 Live from Barcelona, Spain, it's theCUBE! Covering Cisco Live Europe, brought to you by Cisco and its ecosystem partners. Welcome back, everyone, live here in Barcelona, this is theCUBE's exclusive coverage of Cisco Live 2019. I'm John Furrier with Dave Vellante, my co-host for the week and Stu Miniman, who's also here doing interviews. Our next two guests is Mike Bundy, Senior Director of Global Cisco Lines with Pure Storage and Zee, who's in charge of product strategy with Cisco. Welcome to theCUBE, thanks for joining us. Thank you for having us here. Welcome. Thank you. We're in the DevNet zone, it's packed with people learning, real use cases, getting, rolling up the sleeves. Talk about the Cisco Pure relationship. How do you guys fit into all this? What's the alliance? You want to start? Sure. So, we have a partnership with Cisco, primarily around a solution called FlashTac in the Converged Infrastructure space. And most recently, we've evolved a new use case and application together for artificial intelligence that Zee's business unit have just released, a new platform that works with Cisco and NVIDIA to accomplish customer application needs, mainly in machine learning, but all aspects of artificial intelligence. So, AI is obviously hot trend in machine learning, but today at Cisco, the big story was, it's not about the data center as much anymore as it's the data at the center of the value proposition which spans the on-premises, IoT edge, and multiple clouds. So, data now is everywhere. You got to store it. It's going to store it in the cloud, it's on-premise. So, data at the center means a lot of things. You can program with it, it's got to be addressable, and it has to be smart and aware and take advantage of the networking. So, with all that as a background backdrop, what is the AI approach? How should people think about AI in context to storing data, using data, not just moving packets from point A to point B, but you're storing it, you're pulling it out, you're integrating it to applications, there's a lot of moving parts there. What's the- Yeah, you got a really good point here. When people think about machine learning, traditionally, they just think about training. But we look at this more than just training. It's the whole data pipeline that starts with collecting the data, store the data, analyze the data, train the data, and then deploy it, and then put the data back. So, it's really a very, it's a cycle there, right? It's where you need to consider how you actually collect the data from the edge, how you store them in the speed that you can, and give the data to the training side. So, I believe when we work with Pure, we try to create this as a whole data pipeline, and thinking about entire data movement and the storage need that we look at here. So, we're in the DevNet zone, and I'm looking at the machine learning with Python, ML library, TensorFlow, Apache Spark, a lot of this data science type stuff. But increasingly, AI is a workload that's going mainstream. But what are the trends that you guys are seeing in terms of traditional ITs involvement? Is it still sort of AI off on an island? What are you seeing there? So, I'll take a gas stab at it. So, really, every major company industry that we work with have AI initiatives. It's the core of the future for their business. So, what we're trying to do is partner with IT to get ahead of the large infrastructure demands that will come from those smaller innovative projects that are in pilot mode, so that they are a partner to the business and the data scientists rather than a laggard in the business, the way that sometimes the reputation that IT gets. So, we want to be the infrastructure solid, like a cloud-like experience for the data scientists, so they can worry more about the applications, the data, what it means to the business, and less about the infrastructure. Okay, and so, you guys are trying to simplify that infrastructure, whether it's converged infrastructure and other sort of unifying approaches. Is, are you seeing the shift of sort of that heavy lifting of people now shifting resources to new workloads like AI? Maybe you could discuss what the trends are there. Yeah, absolutely. So, I think AI started with more like a data science experiment, right? You see one or two data science, a couple of data science experimenting. Now, it's really getting into mainstream. More and more people are put into that, and as, I apologize. Mike. Mike, can we restart that question? My deep apology. I need a GPU or something in my brain and I need to store that data better. You're on Fortnite, go ahead. Yes. So, as Mike has said earlier on, it's not just a data scientist. It's actually an IT challenge as well. And I think with Cisco, what we're trying to do with Pure here is, you know, the Cisco thing we're saying, we're bridge, right? We want to bridge the gap between the data scientist and the IT, and make it not just AI as experiments, but AI at scale, at production level, and be ready to actually create real impact with the technology infrastructure that we can enable. Mike, talk about Pure's position. You guys have announced Pure in the cloud. Yes. You're seeing that software focus. Software is the key here, right? You're getting into a software model, AI, machine learning, all these things we're talking about is software. Data is now available to be addressed and managed in that software lifecycle. How is the role of software for you guys with Converge Infrastructure at the center of all the Cisco announcements that you're on stage today? Sure. Converge infrastructure to the edge. Yeah, so if you look at the platform that we've built, it's referenced by being called the Data Hub. The Data Hub has a very tight synergy with all the applications you're referring to, Spark, TensorFlow, et cetera, et cetera, CAFE. So we look at it as the next generation analytics, and the platform has a super layer on top of all those applications, because that's going to really make the integration possible for the data scientist so they can go quicker and faster. What we're trying to do underneath that is use the Data Hub that no matter what the size, whether it's small data, large data, transaction-based or more bulk data warehouse type applications, the Data Hub and the FlashBlade solution are need to handle all of that very, very different and probably more optimized and easier than traditional legacy infrastructures, even flash from some of our competitors because we built this a purpose-built application for that, not trying to go backwards in terms of technology. So I want to put both of you guys on the spot for a question. We hear infrastructure as code for going on many, many years. Since theCUBE started nine years ago, infrastructure as code, now it's here. The network's programmable, infrastructure's programmable, storage is programmable. What a customer or someone asks you, how is infrastructure, networks and storage programmable? And what do I do? I used to provision storage, I've got servers, I'm going to the cloud, what do I do? How do I become AI-enabled so that I could program the infrastructure? How do you guys answer that question? So a lot of that comes to the infrastructure management layer. How do you actually using policy and using the right infrastructure management to make the right configuration you want? And I think one thing from programmability is also flexibility. Instead of having just a fixed configuration, what we're doing with Pure here is really having that flexibility where you can put pure storage, different kind of storage, with different kind of compute that we have. No matter it's, we're talking about two RU's or four RU. That kind of compute power is different and connects with a different storage depending on what the customer use case is. So that flexibility driven to the programmability that is managed by the infrastructure management layer. And we're extending that so pure and Cisco's infrastructure management actually tying together, it's really single pane of glass within to decide that we can actually manage both pure and Cisco. That's the programmability that we're talking about here. And your customers get pure storage end-to-end manageability. Where's the Cisco compute? It's a single pane of glass. So what do I buy? I want to get started. What do you got for me? What do you have? So it's pretty simple. There's three basic components. You know Cisco compute and a platform for machine learning that's powered by NVIDIA GPUs. Cisco flash blade which is the data hub and storage component and then network connectivity from the number one network provider in the world from Cisco. Very simple. And it's a SKU, it's a solution. Yep, it's very SKU. It's very simple. It's data driven. So it's not tied to a specific SKU. It's more flexible than that. So you have better optimization of the network. You don't buy a 1000 series X and then only use 50% of it. It's very customizable. Okay, so I can customize it for my whatever data science team or my IT workloads. And provision it for multi-purpose same way a service provider would if you're a large IT organization. Trend around breaking silos has been being discussed heavily when you talk about multiple clouds on-premise and cloud and edge all coming together. How should companies think about their data architecture on, because silos are good for certain things but to make multi-cloud work and all this end to end intent-based networking and all the power of AIs around the corner, you got to have the data out there. And it's got to be horizontally scalable if you will. How do you break down those silos? What's your advice? Is there use cases for architecture? Yeah, well I think it's a classic example of how IT has evolved to not think just silos and be multi-cloud. So what we advocate is you have a data platform that transpires the entire community whether it's development, test, engineering, production applications. And that runs holistically across the entire organization. That would include on-prem, it would include integration with the cloud because most companies now require that. So you could have different levels of high availability or lower cost if your data needs to be archived. So it's really building and thinking about the data is a platform across the company and not just silos for various applications. So replication never goes away. Never goes away. It's going to be around for a long, long time. Dev test never goes away either. Your thoughts on this? Yeah, so I think on top of that, we believe where your infrastructure should go is where the data goes, right? You want to follow where the data is and that's exactly why we want to partner with Pure here because we see a lot of the data are sitting today in the very important infrastructure which is built by Pure Storage and we want to make sure that we're not just building a saddle box sitting there where you have to put the data in there all the time but actually connect our server with Pure Storage in the most manageable way. And for IT, it's the same kind of management layer. You're not thinking about, oh, I have to manage all the saddle box or the shadow IT that some data scientists would have under their desk, right? That's the least thing you want it. And the other thing that came up in the keynote today which we've been saying in theCUBE and all the experts reaffirm is moving data costs money. You got latency costs and also just costs to move traffic around. So moving compute to the edge and moving compute to the data has been a big hot trend. How has the compute equation changed? I got storage, I'm not just moving packets around, I'm storing it, I'm moving it around. How does it change the compute? Does it put more emphasis on the compute? It's definitely putting a lot more emphasis on compute. I think it's where you want to compute to happen, right? You can pull all the data when it happened in the center place. That's fine if that's the way you want to manage it. If you have already simplified the data, you want to pull in that way. If you want to do it at the edge, near where the data source is, you can also do the cleaning there. So we want to make sure that no matter how you want to manage it, we have the portfolio that can actually help you to manage that. And there's alternative processors that you mentioned in video. You guys are the first to do a deal with them. In other ways too. I mean, you've got to take advantage of technologies like Kubernetes as an example. So you can move the containers where they need to be and have policy managers for the compute requirements and also storage, and so you don't have the contention or data integrity issues. So embracing those technologies in a multi-cloud world is very, very exciting. Mike, I want to ask you a question around customer trends. What are you seeing as a pattern from a customer standpoint as they prepare for AI and start refactoring some of their IT and or resources? Is there a certain use case that they set up with Pure in terms of how they set up their storage? Is it different by customer? Is there a common trend that you see? There are some commonalities, like take financial services, quant trading as an example. We have a number of customers that leverage our platform for that, because it's very time sensitive, high availability data. So really, I think that customers, the trend overall of that would be, step back, take a look at your data and focus on how can I correlate and organize that and really get it ready so that whatever platform you use from a storage standpoint, you're thinking about all aspects of data and get it in a format, in a forum where you can manage and catalog, because that's kind of essential to that. It really highlights all the key things that have been saying in storage for a long time. High availability, integrity of the data, and now you got application developers programming with data. This is a halt with APIs now, you're slinging APIs around, like it's a ton of... It's the way it should be. It's the way it should be. This is like Nirvana, finally got here. How far along are we in the progress? How far are we early? Are we moving the needle? Where are the customers? In terms of the partnership, in terms of the AIMR. Partnership, customer, AI in general, you guys working with us. You got storage, you got networking and compute, all kind of working together. Has to be flexible, elastic, like the cloud. My feeling, Mike can correct me, or you can disagree with me. I'll use data. I think right now, if we look at all the analysts are saying, and what we're saying, I think most of the companies, more than 50% of companies, either have deployed AIMR, or are considering in plan of deploying that. But having said that, we do see that we're still at a relatively early stage, because the challenges of making AI deployment at scale, where data scientists and IT are really working together. You need that level of security and that level of skill of infrastructure and software involving DevNet. So my feeling is we're still at a relatively early stage. I think we are in the early adopter phase. We've had customers for the last two years that really been driving this. We've worked with about seven of the automated car, driving companies. But if you look at the data from Morgan Stanley and other analysts, there's about a $13 billion infrastructure that's required for AI over the next three years, from 2019 to 2021. So that is probably 6x, 7x what it is today. So we haven't quite hit that. So people are doing their homework right now and setting up the architecture. It's the leaders in the industry, not the industry. Yeah. Everybody else is going to close that gap. Absolutely. That's where you guys come in, it's helping them do that. That's what we built this platform with Cisco on, is really the flashback for AI is around scale for tens and 20s of petabytes of data. That will be required for the exact one. And it's a targeted solution for AI with all the integration pieces with Cisco built in. Yes. Great, awesome. We'll keep track of it. It looks exciting. We think it's a cliche to say future proof, but this in this case, literally is preparing for that future. The bridge for the future. Yes. As the news is saying. Absolutely. Cisco goes, it's theCUBE coverage here live in Barcelona with back a more live coverage after this short break. Thanks for watching. I'm John Furrier with Dave Vellante. Stay with us. Thank you.