[Narrator] From Sunnyvale, California, in the heart of Silicon Valley, it's theCUBE. Covering Accelerate Your Journey to AI, brought to you by NetApp. >> Hi, I'm Peter Burris and welcome to a great conversation here from NetApp's Data Visionary Center. Specifically, we've got Monty Barlow from Cambridge Consultants. Monty is the head of Artificial Intelligence at a relatively well-known, august, consultant group. Monty, welcome to the Cube. >> Thank you, Peter. Pleased to be here. >> Monty, what we're going to do is we're going to spend a number of minutes talking about some of the trends and transformations that are being made by and wrought by AI. But let's start, what is Cambridge Consultants? >> So, Cambridge Consultants does technology, product, and service development for customers all across the world. Probably about 400 new projects starting each year. And what's a common thread in them is that they're technically difficult and innovative, so there's something really challenging about them they're typically strategic for our customers, and they're looking to do something disruptive and do it fast. >> So, you have a pretty broad range of customers that you're utilizing or you're working with. Let's ask the question then, give us some examples of some of the customer cases that you've been working on, specifically as it relates to AI. >> Sure, so we're working with everything from blue chips to startups, and what they're looking for is slightly different from AI. I can't talk about some of the confidential details but some really interesting applications, for example, one for me, is precision agriculture. We've heard a lot about improving crop yield, but we're reaching the point now where you can drive over a crop and recognize it from a weed and put water on just the crop and pesticide on just the weed, so you get a much better yield, you cut down on water, you cut down on pesticide, and it's a really nice application where it's a win-win for everything. >> So, as we think about some of these big issues associated with inventing technology and inventing AI-related stuff to do many of the things we're talking about, we also have to recognize that there's a social side of introducing AI. There's the invention and then there's the innovative side, which is in many respects the social things. How do you get people to adopt this stuff? What are the challenges that you're seeing customers face as they conceive how best to adopt AI and AI-related capabilities within markets? >> Sure. I think in most of the markets we work in, the benefits are becoming so clear that there's not a massive reluctance to adopt or difficulty. There's obviously in the public, those normal fears about loss of jobs, or safety or security, having machines do jobs for you that you might wish a person to do for you. And those are there in some markets like healthcare, in particular, but many markets see no such problems and the benefits of being able to do innovative things scaleably, flexibly, out performing humans in many cases, it just makes economic sense. >> So, is it just the numbers? Is that what big companies are doing to ensure more rapid time to value for AI related things, or are there other things big companies are doing to try to facilitate the introduction of some of these advanced technologies? >> It depends from company to company, there's all sorts of ways they're approaching this. Maybe trialing early services that introduce people gently to AI, get them accustomed to it, of course, that's what's been the case for social media. None of us believed we were using AI in the early days and then suddenly we realized that we're interacting with it on an almost daily basis. Through to targeted trials, all sorts of different approaches being taken. >> AI's been associated with a lot of different algorithmic forms. It's been a lot of different basic models for thinking about how you do it. Machine learning, deep learning, predictive analysis, recommendation analysis. What's the difference particularly between AI, machine learning, ML and deep learning, DL? >> OK, if I could take a step back for a moment, we've been working with AI for decades, and as you say, there's some really quite old school techniques out there. Decision support, expert systems, where the idea was that you embody the coder's, the programmer's knowledge in a system, and really, all it could do is replay that. So, at best it could act as well as the person who programmed it. >> Very rules-driven. >> Very, very rules-driven. We then, in the early 2000s, saw machine learning beginning to surface more, that's where a system learns, perhaps a few parameters from some data it does learn by itself, but it's doing something quite simple, you know it's from the vibrations in the road, counting the axles of the vehicles going past. Or in an industrial process monitoring temperature, pressure, and saying, "This process is going well." >> But not rules-driven, still data-driven? >> Data-driven. Deep learning just takes, that to a whole new scale. It is still machines learning from data but now a few parameters has become millions or billions. You can now point a camera at a road and recognize all of the different vehicle types, instead of just how many axles they've got, for example. >> And so, the notion of that is that it's a focus on patterns that it discovers out of the data, as opposed to rules or patterns that are put into the data by a developer or by a data person. >> Absolutely, you don't always know what insight you're going to derive from a data set. >> So, I understand that Cambridge Consultants uses a variety of technologies, but specifically, you're utilizing this NetApp and NVIDIA gear in your labs. Talk a little about that experience, how's that been? >> Sure. So, time is everything for us as a business, and for our customers. People want to be first to a particular market window and this AI is still at some level, experimental. We don't know what it's going to do in three or five years time. So, key to our business is a fast turnaround on proof of concepts, how would this work? What would happen? Perhaps our customer's got some data and they need to know if they need a trial to collect more. So, getting through jobs quickly is what matters most to us, and that's what the NVIDIA and NetApp equipment is all about. For GPUs, its the case of big parallel processing large models, crunching the numbers and adjusting the parameters quickly, but equally important is the ability to get data from storage into those GPUs, quickly. >> And so, there is a relationship between the characteristics of the hardware and the success of the AI efforts? >> Absolutely. And it's a really demanding application for file serving. It's the most demanding we've ever seen because it's potentially millions or billions of tiny files that have to be called up in different patterns, quite randomly, it's not just like for example, streaming video, it's too much to cache locally. You need really high performance equipment to manage the data quickly enough that you can learn something in days, and not in months. >> One of the crucial features of any AI development effort is this notion of a data pipeline. How you stage change to the data, where it is, knowing how to move it, when to move it, do it with speed, do it at scale. Talk a little bit about the differences between AI-driven data pipelines and some of the other data pipelines that have been out there. >> Sure. The difference we tend to see on AI is it's touching the real world more directly. So, you may have data coming in live from the edge, from sensors, and that's not as carefully clean, sanitized, formatted as you might expect in a normal, say, enterprise database or data application. So, knowing what to do with those difficult cases, how to format it, what to reject, what to feed in, and then at the other end, how to present that decision, because AI is often making some form of decision, how to present that efficiently back to humans or how to make a quick sensible decision based on that, how to steer the vehicle in the correct direction, how to highlight a cancer, whatever it is we're doing. That pipeline from data first coming in through intelligence and back again to the real world is longer and more complicated and sophisticated than any other data pipeline we've seen before. >> Now, it's that sophistication, that length, the duration of the transactions, for example, that increases the complexity, that ultimately, big companies working with Cambridge Consultants and others, have to address so that they can be successful, get that time to value. But as you think about ultimately the challenges that you're trying to address with customers, what is that you're seeing in their AI projects that are more consistently associated with success, or more, unfortunately, perhaps, consistently associated with having to do it again? >> Sure, I'll limit my answer to those I feel who are doing genuine AI because there is an element of people labeling anything AI. But assuming they are doing something that's only been possible in the last few years that is innovative, difficult and complicated, it's really reaching the right distance. It's stretching themselves the correct amount. So, going into a new market, with new data, a new algorithmic approach is dangerous. There'll be a lot of iteration, a lot of learning needed before that'll come good. If you can take an approach that's beginning to work in one vertical to another, or you can start with data you understand and know perhaps from a previous big data application and start to do more intelligent things with it, then you can achieve these kind of breakthrough innovations and really impressive systems that AI can today. >> So, novel data, practiced algorithms and hardware that works. >> And don't mix up too many new factors together, absolutely. >> Monty Barlow, head of Artificial Intelligence at Cambridge Consultants, thanks very much for being on theCUBE. >> Thank you, Peter. (upbeat electronic music)

welcome back to the queue we are live at peer storage accelerate 2089 Lisa Martin with Dave Allen say we are here in San Francisco at the Bill Graham Civic Auditorium which is why we're sporting concert t-shirts through the WHO and the Quan Rocha [Music] I really view technology as being a three-legged store compute networking and storage and storage didn't keep up at the same time that data was exploding we spent a good chunk of time troubleshooting issues directly related to storage before whether it was storage creep where we had too much data versus of the capacity of the array or the input-output problems in terms of i/o latency those types of issues we don't see any of that anymore pure has always said we're gonna make flash cheaper than or as cheap as spinning disk and we're gonna drive performance and we're gonna differentiate from the market and we're gonna be first right now only about 1/2 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 gonna be hot it's all got to be available able to be analyzed able to be mined and they talked about going all-in on shared accelerated storage it's gonna be multi cloud and so you wanted to provide an architecture a platform that removed the trade-offs of the bottlenecks while also being open and allowing customers to take advantage of container technologies and platform as-a-service technology was the CIO I would imagine you don't want to wake up every day and think about storage that's all without respect to our friends at pure storage should be self-driving infrastructure should be self-driving these are not things that you know in a boardroom people care about but they care about is how can they can change their business and have a competitive edge one of the things that really impresses me is their focus on sharing multiple workloads in one place we get a lot of virtual servers running on you know relatively small amount of storage we went from 40 you of old-school spinning disk lots of complexity and cabling administration down to 2/3 you m20 arrays they're more heat tolerant I have two power cables in each and two network cables so complexity is gone they're providing the pickaxes in the shovels and the basic tools but the real challenges of AI are where do I apply it 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 if you understand the way machine learning operates it has to practice on tens of thousands millions of samples it could take all year or it can take hours and what wanted to do is take minutes or hours those things will start to identify patterns and genomic sequences that humans aren't you know finding with their typical approaches our genomic platforms built up to the point where they have enough sequences in them to do that sort of analysis and you need you know big compute fast storage to do that you can add capacity and and upgrade your software and move to the next generation non-disruptive Lee why is this a big deal three decades you would have to actually shut down you know the storage array have planned downtime to do an upgrade so pure solve that problem with its evergreen model and its software capability every three years they swap out your controller as part of your support and maintenance agreement which is you know huge for us because we don't have a lot of money we have budget is very small for us it you have a green model was brilliant for us but simplicity was critical I can't just keep adding stuff to look after a new technology you need to look after itself no uplifts on pricing for nvme so everybody's gonna follow that the Evergreen model they can do these things and claim these things as we were first of course we know David for you were first to make the call but but pure was right there with you we've always wanted to be the pioneers you know we always wanted to be the innovators we always wanted to challenge convention the other piece that they've done really well is marketing and marketing is how companies differentiate today you've seen that sea of orange that's here it's a bit infectious people are become dedicated you know not to an entity they become it dedicated to a cause enabling so many 4,800 plus 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 pride and the value of what they're delivering to their customers gratulations thank you we're just getting started and we really appreciate all the work you guys do so thanks for being here thanks so much David for joining me all day who to my awesome David thanks for joining us on the wrap we appreciate you watching the cube from pure storage accelerate 2018 I'm Lisa Martin for Dave and David thanks for watching [Music]

or is done a great job of simplifying the experience for the customer no question much in the same way that 3-part 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 today there's no question about things better today dramatically let's have a plan they get you in the future but also create a community an ecosystem where are aware of what's happening in the DevOps side and connect the dots between IT and the data science you know storage was really I think up until now really viewed as maybe you know an aging technology something that was you know becoming commoditized something where where innovation wasn't really important and fewer was the one company that actually thought that storage was important realized a partnership with your with all flash and the faster network and Tasha compute we realized there is something unique that we can bring to bear for the customer so our partnership mindset really said this is the next big one that we're going to invest time and energy and so we clearly did that and then we continued you know helping physicians when they're working directly with patients there's only these are there's so many systems so many datasets so many ways to analyze and yet like getting it all in front of them in some kind of real-time way so that they can use it effectively as tricky so AI machine learning have a chance to help us like funnel that into something that's immediately useful in the moment

>> Narrator: 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 coverage of Pure Storage Accelerate 2018. I'm Lisa Martin. Been here all day with Dave Vellante. We're joined by David Floyer now. Guys, really interesting, very informative day. We got to talk to a lot of puritans, but also a breadth of customers, from Mercedes Formula One, to Simpson Strong-Tie to UCLA's School of Medicine. Lot of impact that data is making in a diverse set of industries. Dave, you've been sitting here, with me, all day. What are some of the key takeaways that you have from today? >> Well, Pure's winning in the marketplace. I mean, Pure said, "We're not going to bump along. "We're going to go for it. "We're going to drive growth. "We don't care if we lose money, early on." They bet that the street would reward that model, it has. Kind of a little mini Amazon, version of Amazon model. Grow, grow, grow, worry about profits down the road. They're eking out a slight, little positive free cashflow, on a non-gap basis, so that's good. And they were first with All-Flash, really kind of early on. They kind of won that game. You heard David, today. The NVMe, the first with NVMe. No uplifts on pricing for NVMe. So everybody's going to follow that. They can do the Evergreen model. The can do these things and claim these things as we were first. Of course, we know, David Floyer, you were first to make the call, back in 2008, (laughs) on Flash and the All-Flash data center, but Pure was right there with you. So they're winning in that respect. Their ecosystem is growing. But, you know, storage companies never really have this massive ecosystem that follow them. They really have to do integration. So that's, that's a good thing. So, you know, we're watching growth, we're watching continued execution. It seems like they are betting that their product portfolio, their platform, can serve a lot of different workloads. And it's going to be interesting to see if they can get to two billion, the kind of, the next milestone. They hit a billion. Can they get to two billion with the existing sort of product portfolio and roadmap, or do they have to do M&A? >> David: You're right. >> That's one thing to watch. The other is, can Pure remain independent? David, you know well, we used to have this conversation, all the time, with the likes of David Scott, at 3PAR, and the guys at Compellent, Phil Soran and company. They weren't able, Frank Slootman at Data Domain, they weren't able to stay independent. They got taken out. They weren't pricey enough for the market not to buy them. They got bought out. You know, Pure, five billion dollar market cap, that's kind of rich for somebody to absorb. So it was kind of like NetApp. NetApp got too expensive to get acquired. So, can they achieve that next milestone, two billion. Can they get to five billion. The big difference-- >> Or is there any hiccup, on the way, which will-- >> Yeah, right, exactly. Well the other thing, too, is that, you know, NetApp's market was growing, pretty substantially, at the time, even though they got hit in the dot-com boom. The overall market for Pure isn't really growing. So they have to gain share in order to get to that two billion, three billion, five billion dollar mark. >> If you break the market into the flash and non flash, then they're in the much better half of the market. That one is still growing, from that perspective. >> Well, I kind of like to look at the service end piece of it. I mean, they use this term, by Gartner, today, the something, accelerated, it's a new Gartner term, in 2018-- >> Shared Accelerated Storage >> Shared Accelerated Storage. Gartner finally came up with a category that we called service end. I've been joking all day. Gartner has a better V.P. of naming than we do. (chuckles) We're looking' at service end. I mean, I started, first talking about it, in 2009, thanks to your guidance. But that chart that you have that shows the sort of service end, which is essentially Pure, right? It's the, it's not-- >> Yes. It's a little more software than Pure is. But Pure is an awful lot of software, yes. And showing it growing, at the expense of the other segments, you know. >> David: Particularly sad. >> Particularly sad. Very particularly sad. >> So they're really well positioned, from that standpoint. And, you know, the other thing, Lisa, that was really interesting, we heard from customers today, that they switched for simplicity. Okay, not a surprise. But they were relatively unhappy with some of their existing suppliers. >> Right. >> They got kind of crummy service from some of their existing suppliers. >> Right. >> Now these are, maybe, smaller companies. One customer called out SimpliVity, specifically. He said, "I loved 'em when they were an independent company, "now they're part of HPE, meh, "I don't get service like the way I used to." So, that's a sort of a warning sign and a concern. Maybe their, you know, HPE's prioritizing the bigger customers, maybe the more profitable customers, but that can come back to bite you. >> Lisa: Right. >> So Pure, the point is, Pure has the luxury of being able to lose money, service, like crazy, those customers that might not be as profitable, and grow from it's position of a smaller company, on up. >> Yeah, besides the Evergreen model and the simplicity being, resoundingly, drivers and benefits, that customers across, you know, from Formula One to medical schools, are having, you're right. The independence that Pure has currently is a selling factor for them. And it's also probably a big factor in retention. I mean, they've got a Net Promoter Score of over 83, which is extremely high. >> It's fantastic, isn't it? I think there would be VMI, that I know of, has even higher one, but it's a very, very high score. >> It's very high. They added 300 new customers, last quarter alone, bringing their global customer count to over 4800. And that was a resounding benefit that we were hearing. They, no matter how small, if it's Mercedes Formula One or the Department of Revenue in Mississippi, they all feel important. They feel like they're supported. And that's really key for driving something like a Net Promoter Score. >> Pure had definitely benefited from, it's taken share from EMC. It did early on with VMAX and Symmetrix and VNX. We've seen Dell EMC storage business, you know, decline. It probably has hit bottom, maybe it starts to grow again. When it starts to grow again, I think, even last quarter, it's growth, in dollars, was probably the size of Pure. (chuckles) You know, so, but Pure has definitely benefited from stealing share. The flip side of all this, is when you talk to you know, the CxOs, the big customers, they're doing these big digital transformations. They're not buying products, you know, they're buying transformations. They're buying sets of services. They're buying relationships, and big companies like Dell and IBM and HPE, who have large services arms, can vie for certain business that Pure, necessarily, can't. So, they've got the advantage of being smaller, nimbler, best of breed product, but they don't have this huge portfolio of capabilities that gives them a seat at the CxO table. And you saw that, today. Charlie Giancarlo, his talk, he's a techie. The guys here, Kicks, Hat, they're techies. They're hardcore storage guys. They love storage. It reminds me of the early days of EMC, you know, it's-- >> David: Or NetApp. Yeah. Yeah, or NetApp, right. They're really focused on that. So there's plenty of market for them, right now. But I wonder, David, if you could talk about, sort of architecturally, people used to criticize the two controller, you know, approach. It obviously seems to be doing very well. People take shots at their, the Evergreen model, saying "Oh, we can do that too." But, again, Pure was first. Architecturally, what's your assessment of Pure? >> So, the Evergreen, I think, is excellent. They've gone about that, well. I think, from a straighforward architecture, they kept it very simple. They made a couple of slightly, odd decisions. They went with their own NAND chips, putting them into their own stuff, which made them much smaller, much more compact, completely in charge of the storage stack. And that was a very important choice they made, and it's come out well for them. I have a feeling. My own view is that M.2 is actually going to be the form factor of the future, not the SSD. The Ssd just fitted into a hard disk slot. That was it's only benefit. So, when that comes along, and the NAND vendors want to increase the value that they get from these stacks, etc., I'm a little bit nervous about that. But, having said that, they can convert back. >> Yeah, I mean, that seems like something they could respond to, right? >> Yeah, absolutely. >> I was at the Micron financial analysts' meeting, this week. And a lot of people were expecting that, you know, the memory business has always been very cyclical, it's like the disk drive business. But, it looks like, because of the huge capital expenses required, it looks like supply, looks like they've got a good handle on supply. Micron made a good strong case to the street that, you know, the pricing is probably going to stay pretty favorable for them. So, I don't know what your thoughts are on that, but that could be a little bit of a head wind for some of the systems suppliers. >> I take that with a pinch of salt. They always want to have the market saying it's not going to go down. >> Of course, yeah. And then it crashes. (chuckles) >> The normal market place is, for any of that, is go through this series of S-curves, as you reach a certain point of volume, and 3D NAND has reached that point, that it will go down, inevitably, and then cue comes in,and then that there will go down, again, through that curve. So, I don't see the marketplace changes. I also think that there's plenty of room in the marketplace for enterprise, because the biggest majority of NAND production is for consumer, 80% goes to consumer. So there's plenty of space, in the marketplace, for enterprise to grow. >> But clearly, the prices have not come down as fast as expected because of supply constraints And the way in which companies like Pure have competed with spinning disks, go through excellent data reduction algorithms, right? >> Yes. >> So, at one point, you had predicted there would be a crossover between the cost per bit of flash and spinning disk. Has that crossover occurred, or-- >> Well, I added in the concept of sharing. >> Raw. >> Yeah, raw. But, added in the cost of sharing, the cost-benefit of sharing, and one of the things that really impresses me is their focus on sharing, which is to be able to share that data, for multiple workloads, in one place. And that's excellent technology, they have. And they're extending that from snapshots to cloud snaps, as well. >> Right. >> And I understand that benefit, but from a pure cost per bit standpoint, the crossover hasn't occurred? >> Oh no. No, they're never going to. I don't think they'll ever get to that. The second that happens, disks will just disappear, completely. >> Gosh, guys, I wish we had more time to wrap things up, but thanks, so much, Dave, for joining me all day-- >> Pleasure, Lisa. >> And sporting The Who to my Prince symbol. >> Awesome. >> David, thanks for joining us in the wrap. We appreciate you watching theCUBE, from Pure Storage Accelerate, 2018. I'm Lisa Martin, for Dave and David, thanks for watching.

(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. >> Welcome back to theCUBE, we are live at Pure Storage Accelerate, 2018 at the Bill Graham Civic Auditorium in San Francisco. I'm Lisa Martin sporting Prince today, with Dave Vellante sporting The Who. And I'm sandwiched, most importantly, between two Celtics fans. And the Warriors are across the bay. We'll save that for after the conversation. So we want to welcome to theCUBE for the first time Ken Ringdahl the VP of Global alliance Architecture. From Veeam, welcome. >> Great. Thank you, Lisa. >> Dave: Well the truth be told, we're afraid of the warriors, okay. We really don't want to play the Warriors. >> Oh really, alright. >> And we're not afraid of many people in Boston, but I don't know, they look pretty good. >> Well, I appreciate the honesty, that's pretty cool. >> Well... Though they lost last night. Right? We're going to start the sports talk now. >> Yep. >> Iguodala was out, they showed some foulability. So, anyway. >> We digress to- >> We'll be back to it later on in this segment stay tuned. >> Alright, so you're just fresh off Veeam On, last week. We're impressed that you still have a voice, you've recovered from that. Tell us a little bit about some of the things that are new with Veeam and Pure. So just a month ago, in April, new intergradation between VM availability platform, and Pure Storage flash a way to deliver business continuity, agility, intelligence for the Cloud era. Expand a little bit upon that. >> Yeah, sure, I mean really this integration with Pure Storage, in the VM backup and replication product, end of last year we introduced this new functionality called Universal Storage API. And what this really is, is a way for us to enable our partners to take control of their destiny a little bit more. It's a program we invite our partners into, you know Pure is one of the first that we integrated with, and invited into the program very early. We announced this last year, and we've now finished the integration, as you've mentioned, we announced it last month. It's now been out there, and I think the number I heard earlier today is that we've already had a couple hundred downloads and deployments. So that's just great adoption, and just shows the pent up demand for that. But what we've integrated is the ability for our partners, our storage partners in particular to integrate with our storage snapshot technology to really off load the snapshot from the VMware side, and really put more of it on the storage side, and take it really off the production environment. And so it's a better together story where you know we take the feature that we've introduced into the backup and replication, and Pure built this plug-in, and they integrate with their own APIs and we jointly test and develop, and release that plug-in. And they can install it with VM backup and replication, and it really takes the mention, it takes that load off the production environment. So that snapshot without this integration, it's a VMware snapshot, that snapshot stays open as long as the backup is. Which can be minutes, and you know tens of minutes potentially for a large system. But now we shrink that down literally to just seconds. So we take a VMware snapshot, we take the Pure snapshot, we close the VMware snapshot. And typically it's like 10-12 seconds long where as opposed to the minutes, and even tens of minutes from before. So, really it's really offloading a lot of that back up impact, and we're able to do it in a very secure quiesce fashion from the production environment. >> Lets roll back and understand that a little bit better. >> Ken, if you could explain it to us and our audience. In the 2008, seven, eight, nine timeframe. Virtualization Gem of VMware in particular started to take hold. And you ended up replacing a bunch of physical servers with virtual servers, which was awesome, because all those physical servers were underutilized, except for one major workload, which was backup. So when you did want to do the backup, you didn't have enough resources. Veeam's ascendancy coincided with that trend, so there was a simplicity component, but it seems like what you're describing now is another instantiation of offloading that bottle neck. So what was the journey to Veeam's efficiency in a virtualization environment? >> Ken: Yeah if you look at that journey, and Veeam really grew up in the virtualization age, right. So backup prior to VM, or virtualization was all agent based, it was physical. So everything was over the wire, and Veeam went and said, hey look you know we see VMware really sort of growing, and we see that trend towards virtualization, right, and at this point, what's the world 95 percent virtualized, at this point the only workloads that aren't virtualized are really legacy work loads. And so we made a significant leap forward in a data protection stance, by integrating with the hyper visors. So instead of off loading that into the individual guests, right. The Windows guest, the Linux guest. We said, okay we're going to go the hyper visor. Right? And we're going to do this in an agent less fashion, so that you don't have to go an visit every little, every system that you're looking to backup. That was sort of the first step, right. Now what we're saying is we can do even better. And we can off load the hyper visor, and off load that to the storage system. So we can have a very small impact on the hyper visor, really minimize that. And now really put that workload on the storage system which has a lot of extra cycles and availability, and we can go straight to the backup environment. And not through the VM, or through the hypervisor to get there. >> Dave: So VMware admins, they don't like snapshots because it's overhead intensive, it clogs up their system if you will. This capability makes that transparent, or irrelevant to them? >> It does, it minimizes them to such a small degree that it's a blip. You know it's a little blip on the radar, as opposed to when you snapshot a VM you're essentially quiescing that VM, so everything sort of slows down for a very short period of time. And what happens is that it spawns another virtual disc. So while that snapshot is open this other virtual disc is being written to. And then when you close that snapshot, and you remove that snapshot, that disc gets merged back in, right. This is generally how VMware snapshots work. And what we're saying is we're going to minimize as much as we possibly can. The data that goes in there, so if you think of a running virtual machine, if you're merging back in a Gigabyte disc versus a disc that has 10 Megabytes, you know that's going to be really, really quick, as opposed to, you know if you keep that snapshot open for a long period of time that merge operation, and it just slows things down, and we're trying to minimize that impact on the system. >> Lisa: So business benefits; I get the performance improvements that this integration with Pure facilitates, if we think of this in the context of digital business transformation, where companies that are doing well, have the ability to really glean actionable insights from their data to be able to drive, you know, new products and get products to market faster. Is this actually going to facilitate a company being able to get new products to market faster? >> Absolutely, so there a feature inside of VM backup and replication we call data labs. And what data labs is, is the ability to take a production snapshot, in this case, we're talking about a pure snapshot, and be able to stand that up in a sandbox environment. And you can run DEV tests, you can apply your Windows' patches in an environment that literally matches production. And it's a key differentiator. It's a key differentiator for Veeam, and it's enabled by the Pure Snapshot integration that you have this environment, and even if you have an infected system, you go put it over in data labs, it's sandboxed, so you can put in a private network so it doesn't have any connectivity. Say if you have a worm, or some other ransom ware, you can run analytics, you can run diagnosis on any of that, and not worry about it infecting any other environment, nor does it put work load on your production environment. So you get patched Tuesday, right, and we all know that Windows' patches don't always go as they seem, right? So data labs, let's take that Pure snapshot, let's stand up a virtual environment, which exactly matches production, let's test that patch, right. And we have confidence there, so when we go to production, we have confidence because we've already done it. We've already run that in production. So there's a lot of value in that capability. >> So we were at Veeam On last week fresh off the Kool-Aid injection. It's all orange here, it was all green at Veeam in Chicago. The messaging there was all about multi-cloud and hyper availability in this multi-cloud world. We're hearing a lot about cloud like function here, but of on prem activity. Of course multi-cloud includes on prem, so I wonder if you could dove tail your messaging last week, what you're seeing in the field, and what you're seeing with the partnership with companies like Pure. >> Yeah no question. I mean the Veeam platform, and really you saw it last week at Veeam On we talked kind of about sort of private cloud, and public cloud and our ability to orchestrate, and really stretch across all those environments, and we know that customer all the way from SMB all the way up to enterprise, right. They have remote offices, branch offices some of them use the cloud, some of them use multiple data centers, and really they need their data protection to be able to stretch across those environments. They don't want point solutions in each of those locations. They want a platform that they can trust, and have visibility, right. That's one of the five stages that we talked about about hyper availability, like last week. Is visibility, they want visibility across those clouds. Phase two is aggregation, they want to be able to aggregate all these different places. And that's what we provide our customers with the platform is backup, visibility, aggregation, orchestration, automation. And we provide them on different stages of that journey for our customers. We have different products, services and integration actions with our partners, that really help our customers along that journey. >> We know from our research, the crew at Wiki Bond does some great work on this. We know that data protection, and orchestration are moving up on the list of CXO priorities. At the same time, for a lot of IT practitioners who are under real budget constraints it's like trying to sell more insurance to a 24 year old. So those are kind of two countervailing trends, what are you seeing in the market place? >> What we're seeing is customers, you know down time is really is gone. I mean, I think last week we heard in one of our keynotes, you know you roll back a couple of years, you were talking about availability in terms of five-nines, right? Now it's zero. I mean people don't talk about down time because down time can't exist, and customers need that sense of security and availability. You know, it will happen, lets face it even Amazon, the best data centers in the world, go down, right, there's been some notable S3 outages, but it's about how fast can you recover. And you're talking about low RPOs, and one of the things that this week at Pure Accelerate we're hearing a lot about rapid recovery, flash blade, and the ability and you take rapid recovery and flash blade, and you combine that with the Veeam platform and our instant recovery, and you can get to near zero time recovery, in your environments. To really provide that security, and lets face it, time is money for a lot of our customers, right? So they longer they're down, the more time their losing money, they need availability, and the RPOs are near zero these days. = [Dave] The other thing, if I may just follow up, just one follow up. The other thing our research shows is the average Fortune 1000 company, over a three or four year period is leaving, literally, a billion plus dollars on the table because of poorly architected backup, or inadequate backup. So that's a huge opportunity for you and others, obviously. There's a lot of opportunity right now for vendor turn. That's the other thing our research shows, is that people aren't wed to their backup and recovery vendor. So, does that resonate with customers, are they because of digital, for example, are you seeing that tipping point, that critical mass occur, and then if you could tie that in to sort of your partnership with Pure, I'd be interested in that. >> Sure, yeah, no doubt about it. We're seeing customers, you know, they want that flexibility and that portability. One of the things we do with out platform, it's one of our unique selling features is is that it is agnostic, right. And I'll tie it back to Pure in a moment, but you know when we back up, we back up in a storage agnostic fashion. So any Veeam backup that lands on a disc on the tape anywhere, can be reconstituted, can be re imported, so even if you have a full disaster scenario, we can go stand that back up some where else, and fully consume that backup and restore it, and we have direct restore capabilities. We can port those backups and direct restore them. For example, a direct restore Azure, for example. So that flexibility, and portability is extremely valuable. Now, bring that back to Pure, some of the things we're doing around rapid recovery around the snapshot integration, we talked about is we're really enabling customers to have high performing primary storage environments. High performing secondary storage environments. And really bring that together in a way that works. We talked about multi cloud, right, you know, remote data centers and work across, and aggregate and give visibility. That's really where the Veeam Pure story together, becomes really strong because you've got an incredibly high performing primary and secondary with a highly flexible, portable secondary data protection environment. And you get the capability to get to the cloud. You know DL, a lot of customers looking to the cloud for DR, because they don't have to stand up infrastructure there. When they need it, they can spin it up, and then they can bring it back. And there's a lot of value there. >> I hear a lot of harmony, but I actually read recently, online, that a different analyst firm called the Pure Veeam relationship a match of opposites. Now they say opposites attract, and you've done a great job of talking about the integration, do you agree that it's a good blending of opposites, and if so what's that kind of symbiotic benefit that those bring to each other? >> Yeah, I don't know that I saw that report, but what I would say you know, there's a lot of synergy, we're growing at a very rapid rate, I think. When I looked at Pure, and I look at Veeam we grew 36 percent last year, I think Pure is growing at like 50 percent year over year. We have NPS scores, our NPS score is 73, we're really proud of that. The Pure NPS score, I think I saw- >> 83. >> Ken: 83. >> Dave: I didn't think it could be higher than 73. >> It's incredible. It is incredible, and I think there is a lot of synergy, the size of the organizations, I think the age of our organizations, the aggressiveness that we have, we have joint competitors in the market, so I think there's a lot of synergies between where we are as an organization, as Veeam, and where Pure is. I wish I read the article in terms of the opposites, because I'd love to understand. >> Personally, as a long time analyst, I would say the similarities are greater than the differences. >> Sure sounds like it. >> You're both about a billion dollars, you're both growing at lets call it 35-40 percent a year. You're both pursuing platforms, your both really aggressive, you're insanely passionate about your customers and winning. And you like colors, you like green, they like orange. Alright, we got to talk a little sports here. >> Lisa: Speaking of green. >> I'm going to start somewhere else though because I asked this question of a number of folks at Veeam On. If you were, Ken, if you were Robert Kraft would you have traded Tom Brady? >> {Ken] No. >> Elaborate. >> I think when you look at a, the guy was the MVP of the league last year, so that by itself stands on it's own, but you have to look and the Patriots have always been about, sort of you know, trading or moving on a year or two early, versus a year or two late. So you could make that case with Tom Brady, but I think there's always exceptions, and when you look at, I mean he is basically like an adopted son of Robert Kraft and the organization. He's brought five Superbowls, he's basically, he built Patriot place, you know. Robert Kraft built Patriot place on the backs of Tom Brady and Bill Belichik to that extent. But how do you move on from someone who's brought you so much success, that has been under market. You know, get paid under market so that they can go and do other things, and have flexibility with the gap. I just don't know how you could move on from that. >> So, that's consistent now, I think it's four for four of people we've asked, Boston fans. So appreciate that feed back. Let's talk a little hoops, you know Celtics we were feeling pretty good, up two zip, now it's tied two-two. Houston, Golden state, tied two-two. Those two teams have proven they could win on the road, Celtics haven't proven that yet. What are your thoughts on that series? >> Yeah so certainly Cleveland came storming back, I think the stories of the down fall of the Cavs were clearly over exaggerated. They came back in a big way. I think they Celtics started to figure out the Cavs in quarters two, three, and four. They got themselves in a big hole in the first quarter in the last game. I feel good, the Celtics are nine and O at home this year in the post season. You know, it's basically the best of three, and they have two of them at home, so. The Cavs will have to break serve if they want to win the series. >> Dave: If they're lucky enough to get through to the finals, which would be unbelievable, do they have any shot against the Warriors? >> So, I think to say they have no shot is probably going a little too far, but- >> Dave: Got to play the game. >> You know you got to play the games, and the Celtics have, traditionally, matched up well against the Warriors. I mean least year, the Celtic actually came into Oracle, and broke, I don't know, what was it, like a 50 game home winning streak or something. So, you know, and that was a team that didn't have Kyrie, or Gordon Haywood, and I know they're still out so the future looks bright for the Celtics. But in the context of this years finals, certainly, if I were a betting man, I'd be putting my money behind the Warriors, but I don't doubt that Brad Stevens could come up with a scheme that could steal a couple of games, and make people in the Bay area feel a little uneasy. >> Would love to see a non Lebron Final, you know. >> Yeah I think as the words would like the Celts >> Sorry Brandon, sorry buddy. >> A little diversity, you know three years in a row we've had the same things, so I'll extend my support to the Celtics in honor of both of you guys. >> Alright, and we can talk, if they get to the finals then we can take it from there. >> I can't imagine what the day after the Superbowl was like for both of you. We won't go there. >> I still haven't recovered, so. >> (laughs) Awesome, well Ken, thanks so much for stopping by. Congrats on being a CUBE alumni, now. We look forward to seeing you Veeam World in just a few months time. >> Yes, great. Thank you. We'll be there for sure. >> For Dave Vellante, I am Lisa Martin. You're watching theCUBE live from Pure Accelerate 2018. Stick around, Dave and I will be back with a wrap in just a moment. (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 coverage of Pure Accelerate 2018. I'm Lisa Martin with Dave Vellante. We're here in San Francisco at the really cool historic Bill Graham Civic Auditorium. We've been here all day talking with lots of great folks, and we're happy to welcome back another Pure customer, Patrick Welch, the network services manager for the Mississippi Department of Revenue. Welcome Patrick. >> Thank you, appreciate it. >> Tell us a little bit about the Department of Revenue. What do you guys do? What kind of information do you collect? >> Okay, we bring in all tax revenue for the state of Mississippi, including vehicle services. We register all the car tags in Mississippi. Income tax, corporate tax, any revenue that's generated in Mississippi comes through us. >> Tax refunds too? Or do you just take, you give? >> We take and give. I have to do it too. (laughing) >> So talk to us about some of the challenges that you had in your environment. I was reading your case study and what you guys are taking in is totalling $7.8 billion a year. As we just identified, some of it's being given back, but what were some of the, what was the infrastructure like to support that before you became a Pure Storage customer? >> We used an internal Mississippi, they're called ITS, they handle all internal infrastructure, that kind of thing. They were using a mixture of Dell, EMC, Compel that type of thing. We use a third party vendor who has an office shelf software package. And they have about 50 to 60 customers in different states and municipalities and countries around the world. In that environment of Dell, EMC, Compel we were about 47th on their list of productive sites. So we were way far down. We were not performing, latency across the board was horrible. The user experience was the worst. If you've ever been on a website and click the button and seen the spinning wheel, we had that in droves. And not just tax payers, but our internal people that worked DOR were not able to work efficiently. We came in and evaluated, and I looked at the infrastructure, and I said my team can do it better. Then when they said, we'll do it better I was like okay now I have to go out and actually do it better. I started researching other companies, and Pure kind of rose to the top of the list. We talked with other customers and partners, kind of how they tackle those type of challenges. We went through a lot of POC process talked with a lot of vendors, things like that. We ended up buying Pure. We are now number three. We went from almost 50 to three. Out of 50, to three. The only two sites that are ahead of us are smaller sites, their transactions aren't nearly as high as ours. >> Okay hang on, how much of that effect could be attributed to the storage infrastructure? Do you have a sense of that? >> 99% >> Really? >> Yeah because before we had, to be fair Pure is all-flash storage, right? And with Compel and EMC or hybrid arrays, at the end of the day, the latency that we saw was due to read and write input being very low. We implemented Pure, through the roof. Storage is not something we would ever look at if we had a problem. We know that that is performing well above capacity. >> Okay I got another follow up. I asked this earlier to another customer, so you're basically comparing an all-flash array to a sort of previous generation hybrid. So it could have been three, four, five, six years old, it could have been 10 years old, so, you had the option obviously of bringing in an all-flash array from the competition. >> We did. >> And you had processes and procedures tied to that, your data protection and you know those products well, but you chose to switch vendors. Why, you could have gotten comparable all-flash, but you chose Pure. Why did you choose that switch and that disruption? What business benefit did that bring you? >> There were several things that led to that. One of the things that we really liked was the proactive support, in terms of every three years they swap out your controller as part of your support and maintenance agreement. Which is huge for us because we don't have a lot of money, our budget is very small for IT, so I can't afford to replace equipment as often as some people can. Their proactive support model, not just in terms of swapping out equipment, but personnel, our sales team that we deal with, our engineering team that we deal with, we're on a personal basis with these people. I have cellphone numbers, I know who to call. We found that out through talking to other customers that, hey you call these guys, they're going to be there for you. Coming from not having that before, we knew that the people we had before, were not going to perform that same level of service. Even if we went to their all-flash product, we were going to have the same support, that we had had before, which was not good. >> And you didn't have that previously because, why? You weren't like a big bank or you just didn't spend enough? >> Because you're a number and in our business, we didn't spend near enough money to be considered. That's a theory of mine, I'm not sure exactly what the actual issue was, but it felt like we were not big enough to get that kind of attention. >> You're the little guy. Pure makes you feel like you're the big guy. >> We think we're doing okay. We have six arrays now, so were not tiny tiny, but we're not also we're not Citibank. But I've never felt any different than a Citibank type customer with Pure Accelerate. >> You're in two years you said? >> A little over two years, yeah. >> You've had enough experience to, you know when you first buy something, you go on Amazon you see the reviews this is great, you wonder if it's still great two years in. >> Patrick: Oh absolutely. >> You would still give a five star rating? >> Oh absolutely, I've done a case study, customers call me and I'm happy to talk about Pure to anybody. I have a lot of friends in state government, I try to head them off from making bad decisions. I'm like if you like your job, you want to keep your job, buy this. >> It's interesting to me, now one of the things that the customers tell us is they love a lot about Pure, but they really like the simplicity. You mentioned Compellent before, Compellent, in its day, was known for simplicity, compared to the old main frame storage. It's interesting to note how technology has changed in whatever 10, 12 years, comments? >> Yeah Compellent was a great product. Back in the day when it came time to evaluate products, they had not performed along the same track as a company like Pure, which consistently innovates its products. If this is again about feeling like the big guy, even though you're a small guy, they keep us in the loop of what they're bringing down the pipe, and it really makes us feel like we're invested in that ecosystem, and we know exactly how they're transforming, how they're going to develop their business going forward. It helps keep us as a happy partner. >> So it's, from what I'm hearing, Patrick, better experience all around, very happy. Did it save you any time? Are you able to now do things differently, add more value to your organization as a result of bringing in Pure? I wonder if you can talk about that. >> Oh absolutely, we spent a good chunk of time troubleshooting issues directly related to storage before whether it was storage creep where we had too much data versus the capacity of the array, or the input output problems in terms of IO, latency those types of issues. We don't see any of that anymore. So that frees our engineers up to work on other problems in the environment. >> What workloads are you running on Flashdeck? >> Mostly production sequel, high sequel workloads mostly. >> You mentioned the dreaded spinning color wheel or whatever kind of computer we're running, and that was affecting not just employees, but also Mississippi citizens. Problem gone? >> The problem is gone from the aspect of our side of things, now this is Mississippi so you still got a lot of rural customers who are still on some dial up internet, so we can't solve that problem for them, but in terms of our side of the fence, we know they're not going to see any latency because of us. We're delivering the application as best you can. Like I said, we're number three in the list of their sites, and we came 44 spots down. >> How quickly in the last couple of years alone? >> Patrick: Immediately, yeah. >> You have to wear a neck brace from the whiplash. >> Yeah we put it in and I'm just crossing my fingers, 'cause if I told them I could do this, and we're 45th, what did we really solve? We didn't solve the problem really, but we came from that high up to all the way down to three, it like felt my team had accomplished something really great. >> And pretty dramatic improvements to your database. I was reading the case study, within the context of your IT transformation, that you improved database transaction performance by as much as 20X. Big, also data reduction rates. So I want to get your perspective on the impact of TCO, and why that's so important for a public agency. >> A lot of things go into TCO. I think user experience is one of those things, downtime for the state. The biggest cost we had was not really something you could see before because our system went down all the time due to not being able to meet the requirements of the taxpayers and the people that work at the Department of Revenue. We don't have that problem anymore. We would spend days of downtime before, that's revenue lost for us. So TCO in that instance is kind of hard to calculate, but I know that the number is big. I know we've saved a lot of time and money. >> Why not just forget all this IT stuff, and throw everything into the cloud. I know as an IT pro, them might be fighting words, but it's talked about in the industry all the time. Why the decision to stay on Pram, and was that discussed? >> We definitely look at the cloud, we definitely have Azure workloads that are in testing right now. Unfortunately it's not just as simple as us saying okay let's go to the cloud, 'cause if it was up to me, with limited funding and that type of thing, I would love to move workloads into the cloud. Where it was applicable. The problem for us is IRS. We have a lot of IRS regulations around cloud. So the core infrastructure that we have, has to remain on premise. There's some things that we can do, but the regulations are a mile long. So we have to make sure that we always stay in compliance with the IRS. That limits our mobility a little bit in the cloud, but we're getting there slowly but surely. I feel like in the next 60 years we'll be there. I joke, but everything we do, we have to go through compliance measures, and we have to make sure we're checking all the boxes. There's one thing you don't want to have, and that's the IRS to write you up for non-compliance. If you're attacked or hit by some vector afterwards, then you're on the hook. You weren't in compliance that's why you were vulnerable. We just have to be very careful, but we're definitely interested. And we'll look into the future with the cloud. >> A lot of talk at this show every show we go to about artificial intelligence, machine intelligence. What do you make of it? How does it apply to your organization? Can you use it? Do you plan on using machine intelligence, whether it's fraud detection or tax evasion, et cetera? What's the state of AI in your world? >> I'd say infancy, but we know that due to the fact that the state hasn't kept up in terms of pay and that type of thing with the private industry. We're going to have to rely on artificial intelligence and automation and things like that to remain ahead of the curve in terms of compliance, performance all the metrics we've talked about. You have to have either a very talented and well paid staff or you're going to have to leverage these types of technologies to stay ahead of the game. >> So you have made some big impacts from an IT transformation perspective we talked about a minute ago. Where are you on this journey of digital transformation? What does that digital transformation mean to the Mississippi Department of Revenue? And what stage would you say you're at? >> We're getting there. Like I said before some of Mississippi is still very rural, for the first time ever, we had more online returns processed than mail. Believe it or not, Mississippians still like to mail their returns in. A lot of that is rural location, internet access that type of thing. We're getting there slowly but surely. I feel like in the next five years, we'll be probably 75% to 80% online refund based. I hope anyway, I hope we're still not at 50%. It's a slow crawl, but we're getting there. We do things a little slower than most people, but we get there eventually. >> You're friendlier down in Mississippi. >> We are definitely, you got to have something. >> You do, so in terms of next steps, you've solved the performance challenges, you're kind of on this road to digital transformation. How have you improved the efficiency of your IT team? >> Say that one more time. >> How have you improved the efficiency within network services? >> I think most of it comes down to not having to worry about the equipment and the environment. We have more time to focus on each other, the tasks we have in front of us. Before it was tackling issues that we knew were related to either vendor or product or storage or server. And now we're focused on expanding the skill set of the current staff. It allows us to leverage things like cloud and automation. We didn't have time to look at that stuff before. So when you ask me where we at with automation, we're still in the infancy because before all we did was fight issues related to previous vendors, previous products, that kind of thing. And this, while it's not a magic bullet, we still have, you're always going to have challenges it frees us up to be able to work on those types of-- >> Dave: Close to firefighting and whack-a-mole. >> That's all we did before. This guy is fighting this problem, he's fighting this one, then they don't get time to learn and grow as employees and as people. >> So automation is big priority, what kind of other fun projects you working on? Or techs that you're researching that get you excited? >> So right now we've deployed both of our major applications using Pure. Our big projects are kind of done. Now we're leveraging towards disaster recovery, modern day DR, BCDR, business continuity that type of thing. How do we recover in case of a disaster? That's kind of where my focus lays right now, to make sure the Department of Revenue, if we are affected by some type of disaster, that we're ready for the taxpayers of Mississippi to come up and running in a sister site and be ready to go. >> Okay that's a combination of infrastructure, probably going to use snapshots, remote replication, but there's also got to be a software component as well. What are you thinking about whether if you don't have a specific vendor product, but just architecturally what are you thinking about? >> So we absolutely right now leverage Zerto with Pure. Which is a very good combination, they work very well together and we have a co-low facility, it's about 200 miles north of us. We'd like to get more geographically diverse as budget frees up and that kind of thing, maybe move out into the Colorados or something like that. But our sister site, all of our data is replicated using Zerto. We're on, I believe, every 15 seconds we're tracking journal history. In the event of a disaster, and we've test fail overs. 'Cause you've got RPO and RTO. Real time objective and recovery point objective. It's important for us to be under 10 minutes, in terms of how quickly we can recover the environment. It's a real time objective. The last time we did a test fail over, we were about four minutes. So our business has completely transformed. Before if we had a disaster, we would be lucky to have data available to us number one and within three to five days. Now we are being able to turn around and operate in another location within minutes. >> And your RPO you said was 15 minutes, did I hear that right? >> Recovery point objectives, that is 15 seconds. Recovery points are every 15 seconds. Our recovery times, the total time it takes us to come back up and running, we hope to be under 10 and we got it around four. Now that depends on a lot of different things. Every situation is not the same. >> Very tight RPO. >> Patrick: Oh yeah, absolutely. >> 'Cause you're moving money, I guess. >> We're moving money. And it's very important that we stay up at all times. Obviously there is going to be a little bit of downtime, but we want to minimize that as much as we can. >> Patrick last question before we wrap here, this is your first time at Pure Storage Accelerate. A whole bunch of announcements this morning, anything that you've heard that excites you for expanding this foundation that you have with Flashtech? >> A lot of the stuff we talked about around automation and that kind of thing. We're definitely interested in how Pure is going to evolve to the cloud because we know you all we be ahead of us I say you all, so you all will be ahead of us whenever we do get ready, and that's another big benefit for us. We know that when we get ready to transition to the cloud, you guys are going to have your ducks in a row, and be ready for us to do that. >> You all as in Pure? We all aren't Pure. >> You know what I meant. >> We're the blue guys. >> It's real exciting to hear about automation, And where they're going with the cloud, and storage as a service and that type of thing is very neat. I love reading about and hearing about that stuff, we can't always be there like I said because of compliance issues, but as we can, we will if it makes sense for us. >> How important is it to you, I was asking a couple of the Pure execs what their thoughts were on staying independent. You see a lot of storage companies get bought, they get consolidated. EMC, 20 plus billion they got acquired. How important is it to you as a customer to have a company like Pure be an independent storage company? >> I mean, it's enormous. I can give you an example. We were a SimpliVity customer so HP bought SimpliVity, our experience before the merger, fantastic. We would give them very high marks in every category. After the merger, not so much. Support dropped off for us after SimpliVity was bought by HP. For us it's huge that Pure is, now that's not to say, we know that this is a business, and that things may happen, but we hope that if they don't stay independent, somebody that has the same level of focus and effort and determination and support keeps that going. >> We hope so too, we love the competition on theCUBE. We love the growth that drives innovation. Pure seems to be leading the way. We talked about this earlier, what they're doing with NVME a lot of good marketing, but still they're throwing down the gauntlet. What they've done with Evergreen. Obviously first with AllFlash or at least early on with AllFlash, so got a leader. >> That's what you worry about too, the Evergreen type things are the things you worry about going away. If they get bought by somebody, is that the first casualty? That's the kind of things that happen to companies when they get bought. We do love the fact that they are independent, but we know it's a business at the end of the day. But hopefully that remains the same. >> Keep that feedback coming, I'm sure they appreciate that. And Patrick thanks so much for stopping by theCUBE and sharing the impact that you guys are making at the Mississippi Department of Revenue. >> Sure, thanks for having me, appreciate it. >> We want to thank you for watching theCUBE, I'm Lisa Martin with Dave Vellante from Pure Accelerate 2018. Stick around we'll be right back with our next guest.

>> Narrator: Live from the Bill Graham Auditorium in San Francisco. It's the Cube. Covering Pure Storage Accelerate 2018. Brought to you by Pure Storage. >> Welcome back to Pure Storage Accelerate 2018. I'm Lisa Martin with the Cube. I'm with Dave Vellante. We are here in San Francisco at the Bill Graham Civic Auditorium which is why we're sporting some concert t-shirts. >> Who. >> The Who and the Clong. >> Roger. Roger Delchi. >> Roger. We are here with the CIO of the David Geffen School of Medicine at UCLA, Pure customer, Ben Nathan. Ben, welcome to the Cube. Thanks for having me. So, talk to us about the shool of medicine at UCLA. You are the CIO there, you've been there for about three years. Give us a little bit of the 10,000 foot view of what your organization looks like to support the school of medicine. >> Sure. We're about 170 people. We have changed a lot over the last three years. So, when I got to UCLA there was 25 separate IT organizations, all smaller groups, operating in each individual department. And, they had built their own sets of managed infrastructure, distributed throughout every closet, nook and cranny in the school. We've consolidated all that under one set of service lines, one organization, and that's including consolidating all the systems and applications as well. So, we've brought all those together and now we're additionally running IT for three more health sciences schools at UCLA, nursing, dentistry, and school of public health, Fielding School of Public Health. Like a lot of CIOs, you serve many masters. You got the administration, you got the students, right. You've got the broader constituency. The community, UCLA. Where do you start? What's the quote on quote customer experience that you're trying to achieve? That's a great way to put it. There's really sort of four pillars that we try to serve. The patient being first and foremost. So, for us, everything is built around a great patient experience. And, that means that when we're educating students it's so they can be great providers of patient care. When we're doing research, When we're doing that research in an effort to eradicate disease et cetera. And, when we're doing community outreach it's also around improving health and peoples lives, so, in IT, we try to stay very connected to those missions. I think it's a large part of what drives people to be a part of an organization that's healthcare or that's a provider. That mission is really, really important. So, yes. We're serving all four of those things at once. >> So, you had lots of silos, lots of data, that's all continuing to grow but, this is data that literally life and death decisions can be made on this. Talk to us about the volumes of data, all the different sources that are generating data. People, sensors, things and how did you make this decision to consolidate leveraging Pure Storage as that foundation? >> Yeah, there's and incredible amount of work going on at UCLA. Particularly in their research education and patient care spaces. We had every brand of server in storage that you've never heard of. Things bought at lowest, bitter methods but, the technical data that we had incurred as part of that was enormous. Right, it's unsustainable. It's unsupportable. It's insecure-able. When I got there and we started to think about how do we deal with all of this? We knew we had an opportunity to green field an infrastructure and consolidate everything onto it. That was the first, that was started us down the road that led us to Pure as one of our major storage vendors. I had worked with them before but, they won on their merits, right? We do these very rigorous RFP processes when we buy things. The thing that really, I think, got them the the victory is us is that the deduplication of data got us to something like an eight to one ratio of virtual to physical. So, we get a lot of virtual servers running on relatively small amount of storage. And, that it's encrypted you know, sort of the time, right? There's not like a switch you might flip or something a vendor says they'll do but it >> Always on. >> doesn't really do, it is always on. And, it's critical for us. We're really building a far more secure and manageable set of services and so all the vendors we work with meet that criteria. >> So, is as a CIO, I would imagine you don't want to wake up every day and think of storage. With all due respect to our friends at Pure. >> That's true. >> So, has bringing it in for infrastructure in, like Pure, that prides itself on simplicity, allowed you to do the things that you really want to do and need to do for your organization? >> Yeah. I'll give you a two part answer. I mean one is simply, I think, it's operationally a really great service. I think that it's well designed, and run, and managed. And, we get great use of out it. I think the thing that makes it so that I don't have to think about it is actually, the business model that they have. So, the fact that I know that it's not going to really obsolete on its own, as long as you're like in the support model, you're upgrading the system every few years, changes, you know the, model for me, 'cause I don't have to think about these new, massive capitalization efforts, it's more of a predictable operational costs and that helps me sleep well because I know what we look like over the next few years and I can explain that to my financial organization. >> Just a follow up on that, a large incumbent storage supplier or system vendor might say, "Well, we can make that transparent to you. We can use our financial services to hide that complexity or make a cloud-like rental experience or you know, play financial games to hide that. Why does that not suffice for you? >> Well, I think, first and foremost we sort of want to run our financials on our own and we're pretty anxious about having anyone else in the middle of all that. Number two is it seems to me different in terms of Pure having built that model from the ground up as part of their service offerings. So, I don't think we see that with too many other vendors and I think that obviously there's far less technical than what I had in the previous design but it can still add up if you're not careful about whatever, what server mechanism you have in place, et cetera. >> But, it eliminates the forklift upgrade, right. Even with those financial incentives or tricks, you still got to forklift it and it's a disruption to your operation. >> Yeah, and I'm sure that's true, yeah. >> So, when you guys were back a year and a half or so, maybe two years ago, looking at this consolidation, where were your thoughts in terms of beyond consolidation and looking at being able to harness the power of AI, for example, we heard a lot of AI today already and this need for legacy infrastructures are insufficient to support that. Was that also part of your plan, was not simply to consolidate and bring your (speaks very rapidly) environment unto Pure source but also to leverage a modern platform that can allow you to harness the power of AI? >> Yeah. That was sort of the later phase bonus period that we're starting to enter now. So, after we sort of consolidate and secure everything, now, we can actually do far more interesting things that would've been much more difficult before. And, in terms of Pure, when we had set out to do this we imagined doing a lot of our analytics and AI machine learning kind of cloud only and we tried that. We're doing a lot of really great things in the cloud but not all of it is makes sense in that environment. Either from a cost perspective or from a capabilities perspective. Particularly with what Pure has been announcing lately, I think there's a really good opportunity for us to build high performance computing clusters in our on premise environment that leverage Pure as a potential storage back end. And that's where our really interesting data goes. We can do the analytics or the AI machine learning on the data that's in our electronic medical record or in our genomics workflows or things like that can all flow through a service like that and there's some interesting discoveries that ought to come from it. >> There's a lot of talk at this event about artificial intelligence, machine intelligence, how do you see AI in health care, generally? And specifically, how you're going to apply it? Is it helping doctors with diagnosis? Is it maybe maintaining better compliance? Or, talk about that a little. >> I think there's two things that I can think of off the top of my head. The first is decision support. So this is helping physicians when they're working directly with patients there's only, there's so many systems, so many data sets, so many way to analyze, and yet getting it all in front of them in some kind of real time way so that they can use it effectively is tricky. So, AI, machine learning, have a chance to help us funnel that into something that's immediately useful in the moment. And then the other thing that we're seeing is that most of the research on genomics and the outcomes that have resulted in changes to clinical care are around individualized mutations in a single nucleotide so there's, those are I guess, quote, relatively easy for a researcher to pick out. There's a letter here that is normally a different letter. But, there are other scenarios where there's not a direct easy tie from a single mutation to an outcome. so, like in autism or diabetes, we're not sure what the genetic components are but we think that with AI machine learning, those things will start to identify patterns in genomic sequences that humans aren't finding with their typical approaches and so, we're really excited to see our genomic platforms built up to a point where they have sequences in them to do that sort of analysis and you need big compute, fast storage to do that kind of thing. >> How is it going to help the big compute, fast storage, this modern infrastructure, help whether its genomics or clinicians be able to sort through masses amounts of data to try to find those needles in the haystack 'cause I think the staff this morning that Charlie Jean and Carla mentioned was that half a percent of data in the world is analyzed. So, how would that under the hood infrastructure going to help facilitate your smart folks getting those needles in the haystack just to start really making big impacts? >> UCLA has an incredible faculty, like brilliant researchers, and sometimes what I've found since I've gotten there, the only ingredient that's missing is the platform where they can do some of this stuff. So, some of them are incredibly enterprising, they've built their own platforms for their own analysis. Others we work with they have a lot of data sets they don't have a place to put them where they can properly interrelate them and do, apply their algorithms at scale. So, we've run into people that are trying to do these massive analysis on a laptop or a little computer or whatever it just fails, right? Or it runs forever. So, giving them, providing a way to have the infrastructure that they can run these things is really the ingredient that we're trying to add and so, that's about storage and compute, et cetera. >> How do you see the role of the CIO evolving? We hear a lot of people on the Cube and these conferences talk about digital transformation and the digital CIO, how much of that is permeating your organization and what do you think it means to the CIO world going forward? >> I wish I knew the real answer to that question. I don't know, time will tell. But, I think that certainly we're trying to follow the trends that we see more broadly which is there's a job of keeping the lights on of operations. And you're not really, you shouldn't have a seat at any other table and so those things are quite excellent. >> Table stakes. >> Yeah. Right. Exactly, table stakes. Security, all that stuff. Once, you've got that, you know, my belief is you need to deeply understand the business and find your way into helping to solve problems for it and so, you know, our realm, a lot of that these days is how do we understand the student journey from prior to, from when they maybe want to apply all the way 'til when they go out and become a resident and then a physician. There's a ton of data that's gathered along that way. We got to ask a lot of questions we don't have easy answers to but, if we put the data together properly, we start to, right? On the research side, same sort of idea, right? Where the more we know about the particular clinical outcomes they're trying to achieve or even just basic science research that they're looking into, the better that we can better micro target a solution to them. Whether it's a on prem, private cloud, or public cloud, either one of those can be harnessed for really specific workloads and I think when we start to do that, we've enabled our faculty to do things that have been tougher for them to do before. Once, we understand the business in those ways I think we really start to have an impact at the strategic level, the organization. >> You've got this centralized services model that was a strategic initiative that you put in place. You've got the foundation there that's going to allow you to start opening up other opportunities. I'm curious, in the UCLA system, maybe the UC system, are there other organizations or schools that are looking at what you're doing as a model to maybe replicate across the system? >> I think there's I don't know about a model. I think there's certainly efforts among some to find, to centralize at least some services because of economies to scale or security or all the normal things. With the anticipated, and then anticipating that that could ultimately provide more value once the baseline stuff is out of the way. UC is vast and varied system so there's a lot of amazing things going on in different realms and we're I think, doing more than ever working together and trying to find common solutions to problems. So, we'll see whose model works out. >> Well, Ben. Thanks so much for stopping by the Cube and sharing the impact that your making at the UCLA School of Medicine, leveraging storage and all the different capabilities that that is generating. We thank you for your time. >> Thanks so much for having me. >> We want to thank you for watching the Cube. I'm Lisa Martin with Dave Vellante. We are live at Pure Accelerate 2018 in San Francisco. Stick around, we'll be right back with our next guest.

>> Announcer: Live from the Bill Graham Auditorium in San Francisco, it's The Cube, covering PureStorage Accelerate 2018. Brought to you by PureStorage. (upbeat electronic music) >> Man: Graduated ASU. >> Welcome back to PureStorage Accelerate 2018. I am Lisa Martin with The Cube, sporting the clong of Prince, formerly known as, today because we are at the Bill Graham Civic Auditorium, a really cool concert venue that's been here since 1950 and I'm joined by Dave The Who Vellante today. >> Play the toast and tea. (laughs) >> Pretty groovy T-shirt there. And we're joined by a couple of guys, next we've got Axel Streichart, the senior director of business application solutions from Pure and John Meng, senior director of IT operations at Simpson Strong-Tie. Hi guys! >> Hi. >> Lisa: Welcome to The Cube! >> Thank you. >> Thank you. >> So John, first question to you. Tell us about Simpson Strong-Tie. Who are you guys, obviously you're a Pure customer, but give us a little bit of an orientation to the business. >> Sure, so Simpson Strong-Tie, we're a public company based out of Pleasanton, California. We've been in business since about 1956, if I've got my history right, so we've been around for quite a long time. We're a manufacturing organization. Basically, if you're building a home or a deck or if you're needing to put two by fours together, our niche is that little connector, that bracket that connects those two by fours and we do pretty well in that business. Overall our revenue is just shy of a billion dollars, so a pretty decent sized organization. >> Dave: So Pure passed you. >> Yes, last year, you know. >> You okay with that or? >> I'm okay with that. (all laugh) >> So tell us about, from a business perspective, the need for PureStorage specifically with respect to your SAP journey. >> So a couple of years ago when I came on board, the business had made a decision that they were going to get off of their old ERP system onto a new ERP system. When I say old ERP system, I'm being a little respectful there. It's a homegrown application running on SQL which is basically, they lovingly called it Blue Screen because you go to fileshare and you double click on the executable that you need, for example, if you're doing accounts payable or accounts receivables or purchase orders or what have you, you double click on the executable you want, opens up a nice little blue screen and it's a DOS based blue screen and you tab around and enter all your information. They had been running on that application for about 30 years. >> Lisa: Is that all? (laughs) >> Yeah, so quite a while. >> Dave: It works. >> It works, right. If it ain't broke don't fix it, but it was developed by a single person and it was time that the company put on some bootstraps and hitched them up, so they went to market to decide on what ERP application they were going to move to and SAP won out. They had actually been running for a year on a test system hosted by SAP when I came on board, so the decision had already been made, the application wise from an ERP perspective, but the next step in our journey for Simpson, and my challenge, was how do we host this environment? Do we host it in a cloud, do we host it on-prem? And so as I took a, looking at our environment, a very distributed environment, I said, alright, well first and foremost, SAP is a centralized solution. Is there a way for us to create a single environment that our entire company could run on, not only for SAP but everything else, a mixed use environment? And I started having conversations with Pure. They actually let me talk to a couple of their existing customers who were very happy about their mixed use workload including ServiceNow who talked today, so definitely a shout out to them on the conversations we had back a couple of years ago. Anyways, Pure ended up being our foundation for currently our core tenant, which is SAP, but also the future tenant for everything else that we're going to throw on there. And it's been an incredible journey over these last couple of years with them. >> And why the decision to stay on-prem, versus go to the cloud? Was it a function of SAP really not being there in the cloud or your data, you didn't just want to shove your business into the public cloud? >> So there was definitely a lot of analysis that went into that. Just from a financial perspective, I worked with the CFO and we put together a 12 year ROI on cloud versus on-prem and just to kind of really give ourselves some understanding over time what the impact would be of renting versus owning and it was very clear that on-prem financially made sense. Then we had to talk about the business, what was the best for the business. We looked at it from a, when I came there, there was some, the project team looking at SAP had really already made their mind up. They wanted it off of IT. They wanted it in an environment that they trusted, so when I came on board I said, look this is something I've done before. We have experience, we have the in-house expertise, you just trust me that this is the right thing and let me show you how and that's where, honestly, a lot of the information that I was able to pull off of FlashStack, off of SAP, it's a certified solution, talking to ServiceNow I was able to prove to the business that look, hosting it internally made the most sense financially as well as for our business and what we were trying to achieve. >> Made you happy. >> Yeah and it's not just that, but this is a story we're hearing more often now. So customers actually trying this out in the cloud and realizing, number one, the cost, it's not that cost-efficient and effective as they were planning for and seeing, especially when you're making multiple copies of this SAP environments. The costs go through the roof and the other thing is also what a lot of customers then realize is how do you actually get your data and get your communication from your data center back to the cloud provider? You need a big pipe and this communication cost just to get the data out is huge, is sometimes huge. The other thing is SLAs. It sounds like a good thing, but in many cases, SLA's because they're not flexible, you're ending up quarter end you need help and they're saying, nope, talk to you in four days. It's not really acceptable. And the third one is, there's this whole concept around I don't really have to invest now into the knowledge, into the skill set, because I put it all in the cloud. It's not the reality. The reality, you still have to invest into the skills. Isn't that? >> Everything he has said is actually the conversations that we had in-house, absolutely. If you want to do a data migration from QA to Dev or Dev to Production or whatever your landscape is and how you want to move the data, oh, well, that's going to be a charge. Oh well, okay, well I need to spin up this extra project. Oh, well there's another charge. I mean, it's just constant nickel and diming and another key component that you hit on that I failed to mention was hosting it internally allowed us to control the end to end experience for our end users. When you're talking about hosting it in the cloud, your data is somewhere else and you can not control end to end. You can control it up to a certain extent, but then from there all you can rely on is the SLAs and, to his point, the SLAs are only what's on paper, they're not very flexible at all. >> So the business case didn't pan out for the cloud. >> Correct. >> But there's certainly attributes of the cloud that are attractive, so what are those attributes and how are you bringing those on-prem? >> So flexibility. Flexibility is huge for us, the ability to just quickly be able to spin things up and scale them back as needed. I kind of look of it as, look, there's a water line that you're going to use on a day in and day out basis for your organization. Maximize your investment there. On the peaks and valleys that you're going to have, that's where the cloud can really help and so, is cloud completely off the table for us? No, that's where we're going to be able to burst into that sort of scenario. If we need more compute, we need more spin cycles, whatever we need from the cloud, we can throw it up there and then bring it back down, so have much more controllable costs in our mind. >> So a major change in the application environment, migration, from an old platform. You had to freeze the app. Does that freeze the code? >> John: Yep. >> How long did you have to freeze the code for? >> So, when we're talking about, just making sure I understand your question. >> Your home-grown ERP, blue screen, C prompt to the SAP environment. >> Yeah, so the landscape as we have it today, we actually just went live on SAP early February and it's not company wide. It's only a certain branch. In its strength, the beauty of that previous application, it was very de-centralized and each branch where we have a high consolidation of users and workers, each branch had their own data center hosting their own ERP for their branch, so we could freeze their environment just during their time window. >> I see. >> Now the challenge for us today is as we start consolidating, those windows start to overlap, but that's honestly why we've invested in technologies like FlashStack and so forth that come with the redundancy built in so we can work on the environment without having to freeze it or bring it down. >> So you need the speed to compress those discontinuities. >> Yes, yes. >> Dave: In data. >> Absolutely. >> What about data protection? How do you, I know that's an area of expertise of yours. How do you approach data protection in this new environment? Are you doing anything differently? Where does Pure fit? >> It's actually a huge shift for us on how we do things. From a data protection standpoint, we're talking about disaster recovery, business continuity and so we have active passive data centers. We're utilizing what Pure has under the hood to be able to replicate in multiple ways. And that's the beauty of our setup that we've designed is the ability to replicate in multiple ways, because in a multi-tenant environment, yes, there are certain parts of the stack that one shoe will fit all sizes. I would say that PureStorage is that, but when you start getting to the details of each of the applications, they don't all play the same way when it comes to DR or it comes to replication or data protection and we will need to look at each one of those applications and design a data protection strategy around it as we import it in, so for SAP, we do have differencing of how we're going to protect that versus when we bring in our web servers, versus when we bring in SharePoint and other core applications to the business. >> So Axel, you mentioned, well actually it was John, you mentioned that you had the opportunity to talk to ServiceNow and maybe another customer of Pure as well when you were in this decision making process. I imagine ServiceNow's business is probably quite different from Simpson Strong-Tie, so what, Axel, I guess both of you, help us understand, what were some of the similar changes that, say, a ServiceNow faced that you were facing and then Axel, to your point, tell us a little bit about the SAP alliance that you have with Pure and how customers as big as ServiceNow and Simpson Strong-Tie are helping to evolve that relationship? >> Me first? >> Go for it. >> Alright, so one of the biggest strategies, the focus that I had when I was making the decision around hosting SAP, I really wanted to make sure I understood, did I have to go a siloed approach? Was I buying architecture specifically for SAP or could I do a multi-use workload? Multi-purpose was huge for me. I was really, I couldn't understand how, in 2016 when I was looking at this, I'm like, look, it's 2016, I know there's a solution out there that can solve this problem and so that's what I was challenging Pure and they're like, who do you want to talk to? And I said, "Well I want to talk to somebody "who's running SAP and I want to talk to somebody "who's running SAP in a mixed-workload environment." And that's where ServiceNow came into play. And when I was having conversations with them, I said, alright, so you're running mixed workload. Yes, okay, when you have an SAP performance problem, do you have to, is there a lot of effort to show that there's, where the problem in the performance is? And there was a pause on the phone and the guy actually giggled over the phone. I don't know how else to say it. And he's like, "Performance problems? "We don't have any." And so, when you hear that, especially when you're talking about SAP, which is a known beast of an application inside any environment and it will use whatever resource you throw at it and it won't play nice with other apps, when I heard that, I was like, okay, where do I sign? So it was basically that conversation that really said, alright, let's give this a try. The other thing, honestly, for us is SAP is our first tenant and as we start applying other applications to it, we already have our baseline established and we can watch as the other applications are thrown in and it's not impacting anything, SAP, or on their own. >> So FlashStack is going to be able to give you a foundation to not only scale your SAP infrastructure-- >> Absolutely. >> But also to expand to multiple workloads. >> Yeah, for example, some of our public web facing applications, we've already moved them in-house. We used to use a public service provider, a public cloud offering for this web service that I'm talking about. It would take, so you'd go out there and you'd say, you know what, I want a product catalog of all Simpson products and you hit the button. 45 minutes later, it's downloaded, 45 minutes. I took that workload and I put it in our data center. Three minutes. 45 minutes to three minutes. >> Lisa: Wow. >> And then another test was a web crawler, so we did a web crawler across that same web application to confirm when we moved it from one location to the other we didn't miss anything. In the old environment, running on a public cloud infrastructure, it took 20 minutes. 17 seconds on our own. And it was run from the same PC. There was no, it was pretty clear and honestly, when marketing felt that increase in performance and saw it and realized it, they bragged to the CFO and now the CFO's like, okay, when are we going to get this out of SAP? Well we have to get the whole company on SAP before we can really realize this investment, but they're very excited about the opportunities. >> And how long have you had the Pure infrastructure? >> We installed it probably about year and a half ago, because we had to get it prepared. We installed it about a year and a half ago. >> So you haven't had to do any upgrades yet. >> No, not major ones. We actually have our first major one this week. We're actually scheduling it, but one of the questions I was asked on an earlier panel was how due you handle outages with Pure and how has your experience been with support. Well, I'm sorry we haven't had to call support yet. I've heard great stories about it (Lisa laughs) and I know that our guys that are working with support right now to get our upgrades done, they've had nothing but praise, but honestly we haven't had a lot of interaction yet with their support, just because we haven't needed it yet. >> And you have an in-house development staff, application development team? >> Yes. >> Has their work flow changed at all in terms of being able to share data, share copies of data, are you there yet or? >> We're not there yet, but one of the goals of our environment, so we have two data centers and we have load balancers in front of the two data centers. When it comes to hosting our public web side of things, the goal is to have a green and a red environment where you develop on the red, green is your production and when it comes time, you just flip the switch and your development becomes your active. And so, basically, a lot of the nuances and strategies that you get out of public cloud, we're going to attain those using our private cloud infrastructure. >> Essentially use live data of the test environment-- >> Absolutely, absolutely. >> And then cutting over immediately. You couldn't have done that three, four, five years ago. >> Absolutely, absolutely. >> So Axel, we're just about out of time, but how common is John's story with Simpson Strong-Tie in terms of, we haven't had to call support yet. Are you hearing this resonate pretty pervasively in your SAP install base across industries? >> This is a very typical environment. I would call it almost green field, but most of the environments that we are dealing with are brown field, so customers are long-time SAP users and customers and they're going from, let's say, the Oracle environment into a HANA environment and the nice thing about this is that we are actually providing a platform that can help customers no matter where they are in their journey. If they are still in Oracle, they're already on HANA, they're moving onto AI, whatever it might be, they don't have to change anything on the infrastructure, per se, because there is no configuration or tuning necessary, whether it's Oracle, whether it's HANA, whether it's AI, so you're running it off the same platform. The other thing is that I want to mention is, because you asked me about our relationship with SAP. It's a very strong relationship, so we're actually working with SAP worldwide in their core innovation labs, so they have labs around the world where they develop new solutions together with hardware and software partners and they love to work with PureStorage because it is so simple and they're coming from a functional side. They don't care about the infrastructure at all. They're saying as long as it's simple and you can imagine they are pretty much the Switzerland of ERP. We actually recently published a white paper together with SAP around how to actually save license cost, SAP license cost, of up to 75%. Now you would ask yourself, why would SAP do that? Why would they promote something, push something, that actually cuts into their revenue? But for SAP it is more important to increase the adoption rate of HANA rather than the revenue that's behind it, so that's why we are publishing, and it's on the SAP website that you can download and you can see, together with PureStorage. It's an amazing story that we have. >> Let-- >> And honestly, that was part of why we chose Pure in the beginning, they're certified and now I didn't have to go to the business and try to convince them. It was all on paper for us. >> I can't help but notice that you brought a little kitty cat to the set, Axel. Tell us about this little stuffed animal. >> Maybe you heard it in the keynote this morning. We were talking about PureStorage is actually moving from their solution development towards engineered solutions. We want to actually put more application specific functionality and embed it directly into the array and one of the big challenges that a lot of customers have is how do I create copies, clones, and refreshes of my SAP environment? And we have customers it takes them sometimes nine days just for one copy, nine days. Why? Because it's a very complex and complicated end to end process, so we thought about why don't we take this entire process, automate this entire process, and embed it into our array, and we call this tool that we developed and that's available for everybody that, it's included in the maintenance. We call it Copy Automation Tool, CAT. >> The cat! >> That's the cat. (all laugh) >> And that's what we are, and so if people are asking, why is a cat, Copy Automation Tool. >> That's good. >> Very nice. >> I was like, where is this going? >> I like it. >> Brought it home, brought it home. >> Like you said. >> Do I get to keep this cat? Is this, oh. >> You can. >> Ah, very nice. >> This is pretty cool swag. Well Axel and John, thank you so much for stopping by and sharing with us the innovations that Pure and SAP are doing, how you are being successful, and now you are a reference customer for what you guys are achieving. >> Great story. >> Thank you. >> Thank you. >> Thanks guys, appreciate your time. >> Thank you. >> Yep. >> We want to thank you for watching The Cube. I'm Lisa Martin with Dave Vellante and cat. We are live from PureStorage Accelerate 2018. Stick around. Dave and I will be right back with our next guest. (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. (upbeat music) >> Welcome back to theCUBE's coverage of Pure Storage Accelerate 2018. I'm Lisa Martin with Dave Vellante. We're at the Bill Graham Civic Auditorium, and we are sportin' some. >> You can't see mine-- >> Who are you? >> Because it's chilly-- >> Who are you? >> I'm a symbol. (laughing) >> I don't know, there's a name for that. I'm formally known as Prince. Dave and I are here with Rob Lee, the VP and chief architect at Pure Storage. Hey Rob, welcome to theCUBE. >> Thanks, thanks for having me. >> You're sporting a lot of gray. >> We won't make a comment. >> I don't see any orange. >> I don't have a symbol or T-shirt either. >> I can't believe you haven't been kicked out. Like they didn't just actually eject you. Going to have to fix that. So, you've been at Pure for about five years now. You were one of the founders of FlashBlade. Here we are, third annual Accelerate, packed house this morning in the keynote session. What are some of your observations about the growth that you've seen at this company? >> Well you know, it's really been amazing. When I joined Pure, we were about 150 employees. I joined as part of the founding team for FlashBlade. One of the first two or three people. In fact, my first day on the job was takin' monitors out of boxes and settin' up desks. Since then, we've obviously grown tremendously from 150 employees to over 2,300. But more importantly, what we've been able to grow in terms of customers. So we've went from that tiny size to over 4,800 customers today. From the FlashBlade side of the house, it's been a really, really fun ride. The first couple of years of my time at Pure was spent really heads down building the product, figuring out how do we repeat some of the core philosophies and values that we've brought to FlashArray into FlashBlade and take that product into new markets. We brought that product out and launched it at our first Accelerate conference three years ago. So that first year was really about getting it up to market, growing that customer base. Last year, you saw us take it into a lot of more kind of newer and emerging workloads, analytics, AI, so and so forth. And this past year has really been spent just doubling down on that and not only building a lot more expertise within the company about understanding where that direction of the market is going, but also translating that experience that we're gathering, working with customers on the leading edge of all of those industries into helping our customers, our new and perspective customers. Figure out how do they deploy those solutions into their environments and be maximally successful. So it's really been a very, very exciting ride. >> So Rob, you're the sort of the resident AI expert inside of Pure and I'm sure there are many, but you're on theCUBE now (laughing) so we want to attack that a little bit. AI seems to be this emerging technology that's a horizontal layer of tech that cuts across virtually every industry and every application, but it's application seems to be narrow, whether it's facial recognition or natural language processing, supply chain optimization. So what's Pure's point-of-view on AI, artificial intelligence. I'm not crazy about the name. I like machine intelligence better personally, but what's your point-of-view on the AI space and how it will get adopted. Maybe some of the barriers to that adoption? >> Sure, well so I think. So I share the same distaste for the term mostly because I think it's overused and it's misused in many ways. I think if you look at AI at its heart, it's really about gathering more intelligence and more value from data. Now, more recently, technology advances mostly in compute and algorithms have caused and created an explosion in subsets of AI particularly machine learning or deep learning. And that's really what's driving a lot of these new applications. You mentioned a few, image recognition, voice recognition, so on and so forth. But really what it is, is, it's re-highlighting the focus on the fact that organizations, for decades, have been gathering and collecting and storing and paying to store volumes and volumes of data. But they haven't been able to get the maximum value out of it. And I think one of the most chilling statistics I've seen is that, over 80% of data that's gathered, is unstructured data, but if you look at all of that unstructured data, less than 1% is actually analyzed. What that means is that 99% of data that people have been collecting over the last several decades, they haven't been able to extract maximum value out of it. And I think what we're seeing is that the recent advances in hardware technology, software technology, algorithms to drive a lot of these deep learning type of applications. Even though the applications may be very focused in terms of the types of data they work with, image recognition, object recognition, emotion detection, so on and so forth. It's really bringing the spotlight back across organizations onto how do we get more information out of all of our data. And in a lot of cases, conversations that we get into with customers that start out with the glitzy use cases, the object detection demos. When we start peeling into, so what is it, how are you going to deploy this into your organization, how are you going to translate this into better customer outcomes. We're actually finding ways to apply more traditional data analysis techniques to get better and more information out of people's data. And they may be everything from relational databases to big data analytic stacks. So again, I think the bigger movement here is that recent advances in technology have really re-highlighted the focus on organizations getting more out of their data of all forms. >> When you think about the top market cap companies, Amazon, Facebook, Microsoft, Google, et cetera. They seem to be companies that have mastered or at least are ahead of the pack in terms of machine intelligence. You guys recently conducted a study with MIT. What do you see from that study and the conversations with customers in terms of the incumbence being able to close that gap? >> So, I think there are a couple of really interesting points that came up out of the MIT survey. One is that the prevalence and demand for AI on particularly machine learning applications is both broad-based across all industries, but it's also huge. I think one of the stats that I saw was that over 80% of organizations expect to deploy into production some form of AI or machine learning technology into their companies by 2020. I think the other thing that wasn't in that survey, but was instead, of remarks that Andrew Ng actually from Google made was that, the rapid pace of development in AI research and particularly the algorithm side in terms of different training frameworks and the way that people are working with data, that the rapid advance on that is actually democratizing entry into the AI space. I don't remember the exact quote, but he said something to the effect of, as algorithm research advances, it's easier and easier for new entrants to get into machine learning, to get into data science and make a bigger and bigger impact. And I think that the other thing that we've learned from the large incumbence, is that in many cases, and I think actually Google is the one that came out and said this, they said, the reason why Google is at the head of the pack, if you will in terms of data intelligence and machine intelligence, in some respect, they got their lead by having the most advanced algorithms, most advanced software engineers. But they maintain their lead because they have the most data. Basically the take away point there is having a lot of data trumps having the best algorithm, and we expect that to continue as AI research and algorithms continue to evolve. So I think it's really in many ways, it's much more a democratized landscape than previous approaches to. >> And a lot of that makes sense because the incumbence. You use that word, I like that word. They're going to buy AI from technology suppliers, and then they're going to apply it to their business. At the same time, data generally is not at the core of their business. It tends to be either humans or maybe the bottling plant or some other manufacturing assets or whatever it is. So they have to figure out the data model, and that study suggested that while they were optimistic about AI, they were struggling with trying to figure out how to apply it and the skill sets, et cetera. Maybe share some of your thoughts on that. >> Absolutely. I think one of the things that study really highlighted was that while there was a tremendous excitement and demand from the upper levels of management, the CIO, the kind of see-swee to deploy AI technologies, that there was an increasing and growing disconnect between the policy decision makers, the executive management and the people that are actually doing the work. And I think that disconnect with this technology set is... We see it on a day-to-day basis. We see it with customers that we talk to. I think that a lot of that disconnect actually comes from poor infrastructure planning. One of the things that we see is that many companies go and get really excited about the promise of the AI technology, the promise of hey, I could deploy this solution, I could understand my customers better, great, let's go do it. And they go off and they hire a bunch of data scientists without investing in or thinking about the infrastructure that they're going to put into place to make those data scientists productive. One of the things that I think there was an article in Financial Times that actually looked at hiring and retention for data scientists. And what they found was that the lack of infrastructure, the lack of automation was materially contributing to frustration in terms of data scientists being able to do their jobs. To the point where even those really, really hard to hire data scientists, it's becoming difficult to retain them if you're not giving them, if you're not equipping them with the tools to do their jobs efficiently. So this is an area where there's a growing disconnect between the decision makers that are saying, hey we've got to go that way. Their understanding of the tool sets and the automation of the infrastructure required to get there, and their staffs and their employees that are actually responsible for getting them there, and this is a scenario where as we, one of the exciting parts of my job at Pure is, I get to talk to a lot of customers that are on the bleeding edge of implementing these technologies. One of the things that we get to do by working with each of these customers by understanding what works, what doesn't work, we could help kind of bridge that gap. >> I'll take the bait. (laughs) >> What does that infrastructure for AI look like? I mean it's kind of self-serving. But, describe it. >> Sure. Well, so, I think at the heart of it, it's all about simplicity, it's all about removing friction in bottle necks. There's a Harvard business review article a while ago that looked at data science in general, where time is spent, where resources are spent. And they came up with a statistic that said, more than 80% of the data scientist's time is spent not doing data science, it's actually spent preparing data, moving data, copying data, doing basic data wrangling, data management tasks, and the other 20% is spent complaining about the first 80%. (laughing) >> So I think what we see, Pure helping with, what we see kind of the ideal kind of infrastructure to enable these types of projects, is an infrastructure that is simple, easy to work with, easy to manage. But more importantly, you heard Charlie and Kix during the keynote talk today, talk about data-centered architecture. You heard them talk about the importance of building an architecture, building a practice, building a set of processes around the idea that data is very, very difficult to move. You want to move it as few times as possible. You want to manage it as little as possible. And that really, really applies in a lot of these AI applications. To give you a very, very quick example, if you take a look at an AI pipeline to do something like training and object detection system for self-driving cars, that pipeline, that simple sentence may encapsulate 30 or 40 different applications. You've got video coming off of video cameras that have to be adjusted somewhere. That video has to be cut, downsized, rendered, cut into still images. Those still images have to be warped, noise filters applied, color filters applied. If you play this out, in most cases, there's 30, 40 different applications that are at play here. And without an infrastructure to make it easy to centralize the data management portion of that, you've also potentially got 30 or 40 different data silos. And so when we look at how to make projects successful, and we look at how do you make infrastructure that helps data science teams spend more time doing data science and less time copying data around, tracking where it is, so and so forth. That's all part of what we see as a larger data strategy. >> Oh, sorry Rob. So one of the customers that was shared on stage this morning, Paige AI, how they're leveraging not just pure technology but also really kind of taking what used to be and still is for a lot of organizations, an analog process of actually looking at cancer pathology slides and digitizing that and taking it forward. Did you see in the study any leading industries that are maybe better positioned to align the (mumbling) with the ITDs to take advantage of AI faster? Are there any industries that kind of jumped out in the study as maybe those that are going to be leading edge? >> So I think the thing that actually jumped out was that how broad-based across industries really the AI applications are. I think if you look at specific types of data sets or specific-use cases, if you look at image detection for example. Yes I think you can drive that into specific industries. I think you're going to see a lot in healthcare, in manufacturing, certainly self-driving cars is a big one. I think if you look at natural language processing or speech detect, that sort of thing. A lot of customer service that's being put into use in a lot of automating a lot of chat bots, a lot of customer service kind of call center type applications. So I think if you look at a particular application or at a particular data set or data type, you can drive that to industries that are likely to lead the charge. But what was interesting to me was if you consider all of the machine-learning approaches, all of the AI kind of interests, how broad-based across all industries that was. >> I know we're out of time, but we'd be remiss if we didn't ask you what you guys are doing internally. You're not just selling a infrastructure for AI, you're AI practitioners as well. Can you briefly describe what you're doing? >> Sure, sure. So I think the most interesting application of AI that we've got internally is really the AI engine that powers Meta which is our Pure1 hosted kind of-- (cell phone ringing) (laughing) Our Pure1 offering that helps us predictively and proactively manage customer arrays. We started Pure1 as a remote support offering since the beginning of Pure, since we first shipped FlashArray, and we did it originally to get to the point where we could better understand arrays. The more arrays that we shipped in the field, we want the marginal cost of support, the marginal kind of effort, if you will, to understand that the arrays behavior to decrease with the number of arrays that we ship. And we want our understanding of the array's behavior of the customer use case, of the workload behavior to increase with the number of arrays that we ship. And we started off by using more traditional AI techniques. Basic language processing, basic statistics, so on and so forth. What we've since done is built a machine-learning engine behind it so that we can make more intelligent inferences, more intelligent decisions. And so you've seen this come out as, in the form of tools that we've released as such as Will It Fit, so we can now take a look at an array, and we can say, okay well you've got this many workloads you've got this many VMs sitting on this array and on this volume. What would it look like to put double that? What can you expect in terms of capacity of utilization? What can you expect in terms of performance? We can also take that to a hypothetical kind of hypothesis analysis to different harbor platforms. We can say hey you've got this workload running on a X50 today, what would it look like to double that workload and move it to an X70? What would that look like? And again, a lot of those inferences, we can do that without exactly tracking and exactly testing that workload because we have a broad-based set of data points across our entire fleet. >> Too complicated for humans to do all that. It really is. >> Yes, it really is. >> But generating workload DNA. >> Exactly, exactly. And more importantly, to get to Dave's point, more importantly, doing it an automated way so that you don't have to put an army of human beings, an army of administrators behind it to calculate it by hand. >> Well Rob thanks so much for stopping by theCUBE and sharing with us what's goin' on from your perspective. Go get some orange. (laughing) >> Thanks for having me. >> For Dave Vellante, I am Lisa Martin. You're watching theCUBE. We are live at Pure Storage Accelerate 2018 in San Francisco. Stick around, Dave and I will be right back with our next guest. (upbeat 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. >> 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)

>> Narrator: Live from the Bill Graham Auditorium in San Francisco, it's The Cube. Covering Pure Storage Accelerate 2018. Brought to you by Pure Storage. (techno music) >> Back to The Cube, we are live at Pure Storage Accelerate 2018. We are in San Francisco at the Bill Graham Civic Auditorium. This is a really cool building built in 1915, loads of history with artists. I'm with Dave Vellante. I'm wearing prints today in honor of the venue and we're excited to be joined by longtime Pure Storage customer Mercedes AMG Petronas Motorsport head of IT Matt Harris. Matt, it's great to see you again. >> Hey, good up, good morning I should say. >> I think it is still morning somewhere. (laughter) >> So, Matt, you know, for folks who aren't that familiar with Formula One one of the things, you know I'm a fan. It's such a data intense sport. You've got to set up a data center 21 times a year, across the globe, with dramatically different weather conditions, humidity, etc. Give our viewers an idea of your role as head of IT and what it is that your team needs to enable the drivers to do? >> Okay, so in general terms, we're but like any other normal business around the world. Yeah we have huge amounts of data created depending on what your company is doing. Ours comes from two cars going around the track. That is the lifeblood of our of our work, our day work, and all that data is always analyzed to work out how we can improve the car. But what we really have is an infrastructure the same as many other companies. We have some slight differences as you say. We go to 21 countries. In those countries we turn around and we have 36 hours roughly to put everything together in a different world, different place and then everybody turns up and uses it as though it's a branch office. A hundred people roughly sat there working in the normal environment. We use it for five days and then we take it apart in six hours, put it in two boxes, take it to another country, and we do the same thing again. We do that 21 times. Sometimes back-to-back, sometimes with a week in between. Week in between is quite easy. Back to back sometimes we go from Canada maybe all the way across the world from Monaco within the space of a week so if we've got the flights in the way and everything else and we also end up having to an engineer a car, run a car around the track, and hopefully win races. >> So, you basically got a data kit that you take around with you. >> Yeah. >> And then what did you do before you had this capability? Was it just gut feel? Was it finger in the wind? >> Um, so. For about 15 years, we've been running what everybody's classes and Internet of Things we've been doing for about 15-20 years the car. It's got around these days around 300 sensors on it. Without those sensors realistically we'll be running the car blind and we probably couldn't even start the car let alone actually run it these days or improve things. We turn around and we're always ingesting data from the cars real-time. That real-time data actually we transfer to the garage. That's no problem at all but we also bring it back to the factory because we're limited on the number of people that are allowed to travel with the team. So, we're physically only allowed to take 60 people. Rules tell us we can only take 60 people to work on the car. Now of those, around about 15 are probably looking at data. We're generating around about half a terabyte per race weekend these days and 15 people, it's not enough eyes realistically to turn around and look at all that data all the time. So we take it back to the UK and in the UK, again, we have anywhere between another 30 and maybe 800 staff will be looking at that data to help analyze particularly on a Friday. Friday is about running the car and learning. We discussed a few minutes ago, what's the weather like? What are the tires like? What's the track like? Has there been any change in track? Has it been resurfaced? What's going on with the car compared to what we think is its optimum? And on a Friday's iterative change and learning about tire degradation, tire life, tire wear, the weather conditions, how they're going to interact with the car, all based on data. The interesting thing for me has always been that we have all this data but the two drivers in the car are the biggest sensor for us. They turn around and tell us how they felt. When they were going round corners, Was it good, bad, indifferent? But as soon as they tell us something, we always go to data. We've taken their interpretation of how their body felt, we turn around and then look at the data to prove what they've told us. So, an interesting anecdote very quickly. last year in Singapore, Valtteri was going across the bridge and he said he could feel that the throttle felt like it was cutting and we couldn't see in data and we were looking and looking and eventually he said, "No, it absolutely happens every time I cross the bridge." and they found a 20 millisecond gap in throttle application basically because there was a magnetic field that the bridge was creating so a sensor was actually cutting the throttle. he could feel it. we could fit that eventually see in data, shielded the sensor, everybody's happy. so you go from the human being could feel a 20th, a 20 millisecond gap in throttle application for us finding in data, engineering a solution, and changing things. >> So, the human's still a critical part of? (crosstalk) >> So, where does Pure Storage fit into this whole thing? and give us the before and after on that. >> So, three years ago we started working with Pure because I have two different solutions. one in the track and one in the factory. one in the track realistically I have some constraints around space, power, heat. that most people would love to take the racks as we were talking about we take around the world, they would love to leave in a nice air-conditioned computer room and just leave it there all year. we move it around but that rack of information we have to spend $298 per kilo to transport IT equipment around, well any equipment, around the world. So, we've got tons of equipment that we take around the world. it's thousands and thousands of pounds of freight cost. So, we went from forty U of old-school spinning disk, lots of complexity in cabling, administration, down to 2-3 U and 20 arrays. Now, they're more heat tolerant. I have two power cables in each and two network cables so complexity is gone. it just works. It's heat tolerant. it doesn't create a lot of heat so I haven't got the added issue of that. it's not using a huge amount of power so my UPS solution has to be smaller. so everything just got smaller, cheaper. really simply at the track, we improve the performance for everybody. from an IT point of view, we got very, very simple. incredibly easy to look after and manage but it's very reliable and performant at the same time. we then went to the factory where I've got 800 people looking at data. the problem is when a car goes round and we offload it, there's one single file. we haven't got this distributed amount of data that everybody. so you got one file that everybody's trying to open, old-school discs, you've now got contention for that one file that everybody's opening. So, people would come back from the track and go, "Why is it so slow to open information in the factory compared to at the track?" Trying to explain to them contention of data in those days was a little bit difficult but now we have 800 people that don't need to care and why that matters for us is decision making. So, if you think about qualifying, those that don't understand Formula One, we have three sessions of qualifying and the car goes out roughly two times in each qualifying session with around about a couple of minute gap in between the times the car goes out. that couple of minutes is about changing the car to be optimal for the next run. if it takes you minutes and minutes to offload data, open the data, review the information that the driver told you, and make a change, you can't go back out a second time. So, everything is about optimal performance for those engineers to optimize the performance of the car. what we are able to do now is to turn around and make sure that we're making correct decisions because rather than data taking two or three minutes to open, it's in seconds instead. So, you can look at the data, make an informed decision, change the car, hopefully improve every time the car goes out. >> One of the things, Matt, that Charlie Giancarlo, the CEO of Pure Storage, said this morning during the keynote was that less than half a percent of data in the world is analyzed. talk to us about what Pure Storage is able to facilitate for your team to be able to analyze that data. how much of that data are you able to analyze? and talk to us about the speed criticality. >> Yeah, okay, so, and quite a lot of the work over the previous probably 10 or 15 years has been very human centric. So, it's what data I know I need to go and look at to understand to be able to compute, to turn around and maybe infer information from to be able to make a better decision. So, strategy is probably one of the best places these days where the data that we're learning all the time. we have data about ourselves but we also have data about the other teams. those teams have the same data about us as well, your GPS data, timing data, so we know what's going on so we can infer information on a competitor as well as ourselves. tire degradation, tire wear, tire life, all things that you can infer that mean that you were mentioning earlier on about a pit stop. if a safety car comes out should you pick, shouldn't you pick. those decisions are now based on accurate data about whether we think competitor will pit, whether we think the competitors tires will last, can we overtake that competitor? because actually the track does or doesn't allow overtaking. So, lots of decisions made real-time based on exactly what's happening now but inferred from previous races and we're always learning all the time. everything is about the previous races. information we're learning every time. >> and how much of that heavy lifting of that data is machines versus humans. Are the machines increasingly, I don't want to say making the decisions, but helping? >> Yes, so, we're not in a position at the moment where the machines are making decisions. they're helping us to be informed, to visualize. Yeah, we work with the likes of TIBCO as well as Pure and other partners or sponsors that we have where they turn around and actually they help us to visualize that data. the problem we've got at the moment is we're still looking at all the data. where we really want to get to is looking at exceptions. So, actually the norm, don't show us that data. we don't need to know, don't need to care. >> Want the outliers. >> we want the outliers that. our problem though is that our car changes every time it goes out. So, an outlier could be because we've made a change. So, now you've got to still have some human that's helping at moto. we're trying to understand how we can use machine learning techniques. in certain places we can so image recognition and another bits and piece like that we can actually start to take advantage of but decisions necessarily around configuration and the next change to the car at the moment it's still indicators given to us by simulation and then a human at the end of the day is making the decision. >> and the data that you talked about that is on your competitors, is that a shared data source or is that but it is. >> Yeah. >> everybody shares the same data. >> every car has a transponder on it. basically it's GPS with longitude, latitude, and all sorts but incredibly accurate. if you consider the cars are doing 200 mile-an-hour, we have an accuracy of around about it's less than 10 centimeters accuracy at 200 miles per hour. Now, if you think of your GPS on your phone, you struggle to know whether you're on the right street sometimes. >> but your differentiation there is your your speed at which you can analyze the data, your algorithms, your skill sets you're telling. and then obviously we're here at Pure there's a component of that speed which is Pure. aren't you worried that your competitors are going to get your secrets or is everybody in the track use Pure Storage? >> everybody is turning around and using their own methodologies, their main, their own software. the thing for us at the moment is to make sure that we keep the really secret things ourselves, our IP sensitive, keep those to ourselves. So, what we do with our storage people know about and other teams are copying and seeing the advantages of Pure as well as some of the other tools and partners we partner with. the benefit of us though is that we have a partnership with Pure not just a purchasing so we work, we've known about some of the products. So, flash blade we knew about a long time before it was released. Yeah, we work with the team on what's coming. we know some of the advances in the technology before it's live and that's critical for us because we can get a stick, a march on everybody else even if we're six months ahead of somebody else on a technology or a way of doing something, six months is a long time in F1. >> Yeah. >> sorry Dave, I was going to say, Pure calls this the unfair advantage. (laughter) and you are, Mercedes has last fall won the fourth consecutive Constructors Championship. Coincidence, I don't know, but talk to us about this symbiotic relationship. are you also able to help influence the design of the technologies at Pure? >> Yeah, so, and I wouldn't say that we help design necessarily but they'll take into consideration our requirements and our wishes. like a number of other people that will be here, you've heard other people talking on stage and we'll always be talking about what we would like to be doing, what we could be doing if we had, I don't know, some new technology whether it's s3 connectivity to the flash blade, s whether it's NFS, whether it's SIF, whatever that would be, the containerization of them, the storage front end, whatever that would be we're always talking about how we can work with the Pure Storage to improve what we're doing. so that ideally I take out the way of the business. my ideal is that IT's not seen, it's not heard, and it just works. obviously in IT that's not always the case but. >> I want to unpack something you said earlier. you said it was I believe two or three years ago, three years ago that you brought in Pure and you had substantial performance improvement. I talk to a lot of customers and what they'll typically do in that situation is they'll compare what they saw in 2015 with what they replaced which was probably a five or eight year old array. true in your case or not? if it is true, which I suspect it is, it had to be something else that led you to Pure because you could have bought the incumbents all flash array and got you know much better performance. What, first of all true or not? and what was it that led you to Pure to switch from the incumbent which is not trivial? >> So quickly and was it five or eight year old hardware? in some places yes, some places no. So, it wasn't, we took a decision to take a step back and look at storage from a different standpoint because we just kept adding more discs to try and get around an issue, you know, and we've got a fairly strange data model to compute. we don't need much compute, we need lots of storage. so some of the models that were talked about on stage where I need, you know, Matt Baer was talking about the fact of I want some more storage, you need to buy some more compute and that was just so annoying for us. so there was different reasons but the end goal, you're quite right, performance. Yeah, we could have got it probably from anywhere and being brutally honest lots of other technologies could give the performance 'cause we don't give that level of performance maybe if your a service now or a big financial institution, we've got data, it's important. we've got critical time scales to open and save data, okay critical to us as far as erasing, but what was important for me was simplicity. Absolutely, now we got other benefits. the Evergreen model was brilliant for us but simplicity was critical. we had a storage guy that was spending his life managing storage. nobody manages storage now. they turn around and they go into Vmware. they want a new VMware server, they just spin it up, and the disk is associated. we don't have to think about it. you don't have that storage specialist any longer. Yeah, we started working with other partners, you know, Rubric for instance, integration with them, the Pure arrays as well, again enabling us to get out the way and not having to worry about backup. traditionally or we'd headed a guy that was always changing tape. I saw on the slide several time today about tape archive, I'm going I never want to see a tape archive. I just don't care about it any longer. I just want to be able to turn around and give the business, the SLAs they want on the their data and then not care about it. Also, can I then still turn around and mine that data in those archive or backup, not back up bin, the archive location? So, there's huge differences but simple is the best thing for me. we could have a small IT team that we have to look after a huge amount of kit and if it's complex it's just I can't employ the right people. >> Simplicity, performance, portability, you mentioned integration. you've got a big partner ecosystem here that. >> Yeah. >> So, having the ability to integrate seamlessly with Rubric, TIBCO, Satirize Key. >> and yeah for us, the partners are extension of the team. my team in particular because I can't turn around and just keep adding staff. we have to look after the day-to-day and keep the lights on but I can't just keep adding staff to look after a new technology. it needs to look after itself so the simplicity is absolutely. performance was a sort of a no-brainer. evergreen was a brilliant one for us because just not having to do those forklift upgrades. I think in the three years, we've gone from M450s to M70s, we've gone from M20s to M50s, M50R2s. we've done all of these. I've been stood on stage before in a day when we've been doing an upgrade during the time I've been stood on stage. You know and so people talk about the forklift upgrade, I don't have to worry about it, it doesn't happen. >> totally non-disruptive. >> Yeah, yeah. >> you do change out the controllers right? >> Yeah, so we change out controllers. we've done all sorts, we've gone from capacity upgrade so complete shells of discs and completely different on from I can't remember the exact size from two terabyte to three terabyte drives, new controllers to give us the new functionality with the nvme and all during the day. we don't do it out of hours. there's a lot of the business a scared stiff when we turn around the wisp and they go oh no no no but we're running the winds on low. we're doing this CFD, we go doesn't matter zero downtime no matter zero no planned. obviously no one play it's planned? >> Yes, it's planned downtime but the user doesn't see it they no performance no downtime no nothing that's Nevada for RIT. Yeah, well it means I don't have to keep asking people to do long shifts through the night to do a simple upgrade what should be a simple your weekends are nice back hopefully we end up with we end up racing those unfortunately okay but that's the fun stuff yeah for those who aren't that familiar was Formula One I encourage you to check it out it's one of the coolest strategic sports that is really fueled by technology it's amazing without technology honestly the cars wouldn't be anywhere near their what they are today and IT systems go we underpin everything that the company does nobody really wants to say that I t's the lifeblood of the company they don't but we need to be able to deliver and actually let the business actually take on new technologies new techniques and get out the way so we've got a huge amount of work a lot of what Charlie said on stage earlier on I've been having conversations with the guys here about autonomous data centers immutable infrastructure it's critical for us to go out the way and allow business to if they want some new VMs new storage it just happens not not need a person to be in the way make it sound so simple well you one of your primary sensors Lewis Hamilton is currently in in the number one position battery talked to us in third Monaco coming up this weekend introduction of a new hyper soft tire some pretty exciting stuff yeah so the hope of soft tires going to be interesting first race with it before the Monaco track yeah so and they originally designed it for Monaco I believe it will go to another race as well in the short term but we didn't even run it in winter testing earlier in the year so the first time we ran it was actually Barcelona test last week I've actually heard nothing about it so I don't know whether it's good bad or indifferent I don't know what's going to happen but it's going to be an interesting week because it's a very different track to where we've been to so far traditionally some of the other teams are quite strong there so the this weekend's going to be an interesting one to see where we end up Monica is always exciting grace Matt thanks so much for stopping by the cube and sharing with us what you're doing and how you're enabling technology to drive the Sportage no comatose again I'm Lisa Martin with Dave Volante live at pure storage accelerate 2018 we were at the Bill Graham Civic I'm Prince for the day stick around Dave and I will be right back with our next guest

>> 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)