conversations we're having with customers. >>You mentioned moving data, and that was something that again came up in the keynote yesterday. And he mentioned that Hey, maybe instead of taking the data to the computer, we should bring the computer's data. That's something that Ned Abbas has long actually talked about. I remember when you used to mention data fabric was something about We want to take your data and then make it available to where the computer is. I'd like you to talk it through that, particularly in light of like a I and ML, which is on the tip of everybody's tongue. It's It's a bit of I think, it's possibly reaching the peak of the hype cycle at the moment s o what our customers actually doing with their data to actually analyze it? Are they actually seeing real value from machine learning? And I are We still isn't just kicking the tires on that. >>I mean, the biggest problem with deep learning and machine learning is having our accumulating enough on being able to have the data or lessening that gravity by being able to move it then you can take advantage off states maker in AWS, the big Cleary and Google, whatever fits your needs. And then, if you want to store the results back on premise, that's what we enable. With it out of harbor having that free flowing work clothes migration has to count for data. It's not enough to just move your application that that that's the key for machine learning and thought the lakes and others, >>absolutely in terms of speed. Anthony mentioned that that's the new scale. How is flash changing the game >>with perspective, you know, flashes a media type, but it's just, you know, the prices have come down now that you know the price performance couple flashes an obvious thing. Um, and a lot of people are, I think now, making on premise decisions to get rid of spinning disc and replaced with Flash because the R. O. I is so good. Tco the meantime between failures, that's that's so many advantages that percent workloads. It's a better decision, of course. You know, AWS provides a whole bunch of media Onda again. It's just you like a kid in a candy store, you know, as a developer, you look at Amazon. You're like, Oh, my God. Back in the day, we had to make, like, an Oracle decision and everything was Oracle. And now you can just move things around and you can take advantage of all sorts of different utilities. And now you piece together an application very differently. And so you're able to sort of really think I think Dion sees point. People are telling us they have to have a date, a strategy, and then, based on the data strategy, they will then leverage the right storage with the right protocols. They'll then bring that to compute whatever compute is necessary. I think data science is, you know, a little fashion, you know, conscious. Right now, you know, everybody wants to say how many did a scientist they have on their teams? They're looking for needles in haystacks. Someone, they're finding them. Some of them are but not doing it, I think it is. Makes companies very, very nervous. So they're going the results, gonna trying as hard as they can to leverage that technology. >>And you'll see where is that data strategy conversation happening if we think about the four essentials that Andy Johnson talked about yesterday for transformation in one of the first things he said was, it has to be topped at senior level decision. Then it's going to be aggressively pushed down through the organization. Are you seeing this data strategy at the CEO level yet? >>Yeah, we are. But I'm also seeing it much lower. I mean, with the data engineers with the developers, because it's asked, is it is extremely important to be developing on top off production data, specifically if you're doing machine and deep learning. So I think it's both. I think the decision authority has actually moved lower in the company where the developers are the side reliability engineers are actually choosing more technology to use. That fits the product that they are actually creating off course. The strategy happens at the tall, but the influencer and the decision makers, in my opinion, has been moving lower and within the organization. So I'm basically contradicting what yes is a. But to me that is also important. The days off a C t o r C E o. Forcing a specific platform or strategy on to developers. Those days are hopefully gone. >>I think if you're a CEO and you know of any company in any industry you have to be a tech company, you know, it used to be a tech industry, and now every company in the world is now tech. Everyone's building APS. Everyone's using data. Everybody's, you know, trying to figure out machine learning. And so I think what's happening is CEOs are are increasingly becoming technically literate. They have to Exactly. They're dead if they're not. I mean, you know whether your insurance company, your primary platform, is now digital if you're a medical company or primary platform additional. So I think that's a great stat. I saw that about two and 1/2 years ago. The number of software engineering jobs in non tech surpassed the number of jobs in tech, so we used to have our little industry and all the software engineers came to work for tech companies. Now there are more jobs outside the tech segment for engineers, and there are in the text >>well, and you brought up uber a minute ago and I think of a couple of companies examples in my last question for you is real. Rapid is about industries. You look at uber for example, what the fact that the taxi cab companies were transitional. And we're really eager to, you know, AP, if I their organizations, and meet the consumer demand. And then you look at Airbnb and how that's revolutionized hospitality or pellet on how it's revolutionized. Fitness Last question, Jonesy, Let's go for you. Looking at all of the transformation that cloud has enabled and can enable what industry you mentioned when the gas. But is there any industry that you see right now that is just at the tipping point to be ableto blow the door wide open if they transform successfully? >>Well, I mean way are working with a lot off pharma companies and genome sequencing companies that have not actually working with sensitive data on if those companies, I mean, these are people's medical histories and everything, so we're seeing them moving now in close into the cloud so those companies can move to the cloud. Anybody can move to the cloud. You mean these sort of compliancy scaremongering? You cannot move to the cloud because of P. C. I or hip power. Those days are over because aws, Microsoft and Google, that's the first thing they do they have? Ah, stricter compliancy than most on premise Homemade tartar sentence. So I see. I see that industry really moving into the cloud. Now >>who knows what a ws re invent 2020 will look like Gentlemen I wish we had more time, but thank you. Both Young and Anthony were talking with Justin and me today sharing what's new with netapp. What? You guys are enabling customers. D'oh! In multiple. Same old way. We appreciate your time where my car is. Justin Warren, I'm Lisa Martin. You're watching the Cube from AWS or reinvent 19 from Vegas. Thanks for watching.

>>live from San Francisco. It's the Q covering Micron Insight 2019. Brought to you by Micron. >>Welcome back to Pier 27 sentences. You're beautiful day here. You're watching the Cube, the leader in live tech coverage recovering micron inside 2019 hashtag micron in sight. My co host, David Floy er and I are pleased to welcome Michael Wood, Acre Cube alum and a fellow at Hewlett Packard Enterprise. Michael, good to see you again. Thanks. Coming on. >>Thanks for having me. >>So you're welcome? So you're talking about HBC on a panel today? But of course, your role inside of HP is is a wider scope. Talk about that a little bit. >>She also I'm the lead technologists in our Compute Solutions business unit that pack out Enterprise. So I've come from the group that worked on in memory computing the Superdome flex platform around things like traditional enterprise computing s it, Hannah. But I'm now responsible not only for that mission critical solutions platform, but also looking at our blades and edge line businesses. Well said broader technology. >>Okay. And then, of course, today we're talking a lot about data, the growth of data and As you say, you're sitting on a panel talking about high performance computing and the impact on science. What are you seeing? One of the big trends in terms of the intersection between data in the collision with H. P. C and science. >>So what we're seeing is just this explosion of data and this really move from traditionally science of space around how you put equations into supercomputers. Run simulations. You test your theories out, look at results. >>Come back in a couple weeks, >>exactly a potential years. Now. We're seeing a lot of work around collecting data from instruments or whether it's genomic analysis, satellite observations of the planner or of the universe. These aerial generating data in vast quantities, very high rates. And so we need to rethink how we're doing our science to gain insights from this massive data increase with seeing, >>you know, when we first started covering the 10th year, the Cuban So in 2010 if you could look at the high performance computing market as sort of an indicator of some of the things that were gonna happen in so called big data, and some of those things have played out on I think it probably still is a harbinger. I wonder, how are you seeing machine intelligence applied to all this data? And what can we learn from that? In your opinion, in terms of its commercial applications. >>So a CZ we'll know this massive data explosion is how do we gain insights from this data? And so, as I mentioned, we serve equations of things like computational fluid dynamics. But now things are progressing, so we need to use other techniques to gain understanding. And so we're using artificial intelligence and particularly today, deep learning techniques to basically gain insights from the state of Wei. Don't have equations that we can use to mind this information. So we're using these aye aye techniques to effectively generate the algorithms that can. Then you bring patterns of interest to our you know, focused of them, really understand what is the scientific phenomenon that's driving the things particular pattern we're seeing within the data? So it's just beyond the ability of the number of HPC programmers, we have the sort of traditional equation based methodologies algorithms to gain insight. We're moving into this world where way just have outstripped knowledge and capabilities to gain insight. >>So So how does that? How is that being made possible? What are the differences in the architecture that you've had to put in, for example, to make this sort of thing possible? >>Yeah, it's it's really interesting time, actually, a few years ago seemed like computing was starting to get boring because wears. Now we've got this explosion of new hardware devices being built, basically moving into the more of a hetero genius. Well, because we have this expo exponential growth of data. But traditional computing techniques are slowing down, so people are looking at exaggerate er's to close that gap and all sorts of hatred genius devices. So we've really been thinking. How do we change that? The whole computing infrastructure to move from a compute centric world to a memory centric world? And how can we use memory driven computing techniques to close that gap to gain insight, so kind of rethinking the whole architectural direction basically merge, sort of collapsing down the traditional hierarchy you have, from storage to memory to the CPU to get rid of the legacy bottlenecks in converting protocols from process of memory storage down to just a simple basically memory driven architecture where you have access to the entire data set you're looking at, which could be many terabytes to pad of eyes to exabytes that you can do simple programming. Just directly load store to that huge data set to gain insights. So that's that's really changed. >>Fascinating, isn't it? So it's the Gen Z. The hope of Gen Z is actually taking place now. >>Yes, so Gen Z is an industry led consulting around a memory fabric and the, you know, Hewlett Packard Enterprise Onda whole host of industry partners, a part of the ecosystem looking at building a memory fabric where people can bring different innovations to operate, whether it's processing types, memory types, that having that common infrastructure. I mean, there's other work to in the industry the Compute Express Link Consortium. So there's a lot of interest now in getting memory semantics out of the process, er into a common fabric for people to innovate. >>Do you have some examples of where this is making a difference now, from from the work in the H B and your commercial work? >>Certainly. Yeah, we're working with customers in areas like precision medicine, genomex basically exaggerating the ability to gain insights into you know what medical pathway to go on for a particular disease were working in cybersecurity. Looking at how you know, we're worried about security of our data and things like network intrusion. So we're looking at How can you gain insights not only into known attacking patterns on a network that the unknown patents that just appearing? So we're actually a flying machine learning techniques on sort of graft data to understand those things. So there's there's really a very broad spectrum where you can apply these techniques to Data Analytics >>are all scientists now, data scientists. And what's the relationship between sort of a classic data scientist, where you think of somebody with stats and math and maybe a little bit of voting expertise and a scientist that has much more domain expertise you're seeing? You see, data scientists sort of traversed domains. How are those two worlds coming together? >>It's funny you mentioned I had that exact conversation with one of the members of the Cosmos Group in Cambridge is the Stephen Hawking's cosmology team, and he said, actually, he realized a couple of years ago, maybe he should call himself a day two scientists not cosmologist, because it seemed like what he was doing was exactly what you said. It's all about understanding their case. They're taking their theoretical ideas about the early universe, taking the day to measurements from from surveys of the sky, the background, the cosmic background radiation and trying to pair these together. So I think your data science is tremendously important. Right now. Thio exhilarate you as they are insights into data. But it's not without you can't really do in isolation because a day two scientists in isolation is just pointing out peaks or troughs trends. But how do you relate that to the underlying scientific phenomenon? So you you need experts in whatever the area you're looking at data to work with, data scientists to really reach that gap. >>Well, with all this data and all this performance, computing capacity and almost all its members will be fascinating to see what kind of insights come out in the next 10 years. Michael, thanks so much for coming on. The Cube is great to have you. >>Thank you very much. >>You're welcome. And thank you for watching. Everybody will be right back at Micron Insight 2019 from San Francisco. You're watching the Cube

>> Hey, welcome back already, Jeffrey. Here with the Cube, we are high Top San Francisco in the Salesforce Tower and the brand new A century's Thean Novation hub opened up, I don't know. Six months ago or so, we were here for the opening. It's a really spectacular space with a really cool Cinderella stare. So if you come, make sure you check that out. We're talking about a cloud in the evolution of cloud and hybrid cloud. And clearly two players that are right in the middle is helping customers get through this journey and do these migrations. Our center and h. P. E s were excited to have our next guest, Melissa Bessie. She is the managing director, Intelligent cloud and infrastructure strategic partnerships at a center. Melissa. Welcome. And joining us from HP is David Stone. He's the VP of ecosystem sales. They have a great to see you. >> Thanks for having me. >> So let's just jump into it. The cloud discussion has taken over for the last 10 years, but it's really continuing to evolve. It was kind of this this new entrance with aws kind of coming on the scene. One of the great lines of Jeff Basil's talks about is they had no competition for seven years. Nobody recognized that the the bookseller out on the left hand ah, edges coming in to take the river structure business. But as things have moved to Public Cloud, now there's hybrid cloud. No, no. All applications or work clothes are right for a public cloud. So now, while the enterprises are trying to figure this out, they want to make their moves. But it's complicated. So first of all, let's talk about some of the vocabulary hybrid cloud versus multi cloud one of those terms mean to you and your customers started Melissa. >> Sure. So when you think of multi cloud, right, we're seeing a big convergence of I would say multi Kludd operating model that really has to integrate across all the clouds. So you have your public cloud providers. You have your sass like, uh, sales force at work day, you have your pass right? And so when you think of multi cloud, any customers goingto have a plethora of all of these types of clouds and really being able to manage across those becomes critical. When you think of hybrid cloud hybrid cloud is really thinking about the placement of ill. We usually look at it from a data perspective, right? Are you going to put your data in the public or in the private space? And so you can't look at it from that perspective, and it really enables that data movement across both of those clouds. >> So what would you see? David and your, uh, your customers? I say that a >> lot of the customers that we see today or confused right the people who have gone to the public cloud have scratched their heads and said, Jeez, what do I do? It's not as cheap as I thought it was gonna be. So the ones who were early adopters or confused the ones who haven't moved yet are really scratching their head as well, Right, because if you don't have the right strategy, you'll end up getting boxed in. You'll pay a ton of money to get your data in, and you'll pay a ton of money to get your data out. And so all of our customers, you know, want the right hybrid strategy, and I think that's where the market and I know a center and HPD clearly see them, the market really becoming a hybrid world. >> It's interesting, Melissa, You said it's based on the data, and you just talked about moving data in and out where we more often hear it talked about workload. This kind of horses for courses, you know, it's a workload specific should be deployed in this particular kind of infrastructure configuration. But you both mentioned data, and there's a lot of conversation kind of pre cloud about data, gravity and how expensive it is to move the date and the age old thing. Do you move the compute to the data, or do you move the data to the compute? A lot of advantages if you have that data in the cloud, but you're highlighting a couple of the ah, the real negatives in terms of potential cost implications. And we didn't even get into regulations and some of the other things that drive workloads to stay in the data center. So how should people start thinking about these variables when they're trying to figure out what to do next? >> Ex enters position Definitely like when we started off on our hybrid cloud journey was to capture the workload and once you have that workload you could really balance. It's the public benefits of speed, innovation and consumption with the private benefits of actually regulation, data, gravity and performance. Right? And so our whole approach and big bet has been able is been to basically we had really good leading public capabilities because we got into the market early. But we knew our customers were not going to be able to migrate their entire estate over to public. And so in doing that, we we said, OK, if we create a hybrid capability that is highly automated, that is consumed like public, Um and that is standard. We'd be able to offer our customers a weight of pick, really the right workload in the right place at the right price. And that was really what? Our whole goal waas. >> Yeah, and so just the Adama Melissa said, I think we also think about at least, uh, you know, keeping the data in a place where you want but then being cloud adjacent. So getting in the right data centers and we often use the cloud saying to bring the cloud to the data right? So if you have the right hybrid strategy. You put the data where it makes the most sense where you want to maintain the security and privacy. Ah, but then have access to the AP eyes and whatever else you might need to get the full advantages of the public cloud. >> Yeah, and we hear a lot of the data center providers like quinyx and stuff talking about features like Direct Connect and Noted Toe have this proximity between the public cloud and the and the stuff that's in your private cloud so that you do have no low latent see, and you can when you do have to move things or you do need to access that data. It's not so far away. Um, I'm curious about the impact of companies like Salesforce with Salesforce Tower here in San Francisco at the Centre Offices and Office 3 65 and Work Day on how kind of the adoption of the SAS applications have changed. The conversation about Cloud or what's important and not important, needs to be security. I don't trust eating outside my data center Now, one might argue that public clouds are more secure in some ways than in private cloud. You have disgruntled employees per se running around the data centers on plugging things. So how? How is the adoption of things like Officer 65? Clearly, Microsoft's leverage that in a big way to grow their own cloud presence changed the conversation about what's good about Cloud. What's not good about Chlo? Why should we move in this direction? But if you have thought >> no, look, it's a great question, and I think if you think about that, his Melissa said, the use cases right and Microsoft is have sex. Feli successfully pivoted their business to it as a service model, right? And so what I think it's done is it's opened up innovation, and a lot of the sales forces of the world have adapted their business models. And that's truly to your point, a sass based offer. And so when you could do a work day or a salesforce dot com implementation shirt, it's been built that it's tested and everything else I think, what then becomes the bigger question in the bigger challenges. Most companies air sitting on 1000 applications that have been built over time, and what do you do with those? And so in many cases, you need to be connected to those SAS space providers. But you need the right hybrid strategy again. To be able to figure out how to connect those SAS based service is to whatever you're gonna do with those 1000 workloads and those 1000 workloads running on different things that you need the right strategy to figure out where to put the actual workloads and is people they're trying to go. I know one of the questions that comes up is Do you my grade or do you modernize? And so as people put that strategy together, I think how you tied to those SAS based service is clearly ties into your hybrid strategy, >> I would agree. And so, as David mentioned, right, that's where the clouded Jason see, you're seeing a lot of blur between public and private. I mean, Google's providing bare metal is a service, so it is actually dedicated hybrid cloud capabilities. Right? So you're seeing a lot of everyone. And as as David talked about all of the surrounding applications around your s a P around Oracle, when we created our ex enter hyper cloud, we were going after the enterprise workload. But there is a lot of legacy and other ones that need that data and or the sales force data, whatever the data is right and really be able to utilize it when they need to in a real low leagues. >> So let's I want to get unpacked. The ah central hybrid cloud. Um, what is that exactly is that is that your guys own cloud is, you know, kind of a solution set. I've heard that mentioned a couple of times. So what is the centre? Hyper cloud? >> So eccentric hybrid cloud was a big bet we made as we saw the convergence of multi cloud. We really said, We know we everything is not gonna go public and in some cases it's all coming back. And so customers really needed a way to look at all of their workloads, right? Because part of the issue with the getting the cost of the benefits out of public is the workload goes. But you really don't earn able to get out of the data center. We terminate the wild animal park because there's a lot of applications that right Are you going to modernize? Are you goingto let them to end of life? so there's a lot of things you have to consider to truly exit a data center strategy. And so its center hybrid cloud is actually a big bet we made. It is a highly automated, standard private cloud capability that really augments all of the leading capability we had in the cloud area. It is it's differentiated women, a big bet with HP. It's differentiated on its hardware. One of the reasons when we're going after the enterprise was they need large compute. They have large computer and large storage requirements, and what we were able to dio is when we created this used some of our automation differentiation. We have actually a client that we had an existing Io environment. We were actually able to achieve some significant benefits just from the automation. We got 50% in the provisioning of applications. We got 40% in the provisioning in the V m on, and we were able to take a lot of what I'll call the manual tasks and down Thio. It was like 62% reduction in the effort as well as a 33% savings overall in getting things production ready. So this capability is highly automated. It will actually repeat the provisioning at the application level because we're going after the enterprise workloads and it will create these. It's an asset that came from the government. So it's highly secured. Um, and it really was able to preserve. I think, what our customer needed and being able to span that public private capability they need out there in the hybrid world. >> Yeah, you could say I don't know that there's enough talk aboutthe complexity of the management in these worlds. Nobody ever wants to talk about writing this a sideman piece of the software, right? It's all about the core functionality. Let's shift gears a little bit. Talk about HBC. A lot of conversation about high performance computing, a lot going on with a I and machine learning now, which you know most of those benefits are going to be realized in a specific application, right? It's a machine learning or artificial intelligence apply to a specific application. So again, you guys big, big iron and been making big iron for ah, long time. What is this kind of hybrid cloud open up in terms of HB Ito have the big, heavy big heavy metal instead it and still have kind of the agility and flexibility of a cloud type of infrastructure. >> Yeah, no, I think it's a great question. I think if you think about what HB strategy has been in this area and high performance compute, we bought the company SG eye on. As you've seen, the announcements were hopefully gonna close on the Kree acquisition as well. And so we see in the world of the data continuing to expand in huge volumes, the need to have incredible horsepower to drive that associated with it. Now all of this really requires Where's your data being created and where's it actually being consumed? And so you need to have the right edge to cloud strategy and everything. And so in many cases, you need enough compute at the edge to be able to compute in do stuff in real time. But in many cases, you need to feed all that data back into ah, Mother Cloud or some sort of mother HP, you know, e type of high performance, confused environment that can actually run the more advanced a I in machine learning type of applications to really get the insights and tune the algorithms and then push some of those AP eyes and applications back to the edge. So it's it's an area of huge investment. It's an area where because of the late and see, uh, and you know things like autonomous driving and things like that. You can't put all that stuff into the public cloud. But you need the public cloud or you need cloud type capability if you will have able to compute and make the right decisions at the right time. So it's about having the right computer technology at the right place at the right time. The right cost and the right perform a >> lot of rights. Yeah, good opportunity for a center. So I mean, it's it's funny as we talk about hybrid cloud and and that kind of new new verbs around Cloud and cloud like things is where we're gonna see the same thing. Kind of the edge, the edge versus the data center comparison in terms of where the data is, where the processing is because it's gonna be this really dynamic situation, and how much can you push out? I was like the edge because there was no air conditioning a lot of times, and the power might not be that grade. And maybe connectivity is a little bit limited. So, you know, EJ offers a whole bunch of different challenges that you can control for in a data center. But it is going to be this crazy kind of hybrid world there, too, in terms of where the allocation of those resource is. Are you guys getting deeper into that model, Melissa? >> So we're definitely working with HP again to create some of all call it our edge. Manage. Service is again going back to what we're saying about the data, right? We saw the centralization of data with a cloud with the initial entrance into the cloud. Now we're seeing the decentralization of that data back out to the edge. Um, with that right in these hybrid cloud models, you're really going to need. They require a lot of high performance compute, especially for certain industries, right? If you take a look at gas, oil and exploration, if you look at media processing right, all of these need to be able to do that. One of the things and depending on where it's located, if it's on the edge. How you're gonna feed back the data as we talked about. And so we're looking at How do you take this foundation? Right. This all colonic center hybrid. Um, architecture. I take that and play that intermediate role. I'm gonna call it intermediary. Right, Because you really need a really good you know, global data map. You need a good supply chain, right? Really? To make sure that the data, no matter where it's coming from, is going to be available for that application at the right time, with right, the ability to do it at speed. And so all of these things air factors as we look at our hole ex center, hybrid cloud strategy, right? And being able to manage that EJ decor and then back out to cloud exactly >> right. And I wonder if you could share some stories because the value proposition I think around cloud is significantly shifted for those who are paying attention, right? It's not about cost. It's not about cost savings. I mean, there's a lot of that in there, and that's good. But really, the opportunity is about speed, speed and innovation and enabling more innovation across your enterprise. with more people having more access to more data to build more APS and really to react. Are people getting that or they still the customer still kind of encumbered by this this kind of transition phase. They're still trying to sort it out. Or do they get it? That that really this opportunity is about speed, Speed, speed? No, >> go ahead. I mean, we use a phrase first offices here. No cloud, right. So to your point, you know, how do you figure out the right strategy? But I think within that you you get what's the right application and how do you fit it into the overall strategy of what you're trying to do? >> And I think the other thing that we're seeing is, um, you know, customers are trying to figure that out. We have a whole right. When you start with that application map, you know, there could be 500 to 1000 workloads, write applications. And how are you going to some? You're gonna retain some? You're gonna retire some. You're gonna reap age. You're gonna re factor for the cloud or for your private cloud capability. Whatever it is you're going to be looking at doing? Um, I think, you know, we're seeing early adopters like even the papers killers themselves, right? They recognize the speed. So, you know, we're working with Google. For instance. They wanted to get into the bare metal as a service capability. Write them, actually building it. Getting it out to market would take so much longer. We already had this whole ex center hybrid cloud architecture that was cloud adjacent, so we had sub millisecond latency, and so their loved ones, Right? Everyone's figuring out that utilizing all of these, I'll call it platforms and pre book capabilities. Many of our partners have them as well is really allowing them that innovation, get products to market sooner, be able to respond to their customers because it is, as we talked about this multi cloud were lots of things that you have to manage if you can get pieces from multiple plate, you know, from a partner right that can provide Maur of the service is that you need it really enables the management of right, >> right, So gonna wrap it up. I won't give you the last word in terms of what's the what's the most consistent blind spot that you see when you're first engaging with a customer who's who's relatively early on this journey that that they miss that you see over and over and over. And you're like, you know, these are some of the things you really gotta think about that they haven't thought about >> Yeah, so for me, I think it's the cloud isn't about a destination. It's about an experience. And so how do you get you talked about the operations? But how do you provide that overall experience? I like to use a simple analogy that if you and I needed a car for five or 10 or 15 minutes, you go get a new bir. Uh, because it's easy. It's quick. If you need a car for a couple days to do a rent a car, we need a car for a year. You might do at least you need a car for 34 years. You probably buy it right. And so if you use that analogy and think whom I need a workload or in the application for 56 years putting something out, persistent workload that you know about on a public cloud, maybe the right answer, but it might be a lot more cost prohibitive. But if you need something that you can stand up in five minutes and shut right back down, the public cloud is absolutely the right way to go is long as you can deal with the security requirements and stuff. So if you think you think about what are the actual requirements, is it costs is a performance. You've talked about speed and everything else it really trying to figure out you get an experience and the only experience that can really hit you. What you need to do today is a having the right hybrid strategy and every company and a century was out way in front of the market on Public Cloud, and now they've come to the realization, and so has many other places. The world is going to be hybrid, and it's gonna be multi cloud. And as long as you can have an experience and a partner that can manage, you know, help you to find the right path, you'll be on the right journey. >> I think the blinds, but we run into is it does start off as a cost savings activity, and they're really. It really is so much more about how you're going to manage that enterprise workload. How are we gonna worry about the data? Are you gonna have access to it? Are you gonna be able to make it fluid, right. The whole essence of cloud, right? What? It What it disrupted was the I thought that something had to stay in one place, right? And that you were the real time decisions were being made where things needed to happen. Now, through all the different clouds as well as that, you had to own it yourself, right? I mean, everyone always thought Okay, uh, I'll take all of the I T. Department. Very protective of everything that wanted to keep. Now it's about saying, All right, how do I utilize the best of each of these multi clouds to stand up? What? I'll call what their core capability is as a customer, right? Are they do in the next chip design or hey, you know, doing financial market models right? That requires a high performance capability, right? So when you start to think about all of this stuff, right, that's the true power. Is is having a strategy that looks at those outcomes. What am I trying to achieve in getting my products and service is to market and touching the car customers I need versus Oh, I'm gonna move this out to an infrastructure because that's what God will save me. Money, Right, Bench. Typically the downfall we see because they're not looking at it from the workload of the application. >> Same old story, right? Focus on your core differentiator and outsource the heavy lifting on the stuff that that's not your core. All right, Well, Melissa David, Thanks for taking a minute and really enjoyed the conversation. Is Melissa? He's David. I'm Jeff. Rick, you're watching. The Cube were high above the San Francisco skyline in the sales force. Tyra. The essential innovation up. Thanks for watching. We'll see you next time.

>> live from Boston, Massachusetts. It's the queue covering your red. Have some twenty nineteen brought to you by bread. Welcome back a few, but our way Our red have some twenty nineteen >> center along with Sue Mittleman. I'm John Walls were now joined by Robin Goldstone, who's HBC solution architect at the Lawrence Livermore National Laboratory. Hello, Robin >> Harrier. Good to see you. I >> saw you on the Keystone States this morning. Fascinating presentation, I thought. First off for the viewers at home who might not be too familiar with the laboratory If you could please just give it that thirty thousand foot level of just what kind of national security work you're involved with. >> Sure. So yes, indeed. We are a national security lab. And you know, first and foremost, our mission is assuring the safety, security reliability of our nuclear weapons stockpile. And there's a lot to that mission. But we also have broader national security mission. We work on counterterrorism and nonproliferation, a lot of of cyber security kinds of things. And but even just general science. We're doing things with precision medicine and and just all all sorts >> of interesting technology. Fascinating >> Es eso, Robin, You know so much and i t you know, the buzzword. The vast months years has been scaled on. We talk about what public loud people are doing. It's labs like yours have been challenged. Challenge with scale in many other ways, especially performance is something that you know, usually at the forefront of where things are you talked about in the keynote this morning. Sierra is the latest generation supercomputer number two, you know, supercomputer. So you know, I don't know how many people understand the petaflop one hundred twenty five flops and the like, but tell us a little bit about, you know, kind of the why and the what of that, >> right? So So Sierra's a supercomputer. And what's unique about these systems is that we're solving. There's lots of systems that network together. Maybe you're bigger number of servers than us, but we're doing scientific simulation, and that kind of computing requires a level of parallelism and very tightly coupled. So all the servers are running a piece of the problem. They all have to sort of operate together. If any one of them is running slow, it makes the whole thing goes slow. So it's really this tightly couple nature of super computers that make things really challenging. You know, we talked about performance. If if one servers just running slow for some reason, you know everything else is going to be affected by that. So we really do care about performance. And we really do care about just every little piece of the hardware you know, performing as it should. So So I >> think in national security, nuclear stockpiles. Um I mean, there is nothing more important, obviously, than the safety and security of the American people were at the center of that. Right? You're open source, right? You know, how does that work? How does that? Because as much trust and faith and confidence we have in the open source community. This is an extremely important responsibility that's being consigned more less to this open source community. >> Sure. You know, at first, people do have that feeling that we should be running some secret sauce. I mean, our applications themselves or secret. But when it comes to the system software and all the software around the applications, I mean, open source makes perfect sense. I mean, we started out running really closed source solutions in some cases, the perp. The hardware itself was really proprietary. And, of course, the vendors who made the hardware proprietary. They wanted their software to be proprietary. But I think most people can resonate when you buy a piece of software and the vendor tells you it's it's great. It's going to do everything you needed to do and trust us, right? Okay, But at our scale, it often doesn't work the way it's It's supposed to work. They've never tested it. Our skill. And when it breaks, now they have to fix. They're the only ones that can fix it. And in some cases we found it wasn't in the vendors decided. You know what? No one else has one quite like yours. And you know, it's a lot of work to make it work for you. So we're just not going to fix and you can't wait, right? And so open source is just the opposite of that, right? I mean, we have all that visibility in that software. If it doesn't work for our needs, we can make it work for our needs, and then we can give it back to the community. Because even though people are doing things that the scale that we are today, Ah, lot of the things that we're doing really do trickle down and can be used by a lot of other people. >> But it's something really important because, as you said, you used to be and I was like, OK, the Cray supercomputer is what we know, You know, let's use proprietary interfaces and I need the highest speed and therefore it's not the general purpose stuff. You moved X eighty six. Lennox is something that's been in the shower computers. Why? But it's a finely tuned version there. Let's get you know, the duct tape and baling wire. And don't breathe on it once you get it running. You're running well today and you talk a little bit about the journey with Roland. You know, now on the Super Computers, >> right? So again, there's always been this sort of proprietary, really high end supercomputing. But about in the late nineteen nineties, early two thousand, that's when we started building these these commodity clusters. You know, at the time, I think Beta Wolf was the terminology for that. But, you know, basically looking at how we could take these basic off the shelf servers and make them work for our applications and trying to take advantage of a CZ much commodity technologies we can, because we didn't want to re invent anything. We want to use as much as possible. And so we've really written that curve. And initially it was just red hat. Lennox. There was no relative time, but then when we started getting into the newer architectures going from Mexico six. Taxi, six, sixty for and Itanium, you know the support just wasn't there in basic red hat and again, even though it's open source and we could do everything ourselves, we don't want to do everything ourselves. I mean, having an organization having this Enterprise edition of Red Hat having a company stand behind it. The software is still open. Source. We can look at the source code. We can modify it if we want, But you know what at the end of the day, were happy to hand over some of our challenge is to Red Hat and and let them do what they do best. They have great, you know, reach into the into the colonel community. They can get things done that we can't necessarily get done. So it's a great relationship. >> Yes. So that that last mile getting it on Sierra there. Is that the first time on one kind of the big showcase your computer? >> Sure. And part of the reason for that is because those big computers themselves are basically now mostly commodity. I mean, again, you talked about a Cray, Some really exotic architecture. I mean, Sierra is a collection of Lennox servers. Now, in this case, they're running the power architecture instead of X eighty six. So Red hat did a lot of work with IBM to make sure that that power was was fully supported in the rail stack. But so, you know, again that the service themselves somewhat commodity were running and video GP use those air widely used everywhere. Obviously big deal for machine learning and stuff that the main the biggest proprietary component we're still dealing was is thie interconnect. So, you know, I mentioned these clusters have to be really tightly coupled. They that performance has to be really superior and most importantly, the latent see right, they have to be super low late and see an ethernet just doesn't cut it >> So you run Infinite Band today. I'm assuming we're >> running infinite band on melon oxen finna ban on Sierra on some of our commodity clusters. We run melon ox on other ones. We run intel. Omni Path was just another flavor of of infinite band. You know, if we could use it, if we could use Ethernet, we would, because again, we would get all the benefit in the leverage of what everybody else is doing, but just just hasn't hasn't quite been able to meet our needs in that >> area now, uh, find recalled the history lesson. We got a bit from me this morning. The laboratory has been around since the early fifties, born of the Cold War. And so obviously open source was, you know? Yeah, right, you know, went well. What about your evolution to open source? I mean, ahs. This has taken hold. Now, there had to be a tipping point at some point that converted and made the laboratory believers. But if you can, can you go back to that process? And was it of was it a big moment for you big time? Or was it just a kind of a steady migration? tour. >> Well, it's interesting if you go way back. We actually wrote the operating systems for those early Cray computers. We wrote those operating systems in house because there really was no operating system that will work for us. So we've been software developers for a long time. We've been system software developers, but at that time it was all proprietary in closed source. So we know how to do that stuff. The reason I think really what happened was when these commodity clusters came along when we showed that we could build a, you know, a cluster that could perform well for our applications on that commodity hardware. We started with Red Hat, but we had to add some things on top. We had to add the software that made a bunch of individual servers function as a cluster. So all the system management stuff the resource manager of the thing that lets a schedule jobs, batch jobs. We wrote that software, the parallel file system. Those things did not exist in the open source, and we helped to write those things, and those things took on lives of their own. So luster. It's a parallel file system that we helped develop slow, Erm, if anyone outside of HBC probably hasn't heard of it, but it's a resource manager that again is very widely popular. So the lab really saw that. You know, we got a lot of visibility by contributing this stuff to the community. And I think everybody has embracing. And we develop open source software at all different layers. This >> software, Robin, you know, I'm curious how you look at Public Cloud. So, you know, when I look at the public odd, they do a lot with government agencies. They got cloud. You know, I've talked to companies that said I could have built a super computer. Here's how long and do. But I could spend it up in minutes. And you know what I need? Is that a possibility for something of yours? I understand. Maybe not the super high performance, But where does it fit in? >> Sure, Yeah. I mean, certainly for a company that has no experience or no infrastructure. I mean, we have invested a huge amount in our data center, and we have a ton of power and cooling and floor space. We have already made that investment, you know, trying to outsource that to the cloud doesn't make sense. There are definitely things. Cloud is great. We are using Gove Cloud for things like prototyping, or someone wants a server, that some architecture, that we don't have the ability to just spin it up. You know, if we had to go and buy it, it would take six months because you know, we are the government. But be able to just spin that stuff up. It's really great for what we do. We use it for open source for building test. We use it to conferences when we want to run a tutorial and spin up a bunch of instances of, you know, Lennox and and run a tutorial. But the biggest thing is at the end of the day are our most important work. Clothes are on a classified environment, and we don't have the ability to run those workloads in the cloud. And so to do it on the open side and not be ableto leverage it on the close side, it really takes away some of the value of because we really want to make the two environments look a similar is possible leverage our staff and and everything like that. So that's where Cloud just doesn't quite fit >> in for us. You were talking about, you know, the speed of, Of of Sierra. And then also mentioning El Capitan, which is thie the next generation. You're next, You know, super unbelievably fast computer to an extent of ten X that off current speed is within the next four to five years. >> Right? That's the goal. I >> mean, what those Some numbers that is there because you put a pretty impressive array up there, >> right? So Series about one hundred twenty five PETA flops and are the big Holy Grail for high performance computing is excess scale and exit flop of performance. And so, you know, El Capitan is targeted to be, you know, one point two, maybe one point five exit flops or even Mohr again. That's peak performance. It doesn't necessarily translate into what our applications, um, I can get out of the platform. But the reason you keep sometimes I think, isn't it enough isn't one hundred twenty five five's enough, But it's never enough because any time we get another platform, people figure out how to do things with it that they've never done before. Either they're solving problems faster than they could. And so now they're able to explore a solution space much faster. Or they want to look at, you know, these air simulations of three dimensional space, and they want to be able to look at it in a more fine grain level. So again, every computer we get, we can either push a workload through ten times faster. Or we can look at a simulation. You know, that's ten times more resolved than the one that >> we could do before. So do this for made and for folks at home and take the work that you do and translate that toe. Why that exponential increase in speed will make you better. What you do in terms of decision making and processing of information, >> right? So, yeah, so the thing is, these these nuclear weapons systems are very complicated. There's multi physics. There's lots of different interactions going on, and to really understand them at the lowest level. One of the reasons that's so important now is we're maintaining a stockpile that is well beyond the life span that it was designed for. You know, these nuclear weapons, some of them were built in the fifties, the sixties and seventies. They weren't designed to last this long, right? And so now they're sort of out of their design regime, and we really have to understand their behaviour and their properties as they age. So it opens up a whole nother area, you know, that we have to be able to floor and and just some of that physics has never been explored before. So, you know, the problems get more challenging the farther we get away from the design basis of these weapons, but also were really starting to do new things like eh, I am machine learning things that weren't part of our workflow before. We're starting to incorporate machine learning in with simulation again to help explore a very large problem space and be ableto find interesting areas within a simulation to focus in on. And so that's a really exciting area. And that is also an area where, you know, GPS and >> stuff just exploded. You know, the performance levels that people are seeing on these machines? Well, we thank you for your work. It is critically important, azaz, we all realize and wonderfully fascinating at the same time. So thanks for the insights here on for your time. We appreciate that. >> All right, Thanks for >> thanking Robin Goldstone. Joining us back with more here on the Cube. You're watching our coverage live from Boston of Red Hat Summit twenty nineteen.

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen, Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to Las Vegas, Lisa Martin. With the cue, we're live Day one of our duel set coverage of Del Technologies World twenty nineteen student a menace here with me, and we're welcoming back a couple of alumni. But for the first time together on our set, we've got Terry Pellegrino, the BP of high performance computing at Delhi Emcee and Nick, who grew VP of Data Analytics and Cyber Securities just at MasterCard. Did I get that right? All right, good. So, guys, thanks for joining Suited me this afternoon, by the way. So we will start with you High performance computing. Talk about that a lot. I know you've been on the Cube talking about HPC in the Innovation lab down in in Austin, high performance computing, generating a ton of data really requiring a I. We talk a lot of it II in machine learning, but let's look at it in the context of all this data. Personal data data from that word, you know, it turns out do with mastercard, for example How are you guys working together? Dell Technologies and MasterCard to ensure that this data is protected. It secure as regulations come up as fraud, is a huge, expensive >> issue. Well, I think make way worked together to really well worry about the data being secure, but also privacy being a key item that we worry about every day you get a lot of data coming through, and if we let customer information or any kind of information out there, it can be really detrimental. So we've really spent a lot of time not only helping manage and worked through the data through the infrastructure and the solutions that we've put together for. For Nick, who also partnered with the consortium project that got started Mosaic Crown to try to focus even more on data privacy on Mosaic Crown is is really interesting because it's getting together and making sure that the way we keep that privacy through the entire life cycle of the data that we have the right tools tio have other folks understand that critical point. That's that's how we got all the brains working together. So it's not just Delon DMC with daily emcee and MasterCard It's also ASAP We have use of Milan, you're sort of bergamot and we'Ll solve the only three c and all together back in January decided to get together and out of Nick's idea. Think about how we could put together with all those tools and processes to help everybody have more private data. Other. >> I think this was your idea. >> I can't say it was my idea. The European Union itself with what? The advent of Judy parent privacy. Their biggest concern was we don't want people to stop sharing. Data began with artificial intelligence. The great things that we do with it from the security, you know, carrying diseases all the way through, making sure transactions are safe and secure. Look, we don't want people to stop our organizations to stop sharing that data because they have fear of the regulations. How do we create a date on market? So the U has something called Horizon twenty twenty on one of their initiatives. Wass Way wanted to understand what a framework for data market would look like where organizations can share that data with confidence that they're complying to all the regulations there, doing the anonymous ization of that data, and the framework itself allows someone to say, I could do analysis without worrying that if it's surfacing personally identifiable information or potentially financial information, but I can share it so that it can progress the market data economy. So as a result of that, what we did is we put the guilt. I said, This is a really good idea for us. Went to the partners at del. That's it, guys, this is something we should consider doing now. Organization always been looking at privacy, and as a result, we've done a very good job of putting that consortium together. >> So, Nick, we've talked with you on the Cuba quite a few times about security. >> Can you just give >> us? You know, you talked about that opportunity of a I We don't want people to stop giving data in. There was concerned with GPR that Oh, wait, I need you to stop collecting information because I'm going to get sued out of existence. If it happened, how do we balance that? You know, data is the new oil I need, you know, keep not flowing and oh, my God. I'm going to get hacked. I'm going to get sued. I'm going to have the regulation, You know, people's personal information. I'm goingto walk down the grocery store and they're going to be taking it from me. How do we balance that? >> Well, the nice part is, since State is the new oil, well, we considered it is artificial intelligences that refinery for that oil. So, for our perspective, is the opportunity to say we can use a eye to help. Somebody says, Hey, I don't want you to share my data information. I want to be private, but I can use a I d. S. Okay, let's filter those out so I can use a I'd actually sit on top of that. I can sit down and say, Okay, how do I keep that person's safe, secure and only share the necessary data that will solve the problem again, using artificial intelligence through different types of data classifications, whoever secure that data with different methods of data security, how we secure those types of things come into play. And again, there's also people say, I don't ever want my data to be we identified so we can use different methods to do complete anonymous ation. >> How do you do that when there are devices that are listening constantly, what Walmart's doing? Everybody that has those devices at home with the lady's name. I won't say it. I know it activates it. How How do you draw the line with ensuring that those folks that don't want certain things shared if they're in the island Walmart talking about something that they don't want shared? How do you facilitate that? >> Well, part of that is okay. At a certain point, when it comes to privacy, you've gotta have a little bit of parenting. Just because you have that information doesn't mean you need to use that information. So that's where we as humans have to come into play and start thinking about what is the data that we're collecting And how should we use that information on that person and who is walking through a store? And we say we are listening to what their conversations are? Well, I don't need to identify that you or you. I just didn't know what is the top talking about? Maybe that's the case, but again, you have to make that decision again. It's about being a parent at this point. That's the ethical part of data which we've discussed on this program before. Alright, >> so teary. Talkto us some about the underlying architecture that's going to drive all of this. You know, we we love the shift. For years ago, it was like storing my data. You know, Now we're talking about how do we extract the value of the data? We know data's moving a lot, So you know what's changing And I talk every infrastructure company I talked to, it's like, Oh, well, we've got the best ai ai, you know, x, whatever. So you know what kind of things should custom be looking for To be able to say, Oh, this is something, really. It's about scale. It's about, you know, really focused on my data. Yeah, absolutely. Well, I will say first, the end of underlying infrastructure. We have our set of products that have security intrinsic in the way they're designed. I really worry about ki management for software we have silicon based would have trust throughout a lot of our portfolio. We also think about secure supply chain, even thinking through security race. If you lose your hard drive on, we can go and make sure that the data is not removed. So that's on the security front. On the privacy side, as a corporation, William C. Is very careful about the data that we have access to on. Then you think about a HBC. So being in charge of H. P. C for Cordelia emcee way actually are part of how the data gets created, gets transferred, gets generated, curated and then stored. Of course, storage s O. What we want to make sure is our customers feel like where that one company that can help them through their journey for their data. And as you heard Michael this morning during keynote, >> uh, getting that value out of the data because it's really where that little transformation is going to get everybody to the next level. But right now there's a lot of data. Has Nick stated this data has more personal information at times? Andan i'll add one more thing way. Want to really make sure that innovation is not stifled and the way we get there is to make sure >> that the data sets are as broad as possible, and today it's very difficult to share data. Sets mean that there are parts of the industry there are so worried about data that they will not even get it anywhere else than their own data center and locked behind closed doors. But if you think about all the data scientists, they're craving more data. And the way we can get there is with what make it talked about is making sure that the data that is collected is free of personal information and can still be qualified for some analysis and letting all the data scientists out there to get a lot of value out of it. >> So HBC can help make the data scientist job simpler or simplify evaluating this innumerable amun of data. >> Correct. So what in the days you had an Excel spreadsheet and wanted to run and put the table on it, you could do that on a laptop for end up tablet. When you start thinking about finding a black hole in the galaxy, you can do that on tablet. So you're gonna have to use several computers in a cluster with the right storage of the right interconnect. And that's why it's easy comes in place. >> I mean, if I man a tactical level, what you'LL see with HBC computing is when someone's in the moment, right? You want to be able to recognize that person has given me the right to communicate to them or has not given me the right to communicate to them, even though they're trying to do something that could be a transaction. The ability to say Hey, I have I know that this person's or this device is operating here is this and they have given me these permissions. You've got to do that in real time, and that's what you're looking for. HBC competing to do. That's what you're saying. I need my G p you to process in that way, and I need that cpt kind of meat it from the courts. The edges say Yep, you can't communicate. No, you can't. Here's where your permissions like. So, >> Nick, what should we >> be looking for? Coming out of this consortium is people are watching around the industry. You know what, what, what >> what expect for us? The consortium's about people understand that they can trust that they're data's being used properly, wisely, and it's being used in the way it was intended to be used so again, part of the framework is what do you expect to do with the data so that the person understands what their data is being used for the analysis being done? So they have full disclosure. So the goal here is you can trust your data's being used. The way was intended. You could trust that. It's in a secure manner. You can trust that your privacy is still in place. That's what we want this construction to create that framework to allow people to have that trust and confidence. And we want the organization to be able to not, you know, to be able to actually to share that information to again move that date economy forward. >> That trust is Nirvana. Well, Nick Terry, thank you so much for joining suing me on the cue this afternoon. Fascinating conversation about HPC data security and privacy. We can't wait to hear what's in store next for this consortium. So you're gonna have to come back. Thank >> you. We'LL be back. Excellent. Thanks so much. >> Our pleasure. First Minutemen, I'm Lisa Martin. You're watching us live from Las Vegas. The keeps coverage of day one of del technology World twenty nineteen. Thanks for watching

>> Live from San Francisco. It's the Cube covering artists. A conference twenty nineteen Brought to You by Four Scout >> Welcome back, everybody, Geoffrey. Here with the cue, we're in North America and the newly refinished Mosconi Center Downtown San Francisco in the force Cow boo. Happy to be here first time and we have our next guest. She's Charlotte Wiley, chief of staff from Symantec. Great to meet you. >> Nice to meet you, teacher. Thanks for having >> absolutely so impressions of the show. This is a crazy show. Forty dollars, people. Aren't many shows like this >> it issue just a little overwhelming. It's my second year here, and it's no less overwhelming. Second year here. It's, uh it's just prolific. Everything that say the session, the keynotes all day, all the networking, the basis. Amazing. >> So I'm curious how your perception has changed. I >> was looking at your background, Your hearing a financial institution before your own kind of the purchaser side of the house. >> Now you're over on >> this side of the house. How's that kind of change your perception when you walk this crazy floor, I imagine before you're like, Yeah, how am I going to digest all this? >> Well, no one wants to be my friend anymore, which is interesting. So, um, you know, working on the vendor side of the defense is the dark side. It's It's a very different experience. When I came here a couple years go to bank. Everyone wants to talk to you. Or is this time? Is this a healthy, competitive nature going on between all the vendors, which is great. You want to see that? Yeah. It sze got the same enthusiasm. Same vase on the floor, which is wonderful. >> So semantics. Been a leader in the space for a very, very long time. One of the original, you know, kind of original security companies back in the day when we're just trying to protect that. You know, I guess our Web browser right from from some malicious activity. Wow. The world has changed. And one of the big new components now is his internet of things. In this tie of it with ot operations technology. You know something you've spent some time on a wonderful get your take on how that's increasing the threat surface, you know, increasing the complexity. And yet there's still a lot of value there if you can bring those systems together. >> Yeah, absolutely. So I think that Kate thing is this. You know, this simplicity here is, uh What? What you don't know, You can see. And what you can't see you can't monitor on DH. That's the key thing to remember when you think about t n OT so with Coyote specifically, if you, uh you've definitely got a nice routine, you network somewhere everyone has. But if you can't see that thing, it is incredibly vulnerable Throat vector for any organization. So really, it's it's a point of egress for any doubt of ex filtration. And if you've got someone compromised in the network already on your way, see it as being a very opportune ingress point to getting a lateral move. Right. So they are incredibly, inherently vulnerable. Right? These things are they're usually hard coded, authenticated. They are. They have massive under. Police often remain unpatched. When you cannot see, you don't know, Right? So some of the dirty side of the fence, right? The same problem exists. They typically were not built to connect to the Internet. Right. So this is something very new that we're trying to tackle right. And one of the key things I think about is that it's probably a little bit few tile to make these OT and I and I. A device is inherently secure. You think about in twenty twenty. We're going to see like twenty five billion devices proliferating our globe, which is incredible. So how do we how do we make it more school? Let's back off from becoming inherently secure. Let's up on the visibility. If you visualize you, Khun Segment, and you can enforce. And then you can take control of what has access to your network, right? A >> lot of interesting conversations about this today, obviously or in the force cow boo. But I think one of the people earlier said they had fifty percent more devices on the network than they anticipated. And it turns out his remote offices and people are plugging things in. Another little factoid is that maybe that hit no s on that device is actually windows in tea. Is it a tea? A little box. And nobody even knew because you knew that's an embedded in team. But then on the other side, we had a lease on, and she was talking about great example on security cameras and just that a lot of these newer devices that you can connect have a plethora of services packaged in on the assumption that you might use them. So rather than have not too many, they put them all in. But you don't necessarily need to turn all those things on. So again, you're just opening up this huge kind of exposure. >> Huge explosion. That's it. I think it's a really good conversation to have with your stakeholders about talking about the target breach. So when people start to understand that that really originated from a hate tax system, right compromise haystack system. So when you're talking about T initialization, that's a really good years case to say. Look, this is a huge bridge that was compromised from because we didn't They didn't have visibility over the anxiety. >> It's funny if you each Max keep coming up, over and over and over there. Obviously the biggest threat that way have I'm jacket to see if I could see like a movie with me. Nasty HBC think come until that munching up the company. But it's funny. Different topic. Shifting gears completely, really, about kind of diversity, diversity of opinion, diversity of perspective, diversity of thought and how that's a really important and effective tool use in trying to accomplish missions. In this really crazy, complex task, you can't abs single point of view, single point of reference, kind of a single pain that you think about. I know that's something that you've been in a lot of time on, >> so my role it's semantic because Chief of staff, I own the diversity agenda for the global security office. And it's bean aerial laser focus on me for the past twelve months, which is our industry has a systemic problem around attracting and retaining talent from diverse backgrounds. Right? We're gonna tackle it head on on We don't really successfully in semantics. Oh, wait. Give this fabulous mandate through to our leadership who got on board with laser focus around, making sure that we get a diverse slate of candidates when we bring in new people and that that translated incredibly well. So we saw a rise of interview to conversion. Foreign ft for females in six months off forty percent >> fourteen or forty four zero for zero. >> So just by making it part of the interviewing experience. Having a diverse slate of candidates, making sure that we're really giving a foreign opportunities coming right really has changed playing Plainfield. >> And then the other thing, of course, is the retention, which is a big problem for attention that we're, you know, women dropping out and not coming back. >> That's and this every organization has to step up to make sure that they're waiting, but their making a workforce that is flexible, that accommodates so some of that. Some of the mental load that women have, whether it's through a child, care whether it's to do with older parents. But also when we talk about diversity, it's nothing. You know just about the gender piece, right? We're going to accommodate for other people as well underrepresented minorities. Early Korea, Different people have different socio economic backgrounds, maybe haven't come from a typical university training course, right, Something that we've focused on heavily. We've been working with a large enough profits to bring in early career guys who have not had a university background who may have had a really rough time coming out of school, getting them in, training them up through internships, bringing them up to speed over six months and converting them into FDA, which I feel is really a way tio to build a diverse workforce and get people an opportunity that didn't have it >> now was someone spearheading that before you came on border was there Was there an effort that really kind of put a dedicated resource on it when you when you took it over? >> So I took over about a year ago and I double down on the effort. We were working with Europe before that. Had a fantastic colleague was doing a lot of work with Europe on. We're just seeing fabulous results with converted nearly thirty three percent of our internships into FT. >> Thirty three and you're not in those thirty three or not coming from, you know, kind of a classic. They're not coming pig population. >> Absolutely these air IGA passionate, enthusiastic young people who have a tenacity to just pick things up because they're so grateful to be there right there, so happy to be given the opportunity. And it's some It's an untapped resource that I think a lot of people who are looking to have solved aside the security talent shortages should be looking into great that we get programs in place for a Girl Scout middle school. But let's think about alternative ways of getting new talent in. And I think that they're not for profit right way after >> such a big problem. And like you say, it's a big problem, you know, from from little girls. And, you know, all the way up to mid mid career women that air dropping out and not coming back before you even get into the boardroom. We work with a ton of organization like Athena Alliance with towards that the boardroom level all the way down to Grace Hopper. You know, this working more kind of college graduate level girls intact? I mean, there's a lot of luckily, a lot of people are trying to focus on the problem, but unfortunately, the numbers or not turning in the correct direction, they're actually turning in the wrong direction. Yeah, >> so really, that's it for me. It's about laser focus. You really got it. If you make your party your agenda making party returned right? Don't give it. The nursery had not. Don't say that you will do the things actually commit to it and get it done right. I'm not a huge fan of talk. It's Qatargas work on. So, yeah, I think there's a lot of opportunity. The people they don't step up to the great doing enough >> to to your earlier first line, right? If you're not measuring it, you know, and tracking against it, how do you know if you're being silly and what it's under served? You have to give it a little juice, right? You can't just have to expect the status quo to suddenly change, right? >> Absolutely metrics. Incredibly employed. And start with you metrics. Dashboard record where your tracking, in terms of your representation of females, underrepresented minorities. Your bets. You're early Korea. Really? What you want to see is a huge influx or the interviewing stage into the into the FT conversion. You want to see an influx in your leadership. You want more women in your leadership team because that's the way to drive a better female pipeline, right? Same goes on because I'm are minority. Same guys. Early career. >> Yeah, so important that they look up and see somebody that looks like one hundred percent C. C an opportunity to be that person, something alright. Charlotte. Well, thanks for, uh, for taking a few minutes of your day. And great Teo learned about all your What you working on? That's >> great. Thanks. Having >> alright? She Charlotte? I'm Jeff. You're watching the Cube? Where are, say twenty nineteen in the force Cow booth. Thanks for watching. >> We'LL see you next time.