 Live from San Francisco, it's theCube covering Pure Accelerate 2017. Brought to you by Pure Storage. Welcome back to Pure Accelerate. We're here at Pure 70 in San Francisco and this is theCube, the leader in live tech coverage. I'm Dave Vellante with my co-host, David Floyer. Matt Kicks, Kicks Moller is here. He's the vice president of product and solutions at Pure Storage. Kicks, welcome to theCube. Thanks for having me. My first time on theCube, I'm honored. It's awesome. We're honored to have you. We got to have a nickname on theCube. We had Deets on earlier. Stu had to leave. You can call me V if you want. You really don't have a nickname. We call him Floyer. All right. So anyway, great job today on stage. You got a really engaged audience. You guys have a lot of fun. The orange shoes are cool. How do you feel? I feel great. I think as we said today, this is the biggest year we've ever had in innovation at Pure. And it was fun to really take the focus back to software this release. We spent the last year bringing out our next gen kind of cloud era, all flash platforms between FlashBlade and FlashArrayX. And this was an opportunity to really kind of flex our muscles around software, flex our muscles around IoT and AI and that as well. So it was a fun set of releases. Well, it's been interesting to watch you guys and you watch your product strategy evolve. And of course, coincident to that, is your TAM expands, right? So it started in the sort of lower end of the spectrum and then it went into the 20s and now it's in the 30s. And I was saying to David, it used to be, well, I buy EMC for block and I buy NetApp for file. And you guys are challenging that sort of convention. Maybe talk a little bit about your strategy and how you're penetrating now new markets. Yeah, we think about our market opportunity in kind of three buckets. So first off, we go after the top 500 cloud providers. And we see, one of our biggest segments is really cloud providers. And we see them increasingly not really looking at legacy options for storage. They want a modern storage fabric. And part of why we're so excited in particular about the work we've done around NVME is we feel like it helps us go after some of the more server-dash-centric workloads of the past or of the next-gen workloads. And we can talk a little bit more about that. A second key area that we're focusing on is really going after next-generation data-driven applications. And AI, ML, all these areas are really driving amazing storage growth. It's even, I think, surprised us how quickly it's come up. And you had folks on theCUBE earlier today talking about FlashBlade. But one of the kind of threads that I think unites a lot of the next-gen applications is they're designed to be scale-out and they're designed to really need a lot of parallelism from storage. And so what we're doing with FlashBlade is really designing a storage platform that's kind of parallel from the start and can deliver that massive concurrency that you just can't get from a lot of legacy providers. And then, yeah, I think the third thing we're obviously excited about is going in and kind of ripping out the spinning rest of the past. We've made a lot of innuendos at this conference and how we're in this kind of classic, rusting building. And maybe it's a nice metaphor for some of that. Here it is. Yeah, but we're helping liberate the X world. And I think one of the things that we're excited about today was to announce Purity Active Cluster. You know, that's been kind of that top of the reliability hill feature when people want Metro-clustered applications active, active, and two data centers. That's about as reliable as it gets. And that was a feature that we didn't have in Flashray until now. And so we're excited to kind of have that final area to go in and help liberate. Yeah, so it's not just the sort of disk spinning rust replacement. You talked this morning about SRDF. I remember well in the early 1990s when SRDF came out and it was game changing and it obviously has driven a lot of revenue for EMC, now Dell EMC, and it helped a lot of customers, but there's no question that it was the mother of all complexity and cost. So talk a little bit more about how you guys are going to approach that problem. Yeah, I mean, I think if you look at a lot of what we announced today, there just continues to be a threat of simplicity throughout everything. And, you know, I was employee number six if you're right, I've been on the adventure from day one, right? And I think we always had a fundamental belief in simplicity. But as we started to ship products and started to get customer feedback, there was like this lightning rod within our team, all throughout engineering, where people really understood the power of simplicity. And it kind of went from a belief to a religion, I would almost say. And, you know, we've just always tried to do that with every new feature we come out with. And this felt like an area where there was such a vacuum of simplicity that there was a huge opportunity to rethink things. And so with this feature, it's totally built in, it's totally integrated. You can easily just stretch a volume across now two sites. And one of the problems we went to go solve was the third site media problem where you always need a third site witness in a stretch cluster to kind of determine if there is a failure, who's the surviving site that you want to have actually process the application I.O. And so we're delivering that as a service, as a SaaS service from our Pure One infrastructure. So it's just one more way that we take one more step and one more kind of pain of the infrastructure away. So I'd like to drill in a little bit on the NVME side of this. We've done some research on the architecture we think is coming up, which we're calling Unigrid because it allows this very even access to data at very low latencies across there. And really we'll start, in our view, a different sort of applications. Really very, very different way you can combine legacy state applications with the AI applications and other things like that. How are you going to bring that to market? Who are you selling that to? Yeah, I mean, we're super excited about this transition to NVME and we're trying to take a real leadership role here. And so much of it reminds us actually of the early days of Pure. We started Pure, Flash was expensive, it was exotic, had a bunch of people trying to make it this 1% technology. And our whole idea was, look, let's not make it a Ferrari. Let's democratize it for all and we think everybody deserves Flash and we did a bunch of work to try to mainstream it. And we're trying to take a very similar approach with NVME where a lot of the early folks who approached NVME built very specialized appliances that kind of exotic things. And our view is this should be mainstream. All Flash arrays should be built on NVME. And the real advantage is something you hinted at. It's just massively parallel. And so here you have Flash, this inherently parallel medium on its own. And we're talking to it through these legacy SCSI protocols that have been around forever. NVME is a huge opportunity to finally open that up. But we had an initial insight, I believe, where when we approached this, we didn't just say, look, we should just go get an NVME SSD. We realized that that whole architecture has to be optimized from software to hardware. And so we kind of foregoed or forewent the SSD form factor. We built our own direct Flash module. And the real magic of how we've approached this is not only shipping a device that's massively parallel, but building a bunch of software within purity that knows how to take advantage of that and brings all the Flash management up to the software tier. So we can kind of take advantage of it end to end. And so these are things we just don't see our competitors in the market doing right now. Maybe one more comment on your kind of parallelism. And I think you're right in that if you look at a wide range of kind of the next generation web scale applications, whether they'd be just more classic, no SQL databases on through to analytics, on through to AI and ML, and ML are kind of maybe the most extreme examples, but they're all far more parallel scale out applications than we were used to before. And so they thrive in environments where you have storage that can kind of marry that model. And what we're finding in particular in the AI world is that we're not up against other storage vendors. I mean, the alternative really is to go get a bunch of white box DAS and build your own storage layer and maybe use some open source stuff, but that's cumbersome. And that has all the issues that everyone's aware of with that, right? And so we believe that as a commercialized product, we have something pretty unique to go after these markets and it's been exciting to see it even push us. One of the things I think we surprised people with today was making FlashBlade 5x bigger. We announced it last year, people thought it was pretty big and fast to begin with, but it was these use cases and the early adopters that pushed us to make it larger. We saw people in the early adopter phase of FlashBlade buy in and deploy at much bigger scale than we were expecting. We were kind of used to our experience with FlashArray where people started small, they got to use the technology, then they kind of grew. But I guess you don't do big data on a small scale. So people dive in. So I want to ask you about this whole big data. This is probably the first time we've even used that term today. It's amazing how fast that came and went, even though big data is now mainstream. But in you said, you made the point, Matt, that not a lot of storage competitors are going after that. Well, you think big data storage, they would fit, but I think a lot of the competitors realize, well, there's not a lot of money to be made there. And now it's just hitting its best stride. Here's my question. If you look at Hortonworks in Cloudera in particular, you're starting to see the cloud guys, Amazon with its data pipeline, certainly Google and Microsoft, are picking up a lot of action in the cloud with a full as a service sort of data pipeline. What do you see, and it's affecting some of the on-prem activity. What are you seeing with regard to cloud versus on-prem and how does that affect your business? Yeah, I think you're right in the sense that if you looked at how you could have deployed big data technologies before, I think there were basically two ways to do it. People did it in the cloud, or they did it on-prem with white boxed as, and they've got servers and put disks inside. So much of the first generation of big data was basically driven on Hadoop, which was fairly low cost and fairly focused at streaming workloads where you had this kind of frankly not much performance profile or need for performance on disk. And so what we've found in the early days was, hey, if you tried to put flash underneath it, it didn't help that much. But the thing that's changing now is people want to move away from those kind of slow batch queries to much more interactive analysis, much more real-time. And so Hadoop's giving way to Spark, and so that's changed that discussion quite a bit. Back to the discussion though around kind of on-prem versus the cloud. I think this is an area where as people get more and more invested in their data, they're understanding it's a key control point. And so if I get all my data into one cloud provider, it's pretty hard to get it out of there. This is core to my business. Do I want that level of lock-in? Also, can I do better with my own dedicated solutions? And what we've found is that when we can bring Flash paid to bear, these big data workloads, we can outperform what people do in the cloud handedly at a lower cost. And so there's a proclivity to want to kind of own your own destiny, to own your own infrastructure, and the ability for us to deliver higher performance at a lower cost in the cloud, we think it's a pretty good connection. And of course complexity is hurt, that is a hurt. I mean the market's growing very nicely, but it's actually hurt a lot of the practitioner's ability to absorb technology. I suppose pure and it's insane focus on simplicity helps a little bit. But as the spark, that sort of simplified the whole Hadoop thing, but you've still got, you need a lot of smart people to make this stuff work. So it's going to be interesting to see, but what I'm hearing from you is you don't have a lot of storage competitors going hard after this, right? And so the guys that have done really well with Hadoop that have on-prem infrastructure, you would think would be picking this up quite rapidly. Yeah, well and look, we're having discussions with all of the Hadoop providers as well, because if we can help them deliver a higher customer satisfaction and a better outcome, it's upside for them as well. They don't want to be storage companies. Well, they need help. I mean, the irony is that cloud era's in the cloud era and the cloud is eating away at its base. So they need somebody who's going to help them simplify in their software company, help us simplify the on-prem infrastructure. One other thing you said earlier that I think it's been an interesting learning for us in FlashBlade as well. I mean, when we kind of went into the FlashBlade experience, we kind of expected that people would buy and all they would care about is performance. And so we asked ourselves, well, how much does this user base really care about simplicity? We found the total opposite to be true. Most of who we're selling FlashBlade to are not IT folk. They're data scientists, they're engineers, they're creatives, they're a line of business people and they want nothing to do with managing infrastructure. And so the simplicity oftentimes we're replacing what would have been racks and racks of disks that they didn't want to deal with it to begin with. And so the simplicity value prop, shockingly, is actually more important we were finding for FlashBlade even than FlashArray. Next slide, we have it saying in the cube that data is the new development kit, because it's like you say, it's data engineers, it's data scientists, even application developers starting with the data. And so complexity has choked that whole industry. So that's excellent. Okay. I was going to ask, one of the things you were saying very clearly here is that the drive of getting data up to the cloud to do this AI or up to anywhere to do the processing to create the models is going to have to be ameliorated by reduction of that data, by reduction I mean turning that data into information or tags or whatever it is as it's going up the line very close to where the data is actually produced. I caught the needles in the haystack. Yeah, extract the needles very early on. So can you talk a little bit more about what your vision is there? How are you going to do that? Who you're partnering with to do that? Yeah, so I think you hit on a very important problem. And I think everybody is starting to finally internalize how much faster devices and machines can generate data than humans. So we're used to this kind of human era of cognition of data creation, but this asymptote is happening. And I think it's becoming quite obvious that basically machines have the potential to generate data much faster than it can be stored, used, and especially sent back to the cloud. And so you need some level of local processing to analyze it, to send back more kind of pertinent metadata. The other challenge is that many of the use cases that people want to use at the edge are latency sensitive. And so you can't take the time to think about it, send it all back, think about it, send it back again, and do some real time control thing, right? My favorite anecdote that kind of proves this is some of Amazon's infrastructure where they build out dedicated data centers within their distribution facilities because they need to be able to kind of real time analyze the video feeds of everything that's going on and make decisions, right? And so if they can't send all the data to their cloud, they have to build their own data center. Nobody can. So it's just indicative of a broader to the listener, right? You'll see a demo that we're going to be doing tomorrow where we're doing a great coprocessing app where we're kind of collecting a bunch of data here at the show analyzing it and then sending part of it up to the cloud and partnering with Google to analyze it there and showcasing kind of an example use case of this. And so we think it's an area that's going to be important. You know, part of that also brings us to what we've done with our purity run. So one of the things we announced today was opening up our purity platform to third party code to developers. And we see a number of use cases for this. Many of our cloud customers have asked for this where they want to kind of tie the storage more directly into their application. But the other use case we see is the edge where if we can deploy a local pure device on your oil rig, in your plane, in your factory, whatever and have that processing capability happen there and then have that summarize the data to be able to send it back. It provides more of an all in one solution for that. And so we don't have dedicated products in this space yet but this is our way of opening up the platform to be able to see how people develop on it and how they can start taking advantage of that. Okay, so we got a wrap but you were telling us you were employee number six. So that's quite a ride. I mean, so many companies just don't get to reach escape velocity to use that term. You guys did, what's next for you? Where do you want to take this thing? Yeah, I think we're all extraordinarily excited here at Pure. I mean, so much of this first generation of Pure's growth has been reshaping the existing storage environment and we feel like we're kind of through that mission. Yes, okay, only 20% or so of vendor by storage is flash but the writing is on the wall, we're delivering the products, that is momentum now, right? And so so much of our next generation of innovation is going after these new data-driven use cases, helping cloud providers, just going after what's next and that opens up a much broader definition of what you can be as a data company. We kind of stopped referring to ourselves as a storage company and we might have to get storage out of the name at some point but going after the broader problems around data is a much more exciting mission that we think powers the next decade, so lots to do. Great, all right, Kix, thanks very much for coming to theCUBE. It was great to have you. Appreciate it. All right, keep it right there, everybody. We'll be back to wrap up right after this short break. This is theCUBE, we're live from Pure Accelerate 2017. Right back.