>> Announcer: Live from Las Vegas, it's theCUBE, covering NetApp Insight 2018. Brought to you by NetApp. >> Welcome back to theCUBE, we are live. We've been here all day at NetApp Insight in Las Vegas at the Mandalay Bay. I'm Lisa Martin with Stu Miniman and we're joined by a couple of guests. One of our alumni, Bharat Badrinath, the V.P. of Product Solutions and Marketing at NetApp. Hey, Bharat, welcome back. >> Thank you, thanks for having me. >> And we've also got Renee Yao, who is a Senior Product Marketing Manager for Deep Learning and AI Systems at Nvidia. Renee, welcome to theCUBE. >> Thanks for having me. >> So guys, this is a pretty big event. NetApp's biggest customer-partner event, the keynote, standing room only this morning five thousand plus people, lot of buzz, lot of momentum. Speaking of momentum, NetApp and Nvidia just launched an interesting partnership a couple months ago. Bharat, talk to us about how NetApp is working with Nvidia to really take advantage of AI and allow your customers to do that as well. >> Sure. So, as we started talking to customers and started looking at what they were investing in, AI bubbled up, right up to the top. And given our rich history in NFS, high performance NFS, it became an obvious choice for NetApp to invest in this space. So we've been working with Nvidia for a really long time, probably close to a year, to start integrating our products with their DGX-1 supercomputer and providing it as a single package to our customers, which makes it a lot easier for them to deploy their AI instead of waiting months for testing infrastructure, which the data scientists don't want to do. We get them a pre-tested, pre-validated system and our All-Flash Fast, which has been winning multiple awards and the recent A800 announcement were perfect choice for us to integrate into this architecture for the system. >> Alright, Renee, in the keynote this morning, the Futurist, he said-- We talked about data as the new oil, he said AI is the new electricity. Maybe you can speak a little bit as to why this is so important. Having gone to a lot of shows this year, it felt like every single show I go to, I see Nvidia, arm in arm with partners, because there's a huge wave coming. >> Yes, absolutely, and I think there was this hype about data, there was this hype about AI, and I think the years of Big Data World, that's creating data, absolutely the foundation for AI, and AI as the new electricity is a very, very good analogy. And let's do some math, shall we? So Swiss Federal Railway, it's a very good customer of ours. For those of you who don't know, they're kind of like the heart or center of all the railway tracks going through, serving about 1.2 million passengers on a day-to-day basis. Securing their security is very, very important. Now, they also have a lot of switches that turn on, then the train can go by and with the tunnels and bridges and switches, so they need to make sure that these trains actually don't collide. So when one train goes by with 11 switches, that gives you 30 ways of possible routing. Two trains, 900 ways. 80 trains, 10 to the eightieth power of ways. That's more than the observed atoms in the universe. And they actually have more than 10 thousand trains. So think about, can human being possibly calculate that much data and possibilities in their brain? As smart as we all want to think we all are, they turn to DGX, and the full day of simulation on DGX-1 was only 17 seconds for them to get back results. And I think that analogy of AI as the new electricity, just talking about the speed of light, is very spot on. >> So this isn't hype anymore, this is actually reality. And you gave a really great example of how a large transportation system is using it to get almost real time information. Bharat, talk to us about NetApp storage, history, 26 years, you guys have really made a lot of pivots in terms of your digital transformation, your cultural transformation. How are you helping with, now, kind of the added power of Nvidia, helping customers to, the hype's gone, actually deploy it, live it, and benefit a business from it? >> Yeah, absolutely, I think, as you rightly pointed out, NetApp has made a lot of pivots. Right, and I think the latest journey in terms of being empowering our customers with data has been a very powerful mission for the company. We entered the Flash market a little bit later than our competitors, but we have made dramatic progress in that space. In fact, recently, based on the latest IDC report, we were number one in All-Flash market worldwide, so that is quite an accomplishment for a company which was late to the market. And having said that, that's because of the innovation engine that is still alive and well within NetApp. We're announcing, as you've seen in the conference, we're announcing a lot of new products and technology which are way ahead of what our competitors are offering, but I think it is all hinged on what our customers need. The customer benefits because, yeah, it has profound benefit of changing how customers operate, their entire operations, it can transform dramatically overnight. And as Renee pointed out, Big Data gave the foundation which collected all the data, but wasn't able to process it. But AI with the power of Nvidia and DGX is able to utilize that to create those outcomes for customers. And from our perspective, we bring two key value adds to the space. One, we're able to serve up the data at incredibly high speeds with our award-winning All-Flash systems. But more importantly, data today lives everywhere. If you think about it, edge is becoming even more important. You can't expect an autonomous car to make an instantaneous decision without the backing of data, which means it can't, everything can't reside in the cloud, it may be at the edge. Some of it may be at your data center. How do you tie all three together, edge, core, and cloud? And that's where the data fabric, the vision of data fabric that you saw today comes in the picture. So one is performance, the ability to stream up the kind of data at the speed of the new processors are demanding, at the speed the customers are demanding to make business decisions and also the edge to core to cloud, our data fabric, which is unique and unparalleled in the industry. >> Now, I'm wondering if you could both bring us inside the customers a little bit. If I think of the traditional storage customer, I need performance, I have more and more data that I need to deal with. But Renee pointed out real outcomes, which is beyond what a traditional storage person would be doing. Who are you working with at the customers-- How do they put together-- It almost sounds like you're building a car. I've got the engine, I've got all the pieces. Who helps put this whole solution together? How does the partnership on the customer's side go together? >> That's a great question. I'll give my take and you can jump on it because she's just returned from being on road shows with joint customers and prospects. So I believe it has to be a joint decision. It's not like IT does it first and the data scientists come in later. Although it may be the case in certain instances where the data scientists start the discussion and then the IT gets brought in. In an ideal case, just like building a car, you want all the teams to be sitting together, make sure they're making the right calls because every compromise you make at one end will impact the other. So you want to make sure you make the optimal decision end to end. And that's where some of our channel partners come in who kind of bridge the data scientist team and the IT team. In some cases, customers show up with data scientists and IT teams together and some, it's one after the other. >> Absolutely. We see the same thing when we're on the road show. Literally two weeks ago, in Canada, by the way, there was a snowstorm, and it was an unforeseen snowstorm, you don't get snowstorm in October-- >> Yes, even for Canada, it was unforeseen. >> Yeah, and we had a packed room of people coming to learn about AI and in the audience, we absolutely see people from the infrastructure side, from the data center side, from the data scientist side, and they realized that they really have to start talking because none of them can afford to be reactive. For example, the data scientists, we want to do the innovation. I can't just go to the infrastructure guys and say that, "Hey, this is my workload, do something about it." And the infrastructure guys don't want to hold on to that problem and then don't know what to do with it. They really need to be ahead of everything and I think the interesting thing is, among those four cities that we're at, we see customers from the government, oil and gas, transportation, health care, and just any industry you can think of, they're all here. One specific example, do you know Mike's company that actually came to us, they have about 15 petabytes of data and that's storing 20 years of historical data and they only have two staff and they were not hiring more staff. They were like, "We just want something that's "going to be able to work and we know everything, "so just give us a solution that's going to be able to "easily scale up and out and enable us to continue to "store more data, manage more data, "and get insights out of data fast." So they came to both of us, it's just a very good, natural decision. That's why we have a partnership together as well. >> So you guys talked about kind of connecting the data scientists with the infrastructure folks. Where's the business involved in this conversation? In terms of, we want to identify new products and services to deliver faster than our competition, new markets. Talk to us about, are the data scientists and the infrastructure guys and girls following business initiatives that have been set or are the business leaders involved in these joint conversations? >> Go ahead, you take it. >> Sure. So, I think we see both. We definitely see that there's top-level executives saying that this is our initiative and we have to do it. And they will make the decision that we have to refresh our infrastructure from the ground up to make sure we're supportive of our data scientists' innovation. We've also seen brilliant minds, researchers, data scientists doing amazing things and then roll it up to the VP level and then roll it up to CEO level to say that this has to be done because this-- For example, that simulation of 17 second results, it's things that people used to cannot do in their lifetime, now they can do it in seconds, that kind of innovation just cannot be ignored. >> Yeah, we see the same thing. In fact, any team that has possession of that data or is accountable for that data is the one usually driving the decisions. Because as you mine the data, as you start deploying new techniques, you realize new opportunities, which means the business gets more interested in it and vice versa. If the business is interested, they're going to look for those answers within the data that they have. >> So last thing, Renee, you were on the Women in Tech panel that ended yesterday, Bharat and I were both in the audience, and one of the things that I thought was really inspiring about your story is that you had given us, the audience, an interesting example of a TV opportunity that you were inspired to do by the CEO of Nvidia. Give our audience who didn't have a chance to see that panel a little bit, and in the last minute, of that story and how you were able to step forward and go, "I'm going to try this." >> Yeah, of course. I think that brings us back to the concept that we have at Nvidia, the speed of light concept, and you really have to learn, act, to move at the speed of light, just like our GPUs, with extreme performance. And obviously, at that speed, none of us know everything. So what Jensen, CEO, shared with us was, in an all-hands meeting internally, he told us that none of us are here qualified to do any of our jobs, maybe besides his legal counsel and CFO. And all of us are here to learn, and we need to learn as fast and as much as we can. And we can't really just let the competition determine where our limit is, but instead is by the limit of what is possible. So that is very much a fundamental mindset change in this AI revolution. >> Well thanks so much, Renee and Bharat, for stopping by and sharing with us the exciting things that you guys are doing with NetApp. We look forward to talking with you again soon. >> Thank you. >> Me too, thanks. >> For Stu Miniman, I'm Lisa Martin. You're watching theCUBE, live from NetApp Insight 2018 in Las Vegas. Stu and I will be right back with our next guests after a short break. (techno music)