>> from Austin, Texas. It's Theo Cube, covering pure storage. Accelerate 2019. Brought to you by pure storage. >> Welcome to the Cube. The leader in live tech coverage covering up your accelerate 2019. Lisa Martin with Dave Ilan in Austin, Texas, this year. Pleased to welcome a couple of guests to the program. Please meet Charlie Boyle, VP and GM of DJ X Systems at N Video. Hey, Charlie, welcome back to the Cube, but in a long time ago and we have Brian Schwartz, VP of product management and development at your brain. Welcome. >> Thanks for having me. >> Here we are Day one of the event. Lots of News This morning here is just about to celebrate its 10th anniversary. A lot of innovation and 10 years. Nvidia partnerships. About two is two and 1/2 years old or so. Brian, let's start with you. Give us a little bit of an overview about where pure and and video are, and then let's dig into this news about the Aye aye data hub. >> Cool, it's It's been a good partnership for a couple of years now, and it really was born out of work with mutual customers. You know we brought out the flash blade product, obviously in video was in the market with DJ X is for a I, and we really started to see overlap in a bunch of initial deployments. And we really realized that there was a lot of wisdom to be gained off some of these early I deployments of capturing some of that knowledge and wisdom from those early practitioners and being able to share it with the with the wider community. So that's really kind of where the partnership was born going for a couple of years now, I've got a couple of chapters behind us and many more in the future. And obviously the eye data hub is the piece that we really talked about at this year's accelerate. >> Yeah, areas about been in the market for what? About a year and 1/2 or so Almost >> two years. >> Two years? All right, tell us a little bit about the adoption. What what customers were able to dio with this a ready infrastructure >> and point out the reason we started the partnership was our early customers that were buying dejected product from us. They were buying pure stored. Both leaders and high performance. And as they were trying to put them together, they're like, How should we do this? What's the optimal settings? They've been using storage for years. I was kind of new to them and they needed that recipe. So that's, you know, the early customer experiences turned into airy the solution, and, you know, the whole point of this to simplify. I sounds kind of scary to a lot of folks and the data scientists really just need to be productive. They don't care about infrastructure, but I t s to support this. So I t was very familiar with pure storage. They used them for years for high performance data and as they brought in the Nvidia Compute toe work with that, you know, having a solution that we both supported was super important to the I T practitioners because they knew it worked. They knew we both supported it. We stood behind it and they could get up and running in a matter of days or weeks versus 6 to 9 months if they built it >> themselves. >> You look at companies that you talk to customers. Let's let's narrow it down to those that have data scientists least one day to scientists and ask him where they are in their maturity model, if one is planning to was early threes, they got multiple use cases and four is their enterprise wide. How do you see the landscape? Are you seeing pretty aggressive adoption in those as I couched it, or is it still early? >> I mean so every customers in a different point. So there's definitely a lot of people that are still early, but we've seen a lot of production use cases. You know, everyone talks about self driving cars, but that's, you know, there's a lot behind that. But real world use cases say medicals got a ton? You know, we've got partner companies that you are looking at a reconstruction of MRI's and CT scans cutting the scan time down by 75%. You know, that's real patient outcome. You know, we've got industrial inspection, we're in Texas. People fly drones around and have a eye. Models that are built in their data center on the drone and the field operators get to re program the drones based on what they see and what is happening. Real time and re trains every night. So depending on the industry really depends on where people are in the maturity her. But you know, really, our message out to the enterprises are start now. You know, whether you've got one data scientist, you've got some community data scientists. There's no reason to wait on a because there's a use case that work somewhere in your inner. >> So so one of the key considerations to getting started. What would you say? >> So one thing I would say is, look any to your stages of maturity. Any good investment is done through some creation of business value, right? And an understanding of kind of what problem you're trying to solve and making sure it's compelling. Problem is an important one, and some industries air farther along. Like you know, one of the ones that most everybody's familiar with is the tech industry itself. Every recommendation engine you've probably ever seen on the Internet is backed by some form of a I behind it because they wanted to be super fast and, you know, customized to you as a user. So I think understanding the business value creation problem is is a really important step of it and many people go through an early stage of experimentation, data modeling really kind of, say, a prototyping stage before they go into a mass production use case. It's a very classic i t adoption curve. Just add a comment to the earlier kind of trend is it's a megatrend. Yes, not everybody is doing it in massive wide scale production today. There's some industries that are farther ahead. If you look forward over the next 15 to 20 years, there's a massive amount of Ai ai coming, and it's a It is a new form of computing, the GPU driven computing and the whole point about areas getting the ingredients right. Thio have this new set of infrastructure have storage network compute on the software stack all kind of package together to make it easier to adopt, to allow people to adopt it faster because some industries are far along and others are still in the earlier stages, >> right? So how do you help for those customers and industries that aren't self driving cards of the drones that you talked about where we use case, we all understand it and are excited about it. But for other customers in different industries. How do you help them even understand the A pipeline? And where did they start? I'm sure that varies very >> a lot. But, you know, the key point is starting a I project. You have a desired outcome from Not everything's gonna be successful, but you know Aye, aye. Projects aren't something that it's not a six month I t project or a big you know, C r m. Refresh it. Something that you could take One of our classes that we have, we do a lot of end user customer training are Deep Learning Institute. You can take 1/2 day class and actually do a deep learning project that day. And so a lot of it is understanding your data, you know, and that's where your and the data hub comes in, understanding the data that you have and then formulating a question like, What could I do if I knew this thing? That's all about a I and deep learning. It's coming up with insights that aren't natural. When you just stare at the data, how can the system understand what you want? And then what are the things that you didn't expect defined that A. I is showing you about your data, and that's really a lot of where the business value comes. And how do you know more about your customer? How do you help that customer better, eh? I can unlock things that you may not have pondered yourself. >> The other thing. I'm a huge fan of analogies when you're trying to describe a new concept of people. And there's a good analogy about Ai ai data pipelines that predates, Aye aye around data warehousing like there's been industry around, extract transformers load E T L Systems for a very long period of time. It's a very common thing for many, many people in the I T industry, and I do think there's when you think about a pipeline in a I pipeline. There's an analogy there, which you have data coming in ingress data. You're cleansing it, you're cleaning it. You're essentially trying to get some value out of it. How you do that in a eyes quite a bit different, cause it's GP use and you're looking, you know, for turning unstructured data into more structure date. It's a little different than data. Warehousing traditionally was running reports, but there's a big analogy, I think, to be used about a pipeline that is familiar to people as a way to understand the new concept. >> So that's good. I like the pipeline concept. One of the one of the counters to that would be that you know, when you think about e. T ells complicated process enterprise data warehouses that were cumbersome Do you feel like automation in the A I Pipeline? When we look back 10 years from now, we'll have maybe better things to say than we do about E D W A R e g l. >> And I think one of the things that we've seen, You know, obviously we've done a ton of work in traditional. Aye, aye, But we've also done a lot in accelerated machine learning because that's a little closer to your traditional Data analytics and one of the biggest kind of ah ha moments that I've seen customers in the past year or so. It's just how quickly, by using GPU computing, they can actually look at their data, do something useful with it, and then move on to the next thing so that rapid experimentation is all you know, what a I is about. It's not a eyes, not a one and done thing. Lots of people think Oh, I have to have a recommend er engine. And then I'm done. No, you have to keep retraining it day in and day out so that it gets better. And that's before you had accelerated. Aye, aye pipeline. Before you had accelerated data pipelines that we've been doing with cheap use. It just took too long so people didn't run those experiments. Now we're seeing people exploring Maur trying different things because when your experiment takes 10 minutes, two minutes versus two days or 10 days, you can try out your cycle time. Shorter businesses could doom or and sure, you're gonna discard a lot of results. But you're gonna find those hidden gems that weren't possible before because you just didn't have the time to do >> it. Isn't a key operational izing it as well? I mean again, one of the challenges with the analogy that you gave a needy W is fine reporting. You can operationalize it for reporting, and but the use cases weren't is rich robust, and I feel as though machine intelligence is I mean, you're not gonna help but run into it. It's gonna be part of your everyday life, your thoughts. >> It's definitely part of our everyday lives. When you talk about, you know, consumer applications of everything we all use every day just don't know it's it's, you know, the voice recognition system getting your answer right the first time. You know there's a huge investments in natural language speech right now to the point that you can ask your phone a question. It's going through searching the Web for you, getting the right answer, combining that answer, reading it back to you and giving you the Web page all in less than a second. You know, before you know that be like you talked to an I. V R system. Wait, then you go to an operator. Now people are getting such a better user experience out of a I back systems that, you know over the next few years, I think end users will start preferring to deal with those based systems rather than waiting on line for human, because it'll just get it right. It'll get you the answer you need and you're done. You save time. The company save time and you've got a better outcome. >> So there's definitely some barriers to adoption skills. Is one obvious one the other. And I wonder if Puritan video attack this problem. I'm sure you have, but I'd like some color on it. His traditional companies, which a lot of your customers, their data is in pockets. It's not at the core. You look at the aye aye leaders, you know, the Big Five data their data cos it's at the core. They're applying machine intelligence to that data. How has this modern storage that we heard about this morning affected that customers abilities to really put data at their core? >> You know, it's It's a great question, Dave and I think one of the real opportunities, particularly with Flash, is to consolidate data into a smaller number off larger kind of islands of data, because that's where you could really drive the insights. And historically, in a district in world, you would never try to consolidate your data because there was too many bad performance implications of trying to do that. So people had all these pockets, and even if you could, you probably wouldn't actually want to put the date on the same system at the same time. The difference with flashes as so much performance at the at the core of it at the foundation of it. So the concept of having a very large scale system, like 150 blade system we announced this morning is a way to put a lot of the year and be able to access it. And to Charlie's point, a lot of people they're doing constant experiment, experimentation and modeling of the data. You don't know that how the date is gonna be consumed and you need a very fast kind of wide platform to do that, Which is why it's been a good fit for us to work together >> now fall upon that. Dated by its very nature. However, Brian is distributed and we heard this morning is you're attacking that problem through in a P I framework that you don't care where it is. Cloud on Prem hybrid edge. At some point in time, your thoughts on that >> well, in again the data t be used for a I I wouldn't say it's gonna be every single piece of data inside an organization is gonna be put into the eye pipeline in a lot of cases, you could break it down again. Thio What is the problem? I'm trying to solve the business value and what is the type of data that's gonna be the best fit for it? There are a lot of common patterns for consumption in a I AA speech recognition image recognition places where you have a lot of unstructured data or it's unstructured to a computer. It's not unstructured to you. When you look at a picture, you see a lot of things in it that a computer can't see right, because you recognize what the patterns are and the whole point about a eyes. It's gonna help us get structure out of these unstructured data sets so the computer can recognize more things. You know, the speech and emotions that we as humans just take for granted. It's about having computers, being able to process and respond to that in a way that they're not really people doing today. >> Hot dog, not a hot dog. Silicon Valley >> Street light. Which one of these is not a street lights and prove you're not about to ask you about distributed environments. You know customers have so much choice for everything these days on Prem hosted SAS Public Cloud. What are some of the trends that you're seeing? I always thought that to really be able to extract a tremendous amount of value from data and to deliver a I from it you needed the cloud because you needed a massive volumes of data. Appears legacy of on print. What are some of the things that you're seeing there and how is and video you're coming together to help customers wherever this data is to really dry Valley business value from these workloads, >> I have to put comments and I'll turn over to Charlie. So one is we get asked this question a lot. Like where should I run my eye? The first thing I always tell people is, Where's your data? Gravity moving these days? That's a very large tens of terror by its hundreds of terabytes petabytes of data moving very large. That's the data is actually still ah, hard challenge today. So running your A II where your date is being generated is a good first principle. And for a lot of folks they still have a lot on premise data. That's where their systems are they're generating the systems, or it's a consolidation point from the edge or other other opportunities to run it there. So that's where your date is. Run your A I there. The second thing is about giving people flexibility. We've both made pretty big investments in the world of containerized software applications. Those things are things that can run on grammar in the cloud. So trying to use a consistent set of infrastructure and software and tooling that allows people to migrate and change over time, I think, is an important strategy not only for us but also for the end users that gives them flexibility. >> So, ideally, on Prem versus Cloud implementations shouldn't be. That shouldn't be different. Be great. It would be identical. But are they today? >> So at the lowest level, there's always technical differences, but at the layers that customers are using it, we run one software stack no matter where you're running. So if it's on one of our combined R E systems, whether it's in a cloud provider, it's the same in video software stack from our lowest end consumer of rage. He views, too. The big £350 dejected too you see back there? You know, we've got one software stack runs everywhere, And when the riders making you know, it's really Renee I where your data is And while a lot of people, if you are cloud native company, if you started that way, I'm gonna tell you to run in the cloud all day long. But most enterprises, they're some of their most valuable data is still sitting on premise. They've got decades of customer experience. They've got decades of product information that's all running in systems on Prem. And when you look at speech, speech is the biggest thing you know. They've got, you know, years of call center data that's all sitting in some offline record. What am I gonna do with that? That stuff's not in the cloud. And so you want to move the processing to that because it's impossible to move that data somewhere else and transform it because you're only gonna actually use a small fraction of that data to produce your model. But at the same time, you don't want to spend a year moving that data somewhere to process it back the truck up, put some DJ X is in front of it. And you're good to go. >> Someone's gonna beat you to finding those insides. Right? So there is no time. >> So you have another question. >> I have the last question. So you got >> so in video, you gotta be Switzerland in this game. So I'm not gonna ask you this question. But, Brian, I will ask you what? Why? You're different. I know you were first. He raced out. You got the press release out first. But now that you've been in the market for a while what up? Yours? Competitive differentiators. >> You know, there's there's really two out netted out for flash played on why we think it's a great fit for an A i N A. I use case. One is the flexibility of the performance. We call multi dimensional performance, small files, large files, meditated intensive workloads. Flash blade can do them all. It's a it's a ground up design. It's super flexible on performance. And but also more importantly, I would argue simplicity is a really hallmark of who we are. It's part of the modern date experience that we're talking about this morning. You can think about the systems. They are miniaturized supercomputers And yes, you could always build a supercomputer. People have been doing it for decades. Use Ph. D's to do it and, like most people, don't want to happen. People focused on that level of infrastructure, so we've tried to give incredible kind of capabilities in a really simple to consume platform. I joke with people. We have storage PhDs like literally people. Be cheese for storage so customers don't have to. >> Charlie, feel free to chime in on your favorite child if you want. I >> need a lot of it comes from our customers. That's how we first started with pure is our joint customers saying we need this stuff to work really fast. They're making a massive investment with us and compute. And so if you're gonna run those systems at 100% you need storage. The confusion, you know, pure is our first in there. There are longest partner in this space, and it's really our joint customers that put us together and, you know, to some extent, yes, we are Switzerland. You know, we love all of our partners, but, you know, we do incredible work with these guys all up and down the stack and that's the point to make it simple. If the customer has data we wanted to make be a simplest possible for them to run a ay, whether it's with my stuff with our cloud stuff, all of our partners, but having that deep level of integration and having some of the same shared beliefs to just make stuff simple so people can actually get value out of the data have I t get out of the way so Data scientists could just get their work done. That's what's really powerful about the partnership. >> And I imagine you know, we're out of time, but I imagine to be able to do this at the accelerated pace accelerated, I'm gonna say pun intended it wasn't but, um, cultural fed has to be pretty align. We know Piers culture is bold. Last question, Brian and we bring it home here. Talk to us about how the cultural cultures appearing and video are stars I lining to be able to enable how quickly you guys are developing together. >> Way mentioned the simplicity piece of it. The other piece that I think has been a really strong cultural fit between the companies. It's just the sheer desire to innovate and change the world to be a better place. You know, our hallmark. Our mission is to make the make the world a better place with data. And it really fits with the level of innovation that obviously the video does so like to Silicon Valley companies with wicked smart folks trying to make the world a better place, It's It's really been a good partnership. >> Echo that. That's just, you know, the rate of innovation in a I changes monthly. So if you're gonna be a good partner to your customers, you gotta change Justus fast. So our partnership has been great in that space. >> Awesome. Next time, we're out of time, But next time, come back, talk to a customer, really wanna understand it, gonna dig into some of the great things that they're extracting from you guys. So, Charlie Brian, thank you for joining David me on the Cube this afternoon. Thanks. Thanks. Thanks for David. Dante. I'm Lisa Martin. You're watching the Cube. Y'all from pure accelerate in Austin, Texas.