(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)

>> Narrator: Live from Madrid, Spain, it's theCUBE, covering HPE Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> Welcome back to Madrid, Spain, everybody. This is theCUBE, the leader in live tech coverage, and this is day two of our exclusive coverage of HPE Discover 2017. I'm Dave Vellante with my co-host Peter Burris. Last night was a great night of customer meetings. We stumbled into the CIO meeting, we were at the-- >> And were quickly ushered out. (both laugh) >> We were at the analyst event, and of course we met our good friend Dr. Tom Bradicich at the analyst meeting. This is the man who brought a lot of the IOT Initiative into HPE. He's the general manager of the IOT and Systems division. Great to see you again, Dr. Tom. Thanks so much for coming on. >> Thank you Dave and Peter, it's great to be here at theCUBE, great to be here at HPE Discover Madrid. Lots of great things happening, I can't wait to tell you about 'em. >> So we're very excited to have you on. John Furg and I interviewed you in the very early days after you came over from your previous company, and you had this sort of vision of, you know, bringing the HPE into the intelligent edge. >> Yes. >> And we're like okay, this sounds really complicated. You got ecosystem, you got all kinds of technologies that you gotta develop. Hardware, software. And you're making it happen. It's become a meaningful portion of HPE's business, so I know you got a long way to go, but congratulations on the progress so far. >> Thank you. Give us the update on the-- >> Well, first of all, thank you for that, I appreciate it. I must give credit to my team, I tell them all the time that if you don't execute and do the work, I'm just a science fiction writer. (interviewers laugh) And the vision has come about, and we have real customer deployments of course that the, you know, the proof of it. >> Right. >> At first we had no products and no customers, now we have these products that we'll talk about, and we have the customer deployments, and we're changing things for businesses at the edge, and again the edge is just not the data center. And the manufacturing floor, we'll talk about refineries, oil rigs, those type of edges. We're doing a lot of work there. And it's been exciting to see the ideas that we have get adopted by not only customers, but the industry, so we're seeing other analysts pick up on two dimensions: computing at the edge, and a little more complicated one, a little more difficult to grasp, is converged OT and IT at the edge, the two worlds of operational technology converging with IT. We were on theCUBE talking with an OT partner, National Instruments, a long while ago, and now we literally have those products in the market in the hands of customers. National Instruments is reselling the Edgeline 1000, the Edgeline 4000 products, as well as of course us selling it, and it's pretty exciting to see this happening. >> Well what I love about that conversation is, you know, when we first started to talk to you, we said okay, let's play the skeptic, analysts are skeptic. >> Sure. >> And we said one of the big problems you're gonna face is bringing the organizations together, OT and IT. They're just different worlds, oil and water, you know, you got hardcore engineers and you got IT guys, and then subsequent to that conversation, you bring on National Instrument, right? >> Yes. >> And we have that conversation. Okay, so we sit down, I check that box, at least they're having conversations. Can you talk about how that convergence is actually occurring, and what's in it for the customer? >> Well great. To talk about this convergence, the best thing to do is say it can happen at several levels. It can happen at a solutions level, it can happen at a software level and a hardware, physical level. Let's talk about a physical level, it's a little more tangible to understand. Let me use the smartphone, which everybody has. Like Peter, you have one there. If you hold that up, you will notice inside the manufacturer of that phone converged, or integrated, those are synonyms, many consumer devices. Such as what? A music player, of course, the phone, of course. But also many other things. A GPS system. >> Camera. >> A camera. The list goes on, right? We can go on. Oh, the flashlight, and by the way, your wallet. Maybe not your wallet, but a millennial and younger's wallet-- >> Yeah, sure. >> Is in that phone. >> My wallet's in it. >> My wallet's in it. >> In it, and-- >> Venmo, baby. >> That's right. (all laugh) >> I have my kids' wallets in there too. >> Oh that's great, you've done that switch. So what is happening there obviously is the notion of we're, you know, software defining and we're converging. Now the benefits of that are irrefutable. One thing you buy, it's less energy. One thing to manage, the convenience of carrying it around. Let's take that metaphor and impute it at, let me say a manufacturing floor edge. There's lots of edges out there. We go to a manufacturing floor edge, we see several devices, just like the early pioneers of the smartphone saw a consumer with a camera around his neck, a GPS on his belt, text, right, a flashlight, a wallet, and all this. We see all these devices out there, and what are they? Some of 'em are OT, as you mentioned. Operational technology devices such as control systems, such as data acquisition systems. >> Real-time systems. >> Real-time systems, industrial networks. CAN, PROFIBUS, SCADA solutions and networks. And the second thing we see is some IT. Most of it's closed, so this is important. It's good IT, meaning computing and storage, but a lot of it is closed systems. It's not the open EXEDY 6 architecture that we so enjoy in the data center. So those things are out there. We looked at 'em and we put them all in one box, just like the smartphone is one device. What are the benefits? Lower space, there's not a lot of space at the edge. Lower energy, there's not a lot of energy, right, at the edge. But the more profound benefits that we're seeing, and we have a large auto manufacturer who has deployed this on their manufacturing line, is it keeps uptime higher. In other words, it reduces downtime. So if the manufacturing line stops, there's nothing worse than a manufacturing line stopped, except perhaps an empty one. But the point is, when a manufacturing line stops, you can't put out product. You can't put out product, you can't recognize revenue get it in the consumer's hands. It's very obvious. It's an air-tight business case, actually. So we're able to reduce any downtime, why? Because first of all, everything's together, and secondly, we're able to manage it just like we're managing the data center because it's an open EXEDY 6 architecture. >> So you're converging tasks as well as hardware. >> As well as hardware, and then the next step is software, you know, as well. We just launched a new class of software called the Edgeline Services Platform, and this is OT software. So we're talking OT functions like aggregators and things that do OT technologies and some IT, but because we have so much compute power and it's open, it's EXEDY 6, it can run software like VMware, Microsoft Products, even database products as well. But because we have that, we're able to software define. When you software define, and I'll use the wallet again. You don't have a billfold with your license anymore. Plastic and leather has been software defined, and therefore it's less to deal with. It's much more efficient. So that announcement of our software strategy along now with our hardware strategy is very exciting for us, and customers are very much interested in it. >> So do you have some examples, you know, some real world examples? Customers that you can talk about where you're bringing together OT and IT disciplines? >> Yeah, you bet. Yeah, you bet. Let me talk about a large global beverage and snack company, and they make snacks, and in this case, potato chips. So a potato chip is a product, and the idea of having them come out of the line in the bag and be a higher quality is important. So we took an Edgeline System, the EL 1000, and we put it at the edge, and we were able to software define several of their IT and OT components and get it to a consolidation and integration in one box. Now what that did is it allowed the, and will do, is allowed the foods to move faster. So if they move across the conveyor belt faster, you can bag them faster, get 'em out to the consumer. The second thing is because it's so powerful, this is interesting. Now they can use video cameras to inspect the quality. Now think about that. That's not necessarily a new idea, but what is new is the notion that you can take video, which I think you'd agree is the largest data, is that right? A video is big, big data. >> We know that well. >> Especially if it's high, Yeah, especially if it's higher resolution, and your hosting costs are telling you that as well, right? Of all these videos. But if it's high resolution, and because you're looking for, you know, defects, indeed, one has to process that not only in high resolution, massive data, number one. Number two, quickly, because the thing is moving, and you wanna know to knock it off or stop or whatever the case may be. So what has happened there is my team and I did not think of that. Our customers thought that, well because you gave us this platform, we can now enhance it with a new type of sensor called a camera, with a new type of data, called video, to enhance our quality and keep our process moving faster. >> So keeping this converged notion going, you're converging the hardware, which is, you know, important. You're converging a lot of the administrative tasks. >> Yes. >> Which reduces the likelihood of any single human failure bringing the whole system down, but now you're talking about, in the whole sense, infer, and act loop that typifies what happens at the edge, you're converging new technologies into that loop by being able to add new data type, bring modeling, machine learning, analytics, in the infer, and then being able to act right there, which allows you to think about new invention, new innovation very, very rapidly because you have the processing power to converge all that new function as it becomes better understood. Have I got that right? >> You got it right. I serve as an adjunct professor at university, so let me position it in an easy way to learn. You said sense, infer, and act. Let's just call 'em the three A's. Acquire, analyze, and act. >> Okay. >> It's just easier to remember. And let me talk to that too, but this is actually just synonyms. So the acquisition of the data is through sensors in D to A conversion, or let me say A to D, analog to digital. Because most of these phenomenon, video for example, it has to be, is a light phenomenon. Moisture, pressure. At Duke Energy, for example, the second largest energy provider I worked on that industrial internet of things solution, and vibration was the thing that needed to be acquired and then analog to digital. Now the analysis has to take place. There are seven reasons to analyze at the edge. There are seven reasons not to send the data to the cloud. In the past, we have talked about it. One of them's latency, one of them's cost, one of them's bandwidth, another one is security, another one is reliability, another one is geofencing and policy, another one is duplication and security, you know, hostile or just, you know, reliability drop packets. There's a lot of issues to do that analysis there. But because we have a non-compromised full EXEDY 6, in fact, 64 in one box. 64 Xeon, Intel Xeon product in one box. We don't have to compromise the stack. We can take it directly out of the data center and run things like artificial intelligence, machine learning algorithms. We can virtualize, we can containerize, we can run Citrix applications at the edge to have better access to the data and of course the application. But you're absolutely right, and then the second thing in this point is we move from the middle A, analysis right, to the action. The reason, I've learned this doing many IOT deployments. The reason people do an IOT deployment is to act. Yes, it's exciting to collect data. It's also exciting to analyze it. But have you ever been in a business meeting where you sit and you analyze data and you give tremendous insights, and one conclusion is pit against another conclusion and it cancels out all conclusiveness, and then you talk and you analyze, and you walk out and nothing happens, there's no action. Many of us have been in that. That's the idea here. You can't stop at the analysis, even though artificial intelligence, deep algorithms, moving averages, signatures that we can compare are very powerful. Well, what do you do when you do that? Because we have control and actuation systems built into Edgeline, we literally in a physically space, as well as in a logical process, as you pointed out, close that loop. >> Right. >> Acquire, analyze, act, acquire, analyze, act. Yes, connect to the cloud or the data center if we need to, but the issue is you don't have to. Now here's what's profound about that. This system at the edge can be managed and run the same stacks as any cloud or data center. I'm gonna use those as synonyms because a cloud is just a data center that nobody's supposed to know where it is. So a data center far away on the corporate campus or in a public or private cloud somewhere, is managed the same way. When that happens, we are revolutionizing workload management. Now, I spent a lot of years in my former time in IT and building data centers and building some of the first clouds, workload management's a big deal. How do you shift the workload to the free server? >> Peter: Right. >> Or to the free resources, right? To optimize, obviously. And it's a packing problem many times in the data center. Well now we've introduced another place to workload manage. >> Right. >> It's called the edge, it's far away. So we workload managed in the data center, then the cloud was invented, that's the first off premises. The next off premises is now the edge. So the other off premise is the edge. So now we have a workload management capability. Do you wanna do 100% processing at the edge where the action is, and where the acquisition is? Do you wanna do 100% in the cloud? That's still possible. Do you wanna do 50-50? Would you like to do 10-90? Would you like to do 30-70? You get my point. >> Totally. >> I can shift this, and depending on the season, depending on issues like disaster recovery, depending on your workloads, you can now do that, and again, you can do this with the Edgeline 1000, the Edgeline 4000, because of the processing power and the converged OT inside it. >> Well our observation is that it's not about bringing your business to the cloud, it's about bringing the cloud to your business. >> Yes. >> So bringing that sense of workload management. You know, you might say the cloud is just a virtualized data center when you come right down to it. So bringing all those capabilities and bringing them to wherever the data requires it. And there's gonna be a lot of instances where the data is gonna be at the edge, stay at the edge, but that doesn't mean you don't want all the benefits of how you run computing data at the edge where that data is. >> Yeah, and we're not obviating, we're offering choice. >> Right. >> But again, there are seven reason I went over why you do it here, but I've had a customer say none of those seven matter. So okay, we send everything to the cloud, and we have great cloud hybrid IT products that do that. >> Yeah. >> And we've envisioned a three-tier data model, you know, real time at the edge. >> Yes. >> Maybe you don't persist everything, but like you said, there are a lot of reasons not to move all the data back. But there is maybe a spot where you aggregate some of that data from discrete devices, and sure, if you wanna do some deep modeling in the cloud, go for it. And that cloud might be the public cloud, it might be your own private cloud. Does that seem reasonable to you? >> Very reasonable, and another reason for a cloud is it's an aggregation point for other, in this case, manufacturing lines where other smart cities to come together, because you're not gonna connect every city, every plant, any to any. You'll have a hub and spoke model where the cloud serves as that hub. So there are always reasons, and that's why, you know, if you look at our company, the pillars of our company, Pointnext services, the second pillar is hybrid IT, primarily focused on cloud and data centers, and the third is the intelligent edge. And those all play very, very closely together, in fact we have edge to core strategies, we have edge to core offerings with partners like NVIDEA, with partners like SAP, with partners like SAS, we have edge to core. For example, Schneider as well, Schneider Electric. All of them are looking at this idea, GE, Microsoft Azure, let's go to the edge. And two years ago, that was not the case, right? Let's go there, when you go to the edge, what are you gonna run it on? Well, let's not force our software partners to re-architect like they used to have to to run at the edge, which is like I'd call that drive-by analytics. You just have to cut out everything because it only ran on a wimpy core somewhere or a little device. No, let's move the entire data center capability out to the edge, when I was presenting this to one of our partners, the CEO of the company, I was presenting this vision, and he was texting during my talk 'cause I was boring. (interviewers laugh) And then I said this, this is a very powerful company, I won't mention names. Then I said, we're gonna move data center class technology out to the edge. It's not gonna be in compromised cores or limited memory or a little bit of storage. It's the very things in the data center we'll harden called Edgeline. We'll add controls systems and data acquisition, we'll put it out at the edge. He stopped texting. Then he looked up at me and said, "Wow, you're really moving a data center out to the edge." and you just said that, right? It's the cloud is coming. It's almost a reverse idea of what was happening before. >> Well you wrote a blog recently. >> Yes. >> About the space edge. So I wanted to ask you about that. What's going on in the space, and that's the ultimate edge, I guess. >> The infinite edge. >> The infinite edge. Explain what you guys are doing there and why it's important. >> Well, this is exciting. Space travel for exploration and eventually colonization, if you would believe that, is happening. We have the first supercomputer technology in a NASA spaceship now. It has orbited the Earth well over 1,000 times and it is doing thousands of benchmarks and is doing very well, isn't failing. Now, why is that profound? Because again, that edge is so far away and the ability to push that back to Earth now, which we could call the data centers on Earth, is limited. It takes minutes, sometimes even longer. There's issues with reliability as well. So we were able to do that, and then we've created a new thing called Project Extreme Edge, where we're going to build Edgeline systems that will fit better with lower energy, smaller size in spaceships, and eventually in colonization, but we're just going into space travel and exploration right now. And I'd like to mention that HP Labs is a great participant in this because they're working on a technology, and the name of it is called the Dot-Product Engine. And dot-product is a mathematical operation needed in high-performance computing and artificial intelligence. But we're able to use that technology because it's small, it's fast, faster than we believe anything else on the market, and also it has a low energy profile. And those are all any edge, obviously, but it's also great for the space edge, and I like to quote Frank Sinatra when he said if I can make it there, I can make it anywhere, New York, New York. (laughs) Well, if we can make it in the space edge, these Earth edges will benefit as well. Some of the same challenges. >> All right, we're out of time, but I gotta ask you. Meg stopped by yesterday, and was giving great support for the intelligence. >> She has, yes. >> The company's now reporting the intelligent edge is gonna be one of the main areas. What about the new guy? Antonio. >> Antonio Neri. >> You know, what's your relationship with him, experience? Has he been focused on this area? >> Support? >> He's been great, he supports in three ways, let me just sum up in three ways. Number one, he supports in customer visits. He and I have been on customer visits together, it's always wonderful to have the president and now the new CEO with you affirming what we're doing. That's number one of three, number two of three, he supports the work we're doing with our new global IoT innovation labs, in fact our first grand opening, the first one in Houston, we will have one in Singapore opening in February, and then we'll have one in Europe and perhaps one in India, we're opening these labs for innovation, but my point is, the one in Houston, our first grand opening, Antonio Neri came personally and did the ribbon cutting and sponsored that as well. And then third, he is of course funding my business unit, and he's been very, very supportive and I'm really happy that he's staying with us and he'll be CEO. >> Excellent, Dr. Tom, thanks so much for coming on theCUBE. Congratulations, as you say, I know there's a long way to go, but looks like you're off to a great start and have some real traction. >> Tom: Thank you very much. >> So we appreciate your time and your insights. Okay, keep it right there buddy, we'll be back with our next guest. 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