>> Good afternoon from Vegas, guys and gals. We're so happy that you're with us. This is theCUBE live at AWS re:Invent '22. This is our third day of coverage. We started Monday night, so we're counting that as day one. Loads of conversations we've had already. We know that you know that 'cause you've been watching. I'm here with Dave Vellante. Dave, great to be here with you with somewhere between 50,000 and 70,000 people. And we're excited for our next conversation. We've got two folks joining us who are new to theCUBE, soon will be alumni. Milan Bhatt joins us, the president and head of Cloud at Hexaware. And Nikhil Date, the Director of Engineering and Application Services at Domestic & General. Guys, welcome to the program. >> Thank you >> Thanks for having us. >> So Domestic & General, or D&G, is a customer of Hexaware, but Milan, we want to start with you. Give the audience an overview of Hexaware. What do you do? What's the business model? >> Yeah. So, Hexaware is a technology services company. We are a global partner of AWS, and essentially, we help customers like Domestic & General, you know, accelerate their digital transformation journeys. We like to think of ourselves as a billion dollar startup. And like Amazon, it is always day one at Hexaware. And, you know, I look forward to the conversation, but any company in the world that is looking at cloud-led digital transformation, they have to put Hexaware on the consideration list. Because, you know, not only do we work with a lot of customers, analysts like Gartner, they have rated us as a visionary in helping customers become, you know, digitally enabled, bring better customer experience to their end customers. >> Excellent. Well, we're glad to feature Hexaware on the program. >> Milan: Thank you. >> Nikhil let's bring you into the conversation. Talk to the audience about Domestic & General. What kind of business is it? What's the business model? >> Sure, thank you. So we are, you know, 110-year-old business, right? I mean, we started insuring sheep in Australia, if you believe it, you know, which is quite an origin story. But at the moment, you know, the primary business is keeping our customers world running. So what do I mean by that? We protect in warranty and out-of-warranty care for domestic appliances. You know, TVs, boilers, refrigerators, washing machines, that kind of thing. But we are also a B2B company in the sense that, you know, you might think you are getting a warranty from some of our biggest customers, like Whirlpool or, you know, Bosch, Siemens, or Samsung, but actually it's D&G at the back trying to administer that for you. So, you know, we are in 13 countries. Just launched in the US last year, but big plans. >> So it's really interesting because we all have appliances, and we can relate to, especially, you know pre or post-pandemic, how difficult it is to get service. So you're kind of like, in a way, you've got to build a digital platform like Uber, connecting drivers and passengers, right? And so you've got the supply of individuals who know how to fix stuff, right? And you want to make it as easy as possible for the customer. So was that the genesis of this digital transformation? Can you talk about those business drivers? >> It was, actually, and it's a fantastic point, because trying to become a platform business is what this journey has been all about for us, right? I think, you know, we are a pioneer in what we consider the subscription model. So customers pay a small amount per month as opposed to a big lump sum amount that they have to pay at the point you buy the appliance. And importantly, you can actually buy our product to pay in installments at the point something breaks down. So it's not just something that you buy at the point of sale or at the point you try to register. You can buy it at any time. And the goal really is to have warranty in a box that you can take anywhere, you know, anywhere in the world. So, you know, but it's a great point. Digital transformation is what it is all about. >> And there is a real lack right now of qualified technicians. >> That's right. >> Is there anything within the platform to incent those individuals to participate in your business? >> You know, this is what we consider a multi-tier approach. I think at the moment, the service that we offer is largely top tier, right? So we will get you an engineer that is certified by the manufacturer with the manufacturer warranty. And it's a no fix, no fee model, you know? So, you know, we guarantee either to repair or replace the appliance, you know? That's the model. But you are right, I think in the future stage would be, you know, why wouldn't we want to have anybody who's got the right skills to come in and work off the platform? Absolutely right. >> Nikhil, talk about, you said this is a legacy business, been around for quite some time. You've been there for not quite two years. What drew you to the organization? And where were they in their digital transformation journey? Because I always think legacy companies, this a big challenge, and it's cultural challenge to really transform, but companies these days have no choice. >> Again, a fantastic point, right? I think some of the, you know, 110-year-old business, right? And some of the tech, you would be forgiven for thinking it's that old. But the assets that we had are our people, right? Who are really passionate about the business. And I think what we had to do is to find a partner that can upskill the tech, but also upskill the people at the same time and upskill the delivery model, right? So we've a very traditional left-to-right waterfall, you know, planet first, big upfront planning, and then deliver kind of organization. And by working with a partner such as Hexaware and embracing cloud, because, you know, our first and our go-to will be a SaaS or a cloud provider. And, you know, doing that was the massive agenda that drew me to the company. But I think what is also fair is, you know, digitization or digitalization, is a misunderstood and often abused term, right? Because for the most part, when companies start, and I'm not saying it's right or wrong, but, you know, for the most part, when companies start on this journey, they take a journey that works in the brick and mortar world, and we were a contact center business, and just try to move it to the digital journey, right? It's not a great customer experience. I'll give you an example, right? Now, if you call our agent and say, "Yeah, I'm trying to register an appliance," they will tell you where to look for the serial number. But if you're on a digital channel, you don't know where to look. There's nobody, you know, who can help you. The model number, who remembers the model number of the washing machine they bought, right? I mean, you know, it's stuff like that, you know, which would feel, you know, for a digital native, my son, you know, for example, would think, "How can you even ask a customer for that?" But, you know, it's that change in the model, that's what this is all about. >> Yeah, it's like when you get to go, "What's your account number?" I have no idea what my account number is. So when did this whole project start? How was Hexaware involved? And where did Hexaware start? Like, how did you sort of gauge what the requirement was? Take us through that little- >> Sure. So, you know, when Nikhil and the rest of the management team came in, they came up with a competitive process where, you know, and it is refreshing to remember, I think they've stuck true to their vision. They were very clear that they were not looking for someone who can just digitize their paper processes, but who can help them completely re-imagine, you know, what the new process would look like what the new experience would look like. And, you know, remember, they were running this process at the height of the pandemic, so we couldn't meet anybody in person. We did everything virtual. And we were using cloud technology, but, you know, the way they run the process, they wanted to make sure that a provider brings in a mix of experience and engineering expertise. And that's really hard to find. But equally importantly, you remember those culture sessions that we did? They figured out some very creative ways of making sure that there is a cultural fit. So, for example, they did virtual breakout sessions where, you know, people were sort of asking each other, you know, if you want to have dinner with someone like a celebrity, who would it be? So, you know, these little things to make sure that there is a match and people can actually work. >> Relationship building too. >> The relationship building. It's hard to do in a virtual environment, but it was a competitive process. They looked at us in terms of engineering, you know, experience, our ability to transcend change and run, and, you know, really focus and align to keep their objectives first, right? Work as a true partnership. Do you agree? >> I would agree. And I think, you know, one of the biggest goals here was to make sure that, this is not an arms length vendor relationship, right? You know, this is an extension of our team. So these are our people, you know, for the people that work on D&G, you know, they work in the D&G way, you know, and that means that they can also challenge us, you know, which is quite refreshing, right? People stopping and saying, "Why are you asking me to do this?" You know, it's very refreshing, I think, you know, to work with a partner that is sold on the vision and committed to helping you achieve success. >> That synergy creates that flywheel. And like you said, at D&G, Hexaware, we're a team, we're working together. Nikhil, share with us some of the significant business outcomes that Hexaware services and AWS are helping the company to achieve? Because there's some big numbers there. >> Indeed. Yeah. So, you know, in the digital journey itself, like I said, we are also a B2B business. You know, one of the key challenges is every client wants their own brand, right? So, you know, a journey for customer X has to look like the customer X brand. And our journey for customer Y will have to do the same. You know, when you try to stretch this to a technology problem though, it means that, you know, we were trying to be too many things for too many people, and that slowed things down and increased complexity. So from our point of view, you know, when we started with the digital journey or in the middle of the digital journey, we thought, we need to have a library of reusable components. We need white labeling, right? So there was a root in branch re-engineering of the digital proposition to allow us to, you know, serve multiple clients with the same underlying technology. And that has meant that, you know, in some cases, we are going to market, you know, two, three times faster than what we were. Costs, obviously, you know, 50% cheaper. But, you know, I think the big thing here, and, you know, this is the unstated benefit, is because now there is a common underlying technology innovation that client X wants to do becomes available for client Y. You know, which means that, you know, there's a virtual circle of, you know, constant improvement. So, you know that, from my point of view, that's the big benefit. >> And would you agree that you are still only in the first quarter of a football game? >> Absolutely. >> I think a lot of ambitious plans. So, you know, this is just the beginning. And the way they have built the organization, the way they have driven the culture change, you know, I'm very hopeful for great things to come. >> Paint a picture of the tech. I'm interested in the architecture, and I'm really interested in the data component and how that's affected your business. >> So I mean, you know, multilayered tech architecture, as you can imagine. Then, you know, we still have a legacy, you know, legacy components running off our own PET mainframe, as we like to call it. But, you know, from a forward point of view, what we really want is to allow clients to self-serve, right? Not have to, you know, because at the moment, the only service we can offer is what I call the white glove, right? Which means, you know, somebody has to sit down with us, have a discussion on the requirements, but people should be able to self-serve, you know, look at the catalog of what it is we can do for them and go for it. Data is a very interesting point, right? Because not only are there, you know, geography restrictions around where customer data can go to, obviously, payments and PCI compliance is an issue. But last but not least, you know, some of this data is very, you know, unique to what the clients want to own and manage. And, you know, if you are a, you know, a typical homeowner, you will have appliance from all kinds of manufacturers, right? Many of whom would be our customers. But how much data we can share, because we recognize you as a person, but how much data we can share, there are restrictions. But, you know, building our data abstraction layer allows us to, you know, take care of that. But you're absolutely right, in terms of, But again, the potential for where the data can be mined, because, you know, the engineer also has to be local to where you live. You know, you can't come from 100 miles away. So, you know, the ability to use data to, you know, not just transform our business, but our client's business is phenomenal, you know? >> Do you actually have a mainframe? >> Yes >> We do do. (laughter) >> Adam Selinsky wants to move it into the cloud. (laughter) >> They have every possible technology that you can think of. I mean, 100-year-old business evolved over a period of time. And, you know, if I could add, you know, what has been really impressive about the decision making at D&G is that they have adopted cloud in the right way, right? So they are one of the few customers who have truly taken AWS well architected to heart. They have taken things like, you know, take the right workloads to the cloud and wait to do the right remediations before you take the rest of the workloads to the cloud. They've used native services available on AWS from apps perspective as well as a data perspective. So that's sort of a little bit more color on the technology and architecture. >> But you've essentially SaaSified your business and you basically have D&G cloud that you're delivering to your customers for self-serve. Is that fair? >> That's the vision, yes. The idea is to get there. And, you know, if we assemble what I call, you know, out-the-box solutions in a clever way, then that becomes the platform that we can replicate success on. And at the moment, our business needs what I call boots on the ground. When we are a true platform business, we should be able to operate without having, you know, any presence in country, with the partners leveraging the platform to do what what's next. >> I'm curious, Milan, you said that one of the great things that D&G has done is really adopted cloud in the right way. Do you, Nikhil, think of cloud first or cloud right approach? Because you've got a mainframe, so I'm just wondering if it's more what's right for cloud versus everything cloud first. >> Correct. I mean, I actually, you know, or we actually tend to start even two steps before that, right? I think it's really whether we need to buy or whether we need to build, right? And if we need to buy, then, you know, how easily would that thing that has been bought fit into what is a very complex architecture, as Milan said, right? I mean, any technology you can imagine we probably have it, but we want to simplify it, right? And this is a journey. So which means that, you know, we start with can SaaS product do it? And then we also want to go wherever we are building, then it has to be on the cloud. It has to be designed for scaling. It has to be designed to be in multiple geographies, multiple countries with the relevant data protection baked in. So, you know, that's the decision-thinking process. You know, that the goal is to not, I mean, you know, we had a project started 18 months ago that wanted to buy more tin, but we put a stop to that, right? And saying that, "You know, come on, you can't have that." Not in this day and age, you know, when the cloud can pretty much do everything that you need. >> Do you think of D&G, this is a question for you. We're almost out of time, but I'm just curious, I'm looking at your website, D&G, the experts who repair and replace the household products everyone relies on. Do you think about it as a repair company? Do you think about it as a tech company that delivers these repair services? >> I mean, this is the conversation we have in our teams all the time, right? That when our vision is successful, we will become a tech business. At the moment, I don't think we are, you know? At the moment, I think we are on a journey, you know, because, you know, we are multi-channel, you know, and our customers love us, you know, touch wood. But are we a true tech company? No, but we are getting there, right? I think, you know, that's the plan. >> You're on the journey? >> Yeah. >> Awesome stuff. Last question for each of you, a little bit different. Milan, question for you. You have a billboard or a bumper sticker, whichever, or maybe a sticker for your laptop and it's about Hexaware, and you want to really convey, in a compelling, but really short way, why are we so great? What would that sticker say? >> Awesome. Like I said at the beginning, if you are thinking about a digital transformation, if you are a company that has been around for a long time, you've got to think of us, you know, as a partner. So that's what I would say, because, you know, the purpose of our company is creating smiles through a combination of great people and technology. So that's what we live for. And, you know, brought a smile to me when Nikhil said that our customers love us, and somewhere, we have a small role to play in that. >> I love that. Nikhil, I'm going to ask the same question. I was going to ask you a different one, but I would love to, I mean, we talked a lot about D&G and the incredible business transformation that you've been on. What's that bumper sticker for D&G? What is that bumper sticker for D&G? >> Oh, yeah. Okay. We want keep your world running, right? I mean, you know, from our point of view, you know, you rely on the appliances to keep your home running, and we want you to rely on us to make sure your world keeps running. You know, that's what this is all about. It has to be slick. Touch wood, hopefully, you never have a problem, but if you do, we want to be there, you know, to make sure that your world keeps running. >> I love it. Awesome, guys. Thank you, Milan. Nikhil, thank you so much for joining Dave and me on the program. >> Thank you. I enjoyed the conversation. >> Great partnership. Hexaware, first time on theCUBE, now you're an alumni. You're an alumni too. We appreciate your insights, sharing the story. It's a really compelling story. Thank you. >> And thank you for all the support, Nikhil. >> Of course. >> All right. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage.

(cheerful music) >> Welcome to theCUBE's continuing coverage of AMD's 4th Generation EPYC launch. I'm Dave Nicholson, and I'm here in our Palo Alto studios talking to Greg Gibby, senior product manager, data center products from AMD, and Mohan Rokkam, technical marketing engineer at Dell. Welcome, gentlemen. >> Mohan: Hello, hello. >> Greg: Thank you. Glad to be here. >> Good to see each of you. Just really quickly, I want to start out. Let us know a little bit about yourselves. Mohan, let's start with you. What do you do at Dell exactly? >> So I'm a technical marketing engineer at Dell. I've been with Dell for around 15 years now and my goal is to really look at the Dell powered servers and see how do customers take advantage of some of the features we have, especially with the AMD EPYC processors that have just come out. >> Greg, and what do you do at AMD? >> Yeah, so I manage our software-defined infrastructure solutions team, and really it's a cradle to grave where we work with the ISVs in the market, so VMware, Nutanix, Microsoft, et cetera, to integrate the features that we're putting into our processors and make sure they're ready to go and enabled. And then we work with our valued partners like Dell on putting those into actual solutions that customers can buy and then we work with them to sell those solutions into the market. >> Before we get into the details on the 4th Generation EPYC launch and what that means and why people should care. Mohan, maybe you can tell us a little about the relationship between Dell and AMD, how that works, and then Greg, if you've got commentary on that afterwards, that'd be great. Yeah, Mohan. >> Absolutely. Dell and AMD have a long standing partnership, right? Especially now with EPYC series. We have had products since EPYC first generation. We have been doing solutions across the whole range of Dell ecosystem. We have integrated AMD quite thoroughly and effectively and we really love how performant these systems are. So, yeah. >> Dave: Greg, what are your thoughts? >> Yeah, I would say the other thing too is, is that we need to point out is that we both have really strong relationships across the entire ecosystem. So memory vendors, the software providers, et cetera, we have technical relationships. We're working with them to optimize solutions so that ultimately when the customer buys that, they get a great user experience right out of the box. >> So, Mohan, I know that you and your team do a lot of performance validation testing as time goes by. I suspect that you had early releases of the 4th Gen EPYC processor technology. What have you been seeing so far? What can you tell us? >> AMD has definitely knocked it out of the park. Time and again, in the past four generations, in the past five years alone, we have done some database work where in five years, we have seen five exit performance. And across the board, AMD is the leader in benchmarks. We have done virtualization where we would consolidate from five into one system. We have world records in AI, we have world records in databases, we have world records in virtualization. The AMD EPYC solutions has been absolutely performant. I'll leave you with one number here. When we went from top of Stack Milan to top of Stack Genoa, we saw a performance bump of 120%. And that number just blew my mind. >> So that prompts a question for Greg. Often we, in industry insiders, think in terms of performance gains over the last generation or the current generation. A lot of customers in the real world, however, are N - 2. They're a ways back, so I guess two points on that. First of all, the kinds of increases the average person is going to see when they move to this architecture, correct me if I'm wrong, but it's even more significant than a lot of the headline numbers because they're moving two generations, number one. Correct me if I'm wrong on that, but then the other thing is the question to you, Greg. I like very long complicated questions, as you can tell. The question is, is it okay for people to skip generations or make the case for upgrades, I guess is the problem? >> Well, yeah, so a couple thoughts on that first too. Mohan talked about that five X over the generation improvements that we've seen. The other key point with that too is that we've made significant process improvements along the way moving to seven nanocomputer to now five nanocomputer and that's really reducing the total amount of power or the performance per watt the customers can realize as well. And when we look at why would a customer want to upgrade, right? And I want to rephrase that as to why aren't you? And there is a real cost of not upgrading. And so when you look at infrastructure, the average age of a server in the data center is over five years old. And if you look at the most popular processors that were sold in that timeframe, it's 8, 10, 12 cores. So now you've got a bunch of servers that you need in order to deliver the applications and meet your SLAs to your end users, and all those servers pull power. They require maintenance. They have the opportunity to go down, et cetera. You got to pay licensing and service and support costs and all those. And when you look at all the costs that roll up, even though the hardware is paid for just to keep the lights on, and not even talking about the soft costs of unplanned downtime, and, "I'm not meeting your SLAs," et cetera, it's very expensive to keep those servers running. Now, if you refresh, and now you have processors that have 32, 64, 96 cores, now you can consolidate that infrastructure and reduce your total power bill. You can reduce your CapEx, you reduce your ongoing OpEx, you improve your performance, and you improve your security profile. So it really is more cost effective to refresh than not to refresh. >> So, Mohan, what has your experience been double clicking on this topic of consolidation? I know that we're going to talk about virtualization in some of the results that you've seen. What have you seen in that regard? Does this favor better consolidation and virtualized environments? And are you both assuring us that the ROI and TCO pencil out on these new big, bad machines? >> Greg definitely hit the nail on the head, right? We are seeing tremendous savings really, if you're consolidating from two generations old. We went from, as I said, five is to one. You're going from five full servers, probably paid off down to one single server. That itself is, if you look at licensing costs, which again, with things like VMware does get pretty expensive. If you move to a single system, yes, we are at 32, 64, 96 cores, but if you compare to the licensing costs of 10 cores, two sockets, that's still pretty significant, right? That's one huge thing. Another thing which actually really drives the thing is we are looking at security, and in today's environment, security becomes a major driving factor for upgrades. Dell has its own setups, cyber-resilient architecture, as we call it, and that really is integrated from processor all the way up into the OS. And those are some of the features which customers really can take advantage of and help protect their ecosystems. >> So what kinds of virtualized environments did you test? >> We have done virtualization across primary codes with VMware, but the Azure Stack, we have looked at Nutanix. PowerFlex is another one within Dell. We have vSAN Ready Nodes. All of these, OpenShift, we have a broad variety of solutions from Dell and AMD really fits into almost every one of them very well. >> So where does hyper-converged infrastructure fit into this puzzle? We can think of a server as something that contains not only AMD's latest architecture but also latest PCIe bus technology and all of the faster memory, faster storage cards, faster nicks, all of that comes together. But how does that play out in Dell's hyper-converged infrastructure or HCI strategy? >> Dell is a leader in hyper-converged infrastructure. We have the very popular VxRail line, we have the PowerFlex, which is now going into the AWS ecosystem as well, Nutanix, and of course, Azure Stack. With all these, when you look at AMD, we have up to 96 cores coming in. We have PCIe Gen 5 which means you can now connect dual port, 100 and 200 gig nicks and get line rate on those so you can connect to your ecosystem. And I don't know if you've seen the news, 200, 400 gig routers and switchers are selling out. That's not slowing down. The network infrastructure is booming. If you want to look at the AI/ML side of things, the VDI side of things, accelerator cards are becoming more and more powerful, more and more popular. And of course they need that higher end data path that PCIe Gen 5 brings to the table. GDDR5 is another huge improvement in terms of performance and latencies. So when we take all this together, you talk about hyper-converged, all of them add into making sure that A, with hyper-converged, you get ease of management, but B, just 'cause you have ease of management doesn't mean you need to compromise on anything. And the AMD servers effectively are a no compromise offering that we at Dell are able to offer to our customers. >> So Greg, I've got a question a little bit from left field for you. We covered Supercompute Conference 2022. We were in Dallas a couple of weeks ago, and there was a lot of discussion of the current processor manufacturer battles, and a lot of buzz around 4th Gen EPYC being launched and what's coming over the next year. Do you have any thoughts on what this architecture can deliver for us in terms of things like AI? We talk about virtualization, but if you look out over the next year, do you see this kind of architecture driving significant change in the world? >> Yeah, yeah, yeah, yeah. It has the real potential to do that from just the building blocks. So we have our chiplet architecture we call it. So you have an IO die and then you have your core complexes that go around that. And we integrate it all with our infinity fabric. That architecture allows you, if we wanted to, replace some of those CCDs with specific accelerators. And so when we look two, three, four years down the road, that architecture and that capability already built into what we're delivering and can easily be moved in. We just need to make sure that when you look at doing that, that the power that's required to do that and the software, et cetera, and those accelerators actually deliver better performance as a dedicated engine versus just using standard CPUs. The other things that I would say too is if you look at emerging workloads. So data center modernization is one of the buzzwords in cloud native, right? And these container environments, well, AMD'S architecture really just screams support for those type of environments, right? Where when you get into these larger core accounts and the consolidation that Mohan talked about. Now when I'm in a container environment, that blast radius so a lot of customers have concerns around, "Hey, having a single point of failure and having more than X number of cores concerns me." If I'm in containers, that becomes less of a concern. And so when you look at cloud native, containerized applications, data center modernization, AMD's extremely well positioned to take advantage of those use cases as well. >> Yeah, Mohan, and when we talk about virtualization, I think sometimes we have to remind everyone that yeah, we're talking about not only virtualization that has a full-blown operating system in the bucket, but also virtualization where the containers have microservices and things like that. I think you had something to add, Mohan. >> I did, and I think going back to the accelerator side of business, right? When we are looking at the current technology and looking at accelerators, AMD has done a fantastic job of adding in features like AVX-512, we have the bfloat16 and eight features. And some of what these do is they're effectively built-in accelerators for certain workloads especially in the AI and media spaces. And in some of these use cases we look at, for example, are inference. Traditionally we have used external accelerator cards, but for some of the entry level and mid-level use cases, CPU is going to work just fine especially with the newer CPUs that we are seeing this fantastic performance from. The accelerators just help get us to the point where if I'm at the edge, if I'm in certain use cases, I don't need to have an accelerator in there. I can run most of my inference workloads right on the CPU. >> Yeah, yeah. You know the game. It's an endless chase to find the bottleneck. And once we've solved the puzzle, we've created a bottleneck somewhere else. Back to the supercompute conversations we had, specifically about some of the AMD EPYC processor technology and the way that Dell is packaging it up and leveraging things like connectivity. That was one of the things that was also highlighted. This idea that increasingly connectivity is critically important, not just for supercomputing, but for high-performance computing that's finding its way out of the realms of Los Alamos and down to the enterprise level. Gentlemen, any more thoughts about the partnership or maybe a hint at what's coming in the future? I know that the original AMD announcement was announcing and previewing some things that are rolling out over the next several months. So let me just toss it to Greg. What are we going to see in 2023 in terms of rollouts that you can share with us? >> That I can share with you? Yeah, so I think look forward to see more advancements in the technology at the core level. I think we've already announced our product code name Bergamo, where we'll have up to 128 cores per socket. And then as we look in, how do we continually address this demand for data, this demand for, I need actionable insights immediately, look for us to continue to drive performance leadership in our products that are coming out and address specific workloads and accelerators where appropriate and where we see a growing market. >> Mohan, final thoughts. >> On the Dell side, of course, we have four very rich and configurable options with AMD EPYC servers. But beyond that, you'll see a lot more solutions. Some of what Greg has been talking about around the next generation of processors or the next updated processors, you'll start seeing some of those. and you'll definitely see more use cases from us and how customers can implement them and take advantage of the features that. It's just exciting stuff. >> Exciting stuff indeed. Gentlemen, we have a great year ahead of us. As we approach possibly the holiday seasons, I wish both of you well. Thank you for joining us. From here in the Palo Alto studios, again, Dave Nicholson here. Stay tuned for our continuing coverage of AMD's 4th Generation EPYC launch. Thanks for joining us. (cheerful music)

(intro upbeat music) >> Hello everyone and welcome to our continued coverage of AWS re:Invent here on theCUBE. My name is Savannah Peterson and I am very excited about the conversation coming up. Not only are we joined by two brilliant minds in the cloud, one of them happens to be a CUBE alumni. Please welcome Mike from AMD and Ali from Dropbox. Ali, welcome back to the show, how you been? >> Thanks Savannah. I'm doing great and really excited to be back on theCUBE. It was great discussion last time and really excited for both re:Invent and also to see how this video turns out. >> Hey, that makes two of us and probably three of us. How are you doing today, Mike? >> Doing great. It's really nice to be getting back to in-person events again and to be out solving problems with customers and partners like Dropbox. >> I know, isn't it? We've all missed each other. Was a lonely couple of years. Mike, I'm going to open it up with you. I'm sure a lot of people are curious. What's new at AMD? >> Well, there's a lot that's new at AMD, so I'll share a subset of what's new and what we've been working on. We've expanded our global coverage in Amazon EC2 with new regions and instance types. So users can deploy any application pretty much anywhere AWS has a presence. Our partner ecosystems for solutions and services has expanded quite a bit. We're currently focused on enabling partners and solutions that focus on cloud cost optimization, modernizing infrastructure, and pushing performance to the limit, especially for HPC. But the biggest buzz, of course, is around AMD's new fourth generation of our EPYC CPU Genoa. It's the world's fastest data center CPU with transformative energy efficiency and that's a really interesting combination, highest performance and most efficient. So on launch day, AWS announced their plans to roll out AMD EPYC Genoa processor-based EC2 instances. So we're pretty excited about that and that's what we'll be working on in the near term. >> Wow, that's a big deal and certainly not a casual announcement. Obviously, power and efficiency hot topics here at re:Invent but also looking at the greater impact on the planet is a big conversation we've been having here as well. So this is exciting and timely and congratulations to you and the team on all that seems to be going on. Ali, what's going on at Dropbox? >> Yeah, thanks Savannah. The Q3 2022 was actually a very strong quarter for Dropbox during a very difficult macroeconomic backdrop. Our focus has continued to be on innovation and this is around both new products and also driving multi-product adoption which is paying a lot of dividends for us, so essentially, bringing products like Dropbox Sign, DocSend, Capture, and other exciting products to our customers. On the infra side, it's all about how do we scale our infrastructure to meet the business needs, right? How do we keep up with the accelerated growth during the pandemic and also leveraging both AMD and AWS for investments in our public cloud? >> Let's talk about the cloud a bit. You are both cloud experts and I'm glad that you brought that up. We'll keep it there with Ali. When, why, and how should users leverage public cloud? >> Yeah, so Dropbox is hybrid cloud which means we are running applications both in private and public cloud and within a unique position to leverage the best of both worlds. And Savannah, this is a decision we continue to reevaluate on a regular basis. And there are really three key factors that come into play here. First is scale and scale, are we operating at a scale where customization is cost-efficient for us? Next is uniqueness. Is our workload unique compared to what the public cloud supports? And lastly, innovation. Do we have the expertise to innovate faster than public cloud or not? So based on these three key factors, we try and balance all of them and then come up with the best option for us at Dropbox. And kind of elaborating over here, things like international storage, we're leveraging public cloud, things like AI and ML, we're leveraging public cloud, but when we talk about Magic Pocket, which is our multi-exabyte storage system, that has the scale which is why we are doing that on our own private cloud. >> Wow, I think you just gave everybody a fantastic framework for thinking about their decision matrix there if nothing else. Mike, is there anything that you'd like to add to that? Anything that AMD considers when contemplating public cloud versus private? >> Yeah, so there's really three main drivers that I see when users consider when, why, and how should they leverage public cloud. Three main drivers: establishing a global footprint, accelerating product release cycles, and efficiently rightsizing infrastructure. So customers looking to establish a global footprint often turn to public cloud deployments to quickly reach their clients in workforces around the world, most importantly with minimal capital expense. I understand Dropbox uses public cloud to establish their global presence scaling out from their core data centers in North America. And then a lot of industries have tremendous pressure to accelerate product release cycles. With public cloud, organizations can immediately deploy new applications without a long site and hardware acquisition cycle and then the associated ongoing maintenance and operational overhead. And the third thing is customers that need to rightsize and dynamically scale their infrastructure and application deployments are drawn to public cloud, for example, customers that have cyclical compute or application load peaks can efficiently deploy in the cloud without overdeploying their on-prem infrastructure for most of the year which is off-peak during those off-peak times. That infrastructure idle time is a waste of resources and OPEX. So scalable rightsizing draws a lot of users to cloud deployment. >> Yeah, wow. I think there's a lot of factors to consider but also it seems like a pretty streamlined process for navigating that or at least you two both made it sound that way. Another hot topic in the space right now is security. Mike, let's start with you a little bit. What are the most important security issues for AMD right now that you can talk about? >> Yeah, sure. So, well, first of all, AWS provides a wide variety of really good security services to protect customers that are working in the cloud. Like from a processor technology perspective, there's three main security aspects to consider, two of which are common practice today and one of which AMD brings significant differentiation and value. The first two are protecting data at rest and data in transit. And these two are part of the prevalent security models of today where AMD provides distinct value and differentiation is in protecting data in use. So EPYC Milan and Genoa processors support a function called SEV-SNP and this enables users to reside and their applications to reside within their own cryptographic context and environment with data integrity protection to accomplish what's called comprehensive confidential computing. Ethics confidential computing solution is hardware-based. So it's easy to leverage, there's no code rewrite required unlike comparable solutions that are software-based that require recoding to a proprietary SDK and come with a significant performance trade-off. So with EPYC processors, you can protect your data at rest, in transit, and most importantly, in use. >> Everybody needs to protect their data everywhere it is. So I love that. That's fantastic to hear and I'm sure gives your customers a lot of confidence. What about over at Dropbox? What security issues are you facing, Ali? >> Yeah, so the first company value at Dropbox is actually being worthy of trust, and what this really means from a security perspective is how do we keep all of our users content safe? And this means keeping everything down to all of the infrastructure hardware secure. So partnering with AMD, which is one of our strongest partners out there, the new security features that AMD have and the hardware are critical for us and we are able to take advantage of some of these best security practices within our compute infrastructure by leveraging AMD's secure ship architecture. >> How important, you just touched on it a little bit, and I want to ask, how important are partnerships like the one you have with each other as you innovate at scale? Ali, you're nodding, I'm going to go to you first. >> Yeah, so like I mentioned, the partnership with with AMD is one of the strongest that we have and it just goes beyond like a regular partnership where it's just buy and sell. We talk about technology together, we talk about innovation together, we talk about partnership together, and for us, as I look look at our hybrid cloud strategy, we would not be able to get the benefits in terms of efficiency, scale, or liability performance without having a strong partner like AMD. >> That's awesome. Mike, anything you want to add there? >> I'd reiterate some of what Ali had to say. One of my favorite parts about my job is getting together with partners and customers to figure out how to optimize their applications and deployments around the world to get the most efficient use of the cloud infrastructure for servers that are based on AMD technology. In many cases, we can find 10% or better performance or cost optimization by working closely with partners like Dropbox. And then in addition, if we keep in lock step together to look at what's coming on the roadmap, by the time the latest and greatest technology is finally deployed, our customers and our partners are ready to take advantage of it. So that's the fun part of the job and I really appreciate the Dropbox's cooperation, optimizing their infrastructure, and using AMD products >> Well, what a synergistic relationship of mutual admiration and support. We love to hear it here in the tech world. Mike, last question for you. What's next for AMD? >> Well, heading into 2023, considering the current challenge macroeconomic environment and geopolitical instability, doing more with less will be top of mind for many CFOs and CEOs in 2023. And AMD can help accomplish that. AMD's EPYC processors, leadership performance, and lower EC2 retail costs can help users reduce costs without impacting performance, or the flip side of that, they can scale capacity without increasing costs. And because of EPYC's higher core counts, really high core density, applications can be deployed with fewer servers or smaller instances that has both economic and environmental benefits that reduce usage costs as well as environmental impacts. And that allows customers to optimize their application and infrastructure spend. And then the second thing that I've seen over the last couple of years and I see this trajectory continuing is increased geographic distribution of our colleagues and workforces is here to stay, people work from everywhere. In modern cross platform, collaboration platforms, that bring teams, tools, and content together have a really important role to play to enable that new, more flexible style of working. And those tools need to be really agile and easy to use. I think Dropbox is really well positioned to enable this new style of working. AMD's really happy to work closely with Dropbox to enable these modern work styles, both on premises, hybrid, and fully in the public cloud. >> Well, it sounds like a very exciting and optimistically, bright future for you all at AMD. We love to hear that here at theCUBE. Ali, what about you? What is 2023 going to hold for Dropbox? >> Yeah, so I think we're going to continue on this journey of transformation where our focus is on new products and also multi-product adoption. And from a cloud perspective, how do we continue to evolve our hybrid cloud so that we remain a competitive advantage for our business and also for our customers? I think right now, Savannah, we're in a very unique position to utilize some of the best AMD technology that's out there and that's both on premise and in the cloud. Some of the AMD Epic processors delivered the performance that we need for our hybrid cloud and we want to continue to leverage these also in public cloud which is the EC2 instances that are powered by AMD in the long run. So overall, Dropbox is looking forward to continue to evaluate some of the AMD's Genoa CPUs that are coming out but also want to continue to grow our EC2 footprint powered by AMD in the long run. >> Fantastic. Well, it sounds like this second showing here on theCUBE is just the tee up for your third and we'll definitely have to have Mike back on for the second time around to hear how things are going. Thank you both so much for taking the time today to join me here. Mike and Ali, it was fantastic getting to chat to you and thank you to our audience for tuning into theCUBE's special coverage of AWS re:Invent. My name's Savannah Peterson and I hope we can learn together soon. (outro upbeat music)

(upbeat music) >> Hello everyone. This is Dave Vellante. We're diving into the deep end with AMD and Oracle on the topic of mySQL HeatWave performance. And we want to explore the important issues around machine learning. As applications become more data intensive and machine intelligence continues to evolve, workloads increasingly are seeing a major shift where data and AI are being infused into applications. And having a database that simplifies the convergence of transaction and analytics data without the need to context, switch and move data out of and into different data stores. And eliminating the need to perform extensive ETL operations is becoming an industry trend that customers are demanding. At the same time, workloads are becoming more automated and intelligent. And to explore these issues further, we're happy to have back in theCUBE Nipun Agarwal, who's the Senior Vice President of mySQL HeatWave and Kumaran Siva, who's the Corporate Vice President Strategic Business Development at AMD. Gents, hello again. Welcome back. >> Hello. Hi Dave. >> Thank you, Dave. >> Okay. Nipun, obviously machine learning has become a must have for analytics offerings. It's integrated into mySQL HeatWave. Why did you take this approach and not the specialized database approach as many competitors do right tool for the right job? >> Right? So, there are a lot of customers of mySQL who have the need to run machine learning on the data which is store in mySQL database. So in the past, customers would need to extract the data out of mySQL and they would take it to a specialized service for running machine learning. Now, the reason we decided to incorporate machine learning inside the database, there are multiple reasons. One, customers don't need to move the data. And if they don't need to move the data, it is more secure because it's protected by the same access controlled mechanisms as rest of the data There is no need for customers to manage multiple services. But in addition to that, when we run the machine learning inside the database customers are able to leverage the same service the same hardware, which has been provisioned for OTP analytics and use machine learning capabilities at no additional charge. So from a customer's perspective, they get the benefits that it is a single database. They don't need to manage multiple services. And it is offered at no additional charge. And then as another aspect, which is kind of hard to learn which is based on the IP, the work we have done it is also significantly faster than what customers would get by having a separate service. >> Just to follow up on that. How are you seeing customers use HeatWaves machine learning capabilities today? How is that evolving? >> Right. So one of the things which, you know customers very often want to do is to train their models based on the data. Now, one of the things is that data in a database or in a transaction database changes quite rapidly. So we have introduced support for auto machine learning as a part of HeatWave ML. And what it does is that it fully automates the process of training. And this is something which is very important to database users, very important to mySQL users that they don't really want to hire or data scientists or specialists for doing training. So that's the first part that training in HeatWave ML is fully automated. Doesn't require the user to provide any like specific parameters, just the source data and the task which they want to train. The second aspect is the training is really fast. So the training is really fast. The benefit is that customers can retrain quite often. They can make sure that the model is up to date with any changes which have been made to their transaction database. And as a result of the models being up to date, the accuracy of the prediction is high. Right? So that's the first aspect, which is training. The second aspect is inference, which customers run once they have the models trained. And the third thing, which is perhaps been the most sought after request from the mySQL customers is the ability to provide explanations. So, HeatWave ML provides explanations for any model which has been generated or trained by HeatWave ML. So these are the three capabilities- training, inference and explanations. And this whole process is completely automated, doesn't require a specialist or a data scientist. >> Yeah, that's nice. I mean, training obviously very popular today. I've said inference I think is going to explode in the coming decade. And then of course, AI explainable AI is a very important issue. Kumaran, what are the relevant capabilities of the AMD chips that are used in OCI to support HeatWave ML? Are they different from say the specs for HeatWave in general? >> So, actually they aren't. And this is one of the key features of this architecture or this implementation that is really exciting. Um, there with HeatWave ML, you're using the same CPU. And by the way, it's not a GPU, it's a CPU for both for all three of the functions that Nipun just talked about- inference, training and explanation all done on CPU. You know, bigger picture with the capabilities we bring here we're really providing a balance, you know between the CPU cores, memory and the networking. And what that allows you to do here is be able to feed the CPU cores appropriately. And within the cores, we have these AVX instruc... extensions in with the Zen 2 and Zen 3 cores. We had AVX 2, and then with the Zen 4 core coming out we're going to have AVX 512. But we were able to with that balance of being able to bring in the data and utilize the high memory bandwidth and then use the computation to its maximum we're able to provide, you know, build pride enough AI processing that we are able to get the job done. And then we're built to build a fit into that larger pipeline that that we build out here with the HeatWave. >> Got it. Nipun you know, you and I every time we have a conversation we've got to talk benchmarks. So you've done machine learning benchmarks with HeatWave. You might even be the first in the industry to publish you know, transparent, open ML benchmarks on GitHub. I mean, I, I wouldn't know for sure but I've not seen that as common. Can you describe the benchmarks and the data sets that you used here? >> Sure. So what we did was we took a bunch of open data sets for two categories of tasks- classification and regression. So we took about a dozen data sets for classification and about six for regression. So to give an example, the kind of data sets we used for classifications like the airlines data set, hex sensors bank, right? So these are open data sets. And what we did was for on these data sets we did a comparison of what would it take to train using HeatWave ML? And then the other service we compared with is that RedShift ML. So, there were two observations. One is that with HeatWave ML, the user does not need to provide any tuning parameters, right? The HeatWave ML using RML fully generates a train model, figures out what are the right algorithms? What are the right features? What are the right hyper parameters and sets, right? So no need for any manual intervention not so the case with Redshift ML. The second thing is the performance, right? So the performance of HeatWave ML aggregate on these 12 data sets for classification and the six data sets on regression. On an average, it is 25 times faster than Redshift ML. And note that Redshift ML in turn involves SageMaker, right? So on an average, HeatWave ML provides 25 times better performance for training. And the other point to note is that there is no need for any human intervention. That's fully automated. But in the case of Redshift ML, many of these data sets did not even complete in the set duration. If you look at price performance, one of the things again I want to highlight is because of the fact that AMD does pretty well in all kinds of workloads. We are able to use the same cluster users and use the same cluster for analytics, for OTP or for machine learning. So there is no additional cost for customers to run HeatWave ML if they have provision HeatWave. But assuming a user is provisioning a HeatWave cluster only to run HeatWave ML, right? That's the case, even in that case the price performance advantage of HeatWave ML over Redshift ML is 97 times, right? So 25 times faster at 1% of the cost compared to Redshift ML And all these scripts and all this information is available on GitHub for customers to try to modify and like, see, like what are the advantages they would get on their workloads? >> Every time I hear these numbers, I shake my head. I mean, they're just so overwhelming. Um, and so we'll see how the competition responds when, and if they respond. So, but thank you for sharing those results. Kumaran, can you elaborate on how the specs that you talked about earlier contribute to HeatWave ML's you know, benchmark results. I'm particularly interested in scalability, you know Typically things degrade as you push the system harder. What are you seeing? >> No, I think, I think it's good. Look, yeah. That's by those numbers, just blow me, blow my head too. That's crazy good performance. So look from, from an AMD perspective, we have really built an architecture. Like if you think about the chiplet architecture to begin with, it is fundamentally, you know, it's kind of scaling by design, right? And, and one of the things that we've done here is been able to work with, with the HeatWave team and heat well ML team, and then been able to, to within within the CPU package itself, be able to scale up to take very efficient use of all of the course. And then of course, work with them on how you go between nodes. So you can have these very large systems that can run ML very, very efficiently. So it's really, you know, building on the building blocks of the chiplet architecture and how scaling happens there. >> Yeah. So it's you're saying it's near linear scaling or essentially. >> So, let Nipun comment on that. >> Yeah. >> Is it... So, how about as cluster sizes grow, Nipun? >> Right. >> What happens there? >> So one of the design points for HeatWave is scale out architecture, right? So as you said, that as we add more data set or increase the size of the data, or we add the number of nodes to the cluster, we want the performance to scale. So we show that we have near linear scale factor, or nearly near scale scalability for SQL workloads in the case of HeatWave ML, as well. As users add more nodes to the cluster so the size of the cluster the performance of HeatWave ML improves. So I was giving you this example that HeatWave ML is 25 times faster compared to Redshift ML. Well, that was on a cluster size of two. If you increase the cluster size of HeatWave ML to a larger number. But I think the number is 16. The performance advantage over Redshift ML increases from 25 times faster to 45 times faster. So what that means is that on a cluster size of 16 nodes HeatWave ML is 45 times faster for training these again, dozen data sets. So this shows that HeatWave ML skills better than the computation. >> So you're saying adding nodes offsets any management complexity that you would think of as getting in the way. Is that right? >> Right. So one is the management complexity and which is why by features like last customers can scale up or scale down, you know, very easily. The second aspect is, okay What gives us this advantage, right, of scalability? Or how are we able to scale? Now, the techniques which we use for HeatWave ML scalability are a bit different from what we use for SQL processing. So in the case of HeatWave ML, they really like, you know, three, two trade offs which we have to be careful about. One is the accuracy. Because we want to provide better performance for machine learning without compromising on the accuracy. So accuracy would require like more synchronization if you have multiple threads. But if you have too much of synchronization that can slow down the degree of patterns that we get. Right? So we have to strike a fine balance. So what we do is that in HeatWave ML, there are different phases of training, like algorithm selection, feature selection, hyper probability training. Each of these phases is analyzed. And for instance, one of the ways techniques we use is that if you're trying to figure out what's the optimal hyper parameter to be used? We start up with the search space. And then each of the VMs gets a part of the search space. And then we synchronize only when needed, right? So these are some of the techniques which we have developed over the years. And there are actually paper's filed, research publications filed on this. And this is what we do to achieve good scalability. And what that results to the customer is that if they have some amount of training time and they want to make it better they can just provision a larger cluster and they will get better performance. >> Got it. Thank you. Kumaran, when I think of machine learning, machine intelligence, AI, I think GPU but you're not using GPU. So how are you able to get this type of performance or price performance without using GPU's? >> Yeah, definitely. So yeah, that's a good point. And you think about what is going on here and you consider the whole pipeline that Nipun has just described in terms of how you get you know, your training, your algorithms And using the mySQL pieces of it to get to the point where the AI can be effective. In that process what happens is you have to have a lot of memory to transactions. A lot of memory bandwidth comes into play. And then bringing all that data together, feeding the actual complex that does the AI calculations that in itself could be the bottleneck, right? And you can have multiple bottlenecks along the way. And I think what you see in the AMD architecture for epic for this use case is the balance. And the fact that you are able to do the pre-processing, the AI, and then the post-processing all kind of seamlessly together, that has a huge value. And that goes back to what Nipun was saying about using the same infrastructure, gets you the better TCO but it also gets you gets you better performance. And that's because of the fact that you're bringing the data to the computation. So the computation in this case is not strictly the bottleneck. It's really about how you pull together what you need and to do the AI computation. And that is, that's probably a more, you know, it's a common case. And so, you know, you're going to start I think the least start to see this especially for inference applications. But in this case we're doing both inference explanation and training. All using the the CPU in the same OCI infrastructure. >> Interesting. Now Nipun, is the secret sauce for HeatWave ML performance different than what we've discussed before you and I with with HeatWave generally? Is there some, you know, additive engine additive that you're putting in? >> Right? Yes. The secret sauce is indeed different, right? Just the way I was saying that for SQL processing. The reason we get very good performance and price performance is because we have come up with new algorithms which help the SQL process can scale out. Similarly for HeatWave ML, we have come up with new IP, new like algorithms. One example is that we use meta-learn proxy models, right? That's the technique we use for automating the training process, right? So think of this meta-learn proxy models to be like, you know using machine learning for machine learning training. And this is an IP which we developed. And again, we have published the results and the techniques. But having such kind of like techniques is what gives us a better performance. Similarly, another thing which we use is adaptive sampling that you can have a large data set. But we intelligently sample to figure out that how can we train on a small subset without compromising on the accuracy? So, yes, there are many techniques that you have developed specifically for machine learning which is what gives us the better performance, better price performance, and also better scalability. >> What about mySQL autopilot? Is there anything that differs from HeatWave ML that is relevant? >> Okay. Interesting you should ask. So mySQL Autopilot is think of it to be an application using machine learning. So mySQL Autopilot uses machine learning to automate various aspects of the database service. So for instance, if you want to figure out that what's the right partitioning scheme to partition the data in memory? We use machine learning techniques to figure out that what's the right, the best column based on the user's workload to partition the data in memory Or given a workload, if you want to figure out what is the right cluster size to provision? That's something we use mySQL autopilot for. And I want to highlight that we don't aware of any other database service which provides this level of machine learning based automation which customers get with mySQL Autopilot. >> Hmm. Interesting. Okay. Last question for both of you. What are you guys working on next? What can customers expect from this collaboration specifically in this space? Maybe Nipun, you can start and then Kamaran can bring us home. >> Sure. So there are two things we are working on. One is based on the feedback we have gotten from customers, we are going to keep making the machine learning capabilities richer in HeatWave ML. That's one dimension. And the second thing is which Kamaran was alluding to earlier, We are looking at the next generation of like processes coming from AMD. And we will be seeing as to how we can more benefit from these processes whether it's the size of the L3 cache, the memory bandwidth, the network bandwidth, and such or the newer effects. And make sure that we leverage the all the greatness which the new generation of processes will offer. >> It's like an engineering playground. Kumaran, let's give you the final word. >> No, that's great. Now look with the Zen 4 CPU cores, we're also bringing in AVX 512 instruction capability. Now our implementation is a little different. It was in, in Rome and Milan, too where we use a double pump implementation. What that means is, you know, we take two cycles to do these instructions. But the key thing there is we don't lower our speed of the CPU. So there's no noisy neighbor effects. And it's something that OCI and the HeatWave has taken full advantage of. And so like, as we go out in time and we see the Zen 4 core, we can... we see up to 96 CPUs that that's going to work really well. So we're collaborating closely with, with OCI and with the HeatWave team here to make sure that we can take advantage of that. And we're also going to upgrade the memory subsystem to get to 12 channels of DDR 5. So it should be, you know there should be a fairly significant boost in absolute performance. But more important or just as importantly in TCO value for the customers, the end customers who are going to adopt this great service. >> I love their relentless innovation guys. Thanks so much for your time. We're going to have to leave it there. Appreciate it. >> Thank you, David. >> Thank you, David. >> Okay. Thank you for watching this special presentation on theCUBE. Your leader in enterprise and emerging tech coverage.

(upbeat music) >> For those of you who've been following the cloud database space, you know that MySQL HeatWave has been on a technology tear over the last 24 months with Oracle claiming record breaking benchmarks relative to other database platforms. So far, those benchmarks remain industry leading as competitors have chosen not to respond, perhaps because they don't feel the need to, or maybe they don't feel that doing so would serve their interest. Regardless, the HeatWave team at Oracle has been very aggressive about its performance claims, making lots of noise, challenging the competition to respond, publishing their scripts to GitHub. But so far, there are no takers, but customers seem to be picking up on these moves by Oracle and it's likely the performance numbers resonate with them. Now, the other area we want to explore, which we haven't thus far, is the engine behind HeatWave and that is AMD. AMD's epic processors have been the powerhouse on OCI, running MySQL HeatWave since day one. And today we're going to explore how these two technology companies are working together to deliver these performance gains and some compelling TCO metrics. In fact, a recent Wikibon analysis from senior analyst Marc Staimer made some TCO comparisons in OLAP workloads relative to AWS, Snowflake, GCP, and Azure databases, you can find that research on wikibon.com. And with that, let me introduce today's guest, Nipun Agarwal senior vice president of MySQL HeatWave and Kumaran Siva, who's the corporate vice president for strategic business development at AMD. Welcome to theCUBE gentlemen. >> Welcome. Thank you. >> Thank you, Dave. >> Hey Nipun, you and I have talked a lot about this. You've been on theCUBE a number of times talking about MySQL HeatWave. But for viewers who may not have seen those episodes maybe you could give us an overview of HeatWave and how it's different from competitive cloud database offerings. >> Sure. So MySQL HeatWave is a fully managed MySQL database service offering from Oracle. It's a single database, which can be used to run transactional processing, analytics and machine learning workloads. So, in the past, MySQL has been designed and optimized for transaction processing. So customers of MySQL when they had to run, analytics machine learning, would need to extract the data out of MySQL, into some other database or service, to run analytics or machine learning. MySQL HeatWave offers a single database for running all kinds of workloads so customers don't need to extract data into some of the database. In addition to having a single database, MySQL HeatWave is also very performant compared to one up databases and also it is very price competitive. So the advantages are; single database, very performant, and very good price performance. >> Yes. And you've published some pretty impressive price performance numbers against competitors. Maybe you could describe those benchmarks and highlight some of the results, please. >> Sure. So one thing to notice that the performance of any database is going to like vary, the performance advantage is going to vary based on, the size of the data and the specific workloads, so the mileage varies, that's the first thing to know. So what we have done is, we have published multiple benchmarks. So we have benchmarks on PPCH or PPCDS and we have benchmarks on different data sizes because based on the customer's workload, the mileage is going to vary, so we want to give customers a broad range of comparisons so that they can decide for themselves. So in a specific case, where we are running on a 30 terabyte PPCH workload, HeatWave is about 18 times better price performance compared to Redshift. 18 times better compared to Redshift, about 33 times better price performance, compared to Snowflake, and 42 times better price performance compared to Google BigQuery. So, this is on 30 Terabyte PPCH. Now, if the data size is different, or the workload is different, the characteristics may vary slightly but this is just to give a flavor of the kind of performance advantage MySQL HeatWave offers. >> And then my last question before we bring in Kumaran. We've talked about the secret sauce being the tight integration between hardware and software, but would you add anything to that? What is that secret sauce in HeatWave that enables you to achieve these performance results and what does it mean for customers? >> So there are three parts to this. One is HeatWave has been designed with a scale out architecture in mind. So we have invented and implemented new algorithms for skill out query processing for analytics. The second aspect is that HeatWave has been really optimized for cloud, commodity cloud, and that's where AMD comes in. So for instance, many of the partitioning schemes we have for processing HeatWave, we optimize them for the L3 cache of the AMD processor. The thing which is very important to our customers is not just the sheer performance but the price performance, and that's where we have had a very good partnership with AMD because not only does AMD help us provide very good performance, but the price performance, right? And that all these numbers which I was showing, big part of it is because we are running on AMD which provides very good price performance. So that's the second aspect. And the third aspect is, MySQL autopilot, which provides machine learning based automation. So it's really these three things, a combination of new algorithms, design for scale out query processing, optimized for commodity cloud hardware, specifically AMD processors, and third, MySQL auto pilot which gives us this performance advantage. >> Great, thank you. So that's a good segue for AMD and Kumaran. So Kumaran, what is AMD bringing to the table? What are the, like, for instance, relevance specs of the chips that are used in Oracle cloud infrastructure and what makes them unique? >> Yeah, thanks Dave. That's a good question. So, OCI is a great customer of ours. They use what we call the top of stack devices meaning that they have the highest core count and they also are very, very fast cores. So these are currently Zen 3 cores. I think the HeatWave product is right now deployed on Zen 2 but will shortly be also on the Zen 3 core as well. But we provide in the case of OCI 64 cores. So that's the largest devices that we build. What actually happens is, because these large number of CPUs in a single package and therefore increasing the density of the node, you end up with this fantastic TCO equation and the cost per performance, the cost per for deployed services like HeatWave actually ends up being extraordinarily competitive and that's a big part of the contribution that we're bringing in here. >> So Zen 3 is the AMD micro architecture which you introduced, I think in 2017, and it's the basis for EPIC, which is sort of the enterprise grade that you really attacked the enterprise with. Maybe you could elaborate a little bit, double click on how your chips contribute specifically to HeatWave's, price performance results. >> Yeah, absolutely. So in the case of HeatWave, so as Nipun alluded to, we have very large L3 caches, right? So in our very, very top end parts just like the Milan X devices, we can go all the way up to like 768 megabytes of L3 cache. And that gives you just enormous performance and performance gains. And that's part of what we're seeing with HeatWave today and that not that they're currently on the second generation ROM based product, 'cause it's a 7,002 based product line running with the 64 cores. But as time goes on, they'll be adopting the next generation Milan as well. And the other part of it too is, as our chip led architecture has evolved, we know, so from the first generation Naples way back in 2017, we went from having multiple memory domains and a sort of NUMA architecture at the time, today we've really optimized that architecture. We use a common I/O Die that has all of the memory channels attached to it. And what that means is that, these scale out applications like HeatWave, are able to really scale very efficiently as they go from a small domain of CPUs to, for example the entire chip, all 64 cores that scaling, is been a key focus for AMD and being able to design and build architectures that can take advantage of that and then have applications like HeatWave that scale so well on it, has been, a key aim of ours. >> And Gen 3 moving up the Italian countryside. Nipun, you've taken the somewhat unusual step of posting the benchmark parameters, making them public on GitHub. Now, HeatWave is relatively new. So people felt that when Oracle gained ownership of MySQL it would let it wilt on the vine in favor of Oracle database, so you lost some ground and now, you're getting very aggressive with HeatWave. What's the reason for publishing those benchmark parameters on GitHub? >> So, the main reason for us to publish price performance numbers for HeatWave is to communicate to our customers a sense of what are the benefits they're going to get when they use HeatWave. But we want to be very transparent because as I said the performance advantages for the customers may vary, based on the data size, based on the specific workloads. So one of the reasons for us to publish, all these scripts on GitHub is for transparency. So we want customers to take a look at the scripts, know what we have done, and be confident that we stand by the numbers which we are publishing, and they're very welcome, to try these numbers themselves. In fact, we have had customers who have downloaded the scripts from GitHub and run them on our service to kind of validate. The second aspect is in some cases, they may be some deviations from what we are publishing versus what the customer would like to run in the production deployments so it provides an easy way, for customers to take the scripts, modify them in some ways which may suit their real world scenario and run to see what the performance advantages are. So that's the main reason, first, is transparency, so the customers can see what we are doing, because of the comparison, and B, if they want to modify it to suit their needs, and then see what is the performance of HeatWave, they're very welcome to do so. >> So have customers done that? Have they taken the benchmarks? And I mean, if I were a competitor, honestly, I wouldn't get into that food fight because of the impressive performance, but unless I had to, I mean, have customers picked up on that, Nipun? >> Absolutely. In fact, we have had many customers who have benchmarked the performance of MySQL HeatWave, with other services. And the fact that the scripts are available, gives them a very good starting point, and then they've also tweaked those queries in some cases, to see what the Delta would be. And in some cases, customers got back to us saying, hey the performance advantage of HeatWave is actually slightly higher than what was published and what is the reason. And the reason was, when the customers were trying, they were trying on the latest version of the service, and our benchmark results were posted let's say, two months back. So the service had improved in those two to three months and customers actually saw better performance. So yes, absolutely. We have seen customers download the scripts, try them and also modify them to some extent and then do the comparison of HeatWave with other services. >> Interesting. Maybe a question for both of you how is the competition responding to this? They haven't said, "Hey, we're going to come up "with our own benchmarks." Which is very common, you oftentimes see that. Although, for instance, Snowflake hasn't responded to data bricks, so that's not their game, but if the customers are actually, putting a lot of faith in the benchmarks and actually using that for buying decisions, then it's inevitable. But how have you seen the competition respond to the MySQL HeatWave and AMD combo? >> So maybe I can take the first track from the database service standpoint. When customers have more choice, it is invariably advantages for the customer because then the competition is going to react, right? So the way we have seen the reaction is that we do believe, that the other database services are going to take a closer eye to the price performance, right? Because if you're offering such good price performance, the vendors are already looking at it. And, you know, instances where they have offered let's say discount to the customers, to kind of at least like close the gap to some extent. And the second thing would be in terms of the capability. So like one of the things which I should have mentioned even early on, is that not only does MySQL HeatWave on AMD, provide very good price performance, say on like a small cluster, but it's all the way up to a cluster size of 64 nodes, which has about 1000 cores. So the point is, that HeatWave performs very well, both on a small system, as well as a huge scale out. And this is again, one of those things which is a differentiation compared to other services so we expect that even other database services will have to improve their offerings to provide the same good scale factor, which customers are now starting to expectancy, with MySQL HeatWave. >> Kumaran, anything you'd add to that? I mean, you guys are an arms dealer, you love all your OEMs, but at the same time, you've got chip competitors, Silicon competitors. How do you see the competitive-- >> I'd say the broader answer and the big picture for AMD, we're very maniacally focused on our customers, right? And OCI and Oracle are huge and important customers for us, and this particular use cases is extremely interesting both in that it takes advantage, very well of our architecture and it pulls out some of the value that AMD bring. I think from a big picture standpoint, our aim is to execute, to build to bring out generations of CPUs, kind of, you know, do what we say and say, sorry, say what we do and do what we say. And from that point of view, we're hitting, the schedules that we say, and being able to bring out the latest technology and bring it in a TCO value proposition that generationally keeps OCI and HeatWave ahead. That's the crux of our partnership here. >> Yeah, the execution's been obvious for the last several years. Kumaran, staying with you, how would you characterize the collaboration between, the AMD engineers and the HeatWave engineering team? How do you guys work together? >> No, I'd say we're in a very, very deep collaboration. So, there's a few aspects where, we've actually been working together very closely on the code and being able to optimize for both the large L3 cache that AMD has, and so to be able to take advantage of that. And then also, to be able to take advantage of the scaling. So going between, you know, our architecture is chip like based, so we have these, the CPU cores on, we call 'em CCDs and the inter CCD communication, there's opportunities to optimize an application level and that's something we've been engaged with. In the broader engagement, we are going back now for multiple generations with OCI, and there's a lot of input that now, kind of resonates in the product line itself. And so we value this very close collaboration with HeatWave and OCI. >> Yeah, and the cadence, Nip, and you and I have talked about this quite a bit. The cadence has been quite rapid. It's like this constant cycle every couple of months I turn around, is something new on HeatWave. But for question again, for both of you, what new things do you think that organizations, customers, are going to be able to do with MySQL HeatWave if you could look out next 12 to 18 months, is there anything you can share at this time about future collaborations? >> Right, look, 12 to 18 months is a long time. There's going to be a lot of innovation, a lot of new capabilities coming out on in MySQL HeatWave. But even based on what we are currently offering, and the trend we are seeing is that customers are bringing, more classes of workloads. So we started off with OLTP for MySQL, then it went to analytics. Then we increased it to mixed workloads, and now we offer like machine learning as alike. So one is we are seeing, more and more classes of workloads come to MySQL HeatWave. And the second is a scale, that kind of data volumes people are using HeatWave for, to process these mixed workloads, analytics machine learning OLTP, that's increasing. Now, along the way we are making it simpler to use, we are making it more cost effective use. So for instance, last time, when we talked about, we had introduced this real time elasticity and that's something which is a very, very popular feature because customers want the ability to be able to scale out, or scale down very efficiently. That's something we provided. We provided support for compression. So all of these capabilities are making it more efficient for customers to run a larger part of their workloads on MySQL HeatWave, and we will continue to make it richer in the next 12 to 18 months. >> Thank you. Kumaran, anything you'd add to that, we'll give you the last word as we got to wrap it. >> No, absolutely. So, you know, next 12 to 18 months we will have our Zen 4 CPUs out. So this could potentially go into the next generation of the OCI infrastructure. This would be with the Genoa and then Bergamo CPUs taking us to 96 and 128 cores with 12 channels at DDR five. This capability, you know, when applied to an application like HeatWave, you can see that it'll open up another order of magnitude potentially of use cases, right? And we're excited to see what customers can do do with that. It certainly will make, kind of the, this service, and the cloud in general, that this cloud migration, I think even more attractive. So we're pretty excited to see how things evolve in this period of time. >> Yeah, the innovations are coming together. Guys, thanks so much, we got to leave it there really appreciate your time. >> Thank you. >> All right, and thank you for watching this special Cube conversation, this is Dave Vellante, and we'll see you next time. (soft calm music)

(upbeat music) >> Welcome back to San Francisco, "theCUBE" is live, our day two coverage of VMware Explore 2022 continues. Lisa Martin with Dave Nicholson. Dave and I are pleased to welcome Jason Collier, principal member of technical staff at AMD to the program. Jason, it's great to have you. >> Thank you, it's great to be here. >> So what's going on at AMD? I hear you have some juicy stuff to talk about. >> Oh, we've got a ton of juicy stuff to talk about. Clearly the Project Monterey announcement was big for us, so we've got that to talk about. Another thing that I really wanted to talk about was a tool that we created and we call it, it's the VMware Architecture Migration Tool, call it VAMT for short. It's a tool that we created and we worked together with VMware and some of their professional services crew to actually develop this tool. And it is also an open source based tool. And really the primary purpose is to easily enable you to move from one CPU architecture to another CPU architecture, and do that in a cold migration fashion. >> So we're probably not talking about CPUs from Tandy, Radio Shack systems, likely this would be what we might refer to as other X86 systems. >> Other X86 systems is a good way to refer to it. >> So it's interesting timing for the development and the release of a tool like this, because in this sort of X86 universe, there are players who have been delayed in terms of delivering their next gen stuff. My understanding is AMD has been public with the idea that they're on track for by the end of the year, Genoa, next gen architecture. So can you imagine a situation where someone has an existing set of infrastructure and they're like, hey, you know what I want to get on board, the AMD train, is this something they can use from the VMware environment? >> Absolutely, and when you think about- >> Tell us exactly what that would look like, walk us through 100 servers, VMware, 1000 VMs, just to make the math easy. What do you do? How does it work? >> So one, there's several things that the tool can do, we actually went through, the design process was quite extensive on this. And we went through all of the planning phases that you need to go through to do these VM migrations. Now this has to be a cold migration, it's not a live migration. You can't do that between the CPU architectures. But what we do is you create a list of all of the virtual machines that you want to migrate. So we take this CSV file, we import this CSV file, and we ask for things like, okay, what's the name? Where do you want to migrate it to? So from one cluster to another, what do you want to migrate it to? What are the networks that you want to move it to? And then the storage platform. So we can move storage, it could either be shared storage, or we could move say from VSAN to VSAN, however you want to set it up. So it will do those storage migrations as well. And then what happens is it's actually going to go through, it's going to shut down the VM, it's going to take a snapshot, it is going to then basically move the compute and/or storage resources over. And once it does that, it's going to power 'em back up. And it's going to check, we've got some validation tools, where it's going to make sure VM Tools comes back up where everything is copacetic, it didn't blue screen or anything like that. And once it comes back up, then everything's good, it moves onto the next one. Now a couple of things that we've got feature wise, we built into it. You can parallelize these tasks. So you can say, how many of these machines do you want to do at any given time? So it could be, say 10 machines, 50 machines, 100 machines at a time, that you want to go through and do this move. Now, if it did blue screen, it will actually roll it back to that snapshot on the origin cluster. So that there is some protection on that. A couple other things that are actually in there are things like audit tracking. So we do full audit logging on this stuff, we take a snapshot, there's basically kind of an audit trail of what happens. There's also full logging, SYS logging, and then also we'll do email reporting. So you can say, run this and then shoot me a report when this is over. Now, one other cool thing is you can also actually define a change window. So I don't want to do this in the middle of the afternoon on a Tuesday. So I want to do this later at night, over the weekend, you can actually just queue this up, set it, schedule it, it'll run. You can also define how long you want that change window to be. And what it'll do, it'll do as many as it can, then it'll effectively stop, finish up, clean up the tasks and then send you a report on what all was successfully moved. >> Okay, I'm going to go down the rabbit hole a little bit on this, 'cause I think it's important. And if I say something incorrect, you correct me. >> No problem. >> In terms of my technical understanding. >> I got you. >> So you've got a VM, essentially a virtual machine typically will consist of an entire operating system within that virtual machine. So there's a construct that containerizes, if you will, the operating system, what is the difference, where is the difference in the instruction set? Where does it lie? Is it in the OS' interaction with the CPU or is it between the construct that is the sort of wrapper around the VM that is the difference? >> It's really primarily the OS, right? And we've not really had too many issues doing this and most of the time, what is going to happen, that OS is going to boot up, it's going to recognize the architecture that it's on, it's going to see the underlying architecture, and boot up. All the major operating systems that we test worked fine. I mean, typically they're going to work on all the X86 platforms. But there might be instruction sets that are kind of enabled in one architecture that may not be in another architecture. >> And you're looking for that during this process. >> Well usually the OS itself is going to kind of detect that. So if it pops up, the one thing that is kind of a caution that you need to look for. If you've got an application that's explicitly using an instruction set that's on one CPU vendor and not the other CPU vendor. That's the one thing where you're probably going to see some application differences. That said, it'll probably be compatible, but you may not get that instruction set advantage in it. >> But this tool remediates against that. >> Yeah, and what we do, we're actually using VM Tools itself to go through and validate a lot of those components. So we'll look and make sure VM Tools is enabled in the first place, on the source system. And then when it gets to the destination system, we also look at VM Tools to see what is and what is not enabled. >> Okay, I'm going to put you on the spot here. What's the zinger, where doesn't it work? You already said cold, we understand, you can schedule for cold migrations, that's not a zinger. What's the zinger, where doesn't it work? >> It doesn't work like, live migrations just don't work. >> No live, okay, okay, no live. What about something else? What's the oh, you've got that version, you've got that version of X86 architecture, it-won't work, anything? >> A majority of those cases work, where it would fail, where it's going to kick back and say, hey, VM Tools is not installed. So where you would see this is if you're running a virtual appliance from some vendor, like insert vendor here that say, got a firewall, or got something like that, and they don't have VM Tools enabled. It's going to fail it out of the gate, and say, hey, VM Tools is not on this, you might want to manually do it. >> But you can figure out how to fix that? >> You can figure out how to do that. You can also, and there's a flag in there, so in kind of the options that you give it, you say, ignore VM Tools, don't care, move it anyway. So if you've got less, some VMs that are in there, but they're not a priority VM, then it's going to migrate just fine. >> Got It. >> Can you elaborate a little bit on the joint development work that AMD and VMware are doing together and the value in it for customers? >> Yeah, so it's one of those things we worked with VMware to basically produce this open source tool. So we did a lot of the core component and design and we actually engaged VMware Professional Services. And a big shout out to Austin Browder. He helped us a ton in this project specifically. And we basically worked, we created this, kind of co-designed, what it was going to look like. And then jointly worked together on the coding, of pulling this thing together. And then after that, and this is actually posted up on VMware's public repos now in GitHub. So you can go to GitHub, you can go to the VMware samples code, and you can download this thing that we've created. And it's really built to help ease migrations from one architecture to another. So if you're looking for a big data center move and you got a bunch of VMs to move. I mean, even if it's same architecture to same architecture, it's definitely going to ease the pain of going through and doing a migration of, it's one thing when you're doing 10 machines, but when you're doing 10,000 virtual machines, that's a different story. It gets to be quite operationally inefficient. >> I lose track after three. >> Yeah. >> So I'm good for three, not four. >> I was going to ask you what your target market segment is here. Expand on that a little bit and talk to me about who you're working with and those organizations. >> So really this is targeted toward organizations that have large deployments in enterprise, but also I think this is a big play with channel partners as well. So folks out there in the channel that are doing these migrations and they do a lot of these, when you're thinking about the small and mid-size organizations, it's a great fit for that. Especially if they're kind of doing that upgrade, the lift and shift upgrade, from here's where you've been five to seven years on an architecture and you want to move to a new architecture. This is really going to help. And this is not a point and click GUI kind of thing. It's command line driven, it's using PowerShell, we're using PowerCLI to do the majority of this work. And for channel partners, this is an excellent opportunity to put the value and the value add and VAR, And there's a lot of opportunity for, I think, channel partners to really go and take this. And once again, being open source. We expect this to be extensible, we want the community to contribute and put back into this to basically help grow it and make it a more useful tool for doing these cold migrations between CPU architectures. >> Have you seen any in the last couple of years of dynamics, obviously across the world, any industries in particular that are really leading edge for what you guys are doing? >> Yeah, that's really, really interesting. I mean, we've seen it, it's honestly been a very horizontal problem, pretty much across all vertical markets. I mean, we've seen it in financial services, we've seen it in, honestly, pretty much across the board. Manufacturing, financial services, healthcare, we have seen kind of a strong interest in that. And then also we we've actually taken this and presented this to some of our channel partners as well. And there's been a lot of interest in it. I think we presented it to about 30 different channel partners, a couple of weeks back about this. And I got contact from 30 different channel partners that said they're interested in basically helping us work on it. >> Tagging on to Lisa's question, do you have visibility into the AMD thought process around the timing of your next gen release versus others that are competitors in the marketplace? How you might leverage that in terms of programs where partners are going out and saying, hey, perfect time, you need a refresh, perfect time to look at AMD, if you haven't looked at them recently. Do you have any insight into that in what's going on? I know you're focused on this area. But what are your thoughts on, well, what's the buzz? What's the buzz inside AMD on that? >> Well, when you look overall, if you look at the Gartner Hype Cycle, when VMware was being broadly adopted, when VMware was being broadly adopted, I'm going to be blunt, and I'm going to be honest right here, AMD didn't have a horse in the race. And the majority of those VMware deployments we see are not running on AMD. Now that said, there's an extreme interest in the fact that we've got these very cored in systems that are now coming up on, now you're at that five to seven year refresh window of pulling in new hardware. And we have extremely attractive hardware when it comes to running virtualized workloads. The test cluster that I'm running at home, I've got that five to seven year old gear, and I've got some of the, even just the Milan systems that we've got. And I've got three nodes of another architecture going onto AMD. And when I got these three nodes completely maxed to the number of VMs that I can run on 'em, I'm at a quarter of the capacity of what I'm putting on the new stuff. So what you get is, I mean, we worked the numbers, and it's definitely, it's like a 30% decrease in the amount of resources that you need. >> That's a compelling number. >> It's a compelling number. >> 5%, 10%, nobody's going to do anything for that. You talk 30%. >> 30%. It's meaningful, it's meaningful. Now you you're out of Austin, right? >> Yes. >> So first thing I thought of when you talk about running clusters in your home is the cost of electricity, but you're okay. >> I'm okay. >> You don't live here, you don't live here, you don't need to worry about that. >> I'm okay. >> Do you have a favorite customer example that you think really articulates the value of AMD when you're in customer conversations and they go, why AMD and you hit back with this? >> Yeah. Actually it's funny because I had a conversation like that last night, kind of random person I met later on in the evening. We were going through this discussion and they were facing exactly this problem. They had that five to seven year infrastructure. It's funny, because the guy was a gamer too, and he's like, man, I've always been a big AMD fan, I love the CPUs all the way since back in basically the Opterons and Athlons right. He's like, I've always loved the AMD systems, loved the graphics cards. And now with what we're doing with Ryzen and all that stuff. He's always been a big AMD fan. He's like, and I'm going through doing my infrastructure refresh. And I told him, I'm just like, well, hey, talk to your VAR and have 'em plug some AMD SKUs in there from the Dells, HPs and Lenovos. And then we've got this tool to basically help make that migration easier on you. And so once we had that discussion and it was great, then he swung by the booth today and I was able to just go over, hey, this is the tool, this is how you use it, here's all the info. Call me if you need any help. >> Yeah, when we were talking earlier, we learned that you were at Scale. So what are you liking about AMD? How does that relate? >> The funny thing is this is actually the first time in my career that I've actually had a job where I didn't work for myself. I've been doing venture backed startups the last 25 years and we've raised couple hundred million dollars worth of investment over the years. And so one, I figured, here I am going to AMD, a larger corporation. I'm just like, am I going to be able to make it a year? And I have been here longer than a year and I absolutely love it. The culture at AMD is amazing. We still have that really, I mean, almost it's like that underdog mentality within the organization. And the team that I'm working with is a phenomenal team. And it's actually, our EVP and our Corp VP, were actually my executive sponsors, we were at a prior company. They were one of my executive sponsors when I was at Scale. And so my now VP boss calls me up and says, hey, I'm putting a band together, are you interested? And I was kind of enjoying a semi-retirement lifestyle. And then I'm just like, man, because it's you, yes, I am interested. And the group that we're in, the work that we're doing, the way that we're really focusing on forward looking things that are affecting the data center, what's going to be the data center like three to five years from now. It's exciting, and I am having a blast, I'm having the time of my life. I absolutely love it. >> Well, that relationship and the trust that you will have with each other, that bleeds into the customer conversations, the partner conversations, the employee conversations, it's all inextricably linked. >> Yes it is. >> And we want to know, you said three to five years out, like what? Like what? Just general futurist stuff, where do you think this is going. >> Well, it's interesting. >> So moon collides with the earth in 2025, we already know that. >> So we dialed this back to the Pensando acquisition. When you look at the Pensando acquisition and you look at basically where data centers are today, but then you look at where basically the big hyperscalers are. You look at an AWS, you look at their architecture, you specifically wrap Nitro around that, that's a very different architecture than what's being run in the data center. And when you look at what Pensando does, that's a lot of starting to bring what these real clouds out there, what these big hyperscalers are running into the grasps of the data center. And so I think you're going to see a fundamental shift. The next 10 years are going to be exciting because the way you look at a data center now, when you think of what CPUs do, what shared storage, how the networking is all set up, it ain't going to look the same. >> Okay, so the competing vision with that, to play devil's advocate, would be DPUs are kind of expensive. Why don't we just use NICs, give 'em some more bandwidth, and use the cheapest stuff. That's the competing vision. >> That could be. >> Or the alternative vision, and I imagine everything else we've experienced in our careers, they will run in parallel paths, fit for function. >> Well, parallel paths always exist, right? Otherwise, 'cause you know how many times you've heard mainframe's dead, tape's dead, spinning disk is dead. None of 'em dead, right? The reality is you get to a point within an industry where it basically goes from instead of a growth curve like that, it goes to a growth curve of like that, it's pretty flat. So from a revenue growth perspective, I don't think you're going to see the revenue growth there. I think you're going to see the revenue growth in DPUs. And when you actually take, they may be expensive now, but you look at what Monterey's doing and you look at the way that those DPUs are getting integrated in at the OEM level. It's going to be a part of it. You're going to order your VxRail and VSAN style boxes, they're going to come with them. It's going to be an integrated component. Because when you start to offload things off the CPU, you've driven your overall utilization up. When you don't have to process NSX on basically the X86, you've just freed up cores and a considerable amount of them. And you've also moved that to where there's a more intelligent place for that pack to be processed right, out here on this edge. 'Cause you know what, that might not need to go into the host bus at all. So you have just alleviated any transfers over a PCI bus, over the PCI lanes, into DRAM, all of these components, when you're like, but all to come with, oh, that bit needs to be on this other machine. So now it's coming in and it's making that decision there. And then you take and integrate that into things like the Aruba Smart Switch, that's running the Pensando technology. So now you got top of rack that is already making those intelligent routing decisions on where packets really need to go. >> Jason, thank you so much for joining us. I know you guys could keep talking. >> No, I was going to say, you're going to have to come back. You're going to have to come back. >> We've just started to peel the layers of the onion, but we really appreciate you coming by the show, talking about what AMD and VMware are doing, what you're enabling customers to achieve. Sounds like there's a lot of tailwind behind you. That's awesome. >> Yeah. >> Great stuff, thank you. >> It's a great time to be at AMD, I can tell you that. >> Oh, that's good to hear, we like it. Well, thank you again for joining us, we appreciate it. For our guest and Dave Nicholson, I'm Lisa Martin. You're watching "theCUBE Live" from San Francisco, VMware Explore 2022. We'll be back with our next guest in just a minute. (upbeat music)

>>Good morning, everyone. Welcome to the cubes day two coverage of VMware Explorer, 2022 live from San Francisco. Lisa Martin here with Dave Nicholson. We're excited to kick off day two of great conversations with VMware partners, customers it's ecosystem. We've got a V an alumni back with us Kumer on Siva corporate VP of business development from AMD joins us. Great to have you on the program in person. Great >>To be here. Yes. In person. Indeed. Welcome. >>So the great thing yesterday, a lot of announcements and B had an announcement with VMware, which we will unpack that, but there's about 7,000 to 10,000 people here. People are excited, ready to be back, ready to be hearing from this community, which is so nice. Yesterday am B announced. It is optimizing AMD PON distributed services card to run on VMware. Bsphere eight B for eight was announced yesterday. Tell us a little bit about that. Yeah, >>No, absolutely. The Ben Sando smart neck DPU. What it allows you to do is it, it provides a whole bunch of capabilities, including offloads, including encryption DEC description. We can even do functions like compression, but with, with the combination of VMware project Monterey and, and Ben Sando, we we're able to do is even do some of the vSphere, actual offloads integration of the hypervisor into the DPU card. It's, it's pretty interesting and pretty powerful technology. We're we're pretty excited about it. I think this, this, this could, you know, potentially, you know, bring some of the cloud value into, in terms of manageability, in terms of being able to take care of bare metal servers and also, you know, better secure infrastructure, you know, cloudlike techniques into the, into the mainstream on-premises enterprise. >>Okay. Talk a little bit about the DPU data processing unit. They talked about it on stage yesterday, but help me understand that versus the CPU GPU. >>Yeah. So it's, it's, it's a different, it's a different point, right? So normally you'd, you'd have the CPU you'd have we call it dumb networking card. Right. And I say dumb, but it's, it's, you know, it's just designed to go process packets, you know, put and put them onto PCI and have the, the CPU do all of the, kind of the, the packet processing, the, the virtual switching, all of those functions inside the CPU. What the DPU allows you to do is, is actually offload a bunch of those functions directly onto the, onto the deep view card. So it has a combination of these special purpose processors that are programmable with the language called P four, which is one, one of the key things that pan Sando brings. Here's a, it's a, it's a real easy to program, easy to use, you know, kind of set so that not some of, some of our larger enterprise customers can actually go in and, you know, do some custom coding depending on what their network infrastructure looks like. But you can do things like the V switch in, in the, in the DPU, not having to all have that done on the CPU. So you freeze up some of the CPU course, make sure, make sure infrastructure run more efficiently, but probably even more importantly, it provides you with more, with greater security, greater separation between the, between the networking side and the, the CPU side. >>So, so that's, that's a key point because a lot of us remember the era of the tonic TCP, I P offload engine, Nick, this isn't simply offloading CPU cycles. This is actually providing a sort of isolation. So that the network that's right, is the network has intelligence that is separate from the server. Is that, is that absolutely key? Is that absolutely >>Key? Yeah. That's, that's a good way of looking at it. Yeah. And that's, that's, I mean, if you look at some of the, the, the techniques used in the cloud, the, you know, this, this, this in fact brings some of those technologies into, into the enterprise, right. So where you are wanting to have that level of separation and management, you're able to now utilize the DPU card. So that's, that's a really big, big, big part of the value proposition, the manageability manageability, not just offload, but you know, kind of a better network for enterprise. Right. >>Right. >>Can you expand on that value proposition? If I'm a customer what's in this for me, how does this help power my multi-cloud organization? >>Yeah. >>So I think we have some, we actually have a number of these in real customer use cases today. And so, you know, folks will use, for example, the compression and the, sorry, the compression and decompression, that's, that's definitely an application in the storage side, but also on the, just on the, just, just as a, as a DPU card in the mainstream general purpose, general purpose server server infrastructure fleet, they're able to use the encryption and decryption to make sure that their, their, their infrastructure is, is kind of safe, you know, from point to point within the network. So every, every connected, every connection there is actually encrypted and that, that, you know, managing those policies and orchestrating all of that, that's done to the DPU card. >>So, so what you're saying is if you have DPU involved, then the server itself and the CPUs become completely irrelevant. And basically it's just a box of sheet metal at that point. That's, that's a good way of looking at that. That's my segue talking about the value proposition of the actual AMD. >>No, absolutely. No, no. I think, I think, I think the, the, the CPUs are always going to be central in this and look. And so, so I think, I think having, having the, the DPU is extremely powerful and, and it does allow you to have better infrastructure, but the key to having better infrastructure is to have the best CPU. Well, tell >>Us, tell >>Us that's what, tell us us about that. So, so I, you know, this is, this is where a lot of the, the great value proposition between VMware and AMD come together. So VMware really allows enterprises to take advantage of these high core count, really modern, you know, CPU, our, our, our, our epic, especially our Milan, our 7,003 product line. So to be able to take advantage of 64 course, you know, VMware is critical for that. And, and so what they, what they've been able to do is, you know, know, for example, if you have workloads running on legacy, you know, like five year old servers, you're able to take a whole bunch of those servers and consolidate down, down into a single node, right. And the power that VMware gives you is the manageability, the reliability brings all of that factors and allows you to take advantage of, of the, the, the latest, latest generation CPUs. >>You know, we've actually done some TCO modeling where we can show, even if you have fully depreciated hardware, like, so it's like five years old plus, right. And so, you know, the actual cost, you know, it's already been written off, but the cost just the cost of running it in terms of the power and the administration, you know, the OPEX costs that, that are associated with it are greater than the cost of acquiring a new set of, you know, a smaller set of AMD servers. Yeah. And, and being able to consolidate those workloads, run VMware, to provide you with that great, great user experience, especially with vSphere 8.0 and the, and the hooks that VMware have built in for AMD AMD processors, you actually see really, really good. It's just a great user experience. It's also a more efficient, you know, it's just better for the planet. And it's also better on the pocketbook, which is, which is, which is a really cool thing these days, cuz our value in TCO translates directly into a value in terms of sustainability. Right. And so, you know, from, from energy consumption, from, you know, just, just the cost of having that there, it's just a whole lot better >>Talk about on the sustainability front, how AMD is helping its customers achieve their sustainability goals. And are you seeing more and more customers coming to you saying, we wanna understand what AMD is doing for sustainability because it's important for us to work with vendors who have a core focus on it. >>Yeah, absolutely. You know, I think, look, I'll be perfectly honest when we first designed our CPU, we're just trying to build the biggest baddest thing that, you know, that, that comes out in terms of having the, the, the best, the, the number, the, the largest number of cores and the best TCO for our customers, but what it's actually turned out that TCO involves energy consumption. Right. And, and it involves, you know, the whole process of bringing down a whole bunch of nodes, whole bunch of servers. For example, we have one calculation where we showed 27, you know, like I think like five year old servers can be consolidated down into five AMD servers that, that ratio you can see already, you know, huge gains in terms of sustainability. Now you asked about the sustainability conversation. This I'd say not a week goes by where I'm not having a conversation with, with a, a, a CTO or CIO who is, you know, who's got that as part of their corporate, you know, is part of their corporate brand. And they want to find out how to make their, their infrastructure, their data center, more green. Right. And so that's, that's where we come in. Yeah. And it's interesting because at least in the us money is also green. So when you talk about the cost of power, especially in places like California, that's right. There's, there's a, there's a natural incentive to >>Drive in that direction. >>Let's talk about security. You know, the, the, the threat landscape has changed so dramatically in the last couple of years, ransomware is a household word. Yes. Ransomware attacks happened like one every 11 seconds, older technology, a little bit more vulnerable to internal threats, external threats. How is AMD helping customers address the security fund, which is the board level conversation >>That that's, that's, that's a, that's a great, great question. Look, I look at security as being, you know, it's a layered thing, right? I mean, if you talk to any security experts, security, doesn't, you know, there's not one component and we are an ingredient within the, the greater, you know, the greater scheme of things. A few things. One is we have partnered very closely with the VMware. They have enabled our SUV technology, secure encrypted virtualization technology into, into the vSphere. So such that all of the memory transactions. So you have, you have security, you know, at, you know, security, when you store store on disks, you have security over the network and you also have security in the compute. And when you go out to memory, that's what this SUV technology gives you. It gives you that, that security going, going in your, in your actual virtual machine as it's running. And so the, the, we take security extremely seriously. I mean, one of the things, every generation that you see from, from AMD and, and, you know, you have seen us hit our cadence. We do upgrade all of the security features and we address all of the sort of known threats that are out there. And obviously this threats, you know, kind of coming at us all the time, but our CPUs just get better and better from, from a, a security stance. >>So shifting gears for a minute, obviously we know the pending impossible acquisition, the announced acquisition of VMware by Broadcom, AMD's got a relationship with Broadcom independently, right? No, of course. What is, how's that relationship? >>Oh, it's a great relationship. I mean, we, we, you know, they, they have certified their, their, their niche products, their HPA products, which are utilized in, you know, for, for storage systems, sand systems, those, those type of architectures, the hardcore storage architectures. We, we work with them very closely. So they, they, they've been a great partner with us for years. >>And you've got, I know, you know, we are, we're talking about current generation available on the shelf, Milan based architecture, is that right? That's right. Yeah. But if I understand correctly, maybe sometime this year, you're, you're gonna be that's right. Rolling out the, rolling out the new stuff. >>Yeah, absolutely. So later this year, we've already, you know, we already talked about this publicly. We have a 96 core gen platform up to 96 cores gen platform. So we're just, we're just taking that TCO value just to the next level, increasing performance DDR, five CXL with, with memory expansion capability. Very, very neat leading edge technology. So that that's gonna be available. >>Is that NextGen P C I E, or has that shift already been made? It's >>Been it's NextGen. P C I E P C E gen five. Okay. So we'll have, we'll have that capability. That'll be, that'll be out by the end of this year. >>Okay. So those components you talk about. Yeah. You know, you talk about the, the Broadcom VMware universe, those components that are going into those new slots are also factors in performance and >>Yeah, absolutely. You need the balance, right? You, you need to have networking storage and the CPU. We're very cognizant of how to make sure that these cores are fed appropriately. Okay. Cuz if you've just put out a lot of cores, you don't have enough memory, you don't have enough iOS. That's, that's the key to, to, to, you know, our approach to, to enabling performance in the enterprise, make sure that the systems are balanced. So you get the experience that you've had with, let's say your, you know, your 12 core, your 16 core, you can have that same experience in the 96 core in a node or 96 core socket. So maybe a 192 cores total, right? So you can have that same experience in, in a tune node in a much denser, you know, package server today or, or using Melan technology, you know, 128 cores, super, super good performance. You know, its super good experience it's, it's designed to scale. Right. And especially with VMware as, as our infrastructure, it works >>Great. I'm gonna, Lisa, Lisa's got a question to ask. I know, but bear with me one bear >>With me. Yes, sir. >>We've actually initiated coverage of this question of, you know, just hardware matter right anymore. Does it matter anymore? Yeah. So I put to you the question, do you think hardware still matters? >>Oh, I think, I think it's gonna matter even more and more going forward. I mean just, but it's all cloud who cares just in this conversation today. Right? >>Who cares? It's all cloud. Yeah. >>So, so, so definitely their workloads moving to the cloud and we love our cloud partners don't get me wrong. Right. But there are, you know, just, I've had so many conversations at this show this week about customers who cannot move to the cloud because of regulatory reasons. Yeah. You know, the other thing that you don't realize too, that's new to me is that people have depreciated their data centers. So the cost for them to just go put in new AMD servers is actually very low compared to the cost of having to go buy, buy public cloud service. They still want to go buy public cloud services and that, by the way, we have great, great, great AMD instances on, on AWS, on Google, on Azure, Oracle, like all of our major, all of the major cloud providers, support AMD and have, have great, you know, TCO instances that they've, they've put out there with good performance. Yeah. >>What >>Are some of the key use cases that customers are coming to AMD for? And, and what have you seen change in the last couple of years with respect to every customer needing to become a data company needing to really be data driven? >>No, that's, that's also great question. So, you know, I used to get this question a lot. >>She only asks great questions. Yeah. Yeah. I go down and like all around in the weeds and get excited about the bits and the bites she asks. >>But no, I think, look, I think the, you know, a few years ago and I, I think I, I used to get this question all the time. What workloads run best on AMD? My answer today is unequivocally all the workloads. Okay. Cuz we have processors that run, you know, run at the highest performance per thread per per core that you can get. And then we have processors that have the highest throughput and, and sometimes they're one in the same, right. And Ilan 64 configured the right way using using VMware vSphere, you can actually get extremely good per core performance and extremely good throughput performance. It works well across, just as you said, like a database to data management, all of those kinds of capabilities, DevOps, you know, E R P like there's just been a whole slew slew of applications use cases. We have design wins in, in major customers, in every single industry in every, and these, these are big, you know, the big guys, right? >>And they're, they're, they're using AMD they're successfully moving over their workloads without, without issue. For the most part. In some cases, customers tell us they just, they just move the workload on, turn it on. It runs great. Right. And, and they're, they're fully happy with it. You know, there are other cases where, where we've actually gotten involved and we figured out, you know, there's this configuration of that configuration, but it's typically not a, not a huge lift to move to AMD. And that's that I think is a, is a key, it's a key point. And we're working together with almost all of the major ISV partners. Right. And so just to make sure that, that, that they have run tested certified, I think we have over 250 world record benchmarks, you know, running in all sorts of, you know, like Oracle database, SAP business suite, all of those, those types of applications run, run extremely well on AMD. >>Is there a particular customer story that you think really articulates the value of running on AMD in terms of enabling bus, big business outcome, safer a financial services organization or healthcare organization? Yeah. >>I mean we, yeah, there's certainly been, I mean, across the board. So in, in healthcare we've seen customers actually do the, the server consolidation very effectively and then, you know, take advantage of the, the lower cost of operation because in some cases they're, they're trying to run servers on each floor of a hospital. For example, we've had use cases where customers have been able to do that because of the density that we provide and to be able to, to actually, you know, take, take their compute more even to the edge than, than actually have it in the, in those use cases in, in a centralized matter. The another, another interesting case FSI in financial services, we have customers that use us for general purpose. It, we have customers that use this for kind of the, the high performance we call it grid computing. So, you know, you have guys that, you know, do all this trading during the day, they collect tons and tons of data, and then they use our computers to, or our CPUs to just crunch to that data overnight. >>And it's just like this big, super computer that just crunches it's, it's pretty incredible. They're the, the, the density of the CPUs, the value that we bring really shines, but in, in their general purpose fleet as well. Right? So they're able to use VMware, a lot of VMware customers in that space. We love our, we love our VMware customers and they're able to, to, to utilize this, they use use us with HCI. So hyperconverge infrastructure with V VSAN and that's that that's, that's worked works extremely well. And, and, and our, our enterprise customers are extremely happy with that. >>Talk about, as we wrap things up here, what's next for AMD, especially AMD with VMwares VMware undergoes its potential change. >>Yeah. So there there's a lot that we have going on. I mean, I gotta say VMware is one of the, let's say premier companies in terms of, you know, being innovative and being, being able to drive new, new, interesting pieces of technology and, and they're very experimentive right. So they, we have, we have a ton of things going with them, but certainly, you know, driving pin Sando is, is very, it is very, very important to us. Yeah. I think that the whole, we're just in the, the cusp, I believe of, you know, server consolidation becoming a big thing for us. So driving that together with VMware and, you know, into some of these enterprises where we can show, you know, save the earth while we, you know, in terms of reducing power, reducing and, and saving money in terms of TCO, but also being able to enable new capabilities. >>You know, the other part of it too, is this new infrastructure enables new workloads. So things like machine learning, you know, more data analytics, more sophisticated processing, you know, that, that is all enabled by this new infrastructure. So we, we were excited. We think that we're on the precipice of, you know, going a lot of industries moving forward to, to having, you know, the next level of it. It's no longer about just payroll or, or, or enterprise business management. It's about, you know, how do you make your, you know, your, your knowledge workers more productive, right. And how do you give them more capabilities? And that, that is really, what's exciting for us. >>Awesome Cooper. And thank you so much for joining Dave and me on the program today, talking about what AMD, what you're doing to supercharge customers, your partnership with VMware and what is exciting. What's on the, the forefront, the frontier, we appreciate your time and your insights. >>Great. Thank you very much for having me. >>Thank you for our guest and Dave Nicholson. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 22 from San Francisco, but don't go anywhere, Dave and I will be right back with our next guest.

>>Everyone welcome back from the cubes coverage here in Las Vegas for Aus re Mars. It's one of the re shows, as we know, reinvent is the big show. Now they have focus, shows reinforces coming up that security Remar is here. Machine learning, automation, robotics, and space. I'm John for your host, Michael Mike Miller here, director of machine learning thought leadership with AWS. Great to see you again. Yeah. Give alumni welcome back here. Back every time we got deep racer, always to talk >>About, Hey John, thanks for having me once again. It's great to be here. I appreciate it. >>So I want to get into the deep racer in context here, but first re Mars is a show. That's getting a lot of buzz, a lot of press. Um, not a lot of news, cuz it's not a newsy show. It's more of a builder kind of a convergence show, but a lot is happening here. It's almost a, a moment in time that I think's gonna be one of those timeless moments where we're gonna look back and saying that year at re Mars was an inflection point. It just seems like everything's pumping machine learning, scaling robotics is hot. It's now transforming fast. Just like the back office data center did years ago. Yeah. And so like a surge is coming. >>Yeah. >>What, what's your take of this show? >>Yeah. And all of these three or four components are all coming together. Right. And they're intersecting rather than just being in silos. Right. So we're seeing machine learning, enabled perception sort of on robots, um, applied to space and sort of these, uh, extra sort of application initiatives. Um, and that's, what's really exciting about this show is seeing all these things come together and all the industry-wide examples, um, of amazing perception and robotics kind of landing together. So, >>So the people out there that aren't yet inside the ropes of the show, what does it mean to them? This show? What, what, what they're gonna be what's in it for me, what's all this show. What does it mean? >>Yeah. It's just a glimpse into where things are headed. Right. And it's sort of the tip of the iceberg. It's sort of the beginning of the wave of, um, you know, these sort of advanced capabilities that we're gonna see imbued in applications, um, across all different industries. >>Awesome. Well, great to have you in the cube. Every time we have an event we wanna bring you on because deep racers become a, the hottest, I won't say cult following because it's no longer cult following. It's become massive following. Um, and which started out as an IOT, I think raspberry pie first time was like a, like >>A, we did a little camera initially camera >>And it was just a kind of a fun, little clever, I won't say hack, but just having a project that just took on a life OFS own, where are we? What's the update with racer you're here with the track. Yeah, >>Possibly >>You got the track and competing with the big dogs, literally dog, you got spot over there. Boston dynamics. >>Well we'll, we'll invite them over to the track later. Yeah. So deep razor, you know, is the fastest way to get hands on with machine learning. You know, we designed it as, uh, a way for developers to have fun while learning about this particular machine learning technique called reinforcement learning, which is all about using, uh, a simulation, uh, to teach the robot how to learn via trial and error. So deep racer includes a 3d racing simulator where you can train your model via trial and error. It includes the physical car. So you can take, uh, the model that you trained in the cloud, download it to this one 18th scale, um, kind of RC car. That's been imbued with an extra sensor. So we have a camera on the front. We've got an extra, uh, Intel, X, 86 processor inside here. Um, and this thing will drive itself, autonomously around the track. And of course what's a track and uh, some cars driving around it without a little competition. So we've got the deep racer league that sort of sits on top of this and adds a little spice to the whole thing. It's >>It's, it's like formula one for nerds. It really is. It's so good because a lot of people will have to readjust their models cuz they go off the track and I see people and it's oh my, then they gotta reset. This has turned into quite the phenomenon and it's fun to watch and every year it gets more competitive. I know you guys have a cut list that reinvent, it's almost like a, a super score gets you up. Yeah. Take, take us through the reinvents coming up. Sure. What's going on with the track there and then we'll get into some of the new adoption in terms of the people. >>Yeah, absolutely. So, uh, you know, we have monthly online races where we have a new track every month that challenges our, our developers to retrain their model or sort of tweak the existing model that they've trained to adapt for those new courses. Then at physical events like here at re Mars and at our AWS summits around the world, we have physical, uh, races. Um, and we crown a champion at each one of those races. You may have heard some cheering a minute ago. Yeah. That was our finals over there. We've got some really fast cars, fast models racing today. Um, so we take the winners from each of those two circuits, the virtual and the physical and they, the top ones of them come together at reinvent every year in November, December. Um, and we have a set of knockout rounds, championship rounds where these guys get the field gets narrowed to 10 racers and then those 10 racers, uh, race to hold up the championship cup and, um, earn, earn, uh, you know, a whole set of prizes, either cash or, or, you know, scholarships or, you know, tuition funds, whatever the, uh, the developer is most interested >>In. You know, I ask you this question every time you come on the cube because I I'm smiling. That's, it's so much fun. I mean, if I had not been with the cube anyway, I'd love to do this. Um, would you ever imagine when you first started this, that it would be such so popular and at the rise of eSports? So, you know, discord is booming. Yeah. The QB has a discord channel now. Sure, sure. Not that good on it yet, but we'll get there, but just the gaming culture, the nerd culture, the robotics clubs, the young people, just nerds who wanna compete. You never thought that would be this big. We, >>We were so surprised by a couple key things after we launched deep racer, you know, we envisioned this as a way for, you know, developers who had already graduated from school. They were in a company they wanted to grow their machine learning skills. Individuals could adopt this. What we saw was individuals were taking these devices and these concepts back to their companies. And they're saying, this is really fun. Like we should do something around this. And we saw companies like JPMC and Accenture and Morningstar into it and national Australia bank all adopting deep racer as a way to engage, excite their employees, but then also create some fun collaboration opportunities. Um, the second thing that was surprising was the interest from students. And it was actually really difficult for students to use deep racer because you needed an AWS account. You had to have a credit card. You might, you might get billed. There was a free tier involved. Um, so what we did this past year was we launched the deep racer student league, um, which caters to students 16 or over in high school or in college, uh, deep Razer student includes 10 hours a month of free training, um, so that they can train their models in the cloud. And of course the same series of virtual monthly events for them to race against each other and win, win prizes. >>So they don't have to go onto the dark web hack someone's credit card, get a proton email account just to get a deep Razer that's right. They can now come in on their own. >>That's right. That's right. They can log into that virtual the virtual environment, um, and get access. And, and one of the other things that we realized, um, and, and that's a common kind of, uh, realization across the industry is both the need for the democratization of machine learning. But also how can we address the skills gap for future ML learners? Um, and this applies to the, the, the world of students kind of engaging. And we said, Hey, you know, um, the world's gonna see the most successful and innovative ideas come from the widest possible range of participants. And so we knew that there were some issues with, um, you know, underserved and underrepresented minorities accessing this technology and getting the ML education to be successful. So we partnered with Intel and Udacity and launched the AI and ML scholarship program this past year. And it's also built on top of deep Bracer student. So now students, um, can register and opt into the scholarship program and we're gonna give out, uh, Udacity scholarships to 2000 students, um, at the end of this year who compete in AWS deep racer student racers, and also go through all of the learning modules online. >>Okay. Hold on, lets back up. Cuz it sounds, this sounds pretty cool. All right. So we kind went fast on that a little bit slow today at the end of the day. So if they sign up for the student account, which is lowered the batteries for, and they Intel and a desk, this is a courseware for the machine learning that's right. So in order to participate, you gotta take some courseware, check the boxes and, and, and Intel is paying for this or you get rewarded with the scholarship after the fact. >>So Intel's a partner of ours in, in putting this on. So it's both, um, helping kind of fund the scholarships for students, but also participating. So for the students who, um, get qualified for the scholarship and, and win one of those 2000 Udacity Nanodegree scholarships, uh, they also will get mentoring opportunities. So AWS and Intel, um, professionals will help mentor these students, uh, give them career advice, give them technical advice. C >>They'll they're getting smarter. Absolutely. So I'm just gonna get to data here. So is it money or credits for the, for the training? >>That's the scholarship or both? Yes. So, so the, the student training is free for students. Yep. They get 10 hours a month, no credits they need to redeem or anything. It's just, you log in and you get your account. Um, then the 2000, uh, Udacity scholarships, those are just scholarships that are awarded to, to the winners of the student, um, scholarship program. It's a four month long, uh, class on Python programming for >>AI so's real education. Yeah. It's like real, real, so ones here's 10 hours. Here's check the box. Here's here's the manual. Yep. >>Everybody gets access to that. That's >>Free. >>Yep. >>To the student over 16. Yes. Free. So that probably gonna increase the numbers. What kind of numbers are you looking at now? Yeah. In terms of scope to scale here for me. Yeah. Scope it >>Out. What's the numbers we've, we've been, uh, pleasantly surprised. We've got over 55,000 students from over 180 countries around the world that have signed up for the deep racer student program and of those over 30,000 have opted into that scholarship program. So we're seeing huge interest, um, from across the globe in, in this virtual students, um, opportunity, you know, and students are taking advantage of those 20 hours of learning. They're taking advantage of the fun, deep racer kind of hands on racing. Um, and obviously a large number of them are also interested in this scholarship opportunity >>Or how many people are in the AWS deep racer, um, group. Now, because now someone's gotta work on this stuff. It's went from a side hustle to like a full initiative. Well, >>You know, we're pretty efficient with what we, you know, we're pretty efficient. You've probably read about the two pizza teams at Amazon. So we keep ourselves pretty streamlined, but we're really proud of, um, what we've been able to bring to the table. And, you know, over those pandemic years, we really focused on that virtual experience in viewing it with those gaming kind of gamification sort of elements. You know, one of the things we did for the students is just like you guys, we have a discord channel, so not only can the students get hands on, but they also have this built in community of other students now to help support them bounce ideas off of and, you know, improve their learning. >>Awesome. So what's next, take us through after this event and what's going on for you more competitions. >>Yeah. So we're gonna be at the remainder of the AWS summits around the world. So places like Mexico city, you know, uh, this week we were in Milan, um, you know, we've got some AWS public sector, um, activities that are happening. Some of those are focused on students. So we've had student events in, um, Ottawa in Canada. We've had a student event in Japan. We've had a student event in, um, Australia, New Zealand. And so we've got events, both for students as well as for the professionals who wanna compete in the league happening around the world. And again, culminating at reinvent. So we'll be back here in Vegas, um, at the beginning of December where our champions will, uh, compete to ho to come. >>So you guys are going to all the summits, absolutely. Most of the summits or >>All of them, anytime there's a physical summit, we'll be there with a track and cars and give developers the opportunity to >>The track is always open. >>Absolutely. All >>Right. Well, thanks for coming on the cube with the update. Appreciate it, >>Mike. Thanks, John. It was great to be >>Here. Pleasure to know you appreciate it. Love that program. All right. Cube coverage here. Deep race are always the hit. It's a fixture at all the events, more exciting than the cube. Some say, but uh, almost great to have you on Mike. Uh, great success. Check it out free to students. The barrier's been lower to get in every robotics club. Every math club, every science club should be signing up for this. Uh, it's a lot of fun and it's cool. And of course you learn machine learning. I mean, come on. There's one to learn that. All right. Cube coverage. Coming back after this short break.

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante predictions are all the rage at this time of year now on december 29th 2020 in collaboration with eric porter bradley of enterprise technology research etr we put forth our predictions for 2021 and the focus of our prognostications included tech spending remote work productivity apps cyber security ipos specs m a data architecture cloud hybrid cloud multi-cloud ai containers automation and semiconductors we covered a lot of ground now over the past several weeks we've been inundated with literally thousands of inbound emails pitching us on various predictions and trends in these and other areas here's my predictions folder and this is only a portion of the documents that i've received by email obviously printed them out killed a few trees sorry hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we're going to review briefly each of our predictions for this past year 2021 and suggest a grade as to how we did we're going to do this as a little warm up for our 2022 predictions which we'll be doing in the next over the next couple of weeks now before we dig in i want to make an observation many of the predictions that we received they were observations of trends and sometimes not really predictions or you know or not surprising we got a lot of self-serving marketing statements you know predictions in our view they should be measurable so you can look back and say okay did they get it right now granted there are gray areas so that's why we'll use a grading system today now there are also many really well done and thought-provoking predictions and there's an example of one that we received that is strong it's from equinix cio milan waglay who said within the decade data centers will be powered by a hundred percent renewable energy okay so you know that's clear and we can measure that but anyway thanks to all the pr folks who sent along like i said literally thousands of predictions we tried to read them all but the volume over the past week or so was just so overwhelming and we'll try to scan them before we do our 2022 predictions but today we want to do that warm up by evaluating how we did in 2021 so let's get started our first prediction was that tech spending would increase by four percent this year coming off of what we had thought was a contraction in 2020 and depending on which data you look at you know best case maybe was flat we definitely correctly called the continuation into 2022 of the remote work trend and the positive impact it would have on pcs and the like but we underestimated the shape of that rebound that that spend back curve idc has tech spending wrote this year at five and a half percent so we feel like while we called the bounce back it was more pronounced than we had thought in fact you know we think that idc number is probably going to go up even higher and we'll address that in our 2022 predictions so so we'll give ourselves a b minus here okay next prediction was remote worker trends become fossilized settling in at an average of 34 percent by year end 2021. so on average 34 of the workers would be remote by the end of this year now you know we made the call but we missed delta no we missed omacrom we said 34 remote which would be 2x the historical norms now the etr data suggests it was 52 in september and it's probably going to be somewhere in the 40 to 45 range by by the end of this month into december and the thing is 75 of the workforce is probably still working either fully remote or in a hybrid model and hybrid work is probably going to be the dominant trend and we're going to have to revisit that framework or how we think about this whole structure and we'll do that again in our 2022 predictions so we'll give ourselves a c on that one we'll take some credit for the permanence of the trend but the percentage was well off the mark you know thanks to the variance as well as some cultural shifts that whole hybrid notion okay so hey not really a great start for eric and me but we rebound with the next one the productivity increases we said seen in 2020 will lead organizations to double down on the successes and certain productivity apps will benefit so to measure this we said let's take a look at the most recent quarterly earnings and gauge the revenue growth year on year as an indicator docusign was up 42 smartsheet who we also called up was up 46 in revenue twilio up 65 zoom growth was 35 down from 325 confirming our layup call the zoom growth would moderate it had nowhere to go but down and microsoft teams has never been more ubiquitous has never seen greater adoption with hundreds of companies having a hundred thousand or more users and thousands of companies with ten thousand users or more so we really feel like we nailed this one so we're gonna give us give ourselves an a plus okay so now on to cyber it's an area that we've been making calls in for a couple of years now and we're really pleased looking back here we said permanent shifts in cso strategies are going to lead to share shifts in network security now we said to give you more detail maybe that sounds like an easy one but we said specifically identity cloud security and endpoint security would continue to benefit and we specifically named crowdstrike octa zscaler and a few others that are targeting their growth rates now gartner has the security market growing at 11 percent octa and zscaler revenues last quarter grew at 62 percent year over year crowdstrike 63 illumia we also called out they raised 225 million dollars on a 2.75 billion valuation on the strength of its growth that was in september now akamai acquired guardiocor for 600 million dollars another company we called out that they would do it they did that as a ransomware protection play and they paid a huge revenue multiple for the company and it seems the guys listed on the last line are all talking about subscriptions sas arr remaining performance obligations or rpo so we feel very good about this look back we'll take an a on this one no it's not an a plus because we're too conservative on the growth of octa crowdstrike and zscaler topping at 50 they they blew that away by another 10 points or so 10 to 15. but look pretty good call nonetheless okay again the next one you might feel like is a layup but not really so we said the increased tech spend would drive even more ipos spax and m a according to spac analytics ipos were up 109 this year the spac attack continued up 109 percent in 2021 on top of a record 2020 and according to kpmg m a dollar volume was up 19 okay you might say uh that was easy call but there was much more underneath this prediction we called out uipass ipo which was a lock but also said automation anywhere would go public uipath did aa didn't we did correctly call the hashicorp ipo we said they'd either get go ipo or get acquired and cloud flare grew revenue 219 percent last quarter but akamai was not acquired so the degree of difficulty on the overall prediction wasn't high but the automation anywhere in akamai events we made those calls that didn't happen and those were you know obviously tougher calls so we think this still deserves a b grade all right as you know data is one of our favorite subjects and we've reported extensively in the successes and failures of so-called big data we said next in the next prediction that in the 2020s 75 percent of large organizations will re-architect their big data platforms and we said this would occur you know in earnest over the next four to five years now again you may say duh dave but you have to evaluate the prediction based on the underlying comments here the jury is still out on things like snowflakes data cloud but we absolutely believe that it's the right direction but then you have then you have data bricks coming in taking a different approach they're coming at the problem from a data science angle trying to take on traditional bi and then you get snowflake coming from the analytics space and moving into ai and data science and you know we asked at aws aws re invent we asked benoit dejaville on the cube if there needs to be a semantic layer to bring these two worlds together and he said yes and that's what he claims snowflake is building meanwhile you got the big whales like oracle they continue to invest in their capabilities to try to eliminate data movement and then there's aws taking a totally different approach to data where it gives customers maximum optionality of offerings and database and other services and you can't forget microsoft and google so many customers might not take the steps that we predicted because they're comfortable where they are specifically we're talking about here a shift toward domain ownership and data product thinking and the reorganization of hyper-specialized technical teams many of the principles put forth by data mesh and we've said this change is going to take a number of years to play out four to five years so we start noticing in 2021 that that's clearly been the case as we reported on parts of jpmorgan chase uh rethinking its data architecture hellofresh and many others so this is still an incomplete the professor we'll give ourselves an incomplete on this one but we think it's trending in the right direction okay the next one is always fun discussion that's the battle to define hybrid and multi-cloud we said that's going to escalate in 2021 and we'll create bifurcated cio strategies now here we go aws sees the world as bringing its apis and primitives and model to the edge and the data center to aws is just another edge node and the company says that in still believes in the fullness of time that all data will be in the cloud however that's defined and aws awareness would say all this talk about hybrid of connecting on-prem to a cloud they would flat out say adam silipsky told us this that's not cloud is what he said then on the other side of the table you have the likes of cisco dell hpe etc saying hold on cloud is an operating model it's not a place and aws might say yeah and aws along with its customers is defining that operating model and these other guys would say no actually you're not we are with our customers and this battle 100 percent escalated in 2021 with the launch of apex by dell hp e double down on green lake cisco's as the service models and then of course oracle which actually announced a true same same public to on-prem hybrid capability two years before aws announced outpost and of course oracle's executing on that strategy in earnest in 2021 and the other nuance here is a concept that we introduced called super cloud which refers to the notion that look something like for example multi-cloud is not about running within a respective cloud it's not about cloud compatibility rather it's about abstracting the complexity of the underlying cloud primitives and building value on top of those cloud services on top of the investments in capex that the hyperscalers have made now some people didn't like the term super cloud maybe uber cloud would be a better term we're going to continue to use it to describe this capability we think it has meaning and we're seeing new examples like goldman sachs's financial cloud running on top of aws so a super cloud is not as an application or a suite of applications running on a single cloud now if those applications span multiple clouds like like snowflake is trying to do okay that's a service that could span multiple clouds or in the case of goldman sachs it's a portfolio of data tools and software that's made accessible as a service that floats on top of a single or even multiple clouds regardless we feel that this was a correct call given the evidence and we'll give ourselves an a minus taking points off for the somewhat anecdotal and observational measurement system that we apply to look back at this prediction okay the next prediction was we made was cloud containers ai and ml automation uh are gonna power that those big four are gonna power 2021 spending here's a graphic we use to predict that it plots survey data for the various technologies within the etr taxonomy net score or spending momentum on the vertical axis and market share or presence in the data set it's a pervasive measurement on the horizontal axis the one that matters here is the vertical that dotted line of 40 percent anything above that is considered highly elevated and these four areas have held served this year based on recent etr survey data that we're not showing here we'll we'll bring that into our 2022 prediction so this prediction came in correctly for the most recent survey data and that's our measurement system on this one so we're going to take an a for this one too now on the penelope ultimate prediction here we came back to automation saying that the automation mandate accelerates in 2021 uipath and automation anywhere we said would go public but microsoft remains a threat to these pure play rpa vendors well we gave ourselves a b on this one doubling down on automation anywhere going public you know that was wrong but we definitely saw this year companies leaning hard into automation and microsoft despite the fact that it doesn't have as feature rich a product and offering as uipath and automation anywhere microsoft remains a very large presence you know we spoke to a lot of customers at the uipath forward four event in october in las vegas physical event and they confirmed you know this is true but at the same time so they're using power automate from microsoft but also using in this case uipath so they've kind of confirmed that yeah it's not the same we use that for some of our productivity we're an azure customer it's easy for us but they're still leaning heavily and investing heavily into uipath and i think the same can be said for automation anywhere but autom but power automate shows up as a big time leader in the magic gartner magic quadrant so it can't be ignored but clearly the two leaders in rpa have a sizable product advantage relative to the legacy software players now if you look at the comment on pega systems they cooled off a bit as measured by their stock price their revenue grew 13 percent last quarter on a year-on-year basis but perhaps we overestimated the tailwind effect and the company's momentum so we'll take a b on this prediction correct call on the automation trend and the big software vendors piling in ibm et cetera but the chance we took on automation anywhere again was a miss so we'll dig ourselves on that and our last prediction for 2021 was 5g rollouts push new edge iot workloads and necessitate new system architectures now much of this prediction you can see in the underlying bullets here really related to the observation that arm was dominating at the edge it would find its way into the mainstream enterprise workloads and we've been asking a lot of the mainstream you know companies the oems you know what do you what do you see with with arm in the enterprise and they say yeah we don't see it yet but very clearly this came into focus in 2021 is aws announced graviton 3 now and new inference and new training silicon these are different types of workloads that are emerging in the enterprise these are all based on arm microsoft google alibaba oracle and others are now shipping or readying arm-based systems for the enterprise when you look at new storage network and security appliances and other systems they're very offering and often including arm-based processors to assist with the offloads and look intel is definitely under product under pressure as we've predicted many times not just in our predictions post even pat gelsinger has admitted this is a turnaround it's going to take at least five years that's kind of new and recent data that he's made public so we're going to take an a minus on this one we're going to take off some points for the fact that you know 5g rollouts in edge are evolving and this is a longer term trend but the underlying points that we made on this slide are still pretty solid now if we use the following scale where a plus is a hundred out of a hundred a minus is a 90 a b is an 85 a b minus is an 80 and a c is a 75 out of 100 and we exclude that incomplete prediction on data architectures we average out to an 87.8 so that's a solid b plus and so the professor in us said hey little yellow sticky good effort as most of the predictions could be quantified and or you know we tried to object objectively score them there were some layups in there so yeah maybe we'll try to take more risks uh you know or not you know we we we'll see we like winning and so you know you always have to couch some of these things with some obvious ones but but really try to give some detail underneath that's maybe non-obvious um and we'll try to keep it down in the legs we did this year to one or two multi-year predictions so what's next well eric bradley and i were working on our 2022 predictions we're going to release those in the next couple of weeks so stay tuned for that you know what do you think how did we do you know we're grading ourselves here love to know you know for we're off base on base we're too hard on ourselves too easy give us your feedback don't forget these episodes are all available as podcasts wherever you listen all you do is search breaking analysis podcast check out etr's website at etr dot plus remember we also publish a full report every week on wikibon.com and siliconangle.com you can always get in touch with email david.velante at siliconangle.com you can dm me at divalante or comment on our linkedin posts this is dave vellante for the cube insights powered by etr have a great week everybody stay safe be well we'll see you next time [Music] you

(bright music) >> Welcome back everyone to theCUBE's coverage of AWS re:Invent 2021 in person. So a live event, physical in-person, also virtual hybrid. So a lot of great action online, check out the website. All the videos are there on theCUBE, as well as what's going on all of the actions on site and theCUBE's here. I'm John Furrier, your host with Dave Vellante, my cohost. Finally, we've got David Brown, VP of Elastic Compute Cloud. EC2, the bread and butter. Our favorite part of Amazon. David, great to have you back on theCUBE in person. >> John, it's great to be back. It's the first time I'd been on theCUBE in person as well. A lot of virtual events with you guys, but it's amazing to be back at re:Invent. >> We're so excited for you. I know, Matt Garman and I've talked in the past. We've talked in the past. EC2 is just an amazing product. It's always been the core block of AWS. More and more action happening and developers are now getting more action and there's well, we wrote a big piece about it. What's going on? The Silicon's really paying off. You've got to also general purpose Intel and AMD, and you've got the custom silicon, all working together. What's the new update? Give us a scoop. >> Well, John, it's actually 15 years of EC2 this year and I've been lucky to be on that team for 14 years and so incredible to see the growth. It's been an amazing journey. The thing that's really driven us, two things. One is supporting new workloads. And so what are the workloads that customers have available out there trying to do on the cloud that we don't support and launch new instance types. And that's the first thing. The second one is price performance. How do we give customers more performance at a continuously decreasing price year-over-year? And that's just driven innovation across EC2 over the years with things like Graviton. All of our inferential chips are custom silicon, but also instance types with the latest Intel Ice Lake CPU's, latest Milan. We just announced the AMD Milan instance. It's just constantly innovation across the ever-increasing list of instances. So super exciting. >> So instances become the new thing. Provision an instance, spin up an instance. Instance becomes, and you can get instances, flavors, almost like flavors, right? >> David: Yeah. >> Take us through the difference between an instance and then the EC2 itself. >> That's correct, yeah. So we actually have, by end of the year, right now we have over 475 different instances available to you whether it's GPU accelerators, high-performance computing instances, memory optimized, just enormous number. We'll actually hit 500 by the end of the year, but that is it. I mean, customers are looking for different types of machines and those are the instances. >> So the Custom Silicon, it's one of the most interesting developments. We've written about it. AWS secret weapon is one of them. I wonder if you could take us back to the decision points and the journey. The Annapurna acquisition, you started working with them as a partner, then you said, all right, let's just buy the company. >> David: Yeah. >> And then now, you're seeing the acceleration, your time to tapeout is way, way compressed. Maybe what was the catalyst and maybe we can get into where it's going. >> Yeah, absolutely. Super interesting story 'cause it actually starts all the way back in 2008. In 2008, EC2 had actually been around for just a little under two years. And if you remember back then, everybody was like, will virtualize and hypervisors, specialization would never really get you the same performances, what they were calling bare metal back then. Everybody's looking at the cloud. And so we took a look at that. And I mean, network latencies, in some cases with hypervisors were as high as 200 or 300 milliseconds. And it was a number of real challenges. And so we knew that we would have to change the way that virtualization works and get into hardware. And so in 2010, 2011, we started to look at how could I offload my network processing, my IO processing to additional hardware. And that's what we delivered our first Nitro card in 2012 and 2013. We actually offloaded all of the processing of network to a Nitro card. And that Nitro card actually had a Annapurna arm chip on it. Our Nitro 1 chip. >> For the offload? >> The offload card, yeah. And so that's when my team started to code for Arm. We started to work on our Linux works for Arm. We actually had to write our own operating system initially 'cause there weren't any operating systems available we could use. And so that's what we started this journey. And over the years, when we saw how well it worked for networking, we said, let's do it for storage as well. And then we said, Hey, we could actually improve security significantly. And by 2017, we'd actually offloaded 100% of everything we did on that server to our offload cards Leaving a 100% of the server available for customers. And we're still actually the only cloud provider that does that today. >> Just to interject, in the data center today, probably 30% of the general purpose cores are used for offloads. You're saying 0% in the cloud. >> On our nitro instances, so every instance we've launched since 2017, our C5. We use 0% of that central core. And you can actually see that in our instance types. If you look at our largest instance type, you can see that we're giving you 96 cores and we're giving you, and our largest instance, 24 terabytes of memory. We're not giving you 23.6 terabytes 'cause we need some. It's all given to you as the customer. >> So much more efficient, >> Much, much more efficient, much better, better price performance as well. But then ultimately those Nitro chips, we went through Nitro 1, Nitro 2, Nitro 3, Nitro 4. We said, Hey, could we build a general purpose server chip? Could we actually bring Arm into the cloud? And in 2018, we launched the A1 instance, which was our Graviton1 instance. And what we didn't tell people at the time is that it was actually the same chip we were using on our network card. So essentially, it was a network card that we were giving to you as a server. But what it did is it sparked the ecosystem. That's why we put it out there. And I remember before launch, some was saying, is this just going to be a university project? Are we going to see people from big universities using Arm in the cloud? Was it really going to take off? And the response was amazing. The ecosystem just grew. We had customers move to it and immediately begin to see improvements. And we knew that a year later, Graviton2 was going to come out. And Graviton2 was just an amazing chip. It continues to see incredible adoption, 40% price performance improvement over other instances. >> So this is worth calling out because I think that example of the network card, I mean, innovation can come from anywhere. This is what Jassy always would say is do the experiments. Think about the impact of what's going on here. You're focused on a mission. Let's get that processing of the lowest cost, pick up some workloads. So you're constantly tinkering with tuning the engine. New discovery comes in. Nitro is born. The chip comes in. But I think the fundamental thing, and I want to get your reaction to this 'cause we've put this out there on our post on Sunday. And I said, in every inflection point, I'm old enough, my birthday was yesterday. I'm old enough to know that. >> David: I saw that. >> I'm old enough to know that in the eighties, the client server shifts. Every inflection point where development changed, the methodology, the mindset or platforms change, all the apps went to the better platform. Who wants to run their application on a slower platform? And so, and those inflects. So now that's happening now, I believe. So you got better performance and I'm imagining that the app developers are coding for it. Take us through how you see that because okay, you're offering up great performance for workloads. Now it's cloud workloads. That's almost all apps. Can you comment on that? >> Well, it has been really interesting to see. I mean, as I said, we were unsure who was going to use it when we initially launched and the adoption has been amazing. Initially, obviously it's always, a lot of the startups, a lot of the more agile companies that can move a lot faster, typically a little bit smaller. They started experimenting, but the data got out there. That 40% price performance was a reality. And not only for specific workloads, it was broadly successful across a number of workloads. And so we actually just had SAP who obviously is an enormous enterprise, supporting enterprises all over the world, announced that they are going to be moving the S/4 HANA Cloud to run on Graviton2. It's just phenomenal. And we've seen enterprises of that scale and game developers, every single vertical looking to move to Graviton2 and get that 40% price performance. >> Now we have to, as analysts, we have to say, okay, how did you get to that 40%? And you have to make some assumptions obviously. And it feels like you still have some dry powder when you looked at Graviton2. I think you were running, I don't know, it's speculated anyway. I don't know if you guys, it's your data, two and a half, 2.5 gigahertz. >> David: Yeah. >> I don't know if we can share what's going on with Graviton3, but my point is you had some dry powder and now with Graviton3, quite a range of performance, 'cause it really depends on the workload. >> David: That's right. >> Maybe you could give some insight as to that. What can you share about how you tuned Graviton3? >> When we look at benchmarking, we don't want to be trying to find that benchmark that's highly tuned and then put out something that is, Hey, this is the absolute best we can get it to and that's 40%. So that 40% is actually just on average. So we just went and ran real world workloads. And we saw some that were 55%. We saw some that were 25. It depends on what it was, but on average, it was around the 35, 45%, and we said 40%. And the great thing about that is customers come back and say, Hey, we saw 40% in this workload. It wasn't that I had to tune it. And so with Graviton3, launching this week. Available in our C7g instance, we said 25%. And that is just a very standard benchmark in what we're seeing. And as we start to see more customer workloads, I think it's going to be incredible to see what that range looks like. Graviton2 for single-threaded applications, it didn't give you that much of a performance. That's what we meant by cloud applications, generally, multi-threaded. In Graviton3, that's no longer the case. So we've had some customers report up to 80% performance improvements of Graviton2 to Graviton3 when the application was more of a single-threaded application. So we started to see. (group chattering) >> You have to keep going, the time to market is compressing. So you have that, go ahead, sorry. >> No, no, I always want to add one thing on the difference between single and multi-threaded applications. A lot of legacy, you're single threaded. So this is kind of an interesting thing. So the mainframe, migration stuff, you start to see that. Is that where that comes in the whole? >> Well, a lot of the legacy apps, but also even some of the new apps, like single threading like video transcoding, for example, is all done on a single core. It's very difficult. I mean, almost impossible to do that multi-threaded way. A lot of the crypto algorithms as well, encryption and cryptography is often single core. So with Graviton3, we've seen a significant performance boost for video encoding, cryptographic algorithms, that sort of thing, which really impacts even the most modern applications. >> So that's an interesting point because now single threaded is where the vertical use cases come in. It's not like more general purpose OS kind of things. >> Yeah, and Graviton has already been very broad. I think we're just knocking down the last few verticals where maybe it didn't support it and now it absolutely does. >> And if an ISV then ports, like an SAP's ports to Graviton, then the customer doesn't see any, I mean, they're going to see the performance difference, but they don't have to think about it. >> David: Yeah. >> They just say, I choose that instance and I'm going to get better price performance. >> Exactly, so we've seen that from our ISVs. We've also been doing that with our AWS services. So services like EMR, RDS, Elastic Cache, it will be moving and making Graviton2 available for customers, which means the customer doesn't have to do the migration at all. It's all done for them. They just pick the instance and get the price performance benefits, and so yeah. >> I think, oh, no, that was serverless. Sorry. >> Well, Lambda actually just did launch on Graviton2. And I think they were talking about a 35% price performance improvement. >> Who was that? >> Lambda, a couple of months ago. >> So what does an ISV have to do to port to Graviton. >> It's relatively straightforward, and this is actually one of the things that has slowed customers down is the, wow, that must be a big migration. And that ecosystem that I spoke about is the important part. And today, with all the Linux operating systems being available for Arm running on Graviton2, with all of the container runtimes being available, and then slowly open source applications in ISV is being available. It's actually really, really easy. And we just ran the Graviton2 four-day challenge. And we did that because we actually had an enterprise migrate one of the largest production applications in just four days. Now, I probably wouldn't recommend that to most enterprises that we see is a little too fast, but they could actually do that. >> But just from a numbers standpoint, that's insanely amazing. I mean, when you think about four days. >> Yeah. >> And when we talked on virtually last year, this year, I can't remember now. You said, we'll just try it. >> David: That's right. >> And see what happens, so I presume a lot of people have tried it. >> Well, that's my advice. It's the unknown, it's the what will it take? So take a single engineer, tell them and give them a time. Say you have one week, get this running on Graviton2, and I think the results are pretty amazing, very surprised. >> We were one of the first, if not the first to say that Arm is going to be dominant in the enterprise. We know it's dominant in the Edge. And when you look at the performance curves and the time to tape out, it's just astounding. And I don't know if people appreciate that relative to the traditional Moore's law curve. I mean, it's a style. And then when you combine the power of the CPU, the GPU, the NPU, kind of what Apple does in the iPhone, it blows away the historical performance curves. And you're on that curve. >> That's right. >> I wonder if you could sort of explain that. >> So with Graviton, we're optimizing just across every single part of AWS. So one of the nice things is we actually own that end-to-end. So when it starts with the early design of Graviton2 and Graviton3, and we obviously working on other chips right now. We're actually using the cloud to do all of the electronic design automation. So we're able to test with AWS how that Graviton3 chip is going to work long before we've even started taping it out. And so those workloads are running on high-frequency CPU's on Graviton. Actually we're using Graviton to build Graviton now in the cloud. The other thing we're doing is we're making sure that the Annapurna team that's building those CPUs is deeply engaged with my team and we're going to ultimately go and build those instances so that when that chip arrives from tapeout. I'm not waiting nine months or two years, like would normally be the case, but I actually had an instance up and running within a week or two on somebody's desk studying to do the integration. And that's something we've optimized significantly to get done. And so it allows us to get that iteration time. It also allows us to be very, very accurate with our tapeouts. We're not having to go back with Graviton. They're all A1 chips. We're not having to go back and do multiple runs of these things because we can do so much validation and performance testing in the cloud ahead of time. >> This is the epiphany of the Arm model. >> It really is. >> It's a standard. When you send it to the fab, they know what's going to work. You hit volume and it's just no fab. >> Well, this is a great thread. We'll stay on this 'cause Adam told us when we met with them for re:Invent that they're seeing a lot more visibility into use cases at the scale. So the scale gives you an advantage on what instances might work. >> And makes the economics works. >> Makes the economics work, hence the timing, the shrinking time to market, not there, but also for the apps. Talk about the scale advantage you guys have. >> Absolutely. I mean, the scale advantage of AWS plays out in a number of ways for our customers. The first thing is being able to deliver highly optimized hardware. So we don't just look at the Graviton3 CPU, you were speaking about the core count and the frequency and Peter spoke about a lot of that in his keynote yesterday. But we look at how does the Graviton3 CPU work with the rest of the instance. What is the right balance between the CPU and memory? The CPU and the Hydro. What's the performance and the drive? We just launched the Nitro SSD, which is now we've actually building our own custom SSDs for Nitro getting better performance, being able to do updates, better security, making it more cloudy. We're just saying, we've been challenged with the SSD in the parts. The other place that scales really helping is in capacity. Being able to make sure that we can absorb things like the COVID spike, or the stuff you see in the financial industry with just enormous demand for compute. We can do that because of our scale. We are able to scale. And the final area is actually in quality because I have such an enormous fleet. I'm actually able to drive down AFR. So annual failure rates, are we well below what the mathematical theoretical tenant or possibility is? So if you look at what's put on that actual sticker on the box that says you should be able to get a full percent AFR. At scale and with focus, we're actually able to get that down to significantly below what the mathematical entitlement was actually be. >> Yeah, it's incredible. I've got a great, and this is the advantage, and that's why I believe anyone who's writing applications that has includes a database, data transfer, any kind of execution of code will use the stack. >> Why would they? Really, why? We've seen this, like you said before, whether it was PC, then the fastest Pentium or somebody. >> Why would you want your app to run slower? >> Unix box, right? ISVS want it to run as fast and as cheaply as possible. Now power plays into it as well. >> Yeah, well, we do have, I agree with what you're saying. We do have a number of customers that are still looking to run on x86, but obviously customers that want windows. Windows isn't available for Arm and so that's a challenge. They'll continue to do that. And you know the way we do look at it is most law kind of died out on us in 2002, 2003. And what I'm hoping is, not necessarily bringing wars a little back, but then we say, let's not accept the 10%, 15% improvement year-over-year. There's absolutely more we can all be doing. And so I'm excited to see where the x86 world's going and they doing a lot of great stuff. Intel Ice Lakes looking amazing. Milan is really great to have an AWS as well. >> Well, I'm thinking it's fair point 'cause we certainly look what Pat's doing it at Intel and he's remaking the company. I've said he's going to follow on the Arm playbook in my mind a little bit, and which is the right thing to do. So competition is a good thing. >> David: Absolutely. >> We're excited for you and a great to see Graviton and you guys have this kind of inflection point. We've been tracking for a while, but now the world's starting to see it. So congratulations to your team. >> David: Thank you. >> Just a couple of things. You guys have some news on instances. Talk about the deprecation issue and how you guys are keeping instances alive real quick. >> Yeah, we're super customer obsessed at Amazon. And so that really drives us. And one of the worst things for us to do is to have to tell a customer that we no longer supporting a service. We recently actually just deprecated the ECG classic network. I'm not sure if you saw that and that's actually off the 10 years of continuing to support it. And the only reason we did it is we have a tiny percentage of customers still using that from back in 2012. But one of the challenges is obviously instance hardware eventually will ultimately time out and fail and have hardware issues as it gets older and older. And so we didn't want to be in a place, in EC2, where we would have to constantly go to customers and say that M1 small, that C3, whatever you were running, it's no longer supported, please move. That's just a text that customers shouldn't have to do. And if they still getting value out of an older instance, let them keep using it. So we actually just announced at re:Invent, in my keynote on Tuesday, the longevity support for EC2 instances, which means we will never come back to you again and ask you to please get off an instance, because we can actually emulate all those instances on our Nitro system. And so all of these instances are starting to migrate to Nitro. You're getting all the benefits of Nitro for now some of our older zen instances, but also you don't have to worry about that work. That's just not something you need to do to get off in all the instance. >> That's great. That's a great test service. Stay on as long as you want. When you're ready to move, move. Okay, final question for you. I know we've got time, I want to get this in. The global network, you guys are known for AWS cloud WAN serve. Gives you updates on what's going on with that. >> So Werner just announced that in his keynote and over the last two to three years or so, we've seen a lot of customers starting to use the AWS backbone, which is extensive. I mean, you've seen the slides in Werner's keynote. It really does span the world. I think it's probably one of the largest networks out there. Customers starting to use that for actually their branch office communication. So instead of going and provisioning the own international MPLS networks and that sort of thing, they say, let me onboard to AWS with VPN or direct connect, and I can actually run the AWS backbone around the world. Now doing that actually has some complexity. You got to think about transit gateways. You got to think about those inter-region peering. And AWS cloud when takes all of that complexity away, you essentially create a cloud WAN, connecting to it to VPN or direct connect, and you can even go and actually set up network segments. So essentially VLANs for different parts of the organization. So super excited to get out that out of there. >> So the ease of use is the key there. >> Massively easy to use. and we have 26 SD-WAN partners. We even partnering with folks like Verizon and Swisscom in Switzerland to telco to actually allow them to use it for their customers as well. >> We'll probably use your service someday when we have a global rollout date. >> Let's do that, CUBE Global. And then the other was the M1 EC2 instance, which got a lot of applause. >> David: Absolutely. >> M1, I think it was based on A15. >> Yeah, that's for Mac. We've got to be careful 'cause M1 is our first instance as well. >> Yeah right, it's a little confusion there. >> So it's a Mac. The EC2 Mac is with M1 silicon from Apple, which super excited to put out there. >> Awesome. >> David Brown, great to see you in person. Congratulations to you and the team and all the work you guys have done over the years. And now that people starting to realize the cloud platform, the compute just gets better and better. It's a key part of the system. >> Thanks John, it's great to be here. >> Thanks for sharing. >> The SiliconANGLE is here. We're talking about custom silicon here on AWS. I'm John Furrier with Dave Vellante. You're watching theCUBE. The global leader in tech coverage. We'll be right back with more covers from re:Invent after this break. (bright music)

>>Mm. Welcome back to the cubes. Continuous coverage of A W s reinvent 2021 were running one of the industry's most important and largest hybrid tech events of the year with A W S and its ecosystem partners. And, of course, special thanks to a M D for supporting this year's editorial coverage at the event we got to live sets we had to remote sets one in Boston, one in Palo Alto. We've got more than 100 guests coming on the programme and we're looking >>deep into >>the next decade of cloud innovation. We're super excited to be joined by Ali Zafar, who is the senior director of platform strategy and operations at Dropbox Ali. Great to see you. Thanks for coming on. >>Awesome. It's a pleasure to be here with you, Dave. >>So Hey, what's your day job like at Dropbox? What's your role? >>Got it? Yeah. So I actually oversee the global supply chain at Dropbox. Also all of the capacity planning which entails both our budget and also capacity requirements and Dropbox. And then I also focus on the platform product management side which is basically building our build vs buy and our overall roadmap for our platform in the long run. >>Great. Thank you. So I mean, everybody knows Dropbox, But maybe you can talk a little bit about your business, your mission and how that's evolved. Over the past several years. >>Dropbox is a global collaboration platform, and our mission at Dropbox is to help design a more enlightened way of working. Dropbox has over 700 million registered users and over 550 billion pieces of content. So taking a step back, they've dropbox health. Let's use all of your content. Think of this as videos as music. Even your tax returns allows you to organise all of this content. And then you can share this content with anybody at any time. You can also take Dropbox to work. And actually, it makes you even more productive in the workplace integrating all of your tools seamlessly, also allowing you to collaborate with all of your teams internally and also externally. >>Yeah, so thank you. Uh, when Dropbox was founded, I mean, the cloud was really nascent, right? So it was early days, and so a lot has changed since you know, the mid last decade. And of course, with remote work and hybrid work that had to be a real tailwind to your business. But maybe you could explain your cloud and your hybrid cloud strategy. >>You're spot on Dave. So Dropbox has always been hybrid since its inception in 2000 and seven. And when I say hybrid, I mean, we have our own on prime infrastructure, and then we also leverage Public Cloud. Now, Public cloud still to these days remains absolutely critical for Dropbox to serve all of its customer needs. And when we talk about the decision between public or private, we think about three or four key things. One is the total cost of ownership. Look at the market. We also look at our customer requirements and the latest technology that's available in the market and then any international data storage requirements to make the decision of going towards public or private for that specific use case. >>So what if we could follow up on that? Like maybe you can talk about the key business, these conditions as a as a SAS storage provider? What are the real drivers in in your business framework? >>Got it at the end of the day, what really matters for us. There is to actually think about our customers and delight them And what better than to focus on performance, reliability and also security. Right. So we want to make sure that the infrastructure that we have today allows Dropbox to actually solve for the specific use case for our customers. What do they care about while also doing this in a very efficient management Manage, uh, way So to summarise that looking at performance, looking at liability, looking at scalability, looking at efficiency and then also compliance >>So that leads me to My next question is about the EC two instances that you use. I know you. You make heavy use of AMG compute. How >>did you >>come to that decision? Was that these factors was all performance. How did you migrate to really enable that capability? How complex was that? >>MD has has been a key strategic partners the partnership as well over 4 to 5 years right now and we've been leveraging them on our on prem infrastructure for compute. So we've always had aimed in our infrastructure. And when the time came where aws was also leveraging some of the MD instances, we wanted to see how we can expand the partnership with AMG and A W S and also experiment with these instances. So we looked at some of the tooling updates that were required. We also looked at specific instances which are either compute optimised and memory optimised instances. And then we actually build our footprint on M D. And what we saw is that the overall performance improvements and also cost improvements that we got for specific workloads. It was actually extremely, uh, overall awesome results for Dropbox and our customers, and we have been using them ever since. >>What kind of business impact did that make that make a difference to your business? That was noticeable >>on the business side, I think primarily it was more on the TCO side, which is where we got most of the benefits on the cost side. Um, and then also for some of our internal work clothes, we also saw benefit, uh, to our internal developers that are using some of those work clothes. >>Well, so you guys have kind of become the poster child for hybrid. A lot has been talked about about you all, but I wonder if you could help us understand what part of your infrastructure is going to be better served by public cloud versus kind of doing your own. I t on Prem. What are some of the value drivers that are that are making, you know, push workloads into the public cloud? Help us understand that better and squint through that >>got ready. I get asked that question a lot. So public cloud in general allows for faster go to market, Think about this as, like product launches teacher launches also international expansion. It allows us to scale and then also leveraging some of the existing technologies out there in the market for some of the common workloads. So just, you know, taking a step back and thinking about Dropbox. We keep on evaluating also the criteria and then also specific workloads on what makes sense on private or public load. And a W s had some instances, like as three rds and EC to that when we started looking at, we knew that some of our key services, like data platform, some parts of our, um, Melania and even paper platform would make more sense for us to actually leverage. Uh, some of these in public cloud for that. >>So what are the sort of characteristics of the workload that are sort of better suited to be in AWS? You know, what's the ideal workload profile? You know, we talk about ideal customer profile. What's the ideal workload profile for the for the AWS Cloud. >>Got it. So the way we think about it, at least we call it the rule of three at Dropbox. Um, and that means we look at scale. First, we look at technology and innovation. Um, and what I mean by that is, is there faster innovation in the public cloud? And is the workload common enough that there's already a lot of work going on in public Cloud? Then there's no reason for us to actually innovate faster than that. We probably can't. And if the scale is not large enough, right? So when we talk about our storage side like magic pocket, the scale is large enough. We're innovating. There makes sense, and it's better for the end customer, so we will probably go towards private cloud there. But then, when we talk about like international expansion, when we talk about, like, faster go to market or some of the innovation in the space. It really makes sense to use public Cloud because of all of the advancements that we've seen there. >>Yeah, so let me circle back to the sort of business benefits and impact of the sort of a MD based compute specifically. But you talked about TCO before. So there's certain things you mentioned on Prem you sometimes use You mean right. If the thing is hardened, you don't want to necessarily rip and replace it. But if you can accelerate, go to market and you spin up things in the cloud that makes sense. You mentioned customer requirements. So that's just kind of depends. And then the international expansion and scale. So it kind of comes down to those whatever. Four or five factors, right? Tco those other factors that I mentioned kind of the high level benefits, if you could, wouldn't mind summarising for us. Ali. >>Yeah, I think you're spot on there. So it's looking at the overall Decio, right? The cost of serving the overall cloud looking at like go to market in general, like can we leverage public cloud and go to market faster? Obviously, meeting that end customer requirements. We also looked at like international expansion, like any of the customer's data that is stored outside of the US is all on public load for Dropbox. Uh, no plans in the short term to do something different there, Um and then also just looking at, like I mentioned anything in the technology space that is ongoing, that we can leverage features side or the product side for our customers, like at Yale or, uh, VRML. We are going to leverage Public cloud there. >>So of course you know we've we've followed the progression of semiconductor technology for decades. This industry has marched to the cadence of performance improvements. What are the one of the futures hold from a technology roadmap standpoint, particularly as it relates to leveraging AMG EC Two instances, Ali >>got it. So drop boxes in a very unique position where we actually leverage AMG both on Prem and for public le leveraging some of the AWS EC two instances like like you mentioned and epic processors from MDR what we're using today, both on the hybrid infrastructure site and the performance and also the d. C o benefits are real and something that we are observing on a day to day basis. So we are gonna be leveraging that technology even in the future. Um, and the partnership with the MD continues to be very, very strong for Dropbox. >>Well, I really, really appreciate you coming on the cube as part of our coverage is great to have You love to have you back sometime. >>Awesome. Thank you. And also just last thing we wanted to also call out that we are also going to be experimenting with probably Milan that is coming out. Uh, room is the current process is from a m D. That we have been leveraging. And as Milan comes available, we do wanna continue to evaluate it and see how we can fit it in our infrastructure. >>Okay, So their their generations are city based, the all Italian city based. They were going to run out of cities soon. >>God, uh, again, the partnership with both A W s and an M. D is something that I'm very proud of. Execution. Thank you, Dave. >>Great to have you, Ali. And really appreciate you watching. Keep it right there for more action on the cube. Your leader in hybrid tech event coverage. Mhm.

(upbeat music) >> Welcome back to "The Cube's" continuous coverage of AWS re:Invent 2021. We're running one of the industry's most important and largest hybrid tech events of the year with AWS and its ecosystem partners. And of course, special thanks to AMD for supporting this year's editorial coverage at the event. We got two live sets. We had two remote sets, one in Boston and one in Palo Alto. We got more than 100 guests coming on the program, and we're looking deep into the next decade of cloud innovation. We're super excited to be joined by Ali Zafar, who's the senior director of platform strategy and operations at Dropbox. Ali, great to see you. Thanks for coming on. >> Awesome, it's a pleasure to be here with you, Dave. >> So hey, what's your day job like at Dropbox and what's your role? >> Got it. So I actually oversee the global supply chain at Dropbox. Also all of the capacity planning, which entails both our budget and also capacity requirements at Dropbox. And then I also focus on the platform product management side, which is basically building our build versus buy and our overall roadmap for our platform in the long run. >> Great, thank you. So, everybody knows Dropbox, but maybe you could talk a little bit about your business, your mission, and how that's evolved over the past several years. >> Got it. So Dropbox is a global collaboration platform and our mission at Dropbox is to help design a more enlightened way of working. Dropbox has over 700 million registered users and over 550 billion pieces of content. So taking a step back Dave, Dropbox helps with all of your content. Think of this as videos as music, even your tax returns, allows you to organize all of this content and then you can share this content with anybody at any time. You can also take Dropbox to work and actually it makes you even more productive in the workplace. Integrating all of your tools seamlessly, also allowing you to collaborate with all of your teams internally and also externally. >> Thank you, Ali. When Dropbox was founded, the cloud was really nascent, right? So it was early days, and so a lot has changed since the mid last decade. And of course with remote work and hybrid work, that's had to be a real tailwind to your business, but maybe you could explain your cloud and your hybrid cloud strategy. >> Got it, you're spot on Dave. So Dropbox has always been hybrid since its inception in 2007. And when I say hybrid, I mean we have our own on-prem infrastructure and then we also leverage public cloud. Now public cloud still to these days, remains absolutely critical for Dropbox to serve all of its customer needs. And when we talk about the decision between public or private, we think about three or four key things. One is the total cost of ownership, we look at go-to-market, we also look at our customer requirements and the latest technology that's available in the market. And then any international data storage requirements to make the decision of going towards public or private for that specific use case. >> So, I wonder if we could follow up on that. Maybe you could talk about the key business considerations as an SAS storage provider. What are the real drivers and in your business framework? >> Got it. At the end of the day, what really matters for us Dave, is to actually think about our customers and delight them. And what better than to focus on performance, reliability and also security, right? So we want to make sure that the infrastructure that we have today, allows Dropbox to actually solve for this specific use case for our customers. You know, what do they care about? While also doing this in a very efficient, managed way. So to summarize that, looking at performance, looking at liability, looking at scalability, looking at efficiency, and then also compliance. >> So that leads me to my next question, is about the EC2 instances that you use. I know you make heavy use of AMD compute. How did you come to that decision? Was it these factors? Was it all performance? How did you migrate to really enable that capability? How complex was that? >> Got it. So AMD has always been our key strategic partners partners. The partnership is well over four to five years right now, and we've been leveraging them on our on-prem infrastructure for compute. So we've always had AMD in our infrastructure and when the time came where AWS was also leveraging some of the AMD instances, we wanted to see how we can expand the partnership with AMD and AWS and also experiment with these instances. So we looked at some of the tooling updates that were required. We also looked at specific instances, which are either compute optimized and memory optimized instances, and then we actually built our footprint on AMD. And what we saw is that the overall performance improvement and also cost improvements that we got per specific workloads was actually extremely, was overall awesome results for Dropbox and our end customers. And we have been using them ever since. >> What kind of business impact did that make? Did it make a difference to your business that was noticeable? >> On the business side, I think primarily it was more on the TCO side, which is where we got most of the benefits on the cost side. And then also for some of our internal workloads, we also saw benefit to our internal developers that are using some of those workloads. >> So, you guys have become the poster child for hybrid and a lot has been talked about about you all, but I wonder if you could help us understand what part of your infrastructure is going to be better served by public cloud versus doing your own I T on-prem. What are some of the value drivers that are making you push workloads into the public cloud? Help us understand that better and squint through that. >> Got it, I get asked that question a lot. So public cloud in general allows for faster go-to-market. Think about this as like product launches, feature launches, also international expansion. It allows us to scale and then also leveraging some of the existing technologies out there in the market for some of the common workloads. So just taking a step back and thinking about Dropbox, we keep on evaluating also the criteria and then also specific workloads on what makes sense on private or public cloud. And AWS had some instances like S3 RDS and EC2 that when we started looking at, we knew that some of our key services like data platform, some parts of the MLN A I, and even paper platform would make more sense for us to actually leverage some of these in a public cloud for that. >> So what are the characteristics of the workload that are better suited to be an AWS? What's the ideal workload profile? You know, we talk about ideal customer profile. What's the ideal workload profile for the AWS cloud? >> Got it, so the way we think about it, at least, we call it the rule of three at Dropbox. And that means we look at scale first, we look at technology and innovation and what I mean by that is, is there a faster innovation in the public cloud and is that workload common enough that there's already a lot of work going on in public cloud, then there's no reason for us to actually innovate faster than that, we probably can't. And if the scale is not large enough. So when we talk about our storage side, like magic pocket, the scale is large enough where innovating there makes sense and it's better for the end customer. So we would probably go towards private cloud there. But then when we talk about international expansion, when we talk about faster go-to-market or some of the innovation in the MLN A I space, it really makes sense to use public cloud because of all of the advancements that we've seen there. >> So let me circle back to the business benefits and impact of the AMD based compute specifically, but you talked about TCO before, so there's certain things you mentioned on-prem you sometimes use. If the thing's hardened, you don't want to necessarily rip and replace it, but if you can accelerate go-to-market and you spin up things in the cloud, that makes sense. You mentioned customer requirements, so that just kind of depends. And then the international expansion and scale. So it comes down to those whatever four or five factors, right? The TCO, those other factors that I mentioned. Kind of the high level benefits, if you wouldn't mind summarizing for us, Ali. >> Yeah, I think you're spot on there. So there's looking at the overall TCO, right? The cost of serving the overall cloud, looking at go-to-market in general, can we leverage public cloud and go-to-market faster, obviously meeting our end customer requirements. We also looked at international expansion, any of the customer's data that is stored outside of the U S is all on public cloud for Dropbox. No plans in the short term to do something different there. And then also just looking at, like I mentioned, anything in the technology space that is ongoing, that we can leverage feature side or the product side for our customers, like A I or M L, we are going to leverage public cloud there. >> So of course you know, we've followed the progression of semiconductor technology for decades. This industry is marched to the cadence of performance improvements. What do the futures hold from a technology roadmap standpoint, and particularly as it relates to leveraging AMD EC2 instances, Ali? >> Got it. So Dropbox is in a very unique position where we actually leverage AMD both on-prem and for public cloud, leveraging some of the AWS EC2 instances like you mentioned. And EPYC processors from AMD are what we're using today, both on the hybrid infrastructure side and the performance. And also the TCO benefits are real and something that we are observing on a day-to-day basis. So we are going to be leveraging that technology even in the future, and the partnership with the AMD continues to be very, very strong for Dropbox. >> Well, Ali, I really appreciate you coming on "The Cube" as part of our coverage. It's great to have you. Love to have you back sometime. >> Awesome, thank you. And also just last thing we wanted to also call out that we are also going to be experimenting with probably Milan that is coming out. Rome is the current processors from AMD that we have been leveraging and as Milan comes available, we do want to continue to evaluate it and see how we can fit it in our infrastructure. >> So their are generations are city-based. Are they all Italian city-based, or are we going to run out of cities soon? (both laughing) >> Got it. Again, the partnership with both AWS and AMD is something that I'm very proud of. >> Excellent. >> Thank you so much. Thank you, Dave. >> Great to have you Ali, and really appreciate you watching. Keep it right there for more action on "The Cube", your leader in hybrid tech event coverage. (calm music)

(upbeat music) >> Welcome back to "The Cube's" continuous coverage of AWS re:Invent 2021. We're running one of the industry's most important and largest hybrid tech events of the year with AWS and its ecosystem partners. And of course, special thanks to AMD for supporting this year's editorial coverage at the event. We got two live sets. We had two remote sets, one in Boston and one in Palo Alto. We got more than 100 guests coming on the program, and we're looking deep into the next decade of cloud innovation. We're super excited to be joined by Ali Zafar, who's the senior director of platform strategy and operations at Dropbox. Ali, great to see you. Thanks for coming on. >> Awesome, it's a pleasure to be here with you, Dave. >> So hey, what's your day job like at Dropbox and what's your role? >> Got it. So I actually oversee the global supply chain at Dropbox. Also all of the capacity planning, which entails both our budget and also capacity requirements at Dropbox. And then I also focus on the platform product management side, which is basically building our build versus buy and our overall roadmap for our platform in the long run. >> Great, thank you. So, everybody knows Dropbox, but maybe you could talk a little bit about your business, your mission, and how that's evolved over the past several years. >> Got it. So Dropbox is a global collaboration platform and our mission at Dropbox is to help design a more enlightened way of working. Dropbox has over 700 million registered users and over 550 billion pieces of content. So taking a step back Dave, Dropbox helps with all of your content. Think of this as videos as music, even your tax returns, allows you to organize all of this content and then you can share this content with anybody at any time. You can also take Dropbox to work and actually it makes you even more productive in the workplace. Integrating all of your tools seamlessly, also allowing you to collaborate with all of your teams internally and also externally. >> Thank you, Ali. When Dropbox was founded, the cloud was really nascent, right? So it was early days, and so a lot has changed since the mid last decade. And of course with remote work and hybrid work, that's had to be a real tailwind to your business, but maybe you could explain your cloud and your hybrid cloud strategy. >> Got it, you're spot on Dave. So Dropbox has always been hybrid since its inception in 2007. And when I say hybrid, I mean we have our own on-prem infrastructure and then we also leverage public cloud. Now public cloud still to these days, remains absolutely critical for Dropbox to serve all of its customer needs. And when we talk about the decision between public or private, we think about three or four key things. One is the total cost of ownership, we look at go-to-market, we also look at our customer requirements and the latest technology that's available in the market. And then any international data storage requirements to make the decision of going towards public or private for that specific use case. >> So, I wonder if we could follow up on that. Maybe you could talk about the key business considerations as an SAS storage provider. What are the real drivers and in your business framework? >> Got it. At the end of the day, what really matters for us Dave, is to actually think about our customers and delight them. And what better than to focus on performance, reliability and also security, right? So we want to make sure that the infrastructure that we have today, allows Dropbox to actually solve for this specific use case for our customers. You know, what do they care about? While also doing this in a very efficient, managed way. So to summarize that, looking at performance, looking at liability, looking at scalability, looking at efficiency, and then also compliance. >> So that leads me to my next question, is about the EC2 instances that you use. I know you make heavy use of AMD compute. How did you come to that decision? Was it these factors? Was it all performance? How did you migrate to really enable that capability? How complex was that? >> Got it. So AMD has always been our key strategic partners partners. The partnership is well over four to five years right now, and we've been leveraging them on our on-prem infrastructure for compute. So we've always had AMD in our infrastructure and when the time came where AWS was also leveraging some of the AMD instances, we wanted to see how we can expand the partnership with AMD and AWS and also experiment with these instances. So we looked at some of the tooling updates that were required. We also looked at specific instances, which are either compute optimized and memory optimized instances, and then we actually built our footprint on AMD. And what we saw is that the overall performance improvement and also cost improvements that we got per specific workloads was actually extremely, was overall awesome results for Dropbox and our end customers. And we have been using them ever since. >> What kind of business impact did that make? Did it make a difference to your business that was noticeable? >> On the business side, I think primarily it was more on the TCO side, which is where we got most of the benefits on the cost side. And then also for some of our internal workloads, we also saw benefit to our internal developers that are using some of those workloads. >> So, you guys have become the poster child for hybrid and a lot has been talked about about you all, but I wonder if you could help us understand what part of your infrastructure is going to be better served by public cloud versus doing your own I T on-prem. What are some of the value drivers that are making you push workloads into the public cloud? Help us understand that better and squint through that. >> Got it, I get asked that question a lot. So public cloud in general allows for faster go-to-market. Think about this as like product launches, feature launches, also international expansion. It allows us to scale and then also leveraging some of the existing technologies out there in the market for some of the common workloads. So just taking a step back and thinking about Dropbox, we keep on evaluating also the criteria and then also specific workloads on what makes sense on private or public cloud. And AWS had some instances like S3 RDS and EC2 that when we started looking at, we knew that some of our key services like data platform, some parts of the MLN A I, and even paper platform would make more sense for us to actually leverage some of these in a public cloud for that. >> So what are the characteristics of the workload that are better suited to be an AWS? What's the ideal workload profile? You know, we talk about ideal customer profile. What's the ideal workload profile for the AWS cloud? >> Got it, so the way we think about it, at least, we call it the rule of three at Dropbox. And that means we look at scale first, we look at technology and innovation and what I mean by that is, is there a faster innovation in the public cloud and is that workload common enough that there's already a lot of work going on in public cloud, then there's no reason for us to actually innovate faster than that, we probably can't. And if the scale is not large enough. So when we talk about our storage side, like magic pocket, the scale is large enough where innovating there makes sense and it's better for the end customer. So we would probably go towards private cloud there. But then when we talk about international expansion, when we talk about faster go-to-market or some of the innovation in the MLN A I space, it really makes sense to use public cloud because of all of the advancements that we've seen there. >> So let me circle back to the business benefits and impact of the AMD based compute specifically, but you talked about TCO before, so there's certain things you mentioned on-prem you sometimes use. If the thing's hardened, you don't want to necessarily rip and replace it, but if you can accelerate go-to-market and you spin up things in the cloud, that makes sense. You mentioned customer requirements, so that just kind of depends. And then the international expansion and scale. So it comes down to those whatever four or five factors, right? The TCO, those other factors that I mentioned. Kind of the high level benefits, if you wouldn't mind summarizing for us, Ali. >> Yeah, I think you're spot on there. So there's looking at the overall TCO, right? The cost of serving the overall cloud, looking at go-to-market in general, can we leverage public cloud and go-to-market faster, obviously meeting our end customer requirements. We also looked at international expansion, any of the customer's data that is stored outside of the U S is all on public cloud for Dropbox. No plans in the short term to do something different there. And then also just looking at, like I mentioned, anything in the technology space that is ongoing, that we can leverage feature side or the product side for our customers, like A I or M L, we are going to leverage public cloud there. >> So of course you know, we've followed the progression of semiconductor technology for decades. This industry is marched to the cadence of performance improvements. What do the futures hold from a technology roadmap standpoint, and particularly as it relates to leveraging AMD EC2 instances, Ali? >> Got it. So Dropbox is in a very unique position where we actually leverage AMD both on-prem and for public cloud, leveraging some of the AWS EC2 instances like you mentioned. And EPYC processors from AMD are what we're using today, both on the hybrid infrastructure side and the performance. And also the TCO benefits are real and something that we are observing on a day-to-day basis. So we are going to be leveraging that technology even in the future, and the partnership with the AMD continues to be very, very strong for Dropbox. >> Well, Ali, I really appreciate you coming on "The Cube" as part of our coverage. It's great to have you. Love to have you back sometime. >> Awesome, thank you. And also just last thing we wanted to also call out that we are also going to be experimenting with probably Milan that is coming out. Rome is the current processors from AMD that we have been leveraging and as Milan comes available, we do want to continue to evaluate it and see how we can fit it in our infrastructure. >> So their are generations are city-based. Are they all Italian city-based, or are we going to run out of cities soon? (both laughing) >> Got it. Again, the partnership with both AWS and AMD is something that I'm very proud of. >> Excellent. >> Thank you so much. Thank you, Dave. >> Great to have you Ali, and really appreciate you watching. Keep it right there for more action on "The Cube", your leader in hybrid tech event coverage. (calm music)

(upbeat music) >> Welcome to the CUBE Conversation. I'm Lisa Martin. I'm talking next with Sanzio Bassini, the Head of High Performance Computing at Cineca, at DELL technologies customer. Sanzio, welcome to the CUBE. >> Thank you, it's a pleasure, it's a pleasure. >> Likewise, nice to see you. So tell us a little bit about Cineca. This is a large computing center, but a very large Italian nonprofit consortium. Tell us about it. >> Yes, Cineca been founded 50 years ago, from the university systems in Italy. For a statutory mission, which is to support, the scientific discovery, and the industry innovations, using the High Performance Computing and the correlated methodologies like a, Artificial Intelligence, which is one of the, you see the more, in a, in a adopted in those days, but together with the big data processing and and simulation. Yes, we are a consortium, which means that this is a private not-for-profit organizations. Currently, our member of the consortium, almost all the universities in Italy and also all the national agencies for those selected structures. Uh. The main quarter of Cineca is in Bologna, which is in the heart Nation, with the bunch of presence in Milan, in Rome and in Naples, so we are a consultation organization. >> And I also read that you were, are the top 10 out of the top 500 of the world's fastest super computers. That's a pretty big accomplishment. >> Yes. That is a part of our institutional missions, the last 10 to 15 years we have been to say, frequent flyers in the top 10. There been at least two, three systems that have been ranked at the top 10. Apart, the.., whatever would be the meaning of such an advance market, there's a lot of its criticalities. We are well aware. The idea is that we're enabling the scientific discovery, by means of providing the most advanced systems and the co-designing, the most advanced HPC systems to promote and support the, what is the, excellence in science. And that being part of European high-performance computing IT system. That is the case. >> Excellent. Now, talk to me about some of the challenges that Cineca is trying to solve in particular, the Human Brain Project. Talk to us a little bit about that and how you're leveraging high-performance computing to accelerate scientific discovery. >> Um, The Human Brain Project is one of the flagship project that has been co-founded by the European commission and that the participating member states. Is not as another situations that are undertaking, it's definitely a joint collaboration between members states and the European commission. There are two different right now, flagships together with another, that is in progress, which is that the quantum of flagship, the first two flagship abroad that that has been lost. The commission for operation with the participating states has been one on the digraph vein on which also we are participating in directly together with the CNR, is the national business counselor. And the second for which we are core partners of the HPC that is, the Human Brain Project. That, that is a big flagship, one million offer, of newer investment, co-founded by the participating states and that the European commission. The project it's going to set up, in what to do be the, third strategic grant agreement that they will go over the next three years, the good, the complete that the, the whole process. Then we see what is going to happen at Africa. We thought that their would be some others progress offer these big projects. It's project that would combine both the technology issues, like the designing the off high-performance computing systems that meet the requirements of the community and the big challenge, scientific challenges correlated to the physiological functions of the human brain center, including the different farm show survey to do with the behavior of the human brain. A from the pathological point of view, from the physiological point of view, that better understand the could be the way for, for a facing that. Let's say the pathology, some of those are very much correlated with respect to aging, and that it would impact the, the health, the public health systems. Some other that are correlating with what would be the support for the physiological knowledge of the, of the human brains. And finally that they, let me say, technological transfer stuff that represented the knowing off at the physiological, behavior of the human brain. Just to use a sort of metaphor to have happen from the point of view of there computational performance, the human brain is a, a, a, more than Exoscale systems, but with a energy consumption, which is very low, we are talking about some hundreds of Watts. So some hundreds of watts of energy, would provide a an extreme and computational performance. So if would could organized the technology of the high-performance computing in terms of interconnections now we're morphing the computing systems and exploitations of that kind of technologies, in I build a system that it might provide the computational power that would represent a tremendous and tremendous step ahead, in order to facing the big challenges of our base, like energies, personalized medicine, try not to change food for all those kinds of big socioeconomic challenges that we are facing. >> Yes I was reading that besides, sorry Sanzio I was reading that besides the Human Brain Project, there are other projects going on, such as that you mentioned, I'd like to understand how Cineca is working with Dell technologies. You have to translate, as you've mentioned a minute ago, the scientific requirements for discovery into high-performance computing requirements. Talk to me about how you've been doing that with partners like Dell technologies. >> Yes, in particularly in our computing architectures, we had the need to address the capability to facing the data processing involved with backed off the Human Brain Project and general speaking that is backed off the science vendor, that would combine the capability also to provide the cloud access to the system. So by main soft containers technologies and the capability also, to address what would be the creation of a Federation. So Piper problems with people proceeded in a new world. So at the end that the requirements and the terms of reference of the would matter will decline and the terms of a system that would be capable to manage, let's say, in a holistic approach, the data processing, the cloud computing services and the opportunity before for being integrated that in a Federation of HSBC system in Europe's, and with this backed off, that kind of thing, we manage a competitive dialogue procurement processor, I think I the sentence would share together with the different potential technology providers, what would be the visuals and those are the constraints (inaudible) and those other kinds of constraints like, I don't want to say, I mean, environmental kind of constraints and uh, sharing with this back of the technology provider what would it be the vision for this solution, in a very, let's say hard, the competitive dialogue, and at the end, results in a sort of, I don't want to say Darwinian processes, okay. So I mean, the survivors in terms of the different technology providers being Dell that shown the characteristics of the solution that it will be more, let's say compliant. And at the same time are flexible with respect of the combinations of very different constraints and requirements that has been the, the process that has been the outcomes of such a process. >> I like that you mentioned that Darwinian survival of the fittest and that Dell technologies has been, it sounds like a pretty flexible partner because you've got so many different needs and scientific needs to meet for different researchers. Talk to me about how you mentioned that this is a multi-national effort. How does Cineca serve and work with teams not only in Italy, but in other countries and from other institutes? >> Definitely the volume commitment that together with the, European member states is that by means of scientific merits and the peer review process, roughly speaking the arc of the production capacity, would be shared at the European level. That it's a commitment that, that there's been, that there's been a shared of that together with France, Germany, Spain, and, and with the London. So, I mean, our, half of our production capacity, it's a share of that at the European level, where also of course the Italian scientist can apply in the participates, but in a sort of offer emulations and the advanced competition for addressing what it would be the excellence in science. The remaining 50% of our production capacity is for, for the national community and, somehow to prepare and support the Italian community to be competitive on the worldwide scenario on the European and international scenario, uh that setting up would lead also to the agreement at the international level, with respect of some of the options that, that are promoted the progress in a US and in Japan also. So from this point of view, that mean that in some cases also the, access that it would come from researchers the best collaborations and the sharing options with the US researchers their or Japanese researchers in an open space. >> Open space for, it sounds like the Human Brain Project, which the HPC is powering, which has been around since 2013 is really facilitating global collaboration. Talk to me about some of the results that the high-performance computing environment has helped the Human Brain Project to achieve so far. >> The main outcomes that it will be consolidated in the next phase that will be need the by rural SPC that is the Jared undertaking um entities, that has been created for consolidating and for progressing the high-performance computing ecosystem in Europe. It represented by the Federations of high-performance computing systems at European level, there is a, a, an option that, that has been encapsulated and the elaborated inside the human brain flagship project which is called the FEHIPCSE that stand for Federation of a High-Performance Computing System in Europe. That uh provide the open service based on the two concepts on one, one is the sharing of the Heidi at a European level, so it means that the, the high demand of the users or researchers more properly. It's unique and Universal at the European level. That didn't mean better the same, we see identity management, education management with the open, and the access to the Cineca system, to the SARS system in France, to the unique system in, uh Germany to the, Diocese system in a Switzerland, to the Morocco System in a Spain. That is the part related to what will be the federated, the ID management, the others, et cetera, related to what will be the Federation off the data access. So from the point of view, again, the scientific community, mostly the community of Human Brain Project, but that will be open at other domains and other community, make sure that data in a seamless mode after European language, from the technological point of view, or let's say from the infrastructure point of view, very strong up, from the scientific point of view, uh what they think they may not, will be the most of the options is being supported by Cineca has to do with the two specific target. One is the elaboration of the data that are provided by the lands. The laws are a laboratory facility in that Florence. That is one of the four parts, and from the bottom view of the provisions of the data that is for the scattering, the, the data that would come from the mouse brains, that are use for, for (inaudible) And then the second part is for the Mayor scale studies of the cortex of the of the human brain, and that got add-on by a couple of groups that are believing that action from a European level their group of the National Researcher Counsel the CNR, that are the two main outcome on which we are in some out reference high-performance computing facilities for supporting that kind of research. Then their is in some situations they combinations of the performance a, capability of the Federation systems for addressing what will be the simulations of the overall human brain would take a lot of performance challenge simulation with bacteria that they would happen combining that they SPC facility as at European level. >> Right! So I was reading there's a case study by the way, on Cynic that Dell technologies has published. And some of the results you talked about, those that the HPC is facilitating research and results on epilepsy, spinal cord injury, brain prostheses for the blind, as well as new insights into autism. So incredibly important work that you're doing here for the Human Brain Project. One last question Sanzio, for you, what advice would you give to your peers who might be in similar situations that need to, to build and deploy and maintain high-performance computing environments? Where should they start? >> (coughs laughs) I think that at, at a certain point, that specific know how would became sort of a know how that is been, I mean, accumulated and then by some facilities and institutions around the world. There are the, the federal labs in US, the main nation model centers in Europe, the big facilities in Japan. And of course the, the big university facilities in China that are becoming, how do you say, evident and our progressively occupied increasing the space, that to say that that is somehow it, that, that, that the, those institutions would continues collaborate and sharing that there are periods I would expect off what to do, be the top level systems. Then there is a continuous sharing of uh knowledge, the experience best practices with respect off, let's say the technologies transfers towards productions and services and boosterism. Where the situation is big parenta, in the sense that, their are focused what it would be, uh the integration of the high-performance computing technology into their production workflow. And from the point of view, there is the sharing of the experience in order to provide the, a sort of, let's say, spreads and amplifications of the opportunity for supporting innovation. That is part of are solution means, in a Italy but it also, eh, er sort of um, see objective, that is addressed by the European options er supported by the European commission. I think that that sort of (inaudible) supply that in US, the, that will be coming there, sort of you see the max practice for the technology transfer to support the innovation. >> Excellent, that sharing and that knowledge transfer and collaboration. It seems to be absolutely fundamental and the environment that you've built, facilitates that. Sanzio thank you so much for sharing with us, what Cineca is doing and the great research that's going on there, and across a lot of disciplines, we appreciate you joining the program today. Thank you. >> Thank you, it's been a pleasure, thank you very much for the opportunity. >> Likewise, for Sanzio Bassini. I'm Lisa Martin. You're watching this cube conversation. (calming music)

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante by our estimates aws will generate around nine billion dollars in storage revenue this year and is now the second largest supplier of enterprise storage behind dell we believe aws storage revenue will hit 11 billion in 2022 and continue to outpace on-prem storage growth by more than a thousand basis points for the next three to four years at its third annual storage day event aws signaled a continued drive to think differently about data storage and transform the way customers migrate manage and add value to their data over the next decade hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll give you a brief overview of what we learned at aws's storage day share our assessment of the big announcement of the day a deal with netapp to run ontap natively in the cloud as a managed service and we'll share some new data on how we see the market evolving with aws executive perspectives on its strategy how it thinks about hybrid and where it fits into the emerging data mesh conversation let's start with a snapshot of the announcements made at storage day now as with most aws events this one had a number of announcements and introduced them at a pace that was predictably fast and oftentimes hard to follow here's a quick list of most of them with some comments on each the big big news is the announcement with netapp netapp and aws have engineered a solution which ports the rich netapp stack onto aws and will be delivered as a fully managed service this is a big deal because previously customers either had they had to make a trade-off they had a settle for cloud-based file service with less functionality than you could get with netapp on-prem or it had to lose the agility and elasticity of the cloud and the whole pay-by-the-drink model now customers can get access to a fully functional netapp stack with services like data reduction snaps clones the full multi-protocol support replication all the services ontap delivers in the cloud as a managed service through the aws console our estimate is that 80 of the data on-prem is stored in file format and that's not the revenue but that's the data and we all know about s3 object storage but the biggest market from a capacity standpoint is file storage you know this announcement reminds us quite a bit of the vmware cloud on aws deal but applied to storage netapp's aunt anthony lai told me dave this is bigger and we're going to come back to that in a moment aws announced s3 multi-region access points it's a service that optimizes storage performance it takes into account latency network congestion and the location of data copies to deliver data via the best route to ensure our best performance this is something we've talked about for quite some time using metadata to optimize that that access aws also announced improvements to s3 tiering where it will no longer charge for small objects of less than 128k so for example customers won't be charged for most metadata and other smaller objects remember aws years ago hired a bunch of emc engineers and those guys built a lot of tiering functionality into their boxes and we'll come back to that later in this episode aws also announced backup and monitoring tools to ensure backups are in compliance with regulations and corporate edicts this frankly is table stakes and was was overdue in my view aws also made a number of other announcements that have been well covered in the press around block storage and simplified data migration tools so we'll leave that to your perusal through other outlets i want to come back to the big picture on the market dynamics now as we've reported in previous breaking analysis segments aws storage revenue is on a path to 10 billion dollars we reported this last year this chart puts the market in context it shows our estimates for worldwide enterprise storage revenue in the calendar year 2021. this data is meant to include all storage revenue including primary secondary and archival storage and related maintenance services dell is the leader in the 60 billion market with aws now hot on its tail with 15 of the market in terms of the way we've cut it now in the pre-cloud days customers would tell us our storage strategy is the following we buy emc for block and netapp for file keeping it simple while remnants of this past habit continue the market is definitely changing as you can see here the companies highlighted in red represent the growing hyperscaler presence and you can see in the pi on the right they now account for around 25 percent of the market and they're growing much much faster than the on-prem vendors well over that thousand basis points when you combine them all a couple of other things to note in the data we're excluding kindrel from ibm's figures that's ibm spinout but including our estimates of storage software for example spectrums protect that is sold as part of the ibm cloud but not reported in ibm's income statement by the way pre-kindred spin ibm storage business we believe would approach the size of netapp's business now in the yellow we've highlighted the portion of hyper-converged that comprises storage this includes vmware nutanix cisco and others vmware and nutanix are the largest hci players but in total the storage piece of that market is less than two billion okay so the way to look at this market is changing traditional on-prem is vying for budgets with cloud storage services which are rapidly gaining presence in the market and we're seeing the on-prem piece evolve of course into as a service models with hpe's green lake dell's apex and other on-prem cloud-like models now let's come back to the netapp aws deal netapp as we know is the gold standard for file services they've been the market leader for a long long time and other than pure which is considerably smaller netapp is the one company that consistently was able to beat emc in the market emc developed its its nas business and developed on its own nasdaq and it bought isilon to compete with netapp with isilon's excellent global file system but generally netapp remains the best file storage company today now emerging disruptors like cumulo vast weka they would take issue with this statement and rightly so as they have really promising technology but netapp remains the king of the file hill you can't debate that now netapp however has had some serious headwinds as the largest independent storage player as seen in this etr chart the data shows a nine-year view of netapp's presence in the etr survey presence is referred to by etr as market share it's not traditional market share it measures the pervasiveness of responses in the etr survey over a thousand customers each quarter so the percentage of mentions essentially that netapp is getting and you can see well netapp remains a leader it has had a difficult time expanding its tam and it's become frankly less relevant in the eye in the grand scheme and the grand eyes of it buyers the company hit headwinds when it began migrating its base to ontap 8 and was late riding a number of new waves including flash but generally it is recovered from those headwinds and it's really now focused on the cloud opportunity opportunity as evidenced by this deal with aws now as i said earlier netapp evp anthony lai told me that this deal is bigger than vmware cloud on aws like me you may be wondering how can that be vmware is the leader in the data center it has half a million customers its deal with aws has been a tremendous success as seen in this etr chart the data here shows spending momentum or net score from when vmware cloud on aws was picked up in the etr surveys with a meaningful n which today is approaching 100 responses in the survey the yellow line is there for context it's vmware's overall business so repeat it buyers who responded vmware versus specifically vmware cloud on aws so you see vmware overall has a huge presence in the survey more than 600 n the red line is vmware cloud on aws and that red dotted line you see that that's that's my magic 40 mark anything above that line we consider elevated net score or spending velocity and while we saw some deceleration earlier this year in that line that top line for vmware cloud vmware cloud and aws has been consistently showing well in the survey well above that 40 percent line so could this netapp deal be bigger than vmware cloud on aws well probably not in our view but we like the strategy of netapp going cloud native on aws and aws's commitment to deliver this as a managed service now where could get interesting is across clouds in other words if netapp can take a page out of snowflake and build an abstraction layer that hides the underlying complexity of not only the aws cloud but also gcp and azure where you log into the netapp cloud netapp data cloud if you will just go ahead and steal steal it from snowflake and then netapp optimizes your on-prem your aws your azure and or your gcp file storage we see that as a winning strategy that could dramatically expand netapp's tam politically it may not sit well with aws but so what netapp has to go multi-cloud to expand that tam when the vmware deal was announced many people felt it was a one-way street where all the benefit would eventually accrue to aws in reality this has certainly been a near-term winner for aws and vmware and of course importantly vmware and aws join customers now longer term it's going to clearly be a win for aws because it gets access to vmware's customer base but we also think it will serve vmware well because it gives the company a clear and concise cloud strategy especially if it can go across clouds and eventually get to the edge so with this netapp aws deal will it be as big probably not in our view but it is big netapp in our view just leapfrogged the competition because of the deep engineering commitment aws has made this isn't a marketplace deal it's a native managed service and we think that's pretty huge okay we're going to close with a few thoughts on aws storage strategy and some other thoughts on hybrid talk about capturing mission critical workloads and where aws fits in the overall data mesh conversation which is one of our favorite topics first let's talk about aws's storage strategy overall as with other services aws approach is to give builders access to tools at a very granular level that means it does mean a lot of apis and access to primitives that are essentially building blocks while this may require greater developer skills it also allows aws to get to market quickly and add functionality faster than the competition enterprises however where they will pay up for solutions so this leaves some nice white space for partners and also competitors and especially the on-prem folks but let's hear from an aws executive i spoke to milan thompson bucheveck an aws vp on the cube and asked her to describe aws's storage strategy here's what she said play the clip we are dynamically and constantly evolving our aws storage services based on what the application and the customer want that is fundamentally what we do every day we talked a little bit about those deployments that are happening right now dave that is something that idea of constant dynamic evolution just can't be replicated by on-premises where you buy a box and it sits in your data center for three or more years and what's unique about us among the cloud services is again that perspective of the 15 years where we are building applications in ways that are unique because we have more customers and we have more customers doing more things so you know i i've said this before uh it's all about speed of innovation dave time and change wait for no one and if you're a business and you're trying to transform your business and base it on a set of technologies that change rapidly you have to use aws services i mean if you look at some of the launches that we talk about today and you think about s3's multi-region access points that's a fundamental change for customers that want to store copies of their data in any number of different regions and get a 60 performance improvement by leveraging the technology that we've built up over over time the the ability for us to route to intelligently router requests across our network that and fsx for netapp ontap nobody else has these capabilities today and it's because we are at the forefront of talking to different customers and that dynamic evolution of storage that's the core of our strategy so as you hear and can see by milan's statements how these guys think outside the box mentality at the end of the day customers want rock solid storage that's dirt cheap and lightning fast they always have and they always will but what i'm hearing from aws is they think about delivering these capabilities in the broader context of an application or a business think deeper business integration not the traditional suppliers don't think about that as well but the services mentality the cloud services mentality is different than dropping off a box at a loading dock turning it over to a professional services organization and then moving on to the next deal now i also had a chance to speak with wayne dusso he's another aws vp in the storage group wayne do so is a long time tech athlete for years he was responsible for building storage arrays at emc aws as i said hired a bunch of emcs years ago and those guys did a lot of tiered storage so i asked wayne what's the difference in mentality when you're building boxes versus cloud services here's what he said you have physical constraints you have to worry about the physical resources on that device for the life of that device which is years think about what changes in three or five years think about the last two years alone and what's changed can you imagine having being constrained by only uh having boxes available to you during this last two years versus having the cloud and being able to expand or contract based on your business needs that would be really tough right and it has been tough and that's why we've seen customers from every industry accelerate uh their use of the cloud during these last two years so i get that so what's your mindset when you're building storage services and data services so so each of the surfaces that we have in object block file movement services data services each of them provides very specific customer value in each are deeply integrated with the rest of aws so that when you need object services you start using them the integrations come along with you when if you're using traditional block we talked about ebs io2 block express when using file just the example alone today with ontap you know you get to use what you need when you need it and the way that you're used to using it without any concern so so the big difference is no constraints in the box but lots of opportunities to blend in with other services now all that said there are cases where the box is gonna win because of locality and and physics and latency issues you know particularly where latency is king that's where a box is gonna be advantageous and we'll come back to that in a bit okay but what about hybrid how does aws think about hybrid and on-prem here's my take and then let's hear from milan again the cloud is expanding it's moving out to the edge and aws looks at the data center as just another edge node and it's bringing its infrastructure as code mentality to that edge and of course to data centers so if aws is truly customer centric which we believe it is it will naturally have to accommodate on-prem use cases and it is doing just that here's how milan thompson-bucheveck explained how aws is thinking about hybrid roll the clip for us dave it always comes back to what the customer is asking for and we were talking to customers and they were talking about their edge and what they wanted to do with it we said how are we going to help and so if i just take s3 for outposts as an example or ebs and outposts you know we have customers like morningstar and morningstar wants outposts because they are using it as a step in their journey to being on the cloud if you take a customer like first adudabi bank they're using outposts because they need data residency for their compliance requirements and then we have other customers that are using outposts to help like dish networks as an example to place the storage as close as account to the applications for low latency all of those are customer driven requirements for their architecture for us dave we think in the fullness of time every customer and all applications are going to be on the cloud because it makes sense and those businesses need that speed of innovation but when we build things like our announcement today of fxs for netapp ontap we build them because customers asked us to help them with their journey to the cloud just like we built s3 and evs for outposts for the same reason so look this is a case where the box or the appliance wins latency matters as we said and aws gets that this is where matt baker of dell is right it's not a zero-sum game this is especially accurate as it pertains to the cloud versus on-prem discussion but a budget dollar is a budget dollar and the dollar can't go to two places so the battle will come down to who has the best solution the best relationships and who can deliver the most rock solid storage at the lowest cost and highest performance let's take a look at mission critical workloads for a second we're seeing aws go after these it's doing a database it's doing it with block storage we're talking about oracle sap microsoft sql server db2 that kind of stuff high volume oltp transactions mission critical work now there's no doubt that aws is picking up a lot of low hanging fruit with business critical workloads but the really hard to move work isn't going without a fight frankly it's not going that fast aws and mace has made some improvements to block storage to remove some of the challenges related but generally we see this is a very long road ahead for aws and other cloud suppliers oracle is the king of mission critical work along with ibm mainframes and those infrastructures generally it's not easy to move to the cloud it's too risky it's too expensive and the business case oftentimes isn't there because very frequently you have to freeze applications to do so what generally what people are doing is they're building an abstraction layer over that putting that abstraction layer maybe in the cloud building new apps that can connect to the back end and the into the cloud but that back end is largely cemented and fossilized look it's all in the definition no doubt there's plenty of mission critical work that is going to move but just really depends on how you define it even aws struggles to move its most critical transaction systems off of oracle but we'll continue to keep an open mind there it's just that today we define the most mission-critical workloads as we define them we don't see a lot of movement to the hyperscale clouds and we're going to close with some thoughts on data mesh so one of our favorite topics we've written extensively about this and interviewed and are collaborating with jamaa dagani who has coined the term and we've announced a media collaboration with the data mesh community and believe it's a strong direction for the industry so we wanted to understand how aws thinks about data mesh and where it fits in the conversation here's what milan had to say about that play the clip we have customers today that are taking the data mesh architectures and implementing them with aws services and dave i want to go back to the start of amazon when amazon first began we grew because the amazon technologies were built in microservices fundamentally a data match is about separation or abstraction of what individual components do and so if i look at data mesh really you're talking about two things you're talking about separating the data storage and the characteristics of data from the data services that interact and operate on that storage and with data mesh it's all about making sure that the businesses the decentralized business model can work with that data now our aws customers are putting their storage in a centralized place because it's easier to track it's easier to view compliance and it's easier to predict growth and control costs but we started with building blocks and we deliberately built our storage services separate from our data services so we have data services like lake formation and glue we have a number of these data services that our customers are using to build that customized data mesh on top of that centralized storage so really it's about at the end of the day speed it's about innovation it's about making sure that you can decentralize and separate your data services from your storage so businesses can go faster so it's very true that aws has customers that are implementing data mess data mesh data mess data mesh can be a data mess if you don't do it right jpmorgan chase is a firm that is doing that we've we've covered that they've got a great video out there check out the breaking analysis archive you'll see that hellofresh has also initiated a data mesh architecture in the cloud and several others are starting to pop up i think the point is the issues and challenges around data mesh are more organizational and process related and less focused on the technology platform look data by its very nature is decentralized so when mylan talks about customers building on centralized storage that's a logical view of the storage but not necessarily physically centralized it may be in a in a hybrid device it may be a copy that lives outside of that same physical location this is an important point as jpmorgan chase pointed out the data mesh must accommodate data products and services that are in the cloud and also on-prem it's got to be inclusive the data mesh looks at the data store as a node on the data mesh it shouldn't be confined by the technology whether it's a data warehouse a data hub a data mart or an s3 bucket so i would say this while people think of the cloud as a centralized walled garden and in many respects it is that very same cloud is expanding into a massively distributed architecture and that fits with the data mesh architectural model as i say the big challenges of data mesh are less technical and more cultural and we're super excited to see how data mesh plays out over time and we're really excited to be part of part of the the community and a media partner of the data mesh community okay that's it for now remember i publish each week on wikibon.com and siliconangle.com and these episodes they're all available as podcasts all you do is search for breaking analysis podcasts you can always connect on twitter i'm at d vellante or email me at david.velante at siliconangle.com i appreciate the comments you guys make on linkedin and don't forget to check out etr.plus for all the survey action this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

>>Mhm Yes. Hello. Welcome back to the cubes coverage of red hat summit 2021 Virtual. I'm john for your host of the cube we're here with Roberto Pellegrini, head of architecture and digital ai services from stam he's remote promoting in from Milan Italy Roberto. Great to see you. Thanks for joining us on the cube. >>Great to see you too john. Thanks for having me here. >>Love the virtual events. We can bring people in from all around the world. I love the virtual. I mean it's one of the trade us for not being in person as we can still get you in. Thanks for coming on. Before we get started. I want to dig into the digital architecture of what you guys are doing. Very compelling in a hybrid cloud. It's got you got all the things going on which I like. But before we start, can you provide a short overview of Snam who are your customers? What is your company's focus and what's your role there? >>Sure. So his name is one of the worst leading energy infrastructure operators. And we basically beat the energy infrastructures and offering the way the services our mission is to guide the evolution of the energy sector and leave the energy transition to a low carbon future. And as you can see in our last investment plan, we declared our net zero-carbon objective to bridge by 2040. This is why we basically are investing a lot in uh, technology in renovating our technology stack in order to provide our business line with the most innovative sustainable energy network, thanks to which we are already guaranteeing stable supplies to europe of natural gas. >>Love your title. Love the fact you've got the ai piece in there. Um, what about specifically is your role? What do you oversee? >>I'm responsible for architecture, digital and artificial intelligence services. That basically means that uh, with with my team and my extended team of the digital technology department are designing the entire technology stack for islam. And I'm specifically focusing more on developing intelligent than usable services for our business lines >>awesome. You guys were there, it's nam have transformed a lot the stack. That's cool. When you get into that, you redesign your applications map. Right? So it's really edge to cloud now. Edge up to the cloud. What were the business drivers and the objectives to reach that goal? Because that's really a great use case. I mean you got the edge to deal with intelligent, you got industrial, global business drivers and objectives. >>Yeah. Our main business drivers has always been to to increase the effectiveness of our processes and business lines so bear support the decision of our internal line of business. And we soon discover that we need a more data in order to do that. And we structure very extensive of your program. But those data provide information about internal states of our assets because they're coming from the census and we thought what about the environment in which our assets are located. So uh following up on that we integrated data coming from remote sensing technologies. So think about drones and satellites. Major data and we soon discover that we needed to renew and extend our technology stack from edge to cloud as you said. And to be the scalable that the platform in order to process this new level of uh data this way we think we will be able to enter the new volume of data that we predict. It will be 100 times what we currently manage and efficiently use AI and machine learning to the riding side from this new scale and complexity. So we're talking about big data >>repairs. I gotta ask you could you take a minute to describe your transformation journey you guys went through and how red hat helped you guys execute the digital transformation? >>Yeah we basically started working in 2018. We read up to set up our 12 grade in a snap. We basically needed to decide what to scale what you lift and shift what we factor in order to move our application to a modern architectural stack. And right up to us with this uh we use open ship for our container orchestration platform and from this we're developing our new application act. Then in 2019 we decided to accelerate the moving of our application workloads. We started moving 10-20 of our work clothes on open shift. And since then most of our new software project Islam, a club native and developed on open shifts. We're still in the process of leveraging modern architecture. So microservices based and using our continual construction platform and other software as a service platform in order to complete the modernization of our application that And we are targeting 2023 2024 to complete the entire process but as you know, is an ever changing landscape. So you basically never complete such a task in some way. >>Do you see red hat technology helping stem in its ecosystem for energy efficiency and aiming for low carbon emissions? >>I think that open ship provide provide us with the right level of flexibility and agility to move at the speed of our new businesses. That's one way to look at the quest and the other one. Uh huh. I think it would be in terms of energy efficiency and the carbon footprint that our application workloads generate. And I think that uh in that in that respect it could happen in the mid to long term, probably so it will be in proportion to the workloads. We will be able to re factor as purely reactive so as non blocking apps. This probably in fact for the same business service could improve the effective resource consumption, so indirectly saving energy and CO two, >>you know, I love this conversation and I know you're in Italy and wish we could be in person but I'm glad to get you on because you guys are kind of an example of the main theme at the conference this year, which is an edge, you know, intelligent edge and IOT, but you know, I O T has been around for a while and we've talked about it before, but now with the cloud and connecting to the cloud, that's a huge topic here at red hat summit 2021 you guys are well versed on the call O. T. Technology operational technologies and what's interesting is kubernetes and containerized orchestration all help operators, operations people. So you have this ot tight integration where the operational technology, old school technology people and the stack and the people in the disciplines are meeting the old I. T. And creating a new thing. So I have to ask you, what are these, what's that world like what are some of the use cases that you're working on and you're planning to deploy? >>Yeah. Yeah. Yeah exactly. It's exactly like that name has a long ot history as you said. So right now we have a complex brownfield uh, situation for our edges and gateways on the field. There are various technical components that resigns on the field. You must consider that Italian network, the Italian Transportation Network has more than 34,000 km of pipeline and differently sides of plants across the country. So we have several already several use cases currently running on our data centers that could benefit we think from distributed processing at the edge. Think about for example physical security. So just to give you an example privacy preserving local video processing for anomaly detection done at the edge, it's much more effective in our opinion. Poor hierarchical processing for data intensive task that involves field data so that you can process the data coming from the field at different level and take to the central data center only was needed. And we're also working on the usual problem there is with with the Widow Tea with the operation of the countries that is standardization. So we have many exogenous components and communication protocol there and you know without a proper rieti stack gathering and normalizing the data for a higher level process could become cumbersome. So security is also is also a relevant topic because it is usually preserve and the physical and natural layer and we we think that we can introduce variety pre main improvements about this. We're expanding the level of cybersec to the food technologists act, bringing modern internet security standard to the edge. We're pushing continue realization to the edge, being able to orchestrate our work clothes from data centers to the cloud. And we think that we this will provide us with a high level of flexibility and a better exploitation of the geographical distribution of our data. And last but not least we're standardizing our gateways and edges and this will help us streamline the message of the transfer conversion and normalization uh of the data we will receive from the field >>awesome. I gotta, I gotta ask for such a great job on the edge. I think that's a great vision. Uh building insecurity, it's important having that edge intelligent is really well done. Congratulations. Love the vision. I gotta ask you, what's your future plans for um Snam technology journey as a whole. What's your vision? What's your next step? >>So well, what we would like to focus on uh in the coming years is how to best leverage the average cloud environment we currently set up. So right now we have an average cloud environment with the data center and one cloud tenant and having our poor clothes running on open ship would make it easier for us to leverage the offering of different club providers and of course to best exploit what we currently have on our tenants. Second one is find the best way to leverage IOT. So as I said before, our focus in the coming years will be to complete our IOT foundation, rolling out our edges are gateways and put our new unified opposition system to work. And this will provide the computational backbone of our intelligent investments. And finally, uh and this is a less objective that is will be built on top of the other two. We must find different ways and export different ways to leverage data and artificial intelligence. So we need to exploit our data uh in order to generate insight for our business lines. Need to the scale of our new data streams, artificial intelligence machine learning, we think will be ubiquitous in our applications right now. We're already using it but not at the scale uh that the new data streams uh well we'll need and most of the algorithms are working on data that are apart from legacy system and scare the system. So they are specifically created for each project. We're about to begin an exciting data journey where everything will reside on a unified data platform and our data scientists, our data analyst in the business lines will be able to derive value from them >>awesome. You know, you guys are great customer use case. I love the real operational impact. I talked with a lot of other practitioners and end user enterprises and I get the same question and I got the statement. They say actually security needs to be built in, but the challenges and where they want to, what they want to do. And I want to get your thoughts on this. If you don't mind commenting, they all say, I want to run cloud native applications, cloud native applications from my data center to the cloud and then out to the edge and with this as a distributed platform, one operation set, whether it's O T I T I want to make that, that's my endgame. In the short term, I get there fast. So I gotta ask you for those people that want that is open shift, a good solution for that. In your opinion, >>we, we of course think it is uh, it is part of our IOT foundation, uh, is not the only technology components, but is one of the, one of the most relevant and it is absolutely happiness in uh, enabling the possibility of orchestrating more clothes uh, from the cloud to the edge. And we will be able to give you more information about that as soon as uh, we will release the first Distribue work clothes within 2021. So I'll be happy to to answer any any questions from our peers or or other colleagues from other industries. >>You guys have thousands and thousands of sites. This is the classic industrial edge implementation, closely monitoring just monitoring the pipes. I mean, you gotta monitoring the system just physically. I mean, this is like a, just a physical thing. So now as you have technology, you guys have to monitor and get that early detection of any gas leaks, this is critical to your business. Um how is that changing? How is that environment changing with technology is more automated? What's your vision? How are you guys looking at that? >>Well, we, we surely are trying to move along to two main drivers. The first is um, unification and standardization of how we monitor all these distributed technology stock. This is very important because even for the simplest use case, you're now dealing with distributed application and this is a entirely different game to what we are used to basically. And and um uh the the other rather the other relevant thing is how can we get the best from um the machines we put on the field. So in other in other in other terms, how can we standardize how we connect to the machines we have on the field and how much intelligence we need to put there and how to test it. And in order to do that, we're thinking about um building a digital twin of our assets that will enable us to being able to test and to end before getting to the real thing on field. How will it work? What are the security vulnerabilities, potential security vulnerability and other aspects uh of the technology infrastructure and the data infrastructure? And we think this is very important because in some way uh in order to provide the acceleration and the scale that we uh are going to provide uh to, to our company, we need to be sure well in advance that what we designed will work in practice without getting to the field. We would like to get into the field where everything is already tested, >>repair too. Great to have you on the cube. Great to see you. Thanks for coming in from Milan Italy. Um cute virtual. It's one of the benefits and hope to see you in person soon at the next event. But great use case, love your environment, love how you're looking at that platform is a distributed platform and bringing that O T T together data center to the cloud to the edge. That's a really relevant use case and architecture. So congratulations. >>Thank you very much, john and I hope to see you too very soon. Alive >>when I'm in Italy, we're gonna come by and do a site visit and uh, see each other coming on. I appreciate it. Thank you. >>Absolutely. >>Okay. Cube coverage for Red Hat Summit 2021. I'm John for your host. Thanks for watching. Mhm. Mhm. Mhm.

>>Welcome lisa and thank you for being here with us today >>Antonio It's wonderful to be here with you as always. And congratulations on your launch. Very, very exciting for you. >>Well, thank you lisa and uh, we love this partnership and especially our friendship, which has been very special for me for many, many years that we have worked together, but I wanted to have a conversation with you today and obviously digital transformation is a key topic. So we know the next wave for digital transformation is here being driven by massive amounts of data and increasingly distributed world and a new set of data intensive workloads. So how do you see a lot of optimization playing a role in addressing these new requirements? >>Yeah, absolutely Antonio. And I think, you know, if you look at the depth of our partnership over the last four or five years, it's really about bringing the best to our customers. And the truth is we're in this compute mega cycle right now. So it's amazing. Um you know, when I know when you talk to customers, when we talk to customers, they all need to do more and frankly, computers becoming quite specialized. So whether, you know, you're talking about large enterprises, um, or you're talking about research institutions trying to get to the next phase of compute so that workload optimization that we're able to do with our processors, your system design and then working closely with our software partners is really the next wave of this, this compute cycle. >>So thanks lisa you talk about mega cycle. So, I want to make sure we take a moment to celebrate The launch of our new generation 10 plus compute products with the latest announcement. Hp now has the broadest a nd server portfolio in the industry spanning from the edge to exa scale. How important is this partnership and the portfolio for our customers? >>Well, um Antonio I'm so excited, first of all, congratulations on your 19 world records with Milan and gen 10 plus. It really is building on sort of our, this is our third generation of partnership with Epic. And you know, you were with me right at the very beginning actually, if you recall you joined us in Austin for our first launch of Epic, you know, four years ago and I think what we've created now is just an incredible portfolio that really does go across. You know, all of the verticals that are required. We've always talked about, how do we customize and make things easier for our customers to use together? And so very excited about your portfolio, very excited about our partnership and more importantly, what we can do for our joint customers. >>It's amazing to see 19 world records. I think I'm really proud of the work our joint team do every generation, raising the bar. And that's where, you know, we, we think we have a shared goal of ensuring our customers get the solution, the services they need any way they want it. And one way we are addressing that need is by offering what we call as a service delivered to HP Green Lake. So let me ask a question, What feedback are you hearing from your customers with respect to choice, meaning consuming as a service? This new solutions? >>Yeah, great point. I think, first of all, you know, HP Green Lake is very, very impressive. So, congratulations to really having that solution. And I think we're hearing the same thing from customers and you know, the truth is, um, the computer infrastructure is getting more complex and everyone wants to be able to deploy, sort of the right compute at the right price point um you know, in in terms of also accelerating um time to deployment with the right security with the right quality. And I think these as a service offerings are going to become more and more important um as we go forward um in the compute capabilities and you know, Green Lake is a leadership product offering and we're very very pleased and honored to be part of it. >>Okay. Yeah. We feel uh lisa we are ahead of the competition and um you know, you think about some of our competitors is not coming with their own offerings, but I think the ability to drive joint innovation is what really differentiates us and that's why we value the partnership and what we have been doing together on given the customer's choice. Finally, you know, I know you and I above incredibly excited about the joint work with you and with the U. S. Department of Energy, the Oak Ridge National Laboratory we think about large data sets and you know and the complexity of the analytics we're running but we both are going to deliver the world first exa scale system. Which is remarkable to me. So what this milestone means to you and what type of impact do you think it will >>make? Yes Antonio I think our work with Oak Ridge National Labs and HP is just really pushing the envelope on what can be done with computing. And if you think about the science that we're going to be able to enable with the first extra scale machine, I would say there's a tremendous amount of innovation that has already gone in to the machine and we're so excited about delivering it together with HP. And you know we also think that the supercomputing technology that we're developing at this broad scale will end up being very, very important for enterprise computer as well. And so it's really an opportunity to kind of take that bleeding edge and really deploy it over the next few years. So super excited about it. I think you and I have a lot to do over the next few months here, but it's an example of the great partnership and and how much we're able to do when we put our teams together, um, to really create that innovation. >>I couldn't agree more. I mean, this is an incredible milestone for for us, for our industry and honestly for the country in many ways. And we have many, many people working 24 by seven to deliver against this mission. And it's going to change the future of compute no question about it. Um, and then honestly put it to work where we needed the most to advance life science to find cures, to improve the way people live and work, lisa, thank you again for joining us today and thank you more most importantly for the incredible partnership and, and the friendship. I really enjoy working with you and your team and together, I think we can change this industry once again. So thanks for your time today. >>Thank you so much Antonio and congratulations again to you and the entire HPI team for just a fantastic portfolio launch. >>Thank you.