(Intro Music) >> Hello everyone and welcome to this special cube conversation with Matt Burr who is 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, Dave. >> Yeah. You know, welcome back. We're going to be broadcasting this is at accelerate. You guys get big news. Of course, FlashBlade S we're going to dig into it. The famous FlashBlade now has new letter attached to it. Tell us what it is, what it's all about. >> (laughing) >> 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 super cluster, which is one of the largest AI super clusters 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 it actually doubles density, performance, power efficiency. On top of that, you can scale independently, storage, networking, and compute, which is pretty big deal because it gives you more flexibility, 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 expensive data tier of cash or expected data cash and 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, their backend cloud is 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 you know, 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 of, Hey, I've got a Greenfield, object environment, or I've got a file environment and my 10 K and 7,500 RPM disc is just spiraling out of control in my environment. It's an environmental problem. It's a management problem, we have effectively, simplified the process of bringing together highly performant, very large multi petabyte 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, read somewhere, protection and the like, where does FlashBlade fit in terms of FlashBlade S in some 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 having a horizontal approach to this market. The unstructured data market has often had vertical specificity. You could see successful infrastructure companies in oil and gas that may not play median entertainment, where you see, successful companies that play in media entertainment, but don't play well in financial services, for example. We're sort of playing the long game here with this and we're focused on, bringing an all Q L C 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 and we look at kind of customers in three categories, right, we have customers that sort of fit into a very traditional, more than three, but kind of make 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 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 Invidia relationship that we have, right. DGX is are a pretty powerful weapon in that market and solving those problems. But they're not cheap. And keeping those DGX's 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 you know, your stack wasn't as hard. And now you've got seven years under your belt. The last time you were on the cube, we talked about some of the things that you guys were doing differently. We talked about UFFO, unified fast file and object. How does this new product, FlashBlade S, compare 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 an engineering philosophy. The belief that everything we build should be modular in nature so that we can have essentially a chassi that has an a 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 you know, 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 your, 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 FlashBlade 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 array side from the FA 400 all the way through the flash array X and Excel, which is about a 10 year time span. So, that then, and of 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, power space clearly 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 size pieces of equipment that once held spinning disc, something that's a size of a microwave that's populated with DFMs with all LC flash that you can actually upgrade over time. So if you want to scale more performance, we can do that through adding CPU. If you want to scale more capacity, we can do that through adding more And we're in control of those parameters because we're building our own DFM, our direct fabric modules on our own storage notes, 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, Matt. I mean, I don't know if you guys invented it, but you certainly popularize the idea of, no Fort lift upgrades and sort of set the industry on its head when you guys really drove that evergreen strategy and kind of on that note, you guys talk about simplicity. I remember last accelerate 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 you a lot of talk these days about outcomes. How does FlashBlade S 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, 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, a good example of that is our Metadata architecture, right? So our scalable Metadata allows us to avoid having that cashing tier, that other architectures have to rely on in order to anticipate, which files are going to need to be in read cash and read misses become very expensive. Now, a good follow up question there, 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, what's the real material advantage of that? Well, the real material advantage of that is that you are in control of the combination and the interaction of those two things you don't give up the 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 to talking about 20, 40, 50, 100, 500, petabyte data sets, every percentage matters. And so those individual percentages equate to space savings. They equate to power and cooling savings. We believe that we're going to have industry best dollars per lot. We're going to have industry best, kind of dollar PRU. So really the whole kind of game here is a round scale. >> Yeah. I mean, look, there's clearly places for the pure software defined. And then when cloud first came out, everybody said, oh, build the cloud and commodity, they don't build custom art. Now you see all the hyper scalers building custom software, custom hardware and software integration, custom Silicon. So co-innovation between hardware and software. It seems pretty as 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 or performance? What should customers be thinking about there? >> I think the downside plays in two ways. First is kind of the future and at scale, as I alluded to earlier around cost and 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- It's wasteful in the environmental sense and wasteful in the sort of computing performance sense. So that's kind of one thing. On the second side, 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 and it gets down to the media, the more control you have over that, the more reliability you have over that piece. >> Yeah. I know. And we talked about ESG earlier. I know you guys, I'm going to talk a little bit about more news from accelerate within Invidia. You've certainly heard Jensen talk about the wasted CPU cycles in the data center. I think he's forecasted, 25 to 30% of the cycles are wasted on doing things like storage offload, or certainly networking and security. So now it sort of confirms your ESG thought, we can do things more efficiently, but as it relates to Invidia and some of the news around AIRI's, what is the AI RI? What's that stand for? What's the high level overview of AIRI. >> So the AIRI has been really successful for both us and Invidia. It's a really great partnership we're appreciative of the partnership. In fact, Tony pack day will be speaking here at accelerate. So, really looking forward to that, Look, there's a couple ways to look at this and 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. 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. We are 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 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 that 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 disc to flash transition was really in the block world. The second decade, which it's really fortunate to have a multi decade company, of course. But the second decade of riding that wave from disk to flash is about improving space, power, efficiency, and density. And we sort of reach that, it's a long way of getting to the point about iMedia where these AI clusters are extremely powerful things. And they're only going to get bigger, right? They're not going to get smaller. It's not like anybody out there saying, oh, it's a Thad, or, this isn't going to be something that's going to yield any results or outcomes. They yield tremendous outcomes in healthcare. They yield tremendous outcomes in financial services. They use tremendous outcome in cancer research, right? These are not things that 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 one of the resources that is required is power. And so when you look at what we've done in particular with Invidia. 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. It's a win-win. >> You know and you're right. It's not going to get smaller. It's just going to continue to in momentum, but it could get increasingly distributed. And 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 Invidia 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 Invidia several years ago, probably four years ago. How is this new solution with a AIRI slash S building on that partnership? What can we expect with Invidia going forward? >> Yeah. I think what you can expect to see is putting the foot on the gas on kind of where we've been with Invidia. So, as I mentioned earlier Meta is by some measurements, the world's largest research super cluster, they're a huge Invidia customer and built on pure infrastructure. So we see kind of those types of well 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 there's already a big business in DGX. There's already a sizable business in performance, unstructured data. But those are only going to get exponentially bigger from here. So kind of what we see is a deepening and a strengthening of the of the relationship and opportunity for us to talk, jointly to customers that are going to be building these big facilities and big data centers for these types of compute related problems and talking about efficiency, right? DGX are much more efficient and Flash Blades are much more efficient. It's a great pairing. >> Yeah. I mean you're definitely, a lot of AI today is modeling in the cloud, seeing HPC and data just slam together all kinds of new use cases. And these types of partnerships are the only way that we're going to solve the future problems and go after these future opportunities. I'll give you a last word you got to be excited with accelerate, what should people be looking for, add accelerate and beyond. >> You know, look, I am really excited. This is going on my 12th year at Pure Storage, which has to be seven or eight accelerates whenever we started this thing. So it's a great time of the year, maybe take a couple off because of because of COVID, but I love reconnecting in particular with partners and customers and just hearing 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 candidly I'm extremely proud of for 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 as accelerate roles around, I think we have some pretty interesting evolutions of the evergreen program coming to be announced. We have some exciting announcements in the other product arenas as well, but the big one for this event is FlashBlade. And I think that we will see. Look, no one's going to completely control this transition from disc to flash, right? That's a 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. This is essentially the equivalent of us going from the 400 series on the block side to the X, for those that you're familiar with the flash array lines. So it's a huge, huge leap for us. I think it's a huge leap for the market. And look, I think you should be proud of the company you work for. And I am 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 ton of inefficiency with this product and looking forward to going and reclaiming a bunch of 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 and the third decade, Matt Burr, thanks so much for coming back in the cubes. 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 for the Cube. And we'll see you next time. (outro music)

(upbeat music) >> Hi I'm Peter Burris. Welcome to another CUBE Conversation from our wonderful studios in beautiful Palo Alto, CA. Today we are going to be talking about new architectures, new disciplines required to really make possible the opportunities associated with digital business. And to do that, we've got Neil Vachharajani, who is the Technical Director at Pure Storage. Neil welcome to theCUBE. >> Thank you for having me, Peter. >> So Neil, we have spent a fair amount of time within Wikibon and within the CUBE community, talking a lot about what is digital business. So, give me a second, run something by ya, tell me if you agree. So we think that there is a difference between business and digital business. And specifically, we think that difference is, a digital business uses data assets differently, than a business does. Walmart beat Sears 'cause it used data differently. AWS is putting the pressure on Walmart, because it uses data differently. Or Amazon is putting the pressure on Walmart, because it uses data differently. So, that is at the centerpiece of a lot of these digital transformations. How are you using data to re-institutionalize your work, realign your resources, reestablish a new engagement model with your marketplace. Would you agree with that? >> Yeah, absolutely agree with that and I think a lot of it has to do with the volume of data, where the data is coming from. If you look at traditional business, it really was about just putting into computers what we used to do on paper. And digital business today I think is about generating huge volumes of data by really looking at every interaction we have no matter how small or how big. >> So, putting telemetry on as many things. So, IoT for machines, mobile for human beings, but it used to be as you said. It was a process, known process, unknown technology world for a long time. And now, these are data driven processes. We're actually using data to describe what their next best action should be, what the recommendation should be. >> That's right. >> So, as we think about this, you know, businesses has been around for a long time. There's this notion of evidence based management, which is the idea that we use data differently, from the boardroom all the way down to the drivers. How does a business start to bring forward the discipline required to really make possible this data driven world. >> Well you know I think the first thing is, to really recognize why does this new paradigm shift changes things? And I think in the old world, if you looked at a piece of data, you actually could articulate all the way from the boardroom down to the stockroom every use of the data. And that meant that you could build a lot of siloed applications and that wasn't a big deal. You got your money's worth out of the data. So for example, recording transactions in store number 17. >> That's right. But in the new world, you actually don't know what the value of the data is ahead of time. Right. You're, in some sense, you're trying to capture a lot of data and then use technology to correlate it with things, mix and mash, mix and match, mash it up, and then drive business decisions that you didn't even know you were making a decision a few weeks ago and that means that you can't really lock up your data, you can't constrain it, because that's going to limit your possibilities. It's going to limit your ROI on that data. >> Yeah, we like to say that data as an asset is different from all other assets, because it is inherently sharable, reusable, it doesn't follow the laws of scarcity. And so, in many respects what the IT organization has had to do is find new ways to privatize that data through things like security, but as you're saying, they don't want to introduce technologies that artificially constrain derivative and future uses of that data. >> And I think, that's where, really the big architectural shift is happening in the data center. Because if you look traditionally, we have siloed the data and it wasn't like this intentional thing that we want to put it into a silo. But that's how we packaged our applications and that's how we deployed our applications. And now, we need a new discipline inside the data center, that makes the data available, lets people put policies on it. Like security policies. But then also makes it available for the innovators all throughout the company to get access to that data. You know, we're trying to crystallize this whole philosophy into something we refer to as the data-centric architecture. Where data is at the center, people have access to the data, and then there's just applications all around it that are all hitting this common pool of data and doing different things, driving new business processes. >> Now, you're talking not about a physical pool of data, but rather a logical pool of data. Data is stil going to be very distributed, right? >> Well you know, data gets generated in a distributed way, data is very large. I think it would be a bit naive to be able to point to one rack and one data center and say all your data center is going to be right here in this one rack. >> Or in one cloud. >> Or in one cloud for that matter. But just from a philosophical perspective, you do want to pull your data out of anything that is, like you said a minute ago, that's constraining it. So, I think, one really good example of this is when we went, quote unquote, web scale, we saw a lot of applications move into direct attached storage, to dive deep into a technology. And that was great if you wanted to only come in the front door and access the data through the application that was managing that das. But, if you wanted to do anything else, you were kind of stuck. >> So as to summarize this point, we're moving from a world in which data is a place to data is a service. >> That's right. >> Have I got that right? >> That's absolutely right. I mean, the way I like to think about it is that data and storage need to really be different things and storage's job is to give you access to the data. Storage in its own right, you know, doesn't solve a business problem. It's the data that solves the business problem. Storage is the vehicle that gets you there. And so I think it's pretty exciting that there's new technologies that are coming out, or that honestly are here, that are enabling that. Things like Flash and NVMe, and you know, it's futures. >> Well let's talk about that because what, the observation that I made to clients for quite some time is that if you go back, disk, was a great technology for persisting data. So again, Store number 17, transaction at a certain time. It's already occurred, we have to record it. So, we record it, we persisted on disk. Now what we are trying to do is we're utilizing technologies that are inherently structured to deliver data so that we can have the data be very distributed, but still look at it from a logical standpoint. And have that data be delivered to a lot of applications whether that is local and as long as we don't undermine basic physics perhaps further away. But even more importantly, deliver it to different roles, different, same day of being delivered to developers, same day to being different, delivered to a new application. What are some of those core technologies that are going to be necessary to do this? You mentioned NVMe, let's start there. >> Yeah, if I just back up a little bit right, that in some sense, even that recording the data workflow that you talked about, we made disk work. But it was actually a pretty challenging media and so we put in a lot of optimizations and things in place, because we said, we know the usage pattern. And if we know the usage pattern, we know how to organize our data. And so as a step one, like the transformation that I think is, in pretty full swing these days was moving from disk to flash. And that was a huge transformation, because it meant that random access to the data was just as performant as this carefully crafted sequential access. That meant you could start accepting unknown workloads into your applications, but you were still stuck behind this very serial, very antiquated SCSI protocol. And NVMe is now bringing a lot more parallels, to play. And that's going to help us to drive things like just simple, plain old data center. Stuff like density, and performance density, and power, and that kind of thing. So, that's sort of step one in terms of the technology that you can package all of this stuff in a pretty dense package and put petabytes of storage with enough I/O to actually access that data. If that's the key that you can have pedabytes, but you can only have one I out for each gig, well you're not going to get a lot out of that data. >> So, just to stop right there, and that leads to a world, in which as long as your disciplined and architected, you do not have to know what workloads are going to access that data near term. >> Well, you know, that's only step one, right. >> Right. >> Because the other challenge is that very few people access storage directly, right. We hide this behind databases, and we hide this behind a whole bunch of other technologies. Now, those technologies might have have their own limitations in place. But we have a lot or really rich things we can do at the storage level to present the same data out multiple frontends. And so the simplest idea is, we don't have one copy of a database, we often will have the transactional database that's using, recording those transactions, but then we'll have an analytics copy of the database and now we need to keep the two of those things in sync. And this is where the discipline and the architecture really comes into place. And we kind of have a lot of that figured out for things like relational databases and best practices there. But in the meantime, the world also moved over to the new world of Node-SQL databases, Queue's, Kafka. Things of that nature. And those, brought direct attached storage as the best practice. And so I think where the discipline comes in and where some of the new technologies that we're talking about right now are: How do you bring those old disciplines that we figured out, on let's say the relational world, how you bring that to bear on the new technologies that are meeting the scale requirements that we have today? >> Well one of the more important workloads that are going to require scale is, for example, AI. So, how are we going to organize some of these technologies, add them to these new disciplines, to be able to make some of these AI workloads run really, really fast. >> You know, I think a lot of this really comes down to pulling the storage out and putting it into it's own tier. And so, Pure Storage has an offering which is called AIRI, which is packaging DGX and Video DGX boxes with FlashBlades. And we say, hey you don't need a whole bunch of direct attached storage which is siloing your data, you can go put it into this common shared pool. And I think that on, you know, the other side the house, our FlashArray business is doing something really similar with NVMe, the FlashArray/X is essentially commoditizing NVMe. It's saying, everybody has access to this high performance density. And looking into the future with technologies like NVMe over Fabric, what we're really saying is your apps that used to use direct attached storage, there's no reason why they can't go to a sand based architecture that offers rich data services and not compromise one iota on latency. >> Or access or any other number of activities as well. So we've got NVMe, NVMe over Fabric, Flash, new approaches for thinking about packaging some of these things. Are there any other technologies that you envision on the horizon that are going to be really important to customers and that Pure is going to take advantage of. >> Yeah, you know, I really think that the other thing is once you collect all this stuff, you need a way to tame the beast. You need a way to deploy your applications. You need a way to catalog everything. And honestly, things like Kubernetes and container orchestration is becoming this platform where you deploy all of this stuff. And some of the assumptions that are baked into that, really go back and tie in nicely with those other technologies. In particular, they assume that I can schedule this compute wherever I want and I have access to the data. So in that way of having a fabric if you will between your compute and your data is essential. And it's just another reason why siloing things off into particular units of compute is just really the architecture of the past. And the architecture going forward is going to be to logically centralize. And maybe put some smarts at that other layer, saying, hey if this data is in the public cloud, let me schedule up there. But if this data is in my data center, let me schedule the compute down there. But then not having to worry about the micro decisions about, does it have to be in this rack or, you know, or on this particular physical node. All your data is accessible. >> But increasingly, we're going to do things that move the compute both physically as well as logically closer to the data. >> You know, 100%. Right. But it's at what scale? That you really want to get the data center right. Your compute should be running in the correct data center. >> Or the center of data right? >> Or the center of data, right, you know. Get it in the right spot, but then you don't want to have to worry about all the other micro constraints. You don't want, you know, if you look on the networking side of the world, Leaf Spy networks are all about say, hey look they're really is a uniform fabric for networking. We're trying to do the same thing in storage and just say, look, the storage is so performant, there's no reason to silo. You can run your compute where ever you want. If you've got a good networking fabric and you've got a good storage fabric, the end of the day, all your data is accessible, to whatever new application you envision. And you just, there's no reason why you have to lock it up. You mentioned security before. You know, you should absolutely be able to orchestrate things like taking a snapshot of your data, putting it through, masking, or whatever anonymization you need to make it safely accessible to new applications and innovators inside of your company to drive that digital business. >> Yes, and we like to talk about moving from a world that is focused on infrastructure, taking cost out, making it static, by removing all uncertainty to a world where we've no workloads, and elastic capacity, or elastic scale to a plastic world. Where plastic, using of the physicals, you know, the physic sense is unknown workload, unknown scale. And just making sure that we have the option to use data any way we want as much as possible in the future. >> And I think that that's why you see the rise of service catalogs and self service coming up in IT, it's that plasticity that you have the brightest minds in your company trying to figure out what to do, and you don't want to have infrastructure be this bottleneck that's causing everything to go slower. Or for people to say no. You just always want to say, yes. And that's where I think it's always exciting to see, see these technologies, NVMe, come out and say, we've now got the performance to say yes. NVMe over Fabric to say there's no compromise over latency. And then honestly, having this stuff packaged in things like FlashArray/X, where the CIO or the CFO, doesn't complain about breaking the bank as well. Because now these technologies are the status quo. They're the standard. There's no premium for them. And if anyone is trying to charge you that premium, you should really, you know, ask them why. This is the new architecture, this should be, this should be, what, the only thing you offer >> Right. >> In some sense >> Yeah, we're bringing all these new technologies into economic envelope that IT has to be in for business today. >> That's right, and you know, you look at something like flash memory, right. It's not a new technology. I remember in college having a flash card to put into like a digital camera in the early days of digital cameras. But for it to make it into the data center, the thing that was critical was that economic aspect of it. So it's not just about being on the bleeding edge of technology, but it's packaging that in a way that's actually palatable for the entire C-Suite to consume inside your organization. >> And I remember my disk pack that I carried around in college from the PDP system that we had to use. (laughter) Alright, Neil Vachharajani, Technical Director of Pure Storage talking about the relationship between new technologies, data centeric architectures, and digital business. Thanks very much for being on theCUBE. >> Thanks so much Peter. >> And once again, I'm Peter Burris, you've been participating in another CUBE conversation. 'Til we talk again. (upbeat music)

>> Announcer: Live from the Bill Graham Auditorium in San Francisco, it's The Cube, covering Pure Storage Accelerate 2018. Brought to you by Pure Storage. (upbeat techno music) >> Welcome back to The Cube, we are live at Pure Accelerate 2018 at the Bill Graham Civic Auditorium in San Francisco. I'm Lisa Martin, moonlighting as Prince today, joined by Dave Vellante, moonlighting as The Who. Should we call you Roger? >> Yeah, Roger. Keith. (all chuckling) I have a moon bat. (laughing) >> It's a very cool concert venue, in case you don't know that. We are joined by a couple of guests, Cube alumnae, welcoming them back to The Cube. Rajiev Rajavasireddy, the VP of Product Management and Solutions at Pure Storage and Siva Sivakumar, the Senior Director of Data Center Solutions at Cisco. Gentlemen, welcome back. >> Thank you. >> Thank you. >> Rajiev: Happy to be here. >> So talk to us about, you know, lots of announcements this morning, Cisco and Pure have been partners for a long time. What's the current status of the Cisco-Pure partnership? What are some of the things that excite you about where you are in this partnership today? >> You want to take that, Siva, or you want me to take it? >> Sure, sure. I think if you look back at what brought us together, obviously both of us are looking at the market transitions and some of the ways that customers were adopting technologies from our site. The converged infrastructure is truly how the partnership started. We literally saw that the customers wanted simplification, wanted much more of a cloud-like experience. They wanted to see infrastructure come together in a much more easier fashion. That we bring the IT, make it easier for them, and we started, and of course, the best of breed technology on both sides, being a Flash leader from their side, networking and computer leader on our side, we truly felt the partnership brought the best value out of both of us. So it's a journey that started that way and we look back now and we say that this is absolutely going great and the best is yet to come. >> So from my side, basically Pure had started what we now call FlashStack, a converged infrastructure offering, roughly about four years ago. And about two and a half years ago, Cisco started investing a lot in this partnership. We're very thankful to them, because they kind of believed in us. We were growing, obviously. But we were not quite as big as we are right now. But they saw the potential early. So about roughly two-and-a-half years ago, I talked about them investing in us. I'm not sure how many people know about what a Cisco validated design is. It's a pretty exhaustive document. It takes a lot of work on Cisco's site to come up with one of those. And usually, a single CVD takes about two or three of their TMEs, highly technical resources and about roughly three to six months to build those. >> Per CVD? >> Per CVD. >> Wow. >> Like I said, it's very exhaustive, I mean you get your building materials, your versions, your interoperability, your, you can actually, your commands that you actually use to stand up that infrastructure and the applications, so on and so forth. So in a nine-month span, they kind of did seven CVDs for us. That was phenomenal. We were very, very thankful that they did that. And over time, that investment paid off. There was a lot of good market investment that Cisco and Pure jointly made, all those investments paid off really well in terms of the customer adoption, the acquisition. And essentially we are at a really good point right now. When we came out with our FlashArray X70 last April, Cisco was about the same time, they were coming out with the M5 servers. And so they invested again, and gave us five more CVDs. And just recently they've added FlashBlade to that portfolio. As you know, FlashBlade is a new product offering. Well not so new, but relatively new, product offering from PR, so we have a new CV that just got released that includes FlashArray and Flash Blade for Oracle. So FlashArray does the online transaction processing, FlashBlade does data warehousing, obviously Cisco networking and Cisco servers do everything OLTB and data warehouse, it's an end to an architecture. So that was what Matt Burr had talked about on stage today. We are also excited to announce that we had that we had introduced AIRI AI-ready infrastructure along with Nvidia at their expo recently. We are excited to say that Cisco is now part of that AIRI infrastructure that Matt Burr had talked about on stage as well. So as you can tell, in a two and half year period we've come a really long way. We have a lot of customer adoption every quarter. We keep adding a ton of customers and we are mutually benefiting from this partnership. >> So I want to ask you about, follow up on the Oracle solution. Oracle would obviously say, "Okay, you buy our database, "buy our SAS, buy the Red Stack, "single throat to choke, "You're going to run better, "take advantage of all the hooks we have." You've heard it before. And it's an industry discussion. >> Rajiev: Of course. >> Customer have it, Oracle comes in hard. So what's the advantage of working with you guys, versus going with an all-Red Stack? Let's talk about that a little bit. >> Sure. Do you want to do it? >> I think if you look at the Oracle databases being deployed, this is a, this really powers many companies. This is really the IT platform. And one of the things that customers, or major customers standardize on this. Again, if they have a standardization from an Oracle perspective, they have a standardization from an infrastructure perspective. Just a database alone is not necessarily easy to put on a different infrastructure, manage them, operate them, go through lifecycle. So they look for a architecture. They look for something that's a overall platform for IT. "I want to do some virtualization. "I want to run desktop virtualization. "I want to do Oracle. "I want to do SAP." So the typical IT operates as more of "I want to manage my infrastructure as a whole. "I want to manage my database and data as its own. "I want its own way of looking." So while there are way to make very appliancey behaviors, that actually operates one better, the approach we took is truly delivering a architecture for data center. The fact that the network as well as the computer is so programmable it makes it easy to expand. Really brings a value from a complete perspective. But if you look at Pure again, their FlashArrays truly have world-class performance. So the customer also looks at, "Well I can get everything from one vendor. "Am I getting the best of breed? "Am I getting the world-class technology from "every one of those aspects and perspectives?" So we certainly think there are a good class of customers who value what we bring to the table and who certainly choose us for what we are. >> And to add to what Siva has just said, right? So if you looked at pre-Flash, you're mostly right in the sense that, hey, if you built an application, especially if it was mission-vertical application, you wanted it siloed, you didn't want another application jumping in and kind of messing up the performance and response times and all that good stuff, right? So in those kind of cases, yeah, appliances made sense. But now, when you have all Flash, and then you have servers and networking that can actually elaborates the performance of Flash, you don't really have to worry about mixing different applications and messing up performance for one at the expense of the other. That's basically, it's a win-win for the customers to have much more of a consolidated platform for multiple applications as opposed to silos. 'Cause silos are always hard to manage, right? >> Siva, I want to ask you, you know, Pure has been very bullish, really, for many years now. Obviously Cisco works with a lot of other vendors. What was it a couple years ago? 'Cause you talked about the significant resource investment that Cisco has been making for a couple of years now in Pure Storage. What is it that makes this so, maybe this Flash tech, I'm kind of thinking of the three-legged stool that Charlie talked about this morning. But what were some of the things that you guys saw a few years ago, even before Pure was a public company, that really drove Cisco to make such a big investment in this? >> I think they, when you look at how Cisco has evolved our data center portfolio, I mean, we are a very significant part of the enterprise today powered by Cisco, Cisco networking, and then we grew into the computer business. But when you looked at the way we walked into this computer business, the traditional storage as we know today is something we actually led through a variety of partnerships in the industry. And our approach to the partnership is, first of all, technology. Technology choice was very very critical, that we bring the best of breed for the customers. But also, again, the customer themself, speaking to us, and then our channel partners, who are very critical for our enablement of the business, is very very critical. So the way we, and when Pure really launched and forayed into all Flash, and they created this whole notion that storage means Flash and that was never the patterning before. That was a game-changing, sort of a model of offering storage, not just capacity but also Flash as my capacity as well as the performance point. We really realized that was going to be a good set of customers will absorb that. Some select workloads will absorb that. But as Flash in itself evolved to be much more mainstream, every day's data storage can be in a Flash medium. They realize, customers realized, this technology, this partner, has something very unique. They've thought about a future that was coming, which we realized was very critical for us. When we evolved network from 10-gig fabric to 40-gig to 100-gig, the workloads that are the slowest part of any system is the data movement. So when Flash became faster and easier for data to be moved, the fabric became a very critical element for the eventual success of our customer. We realized a partnership with Pure, with all Flash and the faster network, and faster compute, we realized there is something unique that we can bring to bear for the customer. So our partnership minds had really said, "This is the next big one that we are going to "invest time and energy." And so we clearly did that and we continue to do that. I mean we continue to see huge success in the customer base with the joint solutions. >> This issue of "best of breed" versus a kind of integrated stacks, it's been around forever, it's not going to go away. I mean obviously Cisco, in the early days of converged infrastructure, put a lot of emphasis on integrating, and obviously partnerships. Since that time, I dunno what it was, 2009 or whatever it was, things have changed a lot. Y'know, cloud was barely a thought back then. And the cloud has pushed this sort of API economy. Pure talks about platforms and integrating through APIs. How has that changed your ability to integrate "best of breed" more seamlessly? >> Actually, you know, I've been working with UCS since it started, right? And it's perhaps, it was a first server system that was built on an API-first philosophy. So everything in the Cisco UCS system can be basically, anything you can do to it GUI or the command line, you can do it their XML API, right? It's an open API that they provide. And they kind of emphasized the openness of it. When they built the initial converged infrastructure stacks, right, the challenge was the legacy storage arrays didn't really have the same API-first programmability mentality, right? If you had to do an operation, you had a bunch of, a ton of CLI commands that you had to go through to get to one operation, right? So Pure, having the advantage of being built from scratch, when APIs are what people want to work with, does everything through rest APIs. All function features, right? So the huge advantage we have is with both Pure, Pure actually unlocks the potential that UCS always had. To actually be a programmable infrastructure. That was somewhat held back, I don't know if Siva agrees or not, but I will say it. That kind of was held back by legacy hardware that didn't have rest space APIs or XML or whatever. So for example, they have Python, and PowerShell-based toolkits, based on their XML APIs that they built around that. We have Python PowerShell toolkits that we built around our own rest APIs. We have puppet integration installed, and all the other stuff that you saw on the stage today. And they have the same things. So if you're a customer, and you've standardized, you've built your automation around any of these things, right, If you have the Intuit infrastructure that is completely programmable, that cloud paradigms that you're talking about is mainly because of programmability, right, that people like that stuff. So we offer something very similar, the joint-value proposition. >> You're being that dev-ops kind of infrastructure-as-code mentality to systems design and architecture. >> Rajiev: Yeah. >> And it does allow you to bring the cloud operating model to your business. >> An aspect of the cloud operating model, right. There's multiple different things that people, >> Yeah maybe not every single feature, >> Rajiev: Right. >> But the ones that are necessary to be cloud-like. >> Yeah, absolutely. >> Dave: That's kind of what the goal is. >> Let's talk about some customer examples. I think Domino's was on stage last year. >> Right. >> And they were mentioned again this morning about how they're leveraging AI. Are they a customer of Flash tech? Is that maybe something you can kind of dig into? Let's see how the companies that are using this are really benefiting at the business level with this technology. >> I think, absolutely, Domino's is one of our top examples of a Flash tech customer. They obviously took a journey to actually modernize, consolidate many applications. In fact, interestingly, if you look at many of the customer journeys, the place where we find it much much more valuable in this space is the customer has got a variety of workloads and he's also looking to say, "I need to be cloud ready. "I need to have a cloud-like concept, "that I have a hybrid cloud strategy today "or it'll be tomorrow. "I need to be ready to catch him and put him on cloud." And the customer also has the mindset that "While I certainly will keep my traditional applications, "such as Oracle and others, "I also have a very strong interest in the new "and modern workloads." Whether it is analytics, or whether it is even things like containers micro-services, things like that which brings agility. So while they think, "I need to have a variety "of things going." Then they start asking the question, "How can I standardize on a platform, "on an architecture, on something that I can "reuse, repeat, and simplify IT." That's, by far, it may sound like, you know, you got everything kind of thing, but that is by far the single biggest strength of the architecture. That we are versatile, we are multi-workload, and when you really build and deploy and manage, everything from an architecture, from a platform perspective looks the same. So they only worry about the applications they are bringing onboard and worry about managing the lifecycle of the apps. And so a variety of customers, so what has happened because of that is, we started with commercial or mid-size customers, to larger commercial. But now we are much more in enterprise. Large, many large IT shops are starting to standardize on Flash tech, and many of our customers are really measured by the number of repeat purchases they will come back and buy. Because once they like and they bought, they really love it and they come back and buy a lot more. And this is the place where it gets very exciting for all of us that these customers come back and tell us what they want. Whether we build automation or build management architecture, our customer speaks to us and says, "You guys better get together and do this." That's where we want to see our partners come to us and say, "We love this architecture but we want these features in there." So our feedback and our evolution really continues to be a journey driven by the demand and the market. Driven by the customers who we have. And that's hugely successful. When you are building and launching something into the marketplace, your best reward is when customer treats you like that. >> So to basically dovetail into what Siva was talking about, in terms of customers, so he brought up a very valid point. So what customers are really looking for is an entire stack, an infrastructure, that is near invisible. It's programmable, right? And it's, you can kind of cookie-cutter that as you scale. So we have an example of that. I'm not going to use the name of the customer, 'cause I'm sure they're going to be okay with it, but I just don't want to do it without asking their permission. It's a healthcare service provider that has basically, literally dozens of these Flash techs that they've standardized on. Basically, they have vertical applications but they also offer VM as a service. So they have cookie-cuttered this with full automation, integration, they roll these out in a very standard way because of a lot of automation that they've done. And they love the Flash tech just because of the programmability and everything else that Siva was talking about. >> With new workloads coming on, do you see any, you know, architectural limitations? When I say new workloads, data-driven, machine intelligence, AI workloads, do we see any architectural limitations to scale, and how do you see that being addressed in the near future? >> Rajiev: Yeah, that's actually a really good question. So basically, let's start with the, so if you look at Bare Metal VMs and containers, that is one factor. In that factor, we're good because, you know, we support Bare Metal and so does the entire stack, and when I say we, I'm talking about the entire Flash tech servers and storage and network, right. VMs and then also containers. Because you know, most of the containers in the early days were ephemeral, right? >> Yeah. >> Rajiev: Then persistent storage started happening. And a lot of the containers would deploy in the public cloud. Now we are getting to a point where customers are kind of, basically experimenting with large enterprises with containers on prem. And so, the persistent storage that connects to containers is kind of nascent but it's picking up. So there's Kubernetes and Docker are the primary components in there, right? And Docker, we already have Docker native volume plug-ins and Cisco has done a lot of work with Docker for the networking and server pieces. And Kubernetes has flex volumes and we have Kubernetes flex volume integration and Cisco works really well with Kubernetes. So there are no issues in that factor. Now if you're talking about machine learning and Artificial Intelligence, right? So it depends. So for example, Cisco's servers today are primarily driven by Intel-based CPUs, right? And if you look at the Nvidia DGXs, these are mostly GPUs. Cisco has a great relationship with Nvidia. And I will let Siva speak to the machine learning and artificial intelligence pieces of it, but the networking piece for sure, we've already announced today that we are working with Cisco in our AIRI stack, right? >> Dave: Right. >> Yeah, no, I think that the next generation workloads, or any newer workloads, always comes with a different set of, some are just software-level workloads. See typically, software-type of innovation, given the platform architecture is more built with programmability and flexibility, adopting our platforms to a newer software paradigm, such as container micro-services, we certainly can extend the architecture to be able to do that and we have done that several times. So that's a good area that covers. But when there are new hardware innovations that comes with, that is interconnect technologies, or that is new types of Flash models, or machine-learning GPU-style models, what we look at from a platform perspective is what can we bring from an integrated perspective. That, of course, allows IT to take advantage of the new technology, but maintain the operational and IT costs of doing business to be the same. That's where our biggest strength is. Of course Nvidia innovates on the GPU factor, but IT doesn't just do GPUs. They have to integrate into a data center, flow the data into the GPU, run compute along that, and applications to really get most out of this information. And then, of course, processing for any kind of real-time, or any decision making for that matter, now you're really talking about bringing it in-house and integrating into the data center. >> Dave: Right. >> Any time you start in that conversation, that's really where we are. I mean, that's our, we welcome more innovation, but we know when you get into that space, we certainly shine quite well. >> Yeah, it's secured, it's protected, it's move it, it's all kind of things. >> So we love these innovations but then our charter and what we are doing is all in making this experience of whatever the new be, as seamless as possible for IT to take advantage of that. >> Wow, guys, you shared a wealth of information with us. We thank you so much for talking about these Cisco-Pure partnership, what you guys have done with FlashStack, you're helping customers from pizza delivery with Domino's to healthcare services to really modernize their infrastructures. Thanks for you time. >> Thank you. >> Thank you very much. >> For Dave Vellante and Lisa Martin, you're watching the Cube live from Pure Accelerate 2018. Stick around, we'll be right back.

>> Announcer: Live from the Bill Graham Auditorium in San Francisco, it's theCUBE! Covering Pure Storage Accelerate 2018 brought to you by Pure Storage. >> Welcome back to theCUBE's continuing coverage of Pure Storage Accelerate 2018, I'm Lisa Martin, sporting the clong and apparently this symbol actually has a name, the clong, I learned that in the last half an hour. I know, who knew? >> Really? >> Yes! Is that a C or a K? >> Is that a Prince orientation or, what is that? >> Yes, I'm formerly known as. >> Nice. >> Who of course played at this venue, as did Roger Daltry, and The Who. >> And I might have been staff for one of those shows. >> You could have been, yeah, could I show you to your seat? >> Maybe you're performing later. You might not even know this. We have a couple of guests joining us. We've got Matt Burr, the GM of FlashBlade, and Rob Ober, the Chief Platform Architect at NVIDIA. Guys, welcome to theCUBE. >> Hi. >> Thank you. >> Dave: Thanks for coming on. >> So, lots of excitement going on this morning. You guys announced Pure and NVIDIA just a couple of months ago, a partnership with AIRI. Talk to us about AIRI, what is it? How is it going to help organizations in any industry really democratize AI? >> Well, AIRI, so AIRI is something that we announced, the AIRI Mini today here at Accelerate 2018. AIRI was originally announced at the GTC, Global Technology Conference, for NVIDIA back in March, and what it is is, it essentially brings NVIDIA's DGX servers, connected with either Arista or Cisco switches down to the Pure Storage FlashBlade, so this is something that sits in less than half a rack in the data center, that replaces something that was probably 25 or 50 racks of compute and store, so, I think Rob and I like to talk about it as kind of a great leap forward in terms of compute potential. >> Absolutely, yeah. It's an AI supercomputer in a half rack. >> So one of the things that this morning, that we saw during the general session that Charlie talked about, and I think Matt (mumbles) kind of a really brief history of the last 10 to 20 years in storage, why is modern external storage essential for AI? >> Well, Rob, you want that one, or you want me to take it? Coming from the non storage guy, maybe? (both laugh) >> Go ahead. >> So, when you look at the structure of GPUs, and servers in general, we're talking about massively parallel compute, right? These are, we're now taking not just tens of thousands of cores but even more cores, and we're actually finding a path for them to communicate with storage that is also massively parallel. Storage has traditionally been something that's been kind of serial in nature. Legacy storage has always waited for the next operation to happen. You actually want to get things that are parallel so that you can have parallel processing, both at the compute tier, and parallel processing at the storage tier. But you need to have big network bandwidth, which was what Charlie was eluding to, when Charlie said-- >> Lisa: You like his stool? >> When Charlie was, one of his stools, or one of the legs of his stool, was talking about, 20 years ago we were still, or 10 years ago, we were at 10 gig networks, in merges of 100 gig networks has really made the data flow possible. >> So I wonder if we can unpack that. We talked a little bit to Rob Lee about this, the infrastructure for AI, and wonder if we can go deeper. So take the three legs of the stool, and you can imagine this massively parallel compute-storage-networking grid, if you will, one of our guys calls it uni-grid, not crazy about the name, but this idea of alternative processing, which is your business, really spanning this scaled out architecture, not trying to stuff as much function on a die as possible, really is taking hold, but what is the, how does that infrastructure for AI evolve from an architect's perspective? >> The overall infrastructure? I mean, it is incredibly data intensive. I mean a typical training set is terabytes, in the extreme it's petabytes, for a single run, and you will typically go through that data set again and again and again, in a training run, (mumbles) and so you have one massive set that needs to go to multiple compute engines, and the reason it's multiple compute engines is people are discovering that as they scale up the infrastructure, you actually, you get pretty much linear improvements, and you get a time to solution benefit. Some of the large data centers will run a training run for literally a month and if you start scaling it out, even in these incredibly powerful things, you can bring time to solution down, you can have meaningful results much more quickly. >> And you be a sensitive, sort of a practical application of that. Yeah there's a large hedge fund based in the U.K. called Man AHL. They're a system-based quantitative training firm, and what that means is, humans really aren't doing a lot of the training, machines are doing the vast majority if not all of the training. What the humans are doing is they're essentially quantitative analysts. The number of simulations that they can run is directly correlative to the number of trades that their machines can make. And so the more simulations you can make, the more trades you can make. The shorter your simulation time is, the more simulations that you can run. So we're talking about in a sort of a meta context, that concept applies to everything from retail and understanding, if you're a grocery store, what products are not on my shelves at a given time. In healthcare, discovering new forms of pathologies for cancer treatments. Financial services we touched on, but even broader, right down into manufacturing, right? Looking at, what are my defect rates on my lines, and if it used to take me a week to understand the efficiency of my assembly line, if I can get that down to four hours, and make adjustments in real time, that's more than just productivity, it's progress. >> Okay so, I wonder if we can talk about how you guys see AI emerging in the marketplace. You just gave an example. We were talking earlier again to Rob Lee about, it seems today to be applied and, in narrow use cases, and maybe that's going to be the norm, whether it's autonomous vehicles or facial recognition, natural language processing, how do you guys see that playing out? Whatever be, this kind of ubiquitous horizontal layer or do you think the adoption is going to remain along those sort of individual lines, if you will. >> At the extreme, like when you really look out at the future, let me start by saying that my background is processor architecture. I've worked in computer science, the whole thing is to understand problems, and create the platforms for those things. What really excited me and motivated me about AI deep learning is that it is changing computer science. It's just turning it on its head. And instead of explicitly programming, it's now implicitly programming, based on the data you feed it. And this changes everything and it can be applied to almost any use case. So I think that eventually it's going to be applied in almost any area that we use computing today. >> Dave: So another way of asking that question is how far can we take machine intelligence and your answer is pretty far, pretty far. So as processor architect, obviously this is very memory intensive, you're seeing, I was at the Micron financial analyst meeting earlier this week and listening to what they were saying about these emerging, you got T-RAM, and obviously you have Flash, people are excited about 3D cross-point, I heard it, somebody mentioned 3D cross-point on the stage today, what do you see there in terms of memory architectures and how they're evolving and what do you need as a systems architect? >> I need it all. (all talking at once) No, if I could build a GPU with more than a terabyte per second of bandwidth and more than a terabyte of capacity I could use it today. I can't build that, I can't build that yet. But I need, it's a different stool, I need teraflops, I need memory bandwidth, and I need memory capacity. And really we just push to the limit. Different types of neural nets, different types of problems, will stress different things. They'll stress the capacity, the bandwidth, or the actual compute. >> This makes the data warehousing problem seem trivial, but do you see, you know what I mean? Data warehousing, it was like always a chase, chasing the chips and snake swallowing a basketball I called it, but do you see a day that these problems are going to be solved, architecturally, it talks about, More's laws, moderating, or is this going to be this perpetual race that we're never going to get to the end of? >> So let me put things in perspective first. It's easy to forget that the big bang moment for AI and deep learning was the summer of 2012, so slightly less than six years ago. That's when Alex Ned get the seed and people went wow, this is a whole new approach, this is amazing. So a little less than six years in. I mean it is a very young, it's a young area, it is in incredible growth, the change in state of art is literally month by month right now. So it's going to continue on for a while, and we're just going to keep growing and evolving. Maybe five years, maybe 10 years, things will stabilize, but it's an exciting time right now. >> Very hard to predict, isn't it? >> It is. >> I mean who would've thought that Alexa would be such a dominant factor in voice recognition, or that a bunch of cats on the internet would lead to facial recognition. I wonder if you guys can comment, right? I mean. >> Strange beginnings. (all laughing) >> But very and, I wonder if I can ask you guys ask about the black box challenge. I've heard some companies talk about how we're going to white box everything, make it open and, but the black box problem meaning if I have to describe, and we may have talked about this, how I know that it's a dog. I struggle to do that, but a machine can do that. I don't know how it does it, probably can't tell me how it does it, but it knows, with a high degree of accuracy. Is that black box phenomenon a problem, or do we just have to get over it? >> Up to you. >> I think it's certain, I don't think it's a problem. I know that mathematicians, who are friends, it drives them crazy, because they can't tell you why it's working. So it's a intellectual problem that people just need to get over. But it's the way our brains work, right? And our brains work pretty well. There are certain areas I think where for a while there will be certain laws in place where you can't prove the exact algorithm, you can't use it, but by and large, I think the industry's going to get over it pretty fast. >> I would totally agree, yeah. >> You guys are optimists about the future. I mean you're not up there talking about how jobs are going to go away and, that's not something that you guys are worried about, and generally, we're not either. However, machine intelligence, AI, whatever you want to call it, it is very disruptive. There's no question about it. So I got to ask you guys a few fun questions. Do you think large retail stores are going to, I mean nothing's in the extreme, but do you think they'll generally go away? >> Do I think large retail stores will generally go away? When I think about retail, I think about grocery stores, and the things that are going to go away, I'd like to see standing in line go away. I would like my customer experience to get better. I don't believe that 10 years from now we're all going to live inside our houses and communicate over the internet and text and half of that be with chat mods, I just don't believe that's going to happen. I think the Amazon effect has a long way to go. I just ordered a pool thermometer from Amazon the other day, right? I'm getting old, I ordered readers from Amazon the other day, right? So I kind of think it's that spur of the moment item that you're going to buy. Because even in my own personal habits like I'm not buying shoes and returning them, and waiting five to ten times, cycle, to get there. You still want that experience of going to the store. Where I think retail will improve is understanding that I'm on my way to their store, and improving the experience once I get there. So, I think you'll see, they need to see the Amazon effect that's going to happen, but what you'll see is technology being employed to reach a place where my end user experience improves such that I want to continue to go there. >> Do you think owning your own vehicle, and driving your own vehicle, will be the exception, rather than the norm? >> It pains me to say this, 'cause I love driving, but I think you're right. I think it's a long, I mean it's going to take a while, it's going to take a long time, but I think inevitably it's just too convenient, things are too congested, by freeing up autonomous cars, things that'll go park themselves, whatever, I think it's inevitable. >> Will machines make better diagnoses than doctors? >> Matt: Oh I mean, that's not even a question. Absolutely. >> They already do. >> Do you think banks, traditional banks, will control of the payment systems? >> That's a good one, I haven't thought about-- >> Yeah, I'm not sure that's an AI related thing, maybe more of a block chain thing, but, it's possible. >> Block chain and AI, kind of cousins. >> Yeah, they are, they are actually. >> I fear a world though where we actually end up like WALLE in the movie and everybody's on these like floating chez lounges. >> Yeah lets not go there. >> Eating and drinking. No but I'm just wondering, you talked about, Matt, in terms of the number of, the different types of industries that really can verge in here. Do you see maybe the consumer world with our expectation that we can order anything on Amazon from a thermometer to a pair of glasses to shoes, as driving other industries to kind of follow what we as consumers have come to expect? >> Absolutely no question. I mean that is, consumer drives everything, right? All flash arrays were driven by you have your phone there, right? The consumerization of that device was what drove Toshiba and all the other fad manufacturers to build more NAM flash, which is what commoditized NAM flash, which what brought us faster systems, these things all build on each other, and from a consumer perspective, there are so many things that are inefficient in our world today, right? Like lets just think about your last call center experience. If you're the normal human being-- >> I prefer not to, but okay. >> Yeah you said it, you prefer not to, right? My next comment was going to be, most people's call center experiences aren't that good. But what if the call center technology had the ability to analyze your voice and understand your intonation, and your inflection, and that call center employee was being given information to react to what you were saying on the call, such that they either immediately escalated that call without you asking, or they were sent down a decision path, which brought you to a resolution that said that we know that 62% of the time if we offer this person a free month of this, that person is going to view, is going to go away a happy customer, and rate this call 10 out of 10. That is the type of things that's going to improve with voice recognition, and all of the voice analysis, and all this. >> And that really get into how far we can take machine intelligence, the things that machines, or the humans can do, that machines can't, and that list changes every year. The gap gets narrower and narrower, and that's a great example. >> And I think one of the things, going back to your, whether stores'll continue being there or not but, one of the biggest benefits of AI is recommendation, right? So you can consider it userous maybe, or on the other hand it's great service, where a lot of, something like an Amazon is able to say, I've learned about you, I've learned about what people are looking for, and you're asking for this, but I would suggest something else, and you look at that and you go, "Yeah, that's exactly what I'm looking for". I think that's really where, in the sales cycle, that's really where it gets up there. >> Can machines stop fake news? That's what I want to know. >> Probably. >> Lisa: To be continued. >> People are working on that. >> They are. There's a lot, I mean-- >> That's a big use case. >> It is not a solved problem, but there's a lot of energy going into that. >> I'd take that before I take the floating WALLE chez lounges, right? Deal. >> What if it was just for you? What if it was just a floating chez lounge, it wasn't everybody, then it would be alright, right? >> Not for me. (both laughing) >> Matt and Rob, thanks so much for stopping by and sharing some of your insights and we should have a great rest of the day at the conference. >> Great, thank you very much. Thanks for having us. >> For Dave Vellante, I'm Lisa Martin, we're live at Pure Storage Accelerate 2018 at the Bill Graham Civic Auditorium. Stick around, we'll be right back after a break with our next guest. (electronic music)

>> Announcer: Live from the Bill Graham Auditorium in San Francisco, it's theCUBE, covering Pure Storage Accelerate 2018. Brought to be you by Pure Storage. >> Welcome back to theCUBE, we are live at Pure Storage Accelerate 2018 in San Francisco. I'm Lisa Prince Martin with Dave The Who Vellante, and we're with David Hatfield, or Hat, the president of Purse Storage. Hat, welcome back to theCUBE. >> Thank you Lisa, great to be here. Thanks for being here. How fun is this? >> The orange is awesome. >> David: This is great. >> Super fun. >> Got to represent, we love the orange here. >> Always a good venue. >> Yeah. >> There's not enough orange. I'm not as blind yet. >> Well it's the Bill Graham, I mean it's a great venue. But not generally one for technology conferences. >> Not it's not. You guys are not conventional. >> So far so good. >> But then-- >> Thanks for keeping us out of Las Vegas for a change. >> Over my dead body I thin I've said once or twice before. >> Speaking of-- Love our customers in Vegas. Unconventional, you've said recently this is not your father's storage company. What do you mean by that? >> Well we just always want to do things a little bit less conventional. We want to be modern. We want to do things differently. We want to create an environment where it's community so our customers and our partners, prospective customers can get a feel for what we mean by doing things a little bit more modern. And so the whole orange thing is something that we all opt in for. But it's more about really helping transform customer's organizations think differently, think out of the box, and so we wanted to create a venue that forced people to think differently, and so the last three years, one was on Pier 48, we transformed that. Last year was in a big steelworkers, you know, 100 year old steel manufacturing, ship building yard which is now long since gone. But we thought the juxtaposition of that, big iron rust relative to what we're doing from a modern solid state perspective, was a good metaphor. And here it's about making music, and how can we together as an industry, develop new things and develop new songs and really help transform organizations. >> For those of you who don't know, spinning disk is known as spinning rust, right? Eventually, so very clever sort of marketing. >> The more data you put on it the slower it gets and it gets really old and we wanted to get rid of that. We wanted to have everything be online in the data center, so that was the point. >> So Hat, as you go around and talk to customers, they're going through a digital transformation, you hear all this stuff about machine intelligence, artificial intelligence, whatever you want to call it, what are the questions that you're getting? CEO's, they want to get digital right. IT professionals are wondering what's next for them. What kind of questions and conversations are you having? >> Yeah, I think it's interesting, I was just in one of the largest financial services companies in New York, and we met with the Chief Data Officer. The Chief Data Officer reports into the CEO. And he had right next to him the CIO. And so they have this development of a recognition that moving into a digital world and starting to harness the power of data requires a business context. It requires people that are trying to figure out how to extract value from the data, where does our data live? But that's created the different organization. It drives devops. I mean, if you're going to go through a digital transformation, you're going to try and get access to your data, you have to be a software development house. And that means you're going to use devops. And so what's happened from our point of view over the last 10 years is that those folks have gone to the public cloud because IT wasn't really meeting the needs of what devops needed and what the data scientists were looking for, and so what we wanted to create not only was a platform and a tool set that allowed them to bridge the gap, make things better today dramatically, but have a platform that gets you into the future, but also create a community and an ecosystem where people are aware of what's happening on the devop's side, and connect the dots between IT and the data scientists. And so we see this exploding as companies digitize, and somebody needs to be there to help kind of bridge the gap. >> So what's your point of view and advice to that IT ops person who maybe really good at provisioning LUNS, should they become more dev like? Maybe ops dev? >> Totally, I mean I think there's a huge opportunity to kind of advance your career. And a lot of what Charlie talked about and a lot of what we've been doing for nine years now, coming up on nine years, is trying to make our customers heroes. And if data is a strategic asset, so much so they're actually going to think about putting it on your balance sheet, and you're hiring Chief Data Officers, who knows more about the data than the storage and infrastructure team. They understand the limitations that we had to go through over the past. They've recognized they had to make trade offs between performance and cost. And in a shared accelerated storage platform where you have tons of IO and you can put all of your applications (mumbles) at the same time, you don't have to make those trade offs. But the people that really know that are the storage leads. And so what we want to do is give them a path for their career to become strategic in their organization. Storage should be self driving, infrastructure should be self driving. These are not things that in a boardroom people care about, gigabytes and petabytes and petaflops, and whatever metric. What they care about is how they can change their business and have a competitive advantage. How they can deliver better customer experiences, how they can put more money on the bottom line through better insights, etc. And we want to teach and work with and celebrate data heroes. You know, they're coming from the infrastructure side and connecting the dots. So the value of that data is obviously something that's new in terms of it being front and center. So who determines the value of that data? You would think it's the business line. And so there's got to be a relationship between that IT ops person and the business line. Which maybe here to for was somewhat adversarial. Business guys are calling, the clients are calling again. And the business guys are saying, oh IT, they're slow, they say no. So how are you seeing that relationship changing? >> It has to come together because, you know, it does come down to what are the insights that we can extract from our data? How much more data can we get online to be able to get those insights? And that's a combination of improving the infrastructure and making it easy and removing those trade offs that I talked about. But also being able to ask the right questions. And so a lot has to happen. You know, we have one of the leaders in devops speaking tomorrow to go through, here's what's happening on the software development and devops side. Here's what the data scientists are trying to get at. So our IT professionals understand the language, understand the problem set. But they have to come together. We have Dr. Kate Harding as well from MIT, who's brilliant and thinking about AI. Well, there's only .5% of all the data has actually been analyzed. You know, it's all in these piggy banks as Burt talked about onstage. And so we want to get rid of the piggy banks and actually create it and make it more accessible, and get more than .5% of the data to be usable. You know, bring as much of that online as possible, because it's going to provide richer insights. But up until this point storage has been a bottleneck to making that happen. It was either too costly or too complex, or it wasn't performing enough. And with what we've been able to bring through solid state natively into sort of this platform is an ability to have all of that without the trade offs. >> That number of half a percent, or less than half a percent of all data in the world is actually able to be analyzed, is really really small. I mean we talk about, often you'll here people say data's the lifeblood of an organization. Well, it's really a business catalyst. >> David: Oil. >> Right, but catalysts need to be applied to multiple reactions simultaneously. And that's what a company needs to be able to do to maximize the value. Because if you can't do that there's no value in that. >> Right. >> How are you guys helping to kind of maybe abstract storage? We hear a lot, we heard the word simplicity a lot today from Mercedes Formula One, for example. How are you partnering with customers to help them identify, where do we start narrowing down to find those needles in the haystack that are going to open up new business opportunities, new services for our business? >> Well I think, first of all, we recognize at Pure that we want to be the innovators. We want to be the folks that are, again, making things dramatically better today, but really future-proofing people for what applications and insights they want to get in the future. Charlie talked about the three-legged stool, right? There's innovations that's been happening in compute, there's innovations that have been happening over the years in networking, but storage hasn't really kept up. It literally was sort of the bottleneck that was holding people back from being able to feed the GPUs in the compute that's out there to be able to extract the insights. So we wanted to partner with the ecosystem, but we recognize an opportunity to remove the primary bottleneck, right? And if we can remove the bottleneck and we can partner with firms like NVIDIA and firms like Cisco, where you integrate the solution and make it self driving so customers don't have to worry about it. They don't have to make the trade offs in performance and cost on the backend, but it just is easy to stamp out, and so it was really great to hear Service Now and Keith walk through is story where he was able to get a 3x level improvement and something that was simple to scale as their business grew without having an impact on the customer. So we need to be part of an ecosystem. We need to partner well. We need to recognize that we're a key component of it because we think data's at the core, but we're only a component of it. The one analogy somebody shared with me when I first started at Pure was you can date your compute and networking partner but you actually get married to your storage partner. And we think that's true because data's at the core of every organization, but it's making it available and accessible and affordable so you can leverage the compute and networking stacks to make it happen. >> You've used the word platform, and I want to unpack that a little bit. Platform versus product, right? We hear platform a lot today. I think it's pretty clear that platforms beat products and that allows you to grow and penetrate the market further. It also has an implication in terms of the ecosystem and how you partner. So I wonder if you could talk about platform, what it means to you, the API economy, however you want to take that. >> Yeah, so, I mean a platform, first of all I think if you're starting a disruptive technology company, being hyper-focused on delivering something that's better and faster in every dimension, it had to be 10x in every dimension. So when we started, we said let's start with tier one block, mission critical data workloads with a product, you know our Flash Array product. It was the fastest growing product in storage I think of all time, and it still continues to be a great contributor, and it should be a multi-billion dollar business by itself. But what customers are looking for is that same consumer like or cloud like experience, all of the benefits of that simplicity and performance across their entire data set. And so as we think about providing value to customers, we want to make sure we capture as much of that 99.5% of the data and make it online and make it affordable, regardless of whether it's block, file, or object, or regardless if it's tier one, tier two, and tier three. We talk about this notion of a shared accelerated storage platform because we want to have all the applications hit it without any compromise. And in an architecture that we've provided today you can do that. So as we think about partnering, we want to go, in our strategy, we want to go get as much of the data as we possibly can and make it usable and affordable to bring online and then partner with an API first open approach. There's a ton of orchestration tools that are out there. There's great automation. We have a deep integration with ACI at Cisco. Whatever management and orchestration tools that our customer wants to use, we want to make those available. And so, as you look at our Flash Array, Flash Deck, AIRI, and Flash Blade technologies, all of them have an API open first approach. And so a lot of what we're talking about with our cloud integrations is how do we actually leverage orchestration, and how do we now allow and make it easy for customers to move data in and out of whatever clouds they may want to run from. You know, one of the key premises to the business was with this exploding data growth and whether it's 30, 40, 50 zettabytes of data over the next you know, five years, there's only two and a half or three zettabytes of internet connectivity in those same period of time. Which means that companies, and there's not enough data platform or data resources to actually handle all of it, so the temporal nature of the data, where it's created, what a data center looks like, is going to be highly distributed, and it's going to be multi cloud. And so we wanted to provide an architecture and a platform that removed the trade offs and the bottlenecks while also being open and allowing customers to take advantage of Red Shift and Red Hat and all the container technologies and platform as a service technologies that exist that are completely changing the way we can access the data. And so we're part of an ecosystem and it needs to be API and open first. >> So you had Service Now on stage today, and obviously a platform company. I mean any time they do M and A they bring that company into their platform, their applications that they build are all part of that platform. So should we think about Pure? If we think about Pure as a platform company, does that mean, I mean one of your major competitors is consolidating its portfolio. Should we think of you going forward as a platform company? In other words, you're not going to have a stovepipe set of products, or is that asking too much as you get to your next level of milestone. >> Well we think we're largely there in many respects. You know, if you look at any of the competitive technologies that are out there, you know, they have a different operating system and a different customer experience for their block products, their file products, and their object products, etc. So we wanted to have a shared system that had these similar attributes from a storage perspective and then provide a very consistent customer experience with our cloud-based Pure One platform. And so the combination of our systems, you hear Bill Cerreta talk about, you have to do different things for different protocols to be able to get the efficiencies in the data servers as people want. But ultimately you need to abstract that into a customer experience that's seamless. And so our Pure One cloud-based software allows for a consistent experience. The fact that you'll have a, one application that's leveraging block and one application that's leveraging unstructured tool sets, you want to be able to have that be in a shared accelerated storage platform. That's why Gartner's talking about that, right? Now you can do it with a solid state world. So it's super key to say, hey look, we want consistent customer experience, regardless of what data tier it used to be on or what protocol it is and we do that through our Pure One cloud-based platform. >> You guys have been pretty bullish for a long time now where competition is concerned. When we talk about AWS, you know Andy Jassy always talks about, they look forward, they're not looking at Oracle and things like that. What's that like at Pure? Are you guys really kind of, you've been also very bullish recently about NVME. Are you looking forward together with your partners and listening to the voice of the customer versus looking at what's blue over the corner? >> Yes, so first of all we have a lot of respect for companies that get big. One of my mentors told me one time that they got big because they did something well. And so we have a lot of respect for the ecosystem and companies that build a scale. And we actually want to be one of those and are already doing that. But I think it's also important to listen and be part of the community. And so we've always wanted to the pioneers. We always wanted to be the innovators. We always wanted to challenge conventions. And one of the reasons why we founded the company, why Cos and Hayes founded the company originally was because they saw that there was a bottleneck and it was a media level bottleneck. In order to remove that you need to provide a file system that was purpose built for the new media, whatever it was going to be. We chose solid state because it was a $40 billion industry thanks to our consumer products and devices. So it was a cost curve where I and D was going to happen by Samsung and Toshiba and Micron and all those guys that we could ride that curve down, allowing us to be able to get more and more of the data that's out there. And so we founded the company with the premise that you need to remove that bottleneck and you can drive innovation that was 10x better in every dimension. But we also recognize in doing so that putting an evergreen ownership model in place, you can fundamentally change the business model that customers were really frustrated by over the last 25 years. It was fair because disk has lots of moving parts, it gets slower with the more data you put on, etc., and so you pass those maintenance expenses and software onto customers. But in a solid state world you didn't need that. So what we wanted to do was actually, in addition to provide innovation that was 10x better, we wanted to provide a business model that was evergreen and cloud like in every dimension. Well, those two forces were very disruptive to the competitors. And so it's very, very hard to take a file system that's 25 years old and retrofit it to be able to really get the full value of what the stack can provide. So we focus on innovation. We focus on what the market's are doing, and we focus on our customer requirements and where we anticipate the use cases to be. And then we like to compete, too. We're a company of folks that love to win, but ultimately the real focus here is on enabling our customers to be successful, innovating forward. And so less about looking sidewise, who's blue and who's green, etc. >> But you said it before, when you were a startup, you had to be 10x better because those incumbents, even though it was an older operating system, people's processes were wired to that, so you had to give them an incentive to do that. But you have been first in a number of things. Flash itself, the sort of All-Flash, at a spinning disk price. Evergreen, you guys set the mark on that. NVME you're doing it again with no premium. I mean, everybody's going to follow. You can look back and say, look we were first, we led, we're the innovator. You're doing some things in cloud which are similar. Obviously you're doing this on purpose. But it's not just getting close to your customers. There's got to be a technology and architectural enabler for you guys. Is that? >> Well yeah, it's software, and at the end of the day if you write a file system that's purpose built for a new media, you think about the inefficiencies of that media and the benefits of that media, and so we knew it was going to be memory, we knew it was going to be silicon. It behaves differently. Reads are effectively free. Rights are expensive, right? And so that means you need to write something that's different, and so you know, it's NVME that we've been plumbing and working on for three years that provides 44,000 parallel access points. Massive parallelism, which enables these next generation of applications. So yeah we have been talking about that and inventing ways to be able to take full advantage of that. There's 3D XPoint and SCM and all kinds of really interesting technologies that are coming down the line that we want to be able to take advantage of and future proof for our customers, but in order to do that you have to have a software platform that allows for it. And that's where our competitive advantage really resides, is in the software. >> Well there are lots more software companies in Silicon Valley and outside Silicon Valley. And you guys, like I say, have achieved that escape velocity. And so that's pretty impressive, congratulations. >> Well thank you, we're just getting started, and we really appreciate all the work you guys do. So thanks for being here. >> Yeah, and we just a couple days ago with the Q1FY19, 40%, you have a year growth, you added 300 more customers. Now what, 4800 customers globally. So momentum. >> Thank you, thank you. Well we only do it if we're helping our customers one day at a time. You know, I'll tell you that this whole customer first philosophy, a lot of customers, a lot of companies talk about it, but it truly has to be integrated into the DNA of the business from the founders, and you know, Cos's whole pitch at the very beginning of this was we're going to change the media which is going to be able to transform the business model. But ultimately we want to make this as intuitive as an iPhone. You know, infrastructure should just work, and so we have this focus on delivering simplicity and delivering ownership that's future proofed from the very beginning. And you know that sort of permeates, and so you think about our growth, our growth has happened because our customers are buying more stuff from us, right? If you look at our underneath the covers on our growth, 70 plus percent of our growth every single quarter comes from customers buying more stuff, and so, as we think about how we partner and we think about how we innovate, you know, we're going to continue to build and innovate in new areas. We're going to keep partnering. You know, the data protection staff, we've got great partners like Veeam and Cohesity and Rubrik that are out there. And we're going to acquire. We do have a billion dollars of cash in the bank to be able to go do that. So we're going to listen to our customers on where they want us to do that, and that's going to guide us to the future. >> And expansion overseas. I mean, North America's 70% of your business? Is that right? >> Rough and tough. Yeah, we had 28%-- >> So it's some upside. >> Yeah, yeah, no any mature B2B systems company should line up to be 55, 45, 55 North America, 45, in line with GDP and in line with IT spend, so we made investments from the beginning knowing we wanted to be an independent company, knowing we wanted to support global 200 companies you have to have operations across multiple countries. And so globalization is always going to be key for us. We're going to continue our march on doing that. >> Delivering evergreen from an orange center. Thanks so much for joining Dave and I on the show this morning. >> Thanks Lisa, thanks Dave, nice to see you guys. >> We are theCUBE Live from Pure Accelerate 2018 from San Francisco. I'm Lisa Martin for Dave Vellante, stick around, we'll be right back with our next guests.

>> Narrator: Live, from the Bill Graham Auditorium in San Francisco, it's theCUBE! Covering, Pure Storage Accelerate, 2018! Brought to you by: Pure Storage. (upbeat electronic music) >> Welcome back to theCUBE, we are live at Pure Storage Accelerate 2018. I am Lisa Martin, supporting the Prince look today. We're at the Bill Graham Civic Auditorium, this is a super cool building, 1915 it was built, and is the home of so many cool artists, so got to represent today. Dave Vellante's my co-host for the day. >> Well, I got to tell you, Charlie, thank you for wearing a tie. >> Yeah, well-- >> My tie's coming off. >> Okay, well, hey, look, you and me both. >> You have to wear yours-- >> Well, I do, I still have investors later. >> I'm not the only one who's representing musicians today. >> I got my tee shirt underneath here, all right. >> Oh, oh oh! >> Ladies and gentlemen, you will not want to miss this. >> Bill Graham, right, I'm on a Who, Lisa. >> "I'm on a Who", oh he said The Who! >> The Who! >> We got Roger Daltrey-- >> Charlie: Oh, that's fantastic. >> (laughing) >> Pete Townshend-- >> The Who! >> That's my deal. >> He's being so careful not to ruin his shirt with the buttons. >> The Who. >> I got to say-- >> Well done. >> Tower of Power was really my band. >> Oh, wow. >> They didn't play here, but Bill Graham was the first to sign him. >> Wow, representing. >> Well, I was an East Coast boy, so it was all the New York concerts and venues for me, but it was fantastic, I used to watch, you remember, Bill Graham presents? That was-- >> Yes! >> Yeah! >> I always thought if I found myself on stage, there'd be a couple of security guys dragging me off. >> Love that line! >> Nobody today, and you got a lot of applause, a lot of confetti. So Charlie, kick things off this morning at the Third Annual Accelerate, packed house, orange as far as the eye can see, but just a couple days ago-- >> Sea of orange. >> Exactly, sea of orange, a proud sea of orange. >> Right. >> Just two days ago, on the 21st of May, you guys announced your fiscal 19 first quarter results. Revenue up 40%, year over year, you added 300 new customers, including the U.S. Department of Energy, Paige.ai, and the really amazing transformational things they're doing for cancer research. You also shared today your NPS score: over 83! >> Correct. >> Big numbers shared today. >> These are big numbers. >> You've been the CEO for about nine months or so now, tell us what's going on, how are you sustaining this? Stocks going up? >> Right, right, stock's up about 80% year over year right now, so that's very good, but really I think it's a recognition that Pure is playing a very important role in the data processing, in the high-tech landscape, right? I think, you know, storage was really, I think up until now, really viewed as maybe an aging technology, something that was becoming commoditized, something where innovation wasn't really important, and Pure was the one company that actually thought that storage was important. As I mention in my keynote talk, you know, I really view technology as being a three-legged stool. That is, it's comprised as three elements: compute, networking, and storage. If any of one of them falls behind, you know, it becomes unbalanced, and frankly, you know, computers has advanced 10X over the last 10 years, networking has advanced more than 10X over the last 10 years, and storage didn't keep up at the same time that data was exploding, right? Pure is the one company that actually believes that there's real innovation to be had in storage. Paige.ai is a great example of that, I know it tugs on all of our heartstrings, but Paige.ai took lots of analog data, what was it, we're talking about cancer samples that were on slides, okay, they took literally millions of samples, digitized it, and fed it into an AI machine learning engine. Now, if you understand the way machine learning operates, it has to practice on thousands, or actually tens of thousands, millions, of samples. It could take all year, or it can take hours. What you want it to do is take minutes or hours, and if the data can't be fed fast enough into that engine, you know, it's going to take all year. You want your cancer pathology to be analyzed, you know, really quickly. >> Immediately. >> Immediately, right? That's what this engine can do, and it can do it because we can feed the data at it fast, at the rate it needs to be able to analyze that cancer. Data is just becoming the core of every company's business, it's becoming, if you will, the currency, it's becoming the gold mine, where companies now want to analyze their data. Right now, only about a half of 1% of the data that companies have can even be analyzed, because it's being kept in cold storage, and at Pure, we believe in no cold storage, you know, it's all got to be hot, it's all got to be available, able to be analyzed, able to be mined. >> Do you think, I got to ask you this, do you think that percentage will rise faster than the amount of data that's going to be created? Especially when you're thinking things at the edge. >> It's a great question, and I think absolutely! The reason is because it's not only the data that's being generated, or saved now, that's important. If you really want to analyze trends and get to know your customers, you know, the last five years, the last 10 years of data, is just as important. Increasingly, I think you may know this just from online banking, right, it used to be that maybe you'd have last month's checks available to you, but now you want to go back a year, you want to go back five years, and see, you know, you get audited by the IRS, they say: "Well, prove to us you did this," you need to find those checks and banks are being expected to have that information available to you. >> I got to ask you, you're what we call a tech-athlete, you were showing your tech-chops on stage, former CTO, but you've been a CEO, a board member of many prominent companies, why, Charlie, did you choose to come back in an operating role? You know, why at Pure, and why in an operating role? >> You know, I love being part of a team, it's really that. You know, I've had great fun throughout my career, but being part of a team that is focused on innovation, and is enabling, you know, not just our industry but frankly, allowing the world's business to do a better job. I mean, that's what gets me thrilled. I like working with customers every day, with our sales people, with our engineers. It's just a thrilling life! >> You did say in your keynote this morning that you leave the office, at the end of the day, with a smile, and you get to the office in the morning with a smile, that's pretty cool. >> I do, and if you asked my wife she'd tell you the same thing right, so I really enjoy being part of the team. >> Dave: So, oh, go ahead, please >> Oh, thank you sir. One of the things that Pure has done well is: partners, partnerships. We're going to be talking with NVIDIA later today, so this is going to be on, you guys just announced the new AIRI mini, and I was just telling Dave: I need to see that box, cause it looks pretty blinged out on the website. Talk to us about, though, what you guys are doing with your partnerships and how you've seen that really be represented in the successes of your customers. >> Right, well there are several different types of partnerships that we could talk about. First of all, we're 100% channel lead in our organization. We believe in the channel. You know, this is ancient history now, but when I arrived at Cisco, they were 100% direct at that time, no partners whatsoever. >> Belly to belly. >> Belly to belly, and I was very much apart of driving Cisco to be 100% partner over that period of time. So, you know, my history and belief in utilizing a channel to go to market is very well known, and my view is: the more we make our partners successful, the more we make our customers successful, the more successful we will be. But then, there are other types of partnerships as well. There are technology partnerships, like what we have with Cisco and NVIDIA, and again, we need to do more with other companies to make the solutions that we jointly provide, easier for our customers to be able to use. Then, there are system integration partners, because, let's face it, with as much technology as we build, customers often need help from experts of system integrators, to be able to pull that all together, to solve their business problems. Again, the more we can work with these system integrators, have them understand our products, train them to use them better, the better off our customers will be. >> Charlie, Pure has redefined, in my opinion, escape velocity in the storage business, it used to be getting to public, you saw that with 3PAR, Compel, Isilon, Data Domain, you guys are the first storage to hit one billion dollars since NetApp-- >> Right, 20 years ago. >> Awesome milestone, I didn't think it was possible eight years ago, to be honest, so now, okay, what's next? Can you remain an independent company? In order to remain independent, you got to grow, NetApp got to five billion in a faster growing market, you guys got to gain-share, how do you continue to do that? >> Well, you're right, each and every day we have to compete. We have to, you know, kill for what we eat. Our European sales lead calls it, our competition, on an account basis, a: knife fight in a phone booth. So the competition is tough out there, but we are bringing innovations to market, and more importantly, we're investing in the technology at a rate that I think our competitors are not going to be able to keep up with. We invest close to 20% of our revenue every year in R&D. Our competitors are in single-digits, okay, and this is a technology business, you know, eventually, if you don't keep up with the technology, you're going to lose, and so, that I think is going to allow us to continue growing and scaling. You're right, growth is important for us to be able to stay independent, but I looked very deeply at the entire industry before joining, and you know, I was in private equity for awhile, so we know how to analyze an industry, right? My view was that all of the other competitors are either no longer investing, and that's either internally, or in terms of large acquisitions, or they've already made their beds, and so I didn't really see a likely acquirer for Pure, and that was going to give us, if you will, the breathing room to be able to grow to a scale where we can continue to be independent. >> Almost by necessity! >> Almost by necessity, yeah. >> It's good to put the pressure on yourselves. >> So, in terms of where you are now, how is Pure positioned to lead storage growth in infrastructure for AI-based apps? There's this explosion of AI, right, fueled by deep-learning, and GPUs, and big data. How are you positioned to lead this charge is storage growth there? >> That's such a great question, you know, to get to the part of, you know, I started hearing about AI when I graduated college, which is a really long time ago now, and yet why is it exploding now? Well, computing has done its job, right, we're here today with NVIDIA, with GPUs that are just, you know, we're talking about, you know, giga-flops, you know, just incredible speeds of compute. Networking has done its job, we're now at 100 gigabits, and we're starting to talk about 400 gigabit per second networks, and storage hadn't kept up, right, even though data is exploding. So, we announced today, as you know, our data-centric architecture, and we believe this is an architecture that really sets our customers' data free. It sets it free in many ways. One of which, it allows it to always be hot, at a price that customers can afford, not only can afford, it's cheaper than what they're doing today, because we're collapsing tiers. No longer a hot tier, warm tier, cold tier, it's all one tier that can serve many, many needs at the same time, and so all of your applications can get access to real-time data, and access it simultaneously with the other applications, and we make sure that they get the quality of service they need, and we protect the data from being, you know, either corrupted or changed when other applications want it to be the same. So, we do what is necessary now, to allow the data to be analyzed for whether it's analytics, or AI, or machine learning, or simply to allow DEV-ops to be able to operate on real-time data, on live data, you know, without upsetting the operation's environment. >> I want to make sure I understand this, so you're democratizing tiering, essentially-- >> Charlie: Democratizing tiering. >> So how do you deal with, you know, different densities, QLC, et cetera, is that through software, is that? >> Well, so we hide that from the customer, right, so we're able to take advantage of the latest storage because we speak directly to the storage chips themselves. All of our competitors use what are called SSDs, solid state drives. Now, think about that for a moment. There's no drive in a solid state drive, these things are designed to allow Flash to mimic hard disk, but hard disk has all these disadvantages, why do you want Flash to mimic hard disk? We also set Flash free. We're able to use Flash in parallel, okay, we're able to take low quality Flash and make it look like high quality Flash, because our software adapts to whatever the specific characteristics of the flash are. So we have this whole layer of software that does nothing other than allow Flash to provide the best possible performance characteristics that Flash can provide. It allows us to mix and match, and completely hide that from the customer. >> With MVME, you're taking steps to eliminate what I call: the horrible storage stack. >> Charlie: That's exactly right. >> So, you talked earlier about the disparity between storage and the other two legs of the stool, so as you attack that bottle neck, what's the new bottle neck? Is it networking, and do you see that shaking out? >> It's a great question, I think the new bottle neck, I would actually put it at a higher layer, it's the orchestration layer that allows all this stuff to work together, in a way that requires less human interaction. There are great new technologies on the horizon, you know, Kubernetes, and Spark, and Kafka, a variety of others that will allow us to create a cloud environment, if you will, both for the applications and for the data, within private enterprises, similar to what they can get in the cloud, in many cases. >> You also talked about, innovation, and I want to ask you about the innovation equation, as both a technologist and a CEO who talks to a lot of other CEOS. We see innovation as coming from data, and the application of machine intelligence on that data, and cloud economics at scale, do you buy that? And where do you guys fit in that? >> We do buy that, although cloud economics, we believe, that we can create an environment where customers and their private data centers can also get cloud economics, and in fact, if you look at cloud economics, they're very good for some workloads, not necessarily good for other workloads. They're good at low scale, but not initially good at high scale. So, how do we allow customers to be able to easily move workloads between these different environments, depending on what their specific needs are, and that's what we view as our job, but also point something else out as well. About 30% of our sales are in the cloud providers themselves. They're in softwares that service, infrastructures that service, platforms as a service. These vendors are using our systems, so as you can see, we are already designed for cloud economics. We also already get to see how these leading-edge, very high scale customers construct their environments, and then we're able to bring that into the enterprise environment as well. >> I mean, I think we buy that. You're an arm's dealer to the cloud, you know, maybe not the tier zero to use that term, which is, but also, you're helping your On-Prem customers bring the cloud operating model to their data, cause they can't just stuff it into the cloud. >> It won't always be the right solution for everyone, now, it'll be the right solution for many, and we're doing more and more to allow the customers to bridge that, but we think that it's a multi-cloud environment, including private data centers, and we want to create as much flexibility as we can. >> Would you say Pure is going to be an enabler of companies being able to analyze way more than a half a percent of their data? >> If we don't do that, then there's no good reason for us to be in business. That is exactly what we're focused on. >> Last question for you Charlie, you've been the CEO about nine months now; cultural observations of Pure Storage? >> Oh, you know, you've seen the sea of orange that's here, and by the way, the orange is being sported not just by Puritans, not just by our employees, but by our partners and our customers as well. It's a bit infections, I have to be honest, I had one piece of orange clothing when I started this job, and you know, my mother's into it, she's sending me orange, you know, all sorts of orange clothing, some of which I'll wear, some of which I won't. My wife, everyone, there's a lot of enthusiasm about this business, it has a bit of a cult-like following, and Puritans are really very, very dedicated, not just to the customer, I mean, people become dedicated, you know, not to an entity, they become dedicated to a cause, and the cause for Pure is really to make our customers successful, and our employees feel that it's what drives them every day, it's what brings them to work, and hopefully it's what puts a smile on their face when they go home at night. >> Charlie Giancarlo, CEO of Pure Storage, thanks so much for joining us on theCUBE today! >> Thank you, thank you. >> For The Who Vallante, I'm Prince Martin, and we are live at Pure Accelerate 2018, in San Francisco, stick around, Who and I will be right back. (upbeat electronic music)

>> Announcer: Live from the Bill Graham Auditorium in San Francisco, it's theCUBE covering Pure Storage Accelerate 2018, brought to you by Pure Storage. (bright music) >> Welcome to theCUBE. We are live at Pure Storage Accelerate 2018. I'm Lisa Martin also known as Prince for today with Dave Vellante. We're at the Bill Graham Civic Auditorium, really cool, unique venue. Dave, you've been following Pure for a long time. Today's May 23rd, they just announced FY19 Q1 earnings a couple days ago. Revenue up 40% year over year, added 300 new customers this last quarter including the Department of Energy, Paige.ai, bringing their customer tally now up to about 4800. We just came from the keynote. What are some of the things that you've observed over the last few years of following Pure that excite you about today? >> Well Lisa, Pure's always been a company that is trying to differentiate itself from the pack, the pack largely being EMC at the time. And what Pure talked about today, Matt Kixmoeller talked about, that in 2009, if you go back there, Fusion-io was all the rage, and they were going after the tip of the pyramid, and everybody saw flash, as he said, his words, as the tip of the pyramid. Now of course back then David Floyer in 2008 called that flash was going to change the world, that is was going to dominate. He'd forecast that flash was going to be cheaper than disk over the long term, and that is playing out in many market segments. So he was one of the few that didn't fall into that trap. But the point is that Pure has always said, "We're going to make flash cheaper than "or as cheap as spinning disk, "and we're going to drive performance, "and we're going to differentiate from the market, "and we're going to be first." And you heard that today with this company. This company is accelerated to a billion dollars, the first company to hit a billion dollars since NetApp. Eight years ago I questioned if any company would do that. If you look at the companies that exited the storage market, that entered and exited the storage market that supposedly hit escape velocity, 10 years ago it was 3PAR hit $250 million. Isilon, Data Domain, Compellent, these companies sold for between $1 and $2.5 billion. None of them hit a billion dollars. Pure is the first to do that. Nutanix, which is really not a storage company, they're hyper-converged infrastructure, they got networking and compute, sort of, hit a billion, but Pure is the the first pure play, no pun intended, storage company to do that. They've got a $5 billion evaluation. They're growing, as you said, at 40% a year. They just announced their earnings they beat. But the street reacted poorly because it interpreted their guidance as lower. Now Pure will say that we know we raised (laughs) our guidance, but they're lowering the guidance in terms of growth rates. So that freaks the street out. I personally think it's pure conservativism and I think that they'll continue to beat those expectations so the stock's going to take a hit. They say, "Okay, if you want to guide lower growth, "you're going to take the hit," and I think that's smart play by Pure because if and when they beat they'll get that updraft. But so that's what you saw today. They're finally free cash flow positive. They've got about a billion dollars in cash on the balance sheet. Now half a billion of that was from a convertible note that they just did, so it's really not coming from a ton of free cash flow, but they've hit that milestone. Now the last point I want to make, Lisa, and we talked about this, is Pure Storage at growing at 40% a year, it's like Amazon can grow even though they make small profit. The stock price keeps going up. Pure has experienced that. You're certainly seeing that with companies like Workday, certainly Salesforce and its ascendancy, ServiceNow and its ascendancy. These companies are all about growth. The street is rewarding growth. Very hard for a company like IBM or HPE or EMC when it was public, when they're not growing to actually have the stock price continue to rise even though they're throwing off way more cash than a company like Pure. >> Also today we saw for the first time the new CEO's been Charlie Giancarlo, been the CEO since August of 2017, sort of did a little introduction to himself, and they talked about going all in on shared accelerated storage, this category that Gartner's created. Big, big focus there. >> Yeah, so it's interesting. When I look at so-called shared accelerated storage it's 2018, Gartner finally came up with a new category. Again, I got to give credit to the Wikibon guys. I think David Floyer in 2009 created the category. He called it Server SAN. You don't know if that's David, but I think maybe shared accelerated storage's a better name. Maybe Gartner has a better V.P. of Naming than they do at Wikibon, but he forecast this notion of Server SAN which really it's not DAS, it's not SAN, it's this new class of accelerated storage that's flash-based, that's NVMe-based, eliminates the horrible storage stack. It's exactly what Pure was talking about. Again, Floyer forecast that in 2009, and if you look at the charts that he produced back then it looks like you see the market like this going shoom, the existing market and the new market just exploding. So Pure, I think, is right on. They're targeting that wide market. Now what they announced today is this notion of their flash array for all workloads, bringing NVMe to virtually their entire portfolio. So they're aiming their platform at the big market. Remember, Pure's ascendancy to a billion really came at the expense of EMC's VMAX and VNX business. They aimed at that and they hit it hard. They positioned flash relative to EMC's either spinning disk or flash-based systems as better, easier, cheaper, et cetera, et cetera, and they won that battle even though they were small. Pure's a billion, EMC at the time was $23, $24 billion, but they gained share very rapidly when you see the numbers. So what they're doing is basically staking a claim, Lisa, saying, "We can point our platform "at the entire $30, $40, $50 billion storage TAM," and their intention, we're going to ask Charlie Giancarlo and company, their aspiration is to really continue to gain share in that marketplace and grow significantly faster than the overall market. >> So they also talked about the data-centric architecture today and gave some great examples of customers. I loved the Domino's Pizza example that they talked about, I think he was here last year, and how they're actually using AI at Domino's to analyze the phone calls using this AI engine to identify accurate order information and get you your pizza as quickly as you want. So not only do we have pizza but we were showered with confetti. Lot of momentum there. What is your opinion of Pure, what they're doing to enable companies to utilize and maximize AI-based applications with this data-centric architecture? >> So Pure started in the what's called block storage, really going after the high-volume, the transaction OLTP business. In the early days of Pure you'd see them at Oracle OpenWorld. That's where the high-volume transactions are taking place. They were the first really, by my recollection, to do file-based flash storage. Back in the day it was you would buy EMC for a block, you'd buy NetApp for file. What Pure did is said, "Okay, let's go after "the biggest market player, EMC, "which we'll gain share there in block, "and then now let's go after NetApp space and file." They were again the first to do that. And now they're extending that to AI. Now AI is a small but growing market, so they want to be the infrastructure for artificial intelligence and machine intelligence. They've struck a partnership with Nvidia, they're using the example of Domino's. It's clearly not a majority of their business today, but they're doing some clever things in marketing, getting ahead of the game. This is Pure's game. Be first, get out in the lead, market it hard, and then let everybody else look like they're following which essentially they are and then claim leadership position. So they are able to punch above their weight class by doing that, and that's what you're seeing with the Domino's example. >> You think they're setting the bar? >> Do I think they're setting the bar? Yeah, in many respects they are because they are forcing these larger incumbents to respond and react because they're in virtually all accounts now. The IT practitioners, they look at the Gartner Magic Quadrant, who's in the upper right, I got to call them in for the RFP. They get a seat at that table. I would say it was interesting hearing Charlie speak today and the rest of the executives. These guys are hardcore storage geeks, and I mean that with all due respect. They love storage. It kind of reminds me of the early days of EMC. They are into this stuff. Their messaging is really toward that storage practitioner, that administrator. They're below the line but those are the guys that are actually making the decisions and affecting transactions. They're touching above the line with AI messages and data growth and things like that, but it's really not a hardcore CIO, CFO, CEO message yet. I think that will come later. They see a big enough market selling to those IT practitioners. So I think they are setting the bar in that IT space, I do. >> One of the things I thought that they did well is kind of position the power of data where, you know people talk about data as fuel. Data's really a business catalyst that needs to be analyzed across multiple areas of a business simultaneously to really be able to extract value. They talked about the gold rush, oh gee, of 1849 and now kind of in this new gold rush enabling IT with the tools. And interestingly they also talked about a survey that they did with the SEE Suite who really believe that analyzing data is going to be key to driving businesses forward, identifying new business models, new products, new services. Conversely, IT concern do we have the right tools to actually be able to evaluate all of these data to extract the value from it? Because if you can't extract the value from the data, is it, it's not useful. >> Yeah, and I think again, I mean to, we give Pure great marketing, and a lot of what they're doing, (laughs) it's technology, it's off-the-shelf technology, it's open source components. So what's their differentiation? Their differentiation is clearly their software. Pure has done a great job of simplifying the experience for the customer, no question, much in the same way that 3PAR did 10 or 15 years ago. They've clearly set the bar on simplicity, so check. The other piece that they've done really well is marketing, and marketing is how companies differentiate (laughs) today. There's no question about it that they've done a great job of that. Now having said that I don't think, Lisa, that storage, I think storage is going to be table stakes for AI. Storage infrastructure for AI is going to have to be there, and they talked about the gold rush of 1849. The guys who made all the money were the guys with the picks and the axes and the shovels supplying them, and that's really what Pure Storage is. They're a infrastructure company. They're providing the pickaxes and the shovels and the basic tools to build on top of that AI infrastructure. But the real challenges of AI are where do I apply and how do I infuse it into applications, how do I get ROI, and then how do I actually have a data model where I can apply machine intelligence and how do I get the skillsets applied to that data? So is Pure playing a fundamental catalyst to that? Yes, in the sense that I need good, fast, reliable, simple-to-use storage so that I don't have to waste a bunch of time provisioning LUNs and doing all kinds of heavy lifting that's nondifferentiated. But I do see that as table stakes in the AI game, but that's the game that Pure has to play. They are an infrastructure company. They're not shy about it, and it's a great business for them because it's a huge market where they're gaining share. >> Partners are also key for them. There's a global partner summit going on. We're going to be speaking, you mentioned Nvidia. We're going to be talking with them. They also announced the AIRI Mini today. I got to get a look at that box. It looks pretty blinged out. (laughing) So we're going to be having conversations with partners from Nvidia, from Cisco as well, and they have a really diverse customer base. We've got Mercedes-AMG Petronas Motorsport Formula One, we've got UCLA on the CIO of UCLA Medicine. So that diversity is really interesting to see how data is being, value, rather, from data is being extracted and applied to solve so many different challenges whether it's hitting a race car around a track at 200 kilometers an hour to being able to extract value out of data to advance health care. They talked about Paige.ai, a new customer that they added in Q1 of FY19 who was able to take analog cancer pathology looking at slides and digitize that to advance cancer research. So a really cool kind of variety of use cases we're going to see on this show today. >> Yeah, I think, so a couple thoughts there. One is this, again I keep coming back to Pure's marketing. When you talk to customers, they cite, as I said before, the simplicity. Pure's also done a really clever thing and not a trivial thing with regard to their Evergreen model. So what that means is you can add capacity and upgrade your software and move to the next generation nondisruptively. Why is this a big deal? For decades you would have to actually shut down the storage array, have planned downtime to do an upgrade. It was a disaster for the business. Oftentimes it turned into a disaster because you couldn't really test or if you didn't test properly and then you tried to go live you would actually lose application availability or worse, you'd lose data. So Pure solved that problem with its Evergreen model and its software capability. So its simplicity, the Evergreen model. Now the reality is typically you don't have to bring in new controllers but you probably should to upgrade the power, so there are some nuances there. If you're mixing and matching different types of devices in terms of protocols there's not really tiering, so there's some nuances there. But again it's both great marketing and it simplifies the customer experience to know that I can go back to serial number 00001 and actually have an Evergreen upgrade is very compelling for customers. And again Pure was one of the first if not the first to put that stake in the ground. Here's how I know it's working, because their competitors all complain about it. When the competitors are complaining, "Wow, Pure Storage, they're just doing X, Y, and Z, "and we can do that too," and it's like, "Hey, look at me, look at me! "I do that too!" And Pure tends to get out in front so that they can point and say, "That's everybody following us, we're the leader." And that resonates with customers. >> It does, in fact. And before we wrap things up here a lot of the customer use cases that I read in prepping for this show all talked about this simplicity, how it simplified the portability, the Evergreen model, to make things much easier to eliminate downtime so that the business can keep running as expected. So we have a variety of use cases, a variety of Puritans on the program today as well as partners who are going to be probably articulating that value. >> You know what, I really didn't address the partner issue. Again, having a platform that's API-friendly, that's simple makes it easier to bring in partners, to integrate into new environments. We heard today about integration with Red Hat. I think they took AIRI. I think Cisco's a part of that partnership. Obviously the Nvidia stuff which was kind of rushed together at the last minute and had got it in before the big Nvidia customer show, but they, again, they were the first. Really made competitors mad. "Oh, we can do that too, it's no big deal." Well, it is a big deal from the standpoint of Pure was first, right? There's value in being first and from a standpoint of brand and mindshare. And if it's easier for you to integrate with partners like Cisco and other go-to-market partners like the backup guys you see, Cohesity and Veeam and guys like Catalogic are here. If it's easier to integrate you're going to have more integration partners and the go-to-market is going to be more facile, and that's where a lot of the friction is today, especially in the channel. >> The last thing I'll end with is we got a rain of confetti on us during the main general session today. The culture of Pure is one that is pervasive. You feel it when you walk into a Pure event. The Puritans are very proud of what they've done, of how they're enabling so many, 4800+ customers globally, to really transform their businesses. And that's one of the things that I think is cool about this event, is not just the plethora of orange everywhere but the value and the pride in the value of what they're delivering to their customers. >> Yeah, I think you're right. It is orange everywhere, they're fun. It's a fun company, and as I say they're alpha geeks when it comes to storage. And they love to be first. They're in your face. The confetti came down and the big firecracker boom when they announced that NVMe was going to be available across the board for zero incremental cost. Normally you would expect it to be a 15 to 20% premium. Again, a first that Pure Storage is laying down the gauntlet. They're setting the bar and saying hey guys, we're going to "give" this value away. You're going to have to respond. Everybody will respond. Again, this is great marketing by Pure because they're >> Shock and awe. going to do it and everybody's going to follow suit and they're going to say, "See, we were first. "Everybody's following, we're the leader. "Buy from us," very smart. >> There's that buy. Another first, this is the first time I have actually been given an outfit to wear by a vendor. I'm the symbol of Prince today. I won't reveal who you are underneath that Superman... >> Okay. >> Exterior. Stick around, you won't want to miss the reveal of the concert tee that Dave is wearing. >> Dave: Very apropos of course for Bill Graham auditorium. >> Exactly, we both said it was very hard to choose which we got a list of to pick from and it was very hard to choose, but I'm happy to represent Prince today. So stick around, Dave and I are going to be here all day talking with Puritans from Charlie Giancarlo, David Hatfield. We've also got partners from Cisco, from Nvidia, and a whole bunch of great customer stories. We're going to be right back with our first guest from the Mercedes-AMG Petronas Motorsport F1 team. I'm Lisa "Prince" Martin, Dave Vellante. We'll be here all day, Pure Storage Accelerate. (bright music)

>> Hi, I'm Peter Burris, and welcome to a Wikibon Action Item Quick Take. Jim Kobielus, lots going on in the world of AI and storage. If we think about what happened in storage over the years, it used to be for disc space, get data into a persistent state, and for some of the flash base, it's get data out faster. What happened this week between Pure and NVIDIA to make it easier to get data out faster, especially for AI applications? >> Yeah Peter, this week at NVIDIA's annual conference, GPU technology conference, they announced a partnership with Pure Storage. In fact they released a jointly developed product called AIRI...A-I-R-I standing for AI Ready Infrastructure. What's significant about AIRI is that it is a... Well, I'll tell you years ago, I'm showing my age there was this constant well of data warehousing appliance, a pre-bundled, pre-integrated assembly of storage and compute and software for specific workloads. Though, I wouldn't use the term appliance here, it's a similar concept. In the AI space, there's a need for pre-integrated storage and compute devices...racks...for training workloads and other core, very compute and very data-intensive workloads for AI And that's what the Pure Storage NVIDIA AIRI is all about. It includes Pure Storage's Flashblade storage technology, plus four NVIDIA DCX supercomputers that are running the latest GPUs, the Tesla V100. As well as providing a fast interconnect of NVIDIA's. Plus, also bundling software, NVIDIA's AI frame was from modeling, there's a management tool from Pure Storage. What this is, this is a harbinger of what we expect, and Wikibon will be a broader range from these vendors and others of pre-built optimized AI storage products for premises based deployment, for hyperquads, really for complex AI pipelines involving data... Scientist data, engineers and others. We're very excited about this particular product, we think it has great potential and we believe there's a lot of pent-up demand for these kinds of pre-built hardware products. And that, in many ways, was by far the most significant story in the AI space this week. >> All right, so this has been...thanks very much for that Jim. So, more to come, moving more compute closer to the data. Part of a bigger trend. This has been a Wikibon Action Item Quick Take. >> (smooth techno music)

(upbeat music) >> Hi, I'm Peter Burris, and welcome once again to another Cube Conversation from our studios here in beautiful Palo Alto, California. Today, we've got a really special guest. We're going to be talking about AI and some of the new technologies that are making that even more valuable to business. And we're speaking with Roy Kim, who's the lead for AI solutions at Pure Storage. Roy, welcome to theCUBE. >> Thank you for having me, very excited. >> Well, so let's start by just, how does one get to be a lead for AI solutions? Tell us a little bit about that. >> Well, first of all, there aren't that many AI anything in the world today. But I did spend eight years at Nvidia, helping build out their AI practice. I'm fairly new to Storage, I'm about 11 months into Pure Storage, so, that's how you get into it, you cut your teeth on real stuff, and start at Nvidia. >> Let's talk about some real stuff, I have a thesis, I (mumbles) it by you and see what you think about it. The thesis that I have: Wikibon has been at the vanguard of talking about the role that flash is going to play, flash memory, flash storage systems, are going to play in changes in the technology industry. We were one of the first to really talk about it. And well, we believe, I believe, very strongly that if you take a look at all the changes that are happening today with AI and the commercialization of AI and even big data and some other things that are happening, a lot of that can be traced back directly to the transition from memory, which had very very long lag times, millisecond speed lag times, to flash, which is microsecond speed. And, when you go to microsecond, you can just do so much more with data, and it just seems as though that transition from disk to flash has kind of catalyzed a lot of this change, would you agree with that? >> Yeah, that transition from disk to flash was the fundamental transition within the storage industry. So the fundamental thing is that data is now fueling this whole AI revolution, and I would argue that the big data revolution with Hadoop Spark and all that is really the essence underneath it is to use data get insight. And so, disks were really fundamentally designed to store data and not to deliver data. If you think about it, the way that it's designed, it's really just to store as much data as possible. Flash is the other way around, it's to deliver data as fast as possible. That transition is fundamentally the reason why this is happening today. >> Well, it's good to be right. (laughs) >> Yeah, you are definitely right. >> So, the second observation I would make is that we're seeing, and it makes perfect sense, a move to start, or trend to start, move more processing closer to the data, especially, as you said, on flash systems that are capable of delivering data so much faster. Is that also starting to happen, in you experience? >> That's right. So this idea that you take a lot of this data and move it to compute as fast as possible-- >> Peter: Or move the compute even closer to the data. >> And the reason for that, and AI really exposes that as much as possible because AI is this idea that you have these really powerful processors that need as much data as quickly as possible to turn that around into neural networks that give you insight. That actually leads to what I'll be talking about, but the thing that we built, this thing called AIRI, this idea that you pull compute, and storage, and networking all into this compact design so there is no bottleneck, that data lives close to compute, and delivers that fastest performance for your neural network training. >> Let's talk about that a little bit. If we combine your background at Nvidia, the fact that you're currently at Pure, the role that flash plays in delivering data faster, the need for that faster delivery in AI applications, and now the possibility of moving GPUs and related types of technology even closer to the data. You guys have created a partnership with Nvidia, what exactly, tell us a little bit more about AIRI. >> Right, so, this week we announced AIRI. AIRI is the industry's first AI complete platform for enterprises. >> Peter: AI Ready-- >> AI Ready Infrastructure for enterprises, that's where AIRI comes from. It really brought Nvidia and Pure together because we saw a lot of these trends within customers that are really cutting their teeth in building an infrastructure, and it was hard. There's a lot of intricate details that go into building AI infrastructure. And, we have lots of mutual customers at Nvidia, and we found is that there some best practices that we can pull into a single solution, whether it's hardware and software, so that the rest of the enterprises can just get up and running quickly. And that is represented in AIRI. >> We know it's hard because if it was easy it would've been done a long time ago. So tell us a little bit about, specifically about the types of technologies that are embedded within AIRI. How does it work? >> So, if you think about what's required to build deep learning and AI practice, you start from data scientists, and you go into frameworks like TensorFlow and PyTorch, you may have heard of them, then you go into the tools and then GPUs, InfiniBand typically is networking of choice, and then flash, right? >> So these are all the components, all these parts that you have access to. >> That's right, that's right. And so enterprises today, they have to build all of this together by hand to get their data centers ready for AI. What AIRI represents everything but data scientists, so start from the tools like TensorFlow all the way down to flash, all built and tuned into a single solution so that all, really, enterprises need to do is give it to a data scientist and to get up and running. >> So, we've done a fair amount of research on this at Wikibon, and we discovered that one of the reasons why big data and AI-related projects have not been as successful as they might have been, is precisely because so much time was spent trying to understand the underlying technologies in the infrastructure required to process it. And, even though it was often to procure this stuff, it took a long time to integrate, a long time to test, a long time to master before you could bring application orientations to bear on the problems. What you're saying is you're slicing all that off so that folks that are trying to do artificial intelligence related workloads can have a much better time-to-value. Have I got that right? >> That's right. So, think about, just within that stack, everything I just talked about InfiniBand. Enterprises are like, "What is InfiniBand?" GPU, a lot of people know what GPU is, but enterprises will say that they've never deployed GPUs. Think about TensorFlow or PyTorch, these are tools that are necessary to data scientists, but enterprises are like, "Oh, my goodness, what is that?" So, all of this is really foreign to enterprises, and they're spending months and months trying to figure out what it is, and how to deploy it, how to design it, and-- >> How to make it work together. >> How to make it work together. And so, what Nvidia and Pure decided to do is take all the learnings that we had from these pioneers, trailblazers within the enterprise industry, bring all those best practices into a single solution, so that enterprises don't have to worry about InfiniBand, or ethernet, or GPUs, or scale out flash, or TensorFlow. It just works. >> So, it sounds like it's a solution that's specifically designed and delivered to increase the productivity of data scientists as they try to do data science. So, tell us a little bit about some of those impacts. What kinds of early insights about more productivity with data science are you starting to see as a consequence of this approach. >> Yeah, you know, you'll be surprised that most data scientists doing AI today, when they kick off a job, it takes a month to finish. So think about that. When someone, I'm a data scientist, I come in on Monday, early February, I kick off a job, I go on vacation for four weeks, I come back and it's still running. >> What do you mean by "kicking off a job?" >> It means I start this workload that helps train neural nets, right? It requires GPUs to start computing, and the TensorFlow to work, and the data to get it consumed. >> You're talking about, it takes weeks to run a job that does relatively simple things in a data science sense, like train a model. >> Train a model, takes a month. And so, the scary thing about that is you really have 12 tries a year to get it right. Just imagine that. And that's not something that we want enterprises to suffer through. And so, what AIRI does, it cuts what used to take a month down to a week. Now, that's amazing, if you think about it. What used to, they only had 12 tries in a year, now they have 48 tries in a year. Transformative, right? The way that that worked is we, in AIRI, if you look at it there's actually four servers with FlashBlade. We figured out a way to have that job run across all four servers to give you 4X the throughput. Think that that's easy to do, but it actually is not. >> So you parallelized it. >> We parallelized it. >> And that is not necessarily easy to do. These are often not particularly simple jobs. >> But, that's why no one's doing it today. >> But, if you think about it, going back to your point, it's like the individual who takes performance-enhancement drugs so they can get one more workout than the competition and that lets them hit another 10, 15 home runs which leads to millions of extra dollars. You're kind of saying something similar. You used to be able to get only 12 workouts a year, now you can do 48 workouts, which business is going to be stronger and more successful as a result. >> That's a great analogy. Another way to look at it is, a typical data scientist probably makes about half a million dollars a year. What if you get 4X the productivity out of that person? So, you get the return of two million dollars in return, out of that $500,000 investment you make. That's another way of saying performance-enhancing drug for that data scientist. >> But I honestly think it's even more than that. Because, there's a lot of other support staff that are today, doing a lot of the data science grunt work, let's call it. Lining up the pipelines, building the, testing pipelines, making sure that they run, testing sources, testing sinks. And, this is reducing the need for infrastructure types of tasks. So, you're getting more productivity out of the data scientitists, but you're also getting more productivity out of all the people who heretofore were, you were spending on doing this type of stuff, when all they were doing was just taking care of the infrastructure. >> Yeah. >> Is that right? >> That's exactly right. We have a customer in the UK, one of the world's largest hedge fund companies that's publicly traded. And, what they told us is that, with FlashBlade, and not necessarily an AIRI customer at this time, but they're actually doing AI with FlashBlade today at Pure, from Pure. What they said is, with FlashBlade they actually got two engineers that were full time taking care of infrastructure, now they're doing data science. Right? To your point, that they don't have to worry about infrastructure anymore, because the simplicity of what we bring from Pure. And so now they're working on models to help them make more money. >> So the half a million dollars a year that you were spending on a data scientist and a couple of administrators, that you were getting two million dollars worth, that you're now getting two million dollars return, you can now take those administrators and have them start doing more data science, without necessarily paying them more. It's a little secret. But you're now getting four, five, six million dollars in return as a consequence of this system. >> That's right. >> As we think about where AIRI is now, and you think about where it's going to go, give us a sense of, kind of, how this presages new approaches to thinking about problem solving as it relates to AI and other types of things. >> One of the beauty about AI is that it's always evolving. What used to be what they call CNNs as the most popular model, now is GANs, which-- >> CNN stands for? >> Convolution Neural Nets. Typically used for image processing. Now, people are using things like Generative Adversarial Networks, which is putting two networks against each other to-- >> See which one works and is more productive. >> And so, that happened in a matter of a couple of years. AI's always changing, always evolving, always getting better and so it really gives us an opportunity to think about how does AIRI evolve to keep up and bring the best, state of the art technology to the data scientist. There's actually boundless opportunities to-- >> Well, even if you talk about GANs, or Generative Adversarial Networks, the basic algorithms have been in place for 15, 20, maybe even longer, 30 years. But, the technology wouldn't allow it to work. And so, really what we're talking about is a combination of deep understanding of how some of these algorithms work, that's been around for a long time, and the practical ability to get business value out of them. And that's kind of why this is such an exploding thing, because there's been so much knowledge about how this stuff, or what this stuff could do, that now we can actually apply it to some of these complex business problems. >> That's exactly right. I tell people that the promise of big data has been around for a long time. People have been talking about big data for 10, 20 years. AI is really the first killer application of big data. Hadoop's been around for a really long time, but we know that people have struggled with Hadoop. Spark has been great but what AI does is it really taps into the big data platform and translates that into insight. And whatever the data is. Video, text, all kinds of data can, you can use AI on. That really is the reason why there's a lot of excitement around AI. It really is the first killer application for big data. >> I would say it's even more than that. It's an application, but it's also, we think there's a bifurcation, we think that we're seeing an increased convergence inside the infrastructure, which is offering up greater specialization in AI. So, AI as an application, but it also will be the combination of tooling, especially for data scientists, will be the new platform by which you build these new classes of applications. You won't even know you're using AI, you'll just build an application that has those capabilities, right? >> Right, that's right, I mean I think it's as technical as that or as simple as when you use your iPhone and you're talking to Siri, you don't know that you're talking to AI, it's just part of your daily life. >> Or, looking at having it recognize your face. I mean, that is processing, the algorithms have been in place for a long time, but it was only recently that we had the hardware that was capable of doing it. And Pure Storage is now bringing a lot of that to the enterprise through this relationship with Nvidia. >> That's right, so AIRI does represent all the best of AI infrastructure from all our customers, we pulled it into what AIRI is, and we're both really excited to give it to all our customers. >> So, I guess it's a good time to be the lead for AI solutions at Pure Storage, huh? >> (laughs) That's right. There's a ton of work, but a lot of excitement. You know, this is really the first time a storage company was spotlighted and became, and went on the grand stage of AI. There's always been Nvidia, there's always been Google, Facebook, and Hyperscalers, but when was the last time a storage company was highlighted on the grand stage of AI? >> Don't think it will be the last time, though. >> You know, it's to your point that this transition from disk to flash is that big transition in industry. And fate has it that Pure Storage has the best flash-based solution for deep learning. >> So, I got one more question for you. So, we've got a number of people that are watching the video, watching us talk, a lot of them very interested in AI, trying to do AI, you've got a fair amount of experience. What are the most interesting problems that you think we should be focusing on with AI? >> Wow, that's a good one. Well, there's so many-- >> Other than using storage better. >> (laughs) Yeah, I think there's so many applications just think about customer experience, just one of the most frustrating things for a lot of people is when they dial in and they have to go through five different prompts to get to the right person. That area alone could use a lot of intelligence in the system. I think, by the time they actually speak to a real live person, they're just frustrated and the customer experience is poor. So, that's one area I know that there's a lot of research in how does AI enhance that experience. In fact, one of our customers is Global Response, and they are a call center services company as well as an off-shoring company, and they're doing exactly that. They're using AI to understand the sentiment of the caller, and give a better experience. >> All that's predicated on the ability to do the delivery. So, I'd like to see AI be used to sell AI. (Roy laughs) Alright, so Roy Kim, who's the lead of AI solutions at Pure Storage. Roy, thank you very much for being on theCUBE and talking with us about AIRI and the evolving relationship between hardware, specifically storage, and new classes of business solutions powered by AI. >> Thank you for inviting me. >> And again, I'm Peter Burris, and once again, you've been watching theCUBE, talk to you soon. (upbeat music)