 I want to welcome the special cube conversation with Matt Burr, who's the general manager of FlashBlade at Pure Storage. Matt, how you doing? Good to see you. I'm doing great. Nice to see you again. Nice to see you again, Dave. Yeah, you know, welcome back. We're going to be broadcasting this. Is it going to accelerate? You guys get big news. Of course, FlashBlade S, we're going to dig into it. The famous FlashBlade now has a new letter attached to it. Tell us what it is, what it's all about. You know, it's easy to say it's just the latest and greatest version of the FlashBlade. But obviously, it's a lot more than that. We've had a lot of success with FlashBlade kind of across the board, in particular with Meta and their research supercluster, which is one of the largest AI superclusters in the world. But it's not enough to just build on the thing that you had, right? So with the FlashBlade S, we've increased modularity. We've done things like building co-design software and hardware and leveraging that into something that increases or actually doubles density, performance, power efficiency. On top of that, you can scale independently, storage, networking, and compute, which is a pretty big deal because it gives you more flexibility. It gives you a little more granularity around performance or capacity, depending on which direction you want to go. And we believe that kind of the end of this is fundamentally the, I guess the way to put it is sort of the highest performance and capacity optimization unstructured data platform on the market today without the need for kind of an inexpensive data tier of cash or expected data caching tier. So we're pretty excited about what we've ended up with here. Yeah, so I think sometimes people forget about how much core engineering Meta does, Facebook, you go on Facebook and play around and post things, but yeah, they're back in cloud. It's just amazing. So talk a little bit more about the problem targets for FlashBlade, I mean, it's pretty wide scope and we're going to get into that, but what's the core of that? Yeah, we've talked about that extensively in the past, the use cases kind of generally remain the same. I know we'll probably explore this a little bit more deeply, but really what we're talking about here is performance and scalability. We have written essentially an unlimited metadata software level, which gives us the ability to expand, we're already starting to think about computing an exabyte scale, okay? So, the problem that the customer has, hey, I've got a green field object environment or I've got a file environment and my 10K and 7,500 RPM disk is just spiraling out of control in my environment. It's an environmental problem. It's a management problem. We've effectively simplified the process of bringing together highly performant, very large multi-pedabyte to eventually exabyte scale unstructured data systems. So people are obviously trying to inject machine intelligence, AI, ML into applications, bring data into applications, bringing those worlds closer together, analytics is obviously exploding. You see some other things happening in the news ransomware, protection and the like. Where does FlashBlade fit in terms of FlashBlade S in terms of some of these new use cases? All those things, we're only going wider and broader. So, we've talked in the past about having a horizontal approach to this market. The unstructured data market has often had vertical specificity. You can see successful infrastructure companies in oil and gas that may not play in media and entertainment, where you see successful companies that play in media and entertainment but don't play well in financial services, for example. We were sort of playing the long game here with this and we're focused on bringing an all QLC architecture that combines our traditional kind of pure DFM with the software that is now, I guess, seven years hardened from the original FlashBlade system. And so, when we look at customers in three categories, right? We have customers that sort of fit into a very traditional, more than three, but they're kind of big bucketized this way. Customers that fit into kind of this EDA, HPC space. Then you have that sort of data protection, which I believe kind of ransomware falls under that as well, the world has changed, right? So, customers want their data back faster. Rapid Restore is a real thing, right? We have customers that come to us and say, anybody can back up my data, but if I want to get something back fast, and I mean in less than a week or a couple of days, what do I do? So we can solve that problem. And then as you sort of accurately pointed out where you started, there is the AI ML side of things where the NVIDIA relationship that we have, right? DGXs are a pretty powerful weapon in that market in solving those problems, but they're not cheap. And keeping those DGXs running all the time requires an extremely efficient underpinning of a flash system, and we believe we have that market as well. It's interesting when Pure was first coming out as a startup, you obviously had some cool new tech, but your stack wasn't as hard. Now you've got seven years under your belt. The last time you were on theCUBE, we talked about some of the things that you guys were doing differently. We talked about UFFO, Unified Fast, Bile, and Object. How does this new product, FlashBlade, as compared to some previous generations of FlashBlade in terms of solving unstructured data and some of these other trends that we've been talking about? Yeah, I touched on this a little bit earlier, but I want to go a little bit deeper on this concept of modularity. So for those that are familiar with Pure Storage, we have what's called the Evergreen Storage Program. It's not as much a program as it is in engineering philosophy. The belief that everything we build should be modular in nature so that we can have essentially a chassis that has 100% modular components inside of it, such that we can upgrade all of those features non-disruptively from one version to the next. You should think about that as, if you have an iPhone, when you go get a new iPhone, what do you do with your old iPhone? You either throw it away or you sell it. Well, imagine if your iPhone just got newer and better each time you renewed whatever it is, two year or three year subscription with Apple. That's effectively what we have as a core philosophy, core operating engineering philosophy within Pure, that is now a completely full and robust program with this instantiation of the Flash Blade S. And so kind of what that means is, for a customer, I'm future-proofed for X number of years, knowing that we have a run rate of being able to keep customers on the Flash RA side from the FA 400 all the way through the Flash RA X and XL, which is about a 10-year time span. So that then and now itself sort of starts to play into customers that have concerns around ESG, right? Last time I checked, power, space and cooling still mattered in data center. So although I have people that tell me all the time that power, space and cooling doesn't matter anymore, but I know at the end of the day, most customers seem to say that it does. You're not throwing away refrigerator-sized pieces of equipment that once held spinning disk. You know, something that's the size of a microwave that's populated with DFMs with all QLC Flash that you can actually upgrade over time. So if you wanna scale more performance, we can do that through adding CPU. If you wanna scale more capacity, we can do that through adding more NAND. And we're in control of those parameters because we're building our own DFMs, our direct fabric modules and our own storage nodes, if you will. So instead of relying on the consumer packaging of an SSD, we're upgrading our own stuff and growing it as we can. So again, on the ESG side, I think for many customers going into the next decade, it's going to be a huge deal. Yeah, interesting comments now. I mean, I don't know if you guys invented it, but you certainly popularized the idea of no forklift upgrades and sort of set the industry, you know, on its head when you guys really drove that evergreen strategy. And, you know, kind of on that note, you guys talk about simplicity. I remember last Accelerate, you know, I went deep with cause on your philosophy of keeping things simple, keeping things uncomplicated. You guys talk about using better science to do that. And a lot of talk these days about outcomes. How does FlashBlade support those claims? And what do you guys mean by better science? Yeah, you know, better science is kind of a funny term. It was an internal term that I was on a sales call actually, and the customer said, well, you know, I understand the difference between these two, but could you tell me how we got there? And I was a little stumped on the answer and I just said, well, I think we have better scientists. And that kind of morphed into better science. You know, a good example of that is our metadata architecture, right? So, you know, our scalable metadata allows us to avoid having that, you know, that cashing tier that other architectures have to rely on in order to anticipate, you know, which files are gonna need to be in read cache and read misses become very expensive. Now, you know, a good follow up question there and not to do your job, but it's the question that I always get is, well, when you're designing your own hardware and your own software, you know, what's the real material advantage of that? Well, the real material advantage of that is that you're in control of the combination and the interaction of those two things. You don't give up, you know, sort of the general purpose nature, if you will, of the performance characteristics that come along with things like commodity. You get a very specific performance profile that's tailored to the software that's being married to it. Now, in some instances, you could say, well, okay, does that really matter? Well, when you start talking about 20, 40, 50, 100, 500, you know, petabyte data sets, every percentage matters. And so those individual percentages equate to space savings, they equate to power and cooling savings. You know, we believe that we're going to have industry best dollars per watt. We're going to have industry best, you know, kind of dollar per RU. So, you know, really the whole kind of game here is around scale. I mean, look, there's clearly places for the pure software to find. I mean, when Cloud first came out, everybody said, oh, build the cloud and commodity. They don't build custom hardware. Now you see all the hyperscalers building, you know, custom software, custom hardware and software integration, custom silicon. So, co-innovation between hardware and software, it seems pretty, that's important, if not more important than ever, especially for some of these new workloads. Who knows what the edge is going to bring? What's the downside of not having that philosophy in your view? Is it just, you can't scale to the degree that you want, you can't support the new workloads, performance, what should customers be thinking about there? I think the downside plays in two ways. You know, first is, you know, kind of the future and at scale as I alluded to earlier around, you know, cost and, you know, just savings over time, right? So if you're using a, you know, a commodity SSD, there's packaging around that SSD that is wasteful, both in terms of, both wasteful in the environmental sense and wasteful in the sort of computing performance sense. So that's, you know, kind of one thing. And then on the second side is it's easier for us to control the controllables around reliability when you can eliminate the number of things that actually sit in that workflow. And by workflow, I mean, when a right is acknowledged from a host that gets down to the media, the more control you have over that, the more reliability you have over that piece. Yeah, you know, and we talked about ESG earlier, I know you guys, I'm going to talk a little bit about more news from Accelerate with NVIDIA. I mean, you've certainly heard Jensen talk about the wasted CPU cycles in the data center. I mean, I think he's forecast that's, you know, 25 to 30% of the cycles are wasted on doing things like storage offload or certainly networking and security. So now it's sort of confirms your ESG thought we can do things more efficiently, but as it relates to NVIDIA and some of the news around areas, what is the AIRI, what's that stand for? What's the high level overview of AIRI? So the AIRI has been really successful for both us and NVIDIA, it's a really great partnership. You know, we're appreciative of the partnership in fact, Tony, back in a will be, you know, speaking here at Accelerate. So, you know, really, really looking forward to that. You know, look, there's a couple of ways to look at this. And, you know, I take the macro view on this. I know that there's a equally as good of a micro example, but I think the macro is really kind of where it's at, which is we don't have data center space anymore. Right? There's only so many data centers we can build. There's only so much power we can create. You know, we're going to reach a point in time where municipalities are going to struggle against the businesses that are in their municipalities for power. And now you're essentially bidding, you know, big corporations against people who have an electric bill. And that's only going to last so long, you know, who doesn't win in that the big corporation doesn't win in that because elected officials will have to find a way to serve the people so they can get power. No matter how skewed we think that may be, that is the reality. And so, as we look at this transition, that first decade of disk to flash transition was really, you know, in the block world. The second decade, which it's really fortunate to have a multi-decade company, of course, the second decade of riding that way from disk to flash is about improving space power efficiency and density. And we sort of reached that, you know, it's a long way of getting to the point about a video where, you know, these AI clusters are extremely powerful things. And, you know, they're only going to get bigger, right? They're not going to get smaller. It's not like anybody out there is saying, oh, it's a bad or, you know, this isn't going to be something that's going to yield any results or outcomes. They yield tremendous outcomes in healthcare. They use tremendous outcomes in financial services. They use, you know, tremendous outcome in cancer research, right? These are not things that, you know, we as a society are going to give up. And in fact, we're going to want to invest more on them. But they come at a cost and they come, one of the resources that is required is power. And so, when you look at what we've done in particular with NVIDIA, you found something that is extremely power efficient that meets the needs of kind of going back to that macro view of both the community and the business. That's a win-win. You know, and you're right, it's not going to get smaller. It's just going to continue to be in momentum. But it could get increasingly distributed. And, you know, you think about, I talked about the edge earlier, you think about AI inferencing at the edge. I think about Bitcoin mining. It's very distributed, but it consumes a lot of power. And so, we're not exactly sure what the next level architecture is, but we do know that science is going to be behind it. Talk a little bit more about your NVIDIA relationship because I think you guys were the first, I might be wrong about this, but I think you were the first storage company to announce a partnership with NVIDIA several years ago, probably four years ago. How is this new solution with ARRI-S building on that partnership? What can we expect with NVIDIA going forward? Yeah, you know, I think what you can expect to see is putting the foot on the gas on kind of where we've been with NVIDIA. So, you know, as I mentioned earlier, you know, Meta is, you know, by some measurements, the world's largest research supercluster. They're a huge NVIDIA customer and, you know, built on pure infrastructure. So we see, you know, kind of those types of reference architectures, not that everyone's going to have a meta scale reference architecture, but the base principles of what they're solving for are the base principles of what we're going to begin to see in the enterprise. I know that begin sounds like a strange word because, you know, there's already a big business in DGX. There's already a sizable business in performance on structured data, but those are only going to get, you know, exponentially bigger from here. So, you know, kind of what we see is a deepening and a strengthening of the relationship and opportunity for us to talk, you know, jointly to customers that are going to be building these big facilities and big data centers for, you know, these types of compute related problems and talking about efficiency, right? DGX's are much more efficient and flash blades are much more efficient. It's a great pairing. You know, I mean, you're definitely, I mean, a lot of the AI today is modeling in the cloud. You're seeing HPC and data just slam together in all kinds of new use cases and these types of partnerships are the only way that we're going to solve, you know, the future problems and go after these future opportunities. I'll give you a last word. You got to be excited with Accelerate. You know, what should people be looking for, you know, at Accelerate and beyond? You know, look, I am really excited. This is my, going on my 12th year of Pure Storage, which has to be seven or eight Accelerates whenever we started this thing. So it's a great time of the year. I guess maybe take a couple off because of COVID, but you know, I love reconnecting in particular with partners and customers and just hearing, you know, kind of what they have to say. And this is kind of a nice one. This is four years or five years worth of work for my team who, you know, candidly, I'm extremely proud of for, you know, choosing to take on some of the solutions that they, or excuse me, some of the problems that they chose to take on and find solutions for. So, you know, as Accelerate rolls around, I think we have some pretty interesting evolutions of the Evergreen program coming to be announced. You know, we have some exciting announcements in the other product arenas as well, but, you know, the big one for this event is FlashBlade. And, you know, I think that we will see, look, no one's gonna completely control this transition from disc to flash, right? That's a macro trend. But there are these points in time where individual companies can sort of accelerate the pace at which it's happening. And that happens through cost, it happens through performance. My personal belief is this will be one of the largest points of those types of acceleration in this transformation from disc to flash and unstructured data. This is such a leap. You know, this is essentially the equivalent of us going from the 400 series on the block side to the X for those of you familiar with the FlashArray line. So it's a huge, huge leap for us. I think it's a huge leap for the market. And, you know, look, I think you should be proud of the company you work for. And I am, you know, immensely proud of what we've created here. And I think one of the things that is a good joy in life is to be able to talk to customers about things you care about. I've always told people my whole life, inefficiency is the bane of my existence. And I think we've rooted out a ton of inefficiency with this product and looking forward to going and reclaiming a bunch of data from data center space and power without sacrificing any performance. Well, congratulations on making it into the second decade. And I'm looking forward to the orange in the third decade, Matt Burr. Thanks so much for coming back in theCUBE. It's good to see you. Thanks, Dave. Nice to see you as well. We appreciate it. All right. And thank you for watching. This is Dave Vellante from theCUBE and we'll see you next time.