(bouncy music) >> 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 it's theCUBE's exclusive coverage of Cisco Live 2019. I'm John Furrier. 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 Alliance with Pure Storage, and Z, who's in charge of product strategy for Cisco. Welcome to theCUBE. Thanks for joining us. >> Thank you for having us here. >> You're welcome. >> Thank you. >> We're in the DevNet zone. It's packed with people learning real use cases, rolling up their 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 Flashstack in the converged infrastructure space. And most recently, we've evolved a new use-case and application together for artificial intelligence that Z's business unit have just released a new platform that works with Cisco and NVIDEA to accomplish customer application needs mainly in machine learning but all aspects of artificial intelligence, so. >> So AI is obviously a hot trend in machine learning but today at Cisco, the big story was 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've got to store it. It's going to be stored 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. It has to be smart and aware and take advantage of the networking. So with all of that as the 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 into applications. 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 it as more than just training. It's the whole data pack line 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. It's where you need to consider how you actually collect the data from 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 pack line and think about the 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, (mumbles) Flow, Appache Spark, a lot of this data science type stuff. >> Yup. >> But increasingly, AI is a workload that's going mainstream. But what are the trends that you guys are seeing in terms of traditional IT's involvement? Is it still sort of AI off on an island? What are you seeing there? >> So I'll take a guess, a 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. 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. 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 unifying approaches. Are you seeing the shift 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. You see a couple of data scientists experimenting. Now it's really getting into mainstream. More and more people are into that. And as, I apologize. >> Mike. >> Mike. >> Mike, can we restart that question? (all laughing) My deep apology. I need a GPU or something in my brain. 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 the data scientists. 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 that Cisco thing, we're saying, "We're a bridge." We want to bridge the gap between the data scientists and the IT and make it not just AI as an experiment 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. >> Absolutely. >> You're getting into a software model. AI and machine learning, all this we're talking about is software. Data is now available to be addressed and managed in that software life cycle. How is the role of the software for you guys with converged infrastructure at the center of all the Cisco announcements. You were out on stage today with converged infrastructure to the edge. >> Yes, so, if you look at the platform that we built, it's referenced back, being called the Data Hub. The Data Hub has a very tight synergy with all the applications you're referring to: Spark, Tensor Flow, 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 scientists 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 underneath handle all of that very, very different and probably more optimized and easier than traditional legacy infrastructures. Even traditional, even Flash, from some of our competitors, because we built this purpose-built application for that. Not trying to go backwards in terms of technology. >> So I want to put both you guys on the spot for a question. We hear infrastructure as code going on many, many years since theCUBE started nine years ago. Infrastructure as code, now it's here. The network is programmable, the infrastructure is programmable, storage is programmable. When 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 we're talking about two hour use, four hour use, that kind of compute power is different and can max with 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 the side that we can actually manage both Pure and Cisco. That's the programmability that we're talking about. >> Your customers get Pure storage, end-to-end manageability? >> With the Cisco compute, it's a single pane of glass. >> Okay. >> So where do I buy? I want to get started. What do you got for me? (laughing) >> It's pretty simple. It's three basic components. Cisco Compute and a platform for machine learning that's powered by NVIDEA GPUs; Cisco FlashBlade, which is the Data Hub and storage component; and then network connectivity from the number one network provider in the world, from Cisco. It's very simple. >> And it's a SKU, it's a solution? >> Yup, it's very simple. It's data-driven. 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, do I can customize it for my, whatever, data science team or my IT workloads? >> Yes, 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 discussed heavily. Can you talk about multiple clouds, on-premise in cloud and edge all coming together? How should companies think about their data architecture because silos are good for certain things, but to make multi-cloud work and all this end-to-end and intent-based networking and all the power of AI's 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 a use case for an architecture? >> 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 to 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 can 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 as a platform across the company and not just silos for various applications. >> So replication never goes away. >> Never goes away. (laughing) >> It's going to be around for a long, long time. >> Dev Test never goes away either. >> Your thoughts on this? >> Yeah, so adding on top of that, we believe where your infrastructure should go is where the data goes. 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 silo box sitting there where you have to pour the data in there all the time, but actually connect to our server with Pure Storage in the most manageable way. And for IT, it's the same kind of manual layer. You're not thinking about, oh, I have to manage all this silo box, or the shadow IT that some data scientists would have under their desk. That's the least thing you want. >> And the other thing that came up in the key note today, which we've been saying on theCUBE, and all the experts reaffirm, is that moving data costs money. You've got latency costs and also just cost to move traffic around. So moving compute to the edge or moving compute to the data has been a big, hot trend. How has the compute equation changed? Because I've got storage. I'm not just moving packets around. I'm storing it, I'm moving it around. How does that change the compute? Does that put more emphasis on the compute? >> It's definitely putting a lot more emphasis on compute. I think it's where you want compute to happen. You can pull all the data and want it to happen 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 put it in that's the 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 it's alternative processors. You mentioned NVIDEA. >> Exactly. >> You guys are the first to do a deal with them. >> And other ways, too. You've got to take advantage of technology 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, so that you don't have contention or data integrity issues. So embracing those technologies in a multi-cloud world is very, very essential. >> 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 re-factoring 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? >> Yeah, there are some commonalities. 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 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 form, where you can manage and catalog, because that's kind of essential to the entire thing. >> It really highlights the key things that we've been saying in storage for a long time. High-availability, integrity of the data, and now you've got application developers programming with data. With APIs, you're slinging APIs around like it's-- >> The way it should be. >> That'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? >> You mean in terms of a partnership? >> Partnership, customer AI, in general. You guys, you've got storage, you've got networking and compute all working together. It has to be flexible, elastic, like the cloud. >> My feeling, Mike can correct me, or you can disagree with me. (laughing) I think right now, if we look at what all the analysts are saying, and what we're saying, I think most of the companies, more than 50% of companies either have deployed AI MO or are considering a 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 and evolving DevNet. So my feeling is we're still at a relatively early stage. >> Yeah, I think we are in the early adopter phase. We've had customers for the last two years that have really been driving this. We work 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 2019-2021, so that is probably 6X, 7X what it is today, so we haven't quite hit that bell curve yet. >> So people are doing their homework right now, setting up their architecture? >> It's the leaders. It's leaders in the industry, not the mainstream. >> Got it. >> And everybody else is going to close that gap, and that's where you guys come in, is helping them do that. >> That's scale. (talking over one another) >> That's what we built this platform with Cisco on, is really, the Flashstack for AI is around scale, for tens and twenties of petabytes of data that will be required for these applications. >> 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's exciting. >> Awesome. >> It's cliche to say future-proof but in this case, it literally is preparing for the future. The bridge to the future, as the new saying at Cisco goes. >> Yes, absolutely. >> This is theCube coverage live in Barcelona. We'll be back with more live coverage after this short break. Thanks for watching. I'm John Furrier with Dave Vallente. Stay with us. (upbeat electronic music)

>> Live from Barcelona, Spain. It's the cue covering Sisqo. Live Europe, Brought to you by Cisco and its ecosystem partners. >> Come back. Everyone live here in Barcelona is the key. Exclusive coverage of Sisqo Live twenty nineteen. John for David Want my co host for the week, and Stupid Man was also here, doing interviews. Our next two guests is Mike Bundy, senior director of Global Cisco Lines with pure storage and Z, who's in charge of Christ Francisco. Welcome to the Cube. Thanks for joining >> us. Thank you for having us here. >> Also one, but we're in the definite zone. It's packed with people learning really use cases. Get rolling up the sleeves. Talk about the Cisco pure relationship. How do you guys fit into all this? What's the alliance? >> You understand? >> Sure. So we have a partnership with Cisco, primarily around a solution called flashback in the Converse infrastructure space. And most recently, we've evolved a new use case, an application together for our official intelligence that Z's business unit have just released a new platform that works with Cisco and in video to accomplish. You know, customer application needs mainly in machine learning, but but all aspects of our official intel it >> Hey, Eyes, obviously hot trend in machine learning. But today it's Cisco. The big story was, it's not about the data center as much anymore is. It's the data at the center of the value proposition, which spans the on premises I ot edge and multiple clouds. So data now is every where you gonna store it? So it's going to start in. The cloud is on premises. Data at the center means a lot of things you can programme with its gotta be addressable and has be smart and aware and take advantage of networking. So, with all that is a background backdrop, what is the A I approach? How should people think about a I in context to storing data using data, not just moving package from point A to point B? But you're storing it? You're pulling it out. You're in agreeing into apple cases. A lot of moving parts there. What's that? >> Yeah, you got a really good point here. When people think about machine learning traditional age, they just think about training. But we look at this more than Chinese. The whole did a pipeline that starts with collecting the data stored the data, analyze the data between the data and didn't deploy it and then for the data back. So it's really a vory. It's a cycle there, right? It's 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 way work was pure. We try to create this as a whole data pipeline and thinking about entire data movement and the star, which need that would look here. >> So we're in the definite zone, and I'm looking at the machine learning with Python ML library >> center >> Flow of Apache sparked a >> lot of this data >> science type stuff, but increasingly a ISA workload that's going mainstream. But what The trends that you guys are seeing in terms of, you know, traditional, I tease involvement is >> it's still sort of >> a I often an island. What are you seeing there? So I'll take a take a gas stab at it. So, really, every major company industry that we work with have you know, Aye, aye. Initiatives. It's the core of the future for their business. So, no, what we're trying to do is partner with I t to get ahead of the large infrastructure demands that will come from those smaller, innovative projects that Aeryn pilot mode so that they are a partner to the business and the data scientist, rather than, you know, a laggard in the business. The way that you know, sometimes there the reputation that that I guess we want to be the infrastructure solid, you know, like a cloud like experience for the data scientists. So they can worry more about the applications, the data, what it means the business and less about the infrastructure. Okay. And so you guys are trying to simplify that >> infrastructure, whether it's converged infrastructure. No other sort of unifying approaches is Are you seeing the shift of a sort of that heavy lifting of people out now? Shifting resource is, too. You work loads like a I Maybe you could discuss trends, are there? >> Yeah, absolutely. So I think I started was more like a data signs experiment. Right? You see, want to date, assigns a couple of data science experiment. Now it's really getting into ministry. More and more people report into that and us. Apologize. Mike, Mike, The way we start that questions my deep apology. I need a GP or something. >> Like, I need to >> store the data better. >> Your fortnight? Yes. >> So as Micah's had early on, right? It's it's not just the data scientist is actually all a challenge as well. And I think was Cisco, where twenty do was pure. Here is, you know, that Cisco thing. We're saying we're breach right. We want to bridge the gap between the data scientists and the it and make it not just as experiments, but a scale at production level and be wedded to actually, Crew will impact with the technology infrastructure that we can table >> might talk about yours position You guys have announced here in the cloud. Yes, he's seeing that software. Focus software is the key here. Or you can get to a software model. Aye, aye. And she learned Only we're talking about is software data is now available to be addressed and managing that software. Lifecycle. How is this Corolla software for you guys? With converge infrastructure at the San Francisco announce your downstage day, we'll converge infrastructure to the edge. >> Yeah, so if you look at the plant, one that we built, that's it's referenced by being called the data hub. The data hub has a very tight synergy, with all the applications referring to spark tenser PLO, etcetera, etcetera cafe. So we look it as the next generation analytics, and the platform has a super layer on top of all those applications because that that's going to really make the integration possible for the data scientists. They could go quicker and faster. What we're trying to do underneath that is used 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, you know the data hub in the flash blade solution or need handle all of that very, very different and probably more optimizing and easier than traditional legacy infrastructures, even tradition, even even even flash, you know, from some of our competitors. Because, you know, we've built this a purpose built application for that, you know, not trying to go backwards in terms of technology, >> I want to put both you guys on the spot for a question. We hear infrastructure is code for going on many, many years since the few started at nine years ago. Infrastructures code. Now it's here. The network's programmable infrastructures, programmable storages, programmable What a customer! Or someone asked you. How is infrastructure Network's in storage, Programmable. And what do I do? I'm used to provisional storage. I've got servers. I'm going cloud. What do I do? How do I become? A. I enabled that I could program the infrastructure. How do you guys answer that question? >> So a lot of that comes to the infrastructure management layer, right? How do you actually using policy and using the white infrastructure managing to make the right configuration want? And I think one thing from program eligibility is also flexibility. Instead of having just a fixed conflagration. What we're doing with pure here is really having that flexibility right where you can put pure Star Ridge different kind of star, which was different, kind off. Compute that you have. No matter. It's we're talking about two are used for you. That kind of computing power is different and connects with a different Star wars, depending on what the customer use cases. So that flexibility driven by the driven to the proper program ability that is managed by the infrastructure. Imagine a layer, and we're extending that So pure and Cisco's infrastructure management actually tying together it's really single pane of glass was in decide that we can actually manage both pure and Cisco. That's the program ability that we're talking >> about. Get pure storage and to end manageability. >> Where's the Cisco compute its A single pane of glass. >> So what do I buy? I want to get started. What? What do you got for me? What you have, it's pretty simple. Three basic components, you know, Cisco Compute and a platform for machine learning that's powered by and video GP. Use Cisco Flash Blade, which is the data hub and storage component and then network connectivity from the number one network provider in the world. Francisco. Very simple. It's askew. It's a solution. It's very, very skewed. It's very simple. It's data driven, so you know it's not tied to a specific skew. It's more flexible than that. So you have a better optimization of the network. You know you don't buy a one thousand Siri's ex. Okay, Only used fifty percent of it. It's very customized. Okay, so I can customize it for my whatever data science team or my workloads and provisioning for multipurpose. Same way of service provider would ifyou're a large organization >> trend trend around Breaking Silas has been being discussed heavily. Talk about multiple clouds on premise and cloud and edge all coming together. How should companies think about their data architecture on? Because Silas Air good for certain things to make multi cloud work and all this and to end and intent based networking and all the power of a eyes around the corner. You gotta have the date out there, right? It's gotta be horizontally scaleable of you. How do you break down those silos? Twitter advises air use cases or anarchic for architecture. >> You know what I think? It's a classic example of how it has evolved to not think just silos and be multi cloud. So you know, we've advocate is is you have a date, a platform that transpires the entire community, whether its development, test engineering production applications and that, you know, runs holistically across the entire organization that would include on from it would include integration with the cloud. Because most you know cos now require, That s so you could have different levels of high availability or lower cost if your data needs to be archived. So it's really, you know, building and thinking about The data is on platform across the across the company and not just you know, silos for >> replication never goes away. Never. It's gonna be around for a long, long time. >> Deaf tests never goes away. Yeah, >> you thought some >> s o i. D On top of that, We believe where you infrastructure should go is where the data goes, right? You want to follow that where the data is, And that's exactly why I want a partner was pure here because we see a lot of the data sitting today in the very important infrastructure which is built by pure storage and want to make sure that we're not just building a sidle box sitting there where you have for the data in there all the time, but actually connected our chips. Silver was pure storage in the most manageable way. And it's the same kind of manager layer you're not thinking about All have to manage all the Sala box or the shadow it that some day that time would have under their desks. Right. That's the least thing you want it. >> And the other thing that came up in the Kino today, which we've been seeing on the Cuban, all the experts reaffirm, is moving data cost money got late in sea. Costs also just cost to move traffic around, so moving compute to the edge of moving. Compute to the data has been a big hot trend. How is the computer equation changed? I got storage. I'm moving. I'm not just moving packets around. I'm storing it and moving it around. How does that changed the computers? It put more emphasis on the computer. >> Wait, It's definitely putting a lot more emphasis on computer. I think it's where you want to compute to happen, right? You can pull all the data and I want it happen in the centre place. That's fine if that's the way you want to manage it. If you have, if you have already simplify the data, you want to put it in that way. If you want to do it at the edge near where the data sources, 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. And >> his alternative alternate processors mentioned video first. Yeah, you would deal with them in other ways to you've got to take advantage of technologies like uber, Nettie says. Example. So you can move the containers where they need to be and have policy managers for the computer requirements. And also, you know, storage so you don't have contention or data and integrity issues. So embracing those technologies and a multi cloud world, it's very, very >> like. 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 a I and start re factory? Some of their end or resource is. 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 customers? Are a common trend that you see >> there are some commonalities, you know, like take financial services want trading as an example. We have a number of customers that leverage our platform for that. Is this very you know, time sensitive, high availability data? So really, I think the customers the trend over all of that would be a step back. Take a look at your data and focus on how can I correlate, Organize that and really get it ready so that whatever platform used from a story standpoint, you're 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 the sentence. >> I mean, it really highlights all the key things that would say it in storage for a long time. I availability integrity of the data. And now you got at patient developers programming with data. This's a hole with a P IIs. Now you're slinging FBI's around like it's Tom mentioned me its weight should be. This is like Nirvana finally got here. How far along are we in the progress? How far we earlier we moving the needle? Where the >> customers himself a partnership partnership. Deanna >> and General, You guys were going to say, You got you got storage, You got networking and compute all kind of working together. That's reflex school elastic like the cloud >> I my feeling, mike, contract me or you can disagree with me. I think right now, if we look at all the wood analysts saying what we're saying, I think most of the companies more than fifty percent of companies either have deployed a Emma or are considering implant off deploying that right. But having said that, we do see that we're seeing at a relatively early stage because the challenges off making a deployment at scale where data scientist and I'd really working together, right? You need that level of security in that level, off skill ofthe infrastructure and software involving Devon I. So my feeling is where stew At a relatively early stage, >> I think we are in the early adopter face. You know, we've had customers for last two years. They've really been driving this way, worked with about seven of the automated car, you know, driving Cos. But, you know, if you look at the data from Morgan Stanley and other analysts, is about a thirteen billion dollars infrastructure that's required for a eye over the next three years from twenty, nineteen, twenty, twenty one. So you know, that is probably six x seven x what it is today, so we haven't quite hit that. >> So people are doing their homework right now. You are the leader. >> Its leaders in the industry, not mastering everybody else is going to close that gap. So that's where you guys come into helping that scale way built this. This platform with Cisco on is really flashback for a I is around scale for, you know, tens and twenties of petabytes of data that will be required for >> these targeted solution for a I with all the integration pieces Francisco built in. Yes. Great. We'll keep track of a look sighting. We think it's cliche to say future proof, but this, in this case, literally is preparing for the future. The bridge? >> Yes. Future. Yes. You >> know, as the news is good, it's acute coverage. He live in Barcelona with more live coverage after this short break. Thanks for watching. I'm John Barrier, but David won't they stay with us. >> Thank you.