(upbeat music) >> I'm Dave Nicholson and welcome to Dell Technologies World 2022. I'm delighted to be joined by Tony Bishop. Tony is senior vice president, enterprise strategy at Digital Realty. Tony, welcome to theCUBE. >> Thank you, Dave. Happy to be here. >> So Tony, tell me about your role at Digital Realty and give us a little background on Digital Realty and what you do. >> Absolutely, so my job is to figure out how to make our product and experience relevant for enterprises and partners alike. Digital Realty is probably one of the best kept secrets in the industry. It's the largest provider of multi-tenant data center capacity in the world, over 300 data centers, 50 submetros, 26 countries, six continents. So it's a substantial provider of data center infrastructure capacity to hyperscale clouds to the largest enterprise in the world and everywhere in between. >> So what's the connection with Dell? What are you guys doing with Dell? >> I think it's going to be a marriage made in heaven in terms of the partnership. You think of Dell as the largest leading provider of critical IT infrastructure for companies around the world. They bring expertise in building the most relevant performant efficient infrastructure, combine that with the largest most relevant full spectrum capability provider of data center capacity. And together you create this integrated pre-engineered kind of experience where infrastructure can be delivered on demand, secure and compliant, performant and efficient and really unlock the opportunity that's trapped in the world around data. >> So speaking of data, you have a unique view at Digital Realty because you're seeing things in aggregate, in a way that maybe a single client wouldn't be seeing them. What are some of the trends and important things we need to be aware of as we move forward from a data center, from an IT perspective, frankly. >> Yeah, it's an excellent question. The good part of the vantage point is we see emerging trends as they start to unfold 'cause you have the most unique diverse set of customers coming together and coming together, almost organized like in a community effect because you have them connecting and attaching to each other's infrastructure sharing data. And what we've seen is in explosion in data being created, data being processed, aggregated, stored, and then being enriched. And it's really around that, what we call the data creation life cycle, where what we're seeing is that data then needs to be shared across many different devices, applications, systems, companies, users, and that ends up creating this new type of workflow driven world that's very intelligent and is going to cause a radical explosion in all our eyes of needing more infrastructure and more infrastructure faster and more infrastructure as a service. >> Yeah, when you talk about data and you talk about all of these connectivity points and communication points, talk about how some of those are explained to us. Some of these are outside of your facilities and some of them are within your facilities. In this virtualized abstracted world we live in it's easy to think that everything lives in our endpoint mobile device but talk about how that gravity associated with data affects things moving forward. >> Absolutely, glad you brought up about the mobile device because I think it's probably the easiest thing to attach to, to think about how the mobile device has radically liberated and transformed end users and in versions of mobile devices, even being sensors, not just people on a mobile phone proliferating everywhere. So that proliferation of these endpoints that are accessing and coming over different networks mobile networks, wifi networks, corporate networks, all end up generating data that then needs to be brought together and processed. And what we found is that we've found a study that we've been spending multiple years and multiple millions of dollars building into an index in a tool called the Data Gravity Index where we've been able to quantify not only this data creation life cycle, but how big and how fast and how it creates a gravitational effect because as more data gets shared with more applications, it becomes very localized. And so we've now measured and predicted for 700 mentors around the world where that data gravity effect is occurring and it's affecting every industry, every enterprise, and it's going to fundamentally change how infrastructure needs to be architected because it needs to become data centric. It used to be connectivity centric but with these mobile phones and endpoints going everywhere you have to create a meeting place. And it has to be a meeting place where the data comes together and then systems and services are brought and user traffic comes in and out of. >> So in other words, despite your prowess in this space you guys have yet to solve the speed of light issue and the cost of bandwidth moving between sites. So is it fair to say that in an ideal world you could have dozens of actually different customers, separate entities that are physically living in data center locations that are built and posted and run by Digital Realty, communicating with one another. So when these services are communicating instead of communicating over a hundred miles or a thousand miles, it's like one side of the chicken wire fence to the other, not that you use chicken wire in your data center but you get the point, is that fair. >> It is, it's like the mall analogy, right? You're building these data malls and everybody's bringing their relevant infrastructure and then using private secure connections between each other and then enabling the ability for data to be exchanged, enriched and new business be conducted. So no, physics hasn't been solved, Dave, just to add to that. And what we're finding is it's not just physics. One of the other things that we're continuing to see and hear from customers and that we continue to study as a trend is regulations, compliance and security are becoming as big a factors as physics is. So it's not just physics and cost which I agree with what you're saying but there's also these other dimensions that's in effect in placement, connectivity in the management of data and infrastructure, basically, in all major metros around the world where companies do business and providers support them, or customers come to meet them both physically and digitally. It's an interesting trend, right? I think a number of the industrians call it a digital twin where there's a virtual version and of a digital version and a physical version and that's probably the best way to think of us, is that secure meeting place where each can have their own secure infrastructure of what's being digitized but actually being placed physically. >> Yeah, that's interesting. When you look at this from the Dell, Digital Realty partnership perspective we know here at theCUBE that Dell is trying to make consumption of what they build, very, very simple for end user customers. Removing the complexity of the underlying hardware. There's a saying that the hardware doesn't matter anymore. You hear things referred to as serverless or no code, low code, those sort of abstract away from the reality of what's going on under the covers. But APEX, as an example from Dell allows things to be consumed as operational expense, dramatically simplifying the process of consuming that hardware. Now, if you go down to almost the concrete layer where Digital Realty starts up, you're looking at things like density and square footage and power consumption, right? >> Yep. >> So tell me, you mentioned infrastructure. Tell me about the kind of optimization from a hardware standpoint that you expect to see from Dell. >> Yeah, in the data center, the subset of an industry, they call it digital or mission critical infrastructure, the space, the power, the secure housing, how do you create physical isolation? How do you deal with cooling and containment? How do you deal with different physical loads? 'Cause some of the more dense computers likely working with Dell and some of the various semiconductors that Dell takes and wraps into intelligent compute and storage blocks, the specialized processing for our use cases like artificial intelligence and machine learning, they run very fast, they generate a lot of heat and they consume a lot of power. So that means you have to be very smart about the critical infrastructure and the type of server infrastructure storage coming together where the heat can be quickly removed. The power is obviously distributed to it, so it can run as constant and as fast as possible to unlock insights and processing. And then you also need to be able to deal with things like, hey, the cabling between the server and the storage has to be that when you're running parallel calculations that there's an equal distance between the cabling. Well, if I don't think about how I'm physically bringing the server storage and all of that together and then having space that can accommodate and ensure the equal cabling in the layout, oh and then handle these very heavy physical computers. So that physical load into the floor, it becomes very problematic. So it's hidden, most people don't understand that engineering but that's the partnership that why we're excited about with Dell is you're bringing all that critical expertise of supporting all those various types of use cases of infrastructure combinations and then combining the engineering understanding of how do I build for the right performance, the right density, the right TCO and also do it where physical layout of having things in proximity and in a contiguous space can then be the way to unlock processing of data and connecting to others. >> Yeah, so from an end user perspective, I don't need to care about any of what you just said. All I heard was wawawawawa (chuckles). I will consume my APEX delivered Dell by the drink, as a service, as OPEX, however I want to consume it. But I can rest assured that Digital Realty and Dell are actually taking care of those meaningful things that are happening under the hood. Maybe I'm revealing my long term knuckle dragging hardware guy credentials when I just get that little mentioning. >> (indistinct) you got it, performance secure compliant and I don't need to worry about it. The two of you're taking care of it and you're taking care of it for me. And every major mentor around the world delivered in the experience it needs to be delivered in. >> So from the Digital Realty point of view, what are the things that not necessarily keep you up at night worrying, but sort of wake you up in the morning early with a sense of renewed opportunity when it comes to the data center space, a lot of people would think, well we're in the era of cloud, no one's building any data centers except for monster cloud players. But that's definitely not the case, is it? There's a demand for what you folks are building and delivering. So first, what's the opportunity look like and then what are the constraints that are out there? Is it dirt, is it power? What are the constraints you face? >> We have probably all the above, is the shortest answer, right? So we're not wawawa, right Dave? But what we are is the opportunity is huge because it's not one platform, there's many platforms there isn't one business that exists today that doesn't use many applications, doesn't consume many different services both internally and externally, and doesn't generate a ton of data that they may not even know where it is. So that's the exciting part. And that continues to force a requirement that says I need to be able to connect to all those clouds which you can do at our platform but I also need to be able to put infrastructure or the storage of data next to it and in between it. So it's like an integration approach that says if I think physical first think physical that's within logical proximity to where I have employees, customers, partners, I have business presence. That's what drives us, and in our industry continues to grow both. And we see it in our own business. It's a double digit growth rate for both commercial oriented enterprises and service providers in the telco cloud, or content kind of space. So it's kind of like a best of both worlds. I think that's what gets us excited. If I should take a second part of the question, what ends up boring is like all of us, it is a physical world, physical world start with, do we have enough power? Is it durable, sustainable and secure? Is it available? Do we have the right connectivity options. Keeping things available is a full-time job, making it so that you can accommodate local nuances when you start going in different regions and countries and metros there's a lot of regional policy compliance or market specific needs that have to be factored in. But you're still trying to deliver that consistent physical availability and experience. So it's a good problem to have but it's a critical infrastructure problem that I would put in the same kind of bucket as power companies, energy companies, telecommunication companies, because it's a meeting place for all of that. >> So you've been in this business, not just at Digital Realty but you you've been in this part of the IT world for a while. >> Yeah. >> How has the persona of a customer for a Digital Realty changed over time? Have we seen the kind of consolidation that people would expect in this space in terms of fewer but larger customers coming in and seeking floor space? >> Well, I think it's been the opposite of what probably people predict. And I pause there intentionally being very candid and open. And it's probably why that using data as the proxy to understand, is that it's a many to many world that's only getting bigger, not smaller. As much as companies consolidate, there's more that appear. Innovation is driving new businesses and new industries or the digitization of old industries which is then creating a whole multiplier effect. So what we're seeing is we're actually seeing a rapid uptake in the enterprise side of our business which is why I'm here in driving that. That really was much more nominal five years ago for being the provider of the space and capabilities for telcos and large hyperscalers continues to go because it's not like a once and done, it's I need to do this in many places. I need to continue to bring as there's a push towards the edge, I need to be able to create meeting places for all of it. And so to us, we're seeing a constant growth in more companies becoming customers on the enterprise side more enterprises deploying in more places solving more use cases. And more service providers figuring out new ways to monetize by bringing their infrastructure and making an accessibility to be connected to on our platform. >> So if I'm here hearing you right, you're saying that people who believe that we are maybe a few years away from everything being in a single cloud are completely off base. >> Mmh hmm. >> That is not the direction that we're heading, from your view, right? >> We love our cloud customers, they're going to continue to grow. But it's not all going to one cloud. I think what you would see is, that you would see where a great way to assess that and break it down is enterprise IT, Gartner's Forecast 4.2, four and a half trillion a year in spend, less than a third of that's hitting public cloud. So there's a long tail first of all, it's not going to one cloud of people. There's like seven or eight major players and then you go, okay, well, what do I do if it's not in seven or eight major players? Well, then I need to put it next to it. Oh, that's why we'll go to a Digital Realty. >> Makes a lot of sense. Tony Bishop, Digital Realty. Thanks for joining us on theCUBE. Have a great Dell Technologies World. For me, Dave Nicholson, stay tuned more live coverage from Dell Technologies World 2022 as we resume in just a moment. (soft music)

>> The Cube on cloud continues. We're here with Ana Pinczuk, who's the chief development officer at Anaplan, and we've been unpacking the future of cloud. We've heard from a number of CIOs how they're thinking about cloud in the coming decade. And first of all, Ana, welcome back to the Cube. Thanks for participating. It's great to see you again >> It's great to see you, Dave, and I'm so excited to be here with you again. So hopefully, we'll be doing this soon. >> I hope in 2021, we'll be able to be face-to-face >> Face-to-face, I know. >> and everybody out there, we miss you all. >> I know, I know. >> Now, Ana, in a lot of respects, you think about the CIO role, something that you're intimately familiar with, and it's unique, because she or he has a very wide observation space across the company. Whereas a GM or a business line manager, they're most concerned with their respective business, the CIO, they got to worry about the whole enchilada. And we've heard a lot in this program about digital transformation. We've heard a lot, of course, in the past couple of years. A lot of it was lip service, but digital transformation is no longer optional. What's changed, in your view, in the way that businesses are going about it? >> You know, Dave, from my perspective, it's interesting, and this year, in particular, has been really telling for us. So I think, before, many companies were thinking about, hey, I want to be online, I want to grow my revenues with digital, I want to have a presence. But what's happened, actually, this year, with COVID in particular, is that it's gone from being a good to have to really a fundamental necessity, we must have it. And so when I talk to CIOs today, they're really thinking about different kinds of things than before, not just going digital, but how do I enable my people to work remotely? I've got to enable that. How do I bring the agility and the flexibility that I need in our business, especially with these new ways of working? How do I look at business resiliency, not just from something happens and then how do I recover from it, but also how do I help our company and our people then actually spring forward and grow from where we are? So it's gone from a topic that was happening at the CIO, maybe at the business level, but now it's really also a fundamental CEO and board conversation, and so now we're seeing the CIOs having to present to boards what is our digital transformation, our digital strategy. >> So I wonder what you've seen in that regard. I'm interested in what role cloud plays in supporting those digital initiatives, but more specifically, cloud migration came off the charts in terms of interest 'cause of COVID, but you had those that were deep into cloud, had a lot of experience, and those maybe not as much. Are you seeing any kind of schism in the marketplace, where there's maybe a great advantage to those who really had years of experience, and maybe disadvantages to those who didn't, or is there an equilibrium you're seeing in the marketplace? How do you see that playing out? >> Yeah, what I'm seeing is that, I think there used to be a spectrum of CIOs, in effect, the ones that were a little bit forward, ahead on the cloud, both on cloud infrastructure as well as SaaS, and what are the services that we have, and then there were some that were really trying to think about what's the security implications of the cloud, and is it more expensive? So there was this spectrum of CIOs. And I think now what's happened is there's such a business imperative that I think CIOs are saying, "Look, I'm either going to survive in this new world with the agility and the flexibility that I need." And so cloud, I'm seeing a lot of CIOs really saying okay, cloud is not just fashionable, but it's in, and a necessity, and we must do it. And I think, frankly, the CIOs that don't embrace the cloud and that level of agility are going to struggle. It's really a personal imperative for a CIO, in addition to for the company. >> So a lot of times, we talk about the the three dimensions of people, process, and technology, and I'm interested in your thoughts on how cloud has affected those traditional structures and the value chains. You've got some people are really good at tech, some people are really good at people, some people are really good at process. Has the cloud affected that? Has it upended it, changed it in any way? >> Yeah, let's unpack that a little bit, Dave, 'cause if you think about process, one of the interesting things about the cloud is that- and if you think about the cloud as going all the way from IaaS, or infrastructure, all the way up this stack to actually providing business processes embedded in a SaaS service, then from a process perspective, and for CIOs, it's really upended how they think about business process re-engineering in their companies. If I think, even five years ago, where you would have a whole organization that's focused on business process re-engineering, you do that, it takes a long time, you get a consultant, maybe, to help you, and then you work through that process. If you look at a SaaS service like Anaplan today, where we- Our goal is, for example, to orchestrate business performance. We are a SaaS business planning platform. We've incorporated into our platform that business process, so the role of the CIO relative to business process, in effect, changes. Now it's about how to leverage a cloud infrastructure, and then how do you enable the customizations on top of that? But generally speaking, that's a lot easier than having to think about re-engineering the whole company. If you think about the technology stack, obviously, the cloud embeds a lot of technology in the cloud, so you have a lot of native services that are available to you. That is awesome from a talent perspective, because before, maybe you needed to have database experts or Kubernetes experts. And not that we don't need those today, but many of those capabilities come native in the cloud today. So, in effect, how it helps the CIO is to provide this ecosystem of talent embedded in what the cloud provider does. >> So I wonder, so let's stay on that for a minute. So I remember, before Amazon announced AWS in, was it 2006, a CIO said to me, "Yeah, I'm thinking about maybe I don't need to run my own email," (chuckles). And so- >> That's right, that was those days. >> And then, of course, it happens that we see the SaaSification of businesses, which, to your point, makes things simpler, in that I can focus on other areas, and not to worry about managing infrastructure to support apps. At the same time, you've had this proliferation of cloud. You mentioned, of course, that you're with Anaplan, you see, you got Workday, you got Salesforce, you got ServiceNow, Oracle apps, and people struggle. How do I get these things talking to others, they're worried about that data layer, so there's this new level of complexity. How do you see that playing out in the next decade? >> Yeah, and we used to say that we shift what we do at a certain level, and now, as an organization, we start to look at higher value outcomes. And so I see that happening, and you're absolutely right. The conversations that I have with customers now are, hey, there's things that are enabled by the cloud, and then on top of that, you need a set of APIs, or connectors, or ways to get data in and out of a particular system, or ways to link. In our case, we're linking with Salesforce, to Anaplan, to Workday, or other tools, and so you start to think more about the business outcome that you want. The CIO needs to be focused on that, instead of maybe the fundamentals of the technology. Those come for you. And then it's really more about the partnership with the business side, to say, okay, what is it that you're trying to do, and can I enable that through my cloud architecture, the Workdays, the Adobes, or the Salesforces of the world? So I think the conversation is changing. And from my perspective, what's really cool about that is it brings the CIO to, really makes the CIO, a business and thought leader, a strategic leader, because the IT shop is not just talking tech, the IT shop has to talk a lot more about the outcome that they're trying to deliver. >> So in the early days of cloud, I just want to pick up on what you just said, a lot of people in IT saw the cloud as a threat to their livelihoods, and I think I'm inferring from your statements that we're largely through that dynamic, and the CIOs are now really trying to make the cloud a platform for transformation, and monetization, or whatever other organizational goal, might be saving lives, or better government. Is that how you see it, that the role >> Look, I talk- >> Has changed to that? >> I know, I talk to so many companies, and we're still going through that transition, so I don't think we're completely over the hump of cloud all day, everywhere, but at the same time, I think what the CIO's really focused on these days is really around business agility and business outcomes for their partners. By the way, that's one of the things. The second thing, specially these days, is around people, collaboration, communication. How do we facilitate interaction of people, whether inside or outside of the company? And so that's a very different conversation for the CIO. It doesn't mean that we're not still having the basic conversation of how safe is the cloud, what security do you have built into the cloud? But I think, frankly, Dave, that we've crossed the chasm, where before it used to be, hey, I'm a lot more secure on prem, and given the tremendous focus that the cloud providers and SaaS companies have put on security, I see many more companies feeling very at ease, and in fact, telling their organizations we actually need to switch to the cloud, including large companies that have compliance issues, or large financial companies. Many of those are making that switch as well. >> Well, it's interesting, we could talk about security, but I think it's a two-edged sword, because I think a lot of, frankly, I think a lot of executives, early days, used security as a way to kick the can down the road. But the reality was the cloud, better or worse, you could make that argument, but it's different, and so different concerns people, but it's still, at the end of the day, bad security practices trump good security, and so that's what we've seen so many times, the shared responsibility model. And so people are still learning there. So security is almost this beast in and of itself. I'm interested in your thoughts on the priorities. Are customers, are they streamlining their tech investments? The major focus, as you pointed out, on cloud has been it's a driver of agility and shifting resources, as we talked about, but there's this constant cost pressure, the procurement, looking at the Amazon bill. Do you see a lot of the same going forward, or do you think the value equation is shifting such that there'll be, maybe, IT is less cost pressure? There's always going to be cost pressure, I know, but more value producer. >> I think you're right. I see it, and over the last six months I've seen it really accelerate, where CIOs are thinking about three things, and one is business resiliency, and when I talk about business resiliency, I talk about the ability to recover from crap that happens, whether it's pandemics or global events and shifts, that companies have to accommodate. So that's one thing that I see them thinking about. The second one that we talked about a little bit is just agility. I see them really focused on that, and the cloud enables that. And the third one in conversations is really speed to innovation, because when companies are talking to the cloud providers, and particularly SaaS companies, what I see them talking about is, look, I've got this particular need, and it would take me two years to do it with a legacy player because I've got to do this on prem, but you have the fundamentals built in, and I think I can do it with you in three months. So I think business resiliency, both to grow and to recover from stuff, agility, and innovation, are really three fundamental levers that I see for movement to the cloud. And any one of those that these days, it's funny, depending on who you talk to, any one of those can propel a CIO to make that choice, and when they have all of that together, they have a lot more lift, in effect, as a CIO. They have a lot more leverage in terms of what they can do for their companies. >> Well, let's stay on innovation. Innovation, I've said many times, in tech, for decades it came from Moore's Law. It seems so '90s to even say that, >> I know, I know. >> but it's true. So what's going to drive innovation in the coming years. I'm interested in your perspective on how machine intelligence, and AI, and ML, and cloud, of course, play into that innovation agenda? >> Yeah, it's interesting. I see it a lot in our business with Anaplan, innovation comes from the ability to bring in what you do internally and match it with what's available in the external world. And you mentioned it earlier, data. Data is like the new currency, that's like software eats the world, now we talk about data. And I think what's really going to drive innovation is being able to have access to the world's data. Once a company builds this digital DNA, this digital foundation, and is able to have access to that data, then you start to make decisions, you start to offer services, you start to bring intelligence that wasn't available before, and that's a really powerful thing for any company, whether you're doing forecasting and you need to bring the world's data, whether you are a agricultural company. And in these days, innovation comes in the form of speed, being able to just deliver something new to an audience faster. So to me, the cloud enables all of that, the ability to bring in data. And then on top of that, think about all the AIML innovation that's happening around the world. We just launched an offer, actually, to be able to do forecasting, intelligent forecasting, on top of the cloud. We partner with AWS Forecast for that. If we didn't have a cloud platform to do that, and a set of APIs, being digital that way really enables us the opportunity to match, one plus one equals 100, really, and bring in the power of that to get two companies together to be able to enable that type of innovation. >> Well, that's interesting. It reminds me of, one of my friends, Ed Walsh, is the CEO of a new startup called ChaosSearch, and he used this statement, he said, "We're standing on the shoulders of the giants. We're not trying to recreate it." And I think what you just said is the same thing. You're relying on others to build out cloud infrastructure. >> Totally. >> So here's a totally left-field question. When you hear all the talks about breaking up big tech, I wonder, is that irrelevant to you because you figured, okay, the cloud's going to be there, it's maybe more about search, or it's about Facebook, or Amazon's dominance. Interestingly, Microsoft's really not in those discussions anymore. They were the center of it back in the '90's. >> I know, I know. >> But as the head of development for a company, does that even factor into the equation, or do you just not worry about that? >> I'll be honest, for me personally, what I do is I compartmentalize my world. In a sense, I view the partnerships, and we have partnerships with Google, and AWS, and Microsoft, and others, so I view those as part of an opportunity to really provide an ecosystem set of solutions to customers, and those are very powerful. I think those partnerships enable companies like ours, like SaaS companies, to innovate faster. And so I compartmentalize, and I say those things are wonderful, I don't know why you would want to break up those companies. At the same time, part of what you're referring to has to do with more the social and the consumer elements of what's going on. But as a business leader, I really focus on what the power is, and particularly in the enterprise, what is it that we can do for global enterprise companies? And at least in my mind, those two things tend to be separate. >> A couple of things you said there that triggered my mind. One was ecosystems. We've been talking about data. One of our guests in this program, Allen Nance, has been talking about ecosystems and the power of ecosystems, and I definitely see cloud as a platform to allow data-sharing across those clouds and then to form ecosystems and share data in ways that we really couldn't have half a decade, or even longer ago. And that seems to be where a lot of the innovation is going to occur. Some of the people talk about the flywheel effect, but it's the power of many versus the resources of a few. >> And I'm such a big believer in the ecosystem play, and part of that is because, frankly, even over the last 20 years, the skills that are required and the knowledge that is required is so specialized, Dave, if you think about AIML and all the algorithms that we need to know and the innovation that's happening there, and so I really don't think that there's any one company that can serve a customer alone. And if you think about it from a customer perspective, their business is made up of needs from a lot of different parties that they're putting together to accommodate their business outcome, and so the only way to play, right now, in tech, is in a collaborative way, in an ecosystem way. I think the more that companies like ours work with other companies on these partnerships, and frankly, by the way, I think in the past, many companies that have made bold announcements and they would say, oh, I'm partnering with so-and-so, and I've got this great partnership, and then nothing would happen (laughs). It was just a lot of talk. But I think what's actually happening now, and it's enabled by the cloud, is we have much more of a show me culture. We can actually say, okay, well, let's say, Anaplan is partnering with Google, show me, show me what you're actually doing. And I see our customers asking for references of how these ecosystem partnerships are playing. And because these stories are out there more, I think partnerships are actually much more feasible, and real, and pragmatic. >> Yeah, Ana, we call those barney deals, I love you, you love me, we do a press release, and then nothing ever happens. >> That's right, that's right. And I think that's not going to work going forward, Dave. People are asking for a lot more transparency, and so when we think about ecosystems, they really want the meat on the bone. They don't want just announcements that don't really help their business move forward. >> Yeah, and the other thing too, we come back to data, it's always coming back to data, every conversation, but the data that's created out of that ecosystem is going to throw off new capabilities, and new data products, data services, and that, to me, is a really exciting new chapter, I think, of cloud. >> Yeah, and it's interesting, the conversations I'm having now are about data, and believe it or not, also about metadata, because people are trying to analyze what's happening among cloud providers, what are customers doing with the data, how are they using data, how often are they accessing data. Security, from that perspective, looking at who's accessing what. So the data conversation and the metadata conversation are truly enabled by the cloud, and they're key. And they weren't that easy to do in a prior legacy environment. >> It's a great point, I'm glad you brought that up, because in a legacy environment, all that metadata, that data about the data, is locked inside of these systems, and if you're going to go across clouds, and you're going to have it secure and governed, you've got to have that metadata visibility and a point of control that actually you can see that and can manage it, so thank you for that point. And thank you, Ana, for coming on the Cube and participating in the Cube on Cloud. It's been great having you. >> Thank you so much for having me, it's been a pleasure. >> All right, keep it right there, everybody. More from the Cube on Cloud right after this short break. (bouncy music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with its ecosystem partners. >> Well, welcome back here on theCUBE. We're at AWS re:Invent 2019. And every once in a while, we have one of these fascinating interviews that really reaches beyond the technological prowess that's available today into almost the human fascination of work, and that's what we have here. >> Big story. >> Dave Vellante, John Walls. We're joined by Sebastien De Halleux, who is the CEO, oh, COO, rather, of a company called Saildrone, and what they feature is wind-powered flying robots, and they've undertaken a project called Seabed 2030 that will encompass mapping the world's oceans. 85% of the oceans, we know nothing about. >> That's right. >> And, yeah, they're going to combine this tremendous technology with 100 of these flying drones. So, Sebastien, we're really excited to have you here. Thanks for joining us, and wow, what a project! So, just paint the high-level view, I mean, not to have a pun here, but just to share with folks at home a little bit about the motivation of this and what gap you're going to fill. Then we'll get into the technology. >> So I think, you know, the first question is to realize the role of oceans and how they affect you on land and all of us. Half the air you breathe, half the oxygen you breathe, comes from the ocean. They cover 70% of the planet and drive global weather, they drive all the precipitation. They also drive sea-level rise, which affects coastal communities. They provide 20% of the protein, all the fish that we all eat. So, you know, it's a very, very important survival system for all of us on land. The problem is, it's also a very hostile environment, very dangerous, and so, we know very little about it. Because we study it with a few ships and buoys, but that's really a few hundred data points to cover 70% of the planet, whereas on land, we have billions of data points that are connected. So, that's why we're trying to fundamentally address, is deploying sensors in the ocean using autonomous surface vehicles, what we call Saildrones, which are essentially, think of them as autonomous sailboats, seven meters, 23 feet, long, bright orange thing with a five-meter-tall sail, which is harnessing wind power for propulsion and solar power for the onboard electronics. >> And then you've got sonar attached to that, that is what's going to do the-- >> The mapping itself. >> The underwater mapping, right, so you can look for marine life, you can look for geographical or topographical anomalies and whatever, and so, it's a multidimensional look using this sonar that, I think, is powered down to seven kilometers, right? >> That's right. >> So that's how far down, 20,000, 30,000 feet. >> That's right. >> So you're going to be able to derive information from it. >> You essentially describe it as, you're painting the ocean with sound. >> That's absolutely right, whereas if you wanted to take a picture of land, you could fly an airplane or satellite and take a photograph, light does not travel through water that well. And so, we use sound instead of light, but the same principle, which is that we send those pulses of sound down, and the echo we listen to from the seabed, or from fish or critters in the water column. And so, yes, we paint the ocean with sound, and then we use machine learning to transform this data into biomass, statistical biomass distribution, for example, or a 3-D surface of the seabed, after processing the sound data. >> And you have to discern between different objects, right? I mean, you (laughs) showed one picture of a seal sunbathing on one of these drones, right? Or is there a boat on the horizon? How do you do that? >> It's an extremely hard problem, because if a human is at sea looking through binoculars at things on the horizon, you're going to become seasick, right? So imagine the state of the algorithm trying to process this in a frame where every pixel is moving all the time, unlike on land, where you have at least a static frame of reference. So it's a very hard problem, and one of the first problems is training data. Where do you get all this training data? So our drones, hundreds of drones, take millions of pictures of the ocean, and then we train the algorithm using either labeled datasets or other source of data, and we teach them what is a boat on the horizon, what does that look like, and what's a bird, what's a seal. And then, in some hard cases, when you have a whale under the Saildrone or a seal lying on it, we have a lot of fun pushing it on our blog and asking the experts to really classify it. (Dave and John laugh) You know, what are we looking at? Well, you see a fin, is it a shark? Is it a dolphin? Is it a whale? It can get quite heated. >> I hope it's a dolphin, I hope it's a dolphin. (Sebastien laughs) All right, so, I want to get into the technology, but I'm just thinking about the practical operation of this. They're wind-powered. >> Sebastien: Yes. >> But they just can't go on forever, right? I mean, they have to touch down at some point somehow, right? They're going to hit water. How do you keep this operational when you've got weather situations, you've got some days maybe where wind doesn't exist or there's not enough there to keep it upright, keep it operational, I mean. >> It's a very good question. I mean, the ocean is often described as one of the toughest environments in the universe, because you have corrosive force, you have pounding waves, you have things you can hit, marine mammals, whales who can breach on you, so it's a very hard problem. They leave the dock on their own, and they sail around the world for up to a year, and then they come back to the same dock on their own. And they harvest all of their energy from the environment. So, wind for propulsion, and there's always wind on the ocean. As soon as you have a bit of pressure differential, you have wind. And then, sunlight and hydrogeneration for electrical power, which powers the onboard computers, the sensors, and the satellite link that tells it to get back to shore. >> It's all solar-powered. >> Exactly, so, no fuel, no engine, no carbon emission, so, a very environmentally friendly solution. >> So, what is actually on them, well, first of all, you couldn't really do this without the cloud, right? >> That's right. >> And maybe you could describe why that is. And I'm also interested in, I mean, it's the classic edge use case. >> Sure, the ultimate edge. >> I mean, if you haven't seen Sebastien's keynote, you got to. There's just so many keynotes here, but it should be on your top 10 list, so Google Saildrone keynote AWS re:Invent 2019 and watch it. It was really outstanding. >> Sebastien: Thank you. >> But help us understand, what's going on in the cloud and what's going on on the drone? >> So it is really an AWS-powered solution, because the drones themselves have a low level of autonomy. All they know how to do is to go from Point A to Point B and take wave, current, and wind into consideration. All the intelligence happens shoreside. So, shoreside, we crunch huge amounts of datasets, numerical models that describe pressure field and wind and wave and current and sea ice and all kinds of different parameters, we crunch this, we optimize the route, and we send those instructions via satellite to the vehicle, who then follow the mission plan. And then, the vehicle collects data, one data point every second, from about 25 different sensors, and sends this data back via satellite to the cloud, where it's crunched into products that include weather forecasts. So you and I can download the Saildrone Forecast app and look at a very beautiful picture of the entire Earth, and look at, where is it going to rain? Where is it going to wind? Should I have my barbecue outside? Or, is a hurricane coming down towards my region? So, this entire chain, from the drone to the transmission to the compute to the packaging to the delivery in near real time into your hand, is all done using AWS cloud. >> Yeah, so, I mean, a lot of people use autonomous vehicles as the example and say, "Oh, yeah, that could never be done in the cloud," but I think we forget sometimes, there are thousands of use cases where you don't need, necessarily, that real-time adjustment like you do in an autonomous vehicle. So, your developers are essentially interacting with the cloud and enabling this, right? >> Absolutely, so we are, as I said, really, the foundation for our data infrastructure is AWS, and not just for the data storage, we're talking about petabytes and petabytes of data if you think about mapping 70% of the world, right, but also on the compute side. So, running weather models, for example, requires supercomputers, and this is how it's traditionally done, so our team has taken those supercomputing jobs and brought them into AWS using all the new instances like C3 and C5 and P3, and all this high-performance computing, you can now move from old legacy supercomputers into the cloud, and so, that really is an amazing new capability that did not exist even five years ago. >> Sebastien, did you ever foresee the day where you might actually have some compute locally, or even some persistent-- >> So on the small Saildrones, which is the majority of our fleet, which is going to number a thousand Saildrones at scale, there is very little compute, because the amount of electrical power available is quite low. >> Is not available, yeah. >> However, on the larger Saildrone, which we announced here, which is called the Surveyor-- >> How big, 72 feet, yeah. >> Which is a 72-foot machine, so this has a significant amount of compute, and it has onboard machine learning and onboard AI that processes all the sonar data to send the finished product back to shore. Because, you know, no matter how fast satellite connectivity's evolving, it's always a small pipe, so you cannot send all the raw data for processing on shore. >> I just want to make a comment. So people often ask Andy Jassy, "You say you're misunderstood. "What are you most misunderstood about?" I think this is one of the most misunderstood things about AWS. The edge is going to be won by developers, and Amazon is basically taking its platform and allowing it to go to the edge, and it's going to be a programmable edge, and that's why I really love the strategy. But please, yeah. >> Yeah, no, we talked about this project, you know, Seabed 2030, but you talked about weather forecasts, and whatever. Your client base already, NASA, NOAA, research universities, you've got an international portfolio. So, you've got a whole (laughs) business operation going. I don't want to give people at home the idea that this is the only thing you have going on. You have ongoing data collection and distribution going on, so you're meeting needs currently, right? >> That's right, we supply governments around the world, from the U.S. government, of course, to Canada, Mexico, Japan, Australia, the European Union, well, you name it. If you've got a coastline, you've got a data problem. And no government has ever come and told us, "We have enough ships or enough data on the oceans." And so, we are really servicing a global user base by using this infrastructure that can provide you a thousand times more data and a whole lot of new insights that can be derived from that data. >> And what's your governance structure? Are you a commercial enterprise, or are you going-- >> We are a commercial enterprise, yes, we're based in San Francisco. We're backed by long-term impact venture capital. We've been revenue-generating since day one, and we just offer a tremendous amount of value for a much cheaper cost. >> You used the word impact. There's a lot of impact funds that are sort of emerging now. At the macro, talk about the global impact that you guys hope to have, and the outcome that you'd like to see. >> Yeah, you know, our planetary data is all about understanding things that impact humanity, right? Right now, here at home, you might have a decent weather forecast, but if you go to another continent, would that still be the case? Is there an excuse for us to not address this disparity of information and data? And so, by running global weather model and getting global datasets, you can really deliver an impact at very low marginal cost for the entire global population with the same level of quality that we enjoy here at home. That's really an amazing kind of impact, because, you know, rich and developed nations can afford very sophisticated infrastructure to count your fish and establish fishing quarters, but other countries cannot. Now, they can, and this is part of delivering the impact, it's leveraging this amazing infrastructure and putting it in the hands, with a simple product, of someone whether they live on the islands of Tuvalu or in Chicago. >> You know, it's part of our mission to share stories like this, that's how we have impact, so thank you so much for-- >> I mean, we-- >> The work that you're doing and coming on theCUBE. >> This is cool. We talk about data lakes, this is data oceans. (Dave laughs) This is big-time stuff, like, serious storage. All right, Sebastien, thank you. Again, great story, and we wish you all the best and look forward to following this for the next 10 years or so. Seabed 2030, check it out. Back with more here from AWS re:Invent 2019. You're watching us live, right here on theCUBE. (upbeat pop music)

>> Announcer: Live from San Jose. It's theCUBE, presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media, and it's ecosystem partners. (techno music) >> Welcome back to theCUBE, our continuing coverage on day one of our event, Big Data SV. I'm Lisa Martin with George Gilbert. We are down the street from the Strata Data Conference. We've got a great, a lot of cool stuff going on. You can see the cool set behind me. We are at Forager Tasting Room & Eatery. Come down and join us, be in our audience today. We have a cocktail event tonight, who doesn't want to join that? And we have a nice presentation tomorrow morning of our Wikibon's 2018 Big Data Forecast and Review. Joining us next is Matthew Baird the co-founder of AtScale. Matthew, welcome to theCUBE. >> Thanks for having me. Fantastic venue, by the way. >> Isn't it cool? >> This is very cool. >> Yeah, it is. So, talking about Big Data, you know, Gardner says, "85% of Big Data projects have failed." I often say failure is not a bad F word, because it can spawn the genesis of a lot of great business opportunities. Data lakes were big a few years ago, turned into swamps. AtScale has this vision of Data Lake 2.0, what is that? >> So, you're right. There have been a lot of failures, there's no doubt about it. And you're also right that is how we evolve, and we're a Silicon Valley based company. We don't give up when faced with these things. It's just another way to not do something. So, what we've seen and what we've learned through our customers is they need to have a solution that is integrated with all the technologies that they've adopted in the enterprise. And it's really about, if you're going to make a data lake, you're going to have data on there that is the crown jewels of your business. How are you going to get that in the hands of your constituents, so that they can analyze it, and they can use it to make decisions? And how can we, furthermore, do that in a way that supplies governance and auditability on top of it, so that we aren't just sending data out into the ether and not knowing where it goes? We have a lot of customers in the insurance, health insurance space, and with financial customers that the data absolutely must be managed. I think one of the biggest changes is around that integration with the current technologies. There's a lot of movement into the Cloud. The new data lake is kind of focused more on these large data stores, where it was HDFS with Hadoop. Now it's S3, Google's object storage, and Azure ADLS. Those are the sorts of things that are backing the new data lake I believe. >> So if we take these, where the Data Lake Store didn't have to be something that's a open source HDFS implementation, it could even be through just through a HDSF API. >> Matthew: Yeah, absolutely. >> What are some of the, how should we think about the data sources and feeds, for this repository, and then what is it on top that we need to put to make the data more consumable? >> Yeah, that's a good point. S3, Google Object Storage, and Azure, they all have a characteristic of, they are large stores. You can store as much as you want. They generally on the Clouds, and in the open source on-prem software for landing the data exists, for streaming the data and landing it, but the important thing there is it's cost-effective. S3 is a cost-effective storage system. HDFS is a mostly cost-effective storage system. You have to manage it, so it has a slightly higher cost, but the advice has been, get it to the place you're going to store it. Store it in a unified format. You get a halo effect when you have a unified format, and I think the industry is coalescing around... I'd probably say ParK's in the lead right now, but once ParK can be read by, let's take Amazon for instance, can be read by Athena, can be read by Redshift Spectrum, it can be read by their EMR, now you have this halo effect where your data's always there, always available to be consumed by a tool or a technology that can then deliver it to your end users. >> So when we talk about ParK, we're talking about columnar serialization format, >> Matthew: Yes. but there's more on top of that that needs to be layered, so that you can, as we were talking about earlier, combine the experience of a data warehouse, and the curated >> Absolutely data access where there's guard rails, >> Matthew: Yes >> and it's simple, versus sort of the wild west, but where I capture everything in a data lake. How do you bring those two together? >> Well, specifically for AtScale, we allow you to integrate multiple data access tools in AtScale, and then we use the appropriate tool to access the data for the use case. So let me give you an example, in the Amazon case, Redshift is wonderful for accessing interactive data, which BI users want, right? They want fast queries, sub-second queries. They don't want to pay to have all the raw data necessarily stored in Redshift 'cause that's pretty expensive. So they have this Redshift spectrum, it's sitting in S3, that's cost effective. So when we go and we read raw data to build these summary tables, to deliver the data fast, we can read from Spectrum, we can put it all together, drop it into Redshift, a much smaller volume of data, so it has faster characteristics for being accessed. And it delivers it to the user that way. We do that in Hadoop when we access via Hive for building aggregate tables, but Spark or Impala, is a much faster interactive engine, so we use those. As I step back and look at this, I think the Data Lake 2.0, from a technical perspective is about abstraction, and abstraction's sort of what separates us from the animals, right? It's a concept where we can pack a lot of sophistication and complexity behind an interface that allows people to just do what they want to do. You don't know how, or maybe you do know how a car engine works, I don't really, kind of, a little bit, but I do know how to press the gas pedal and steer. >> Right. >> I don't need to know these things, and I think the Data Lake 2.0 is about, well I don't need to know how Century, or Ranger, or Atlas, or any of these technologies work. I need to know that they're there, and when I access data, they're going to be applied to that data, and they're going to deliver me the stuff that I have access to and that I can see. >> So a couple things, it sounded like I was hearing abstraction, and you said really that's kind of the key, that sounds like a differentiator for AtScale, is giving customers that abstraction they need. But I'm also curious from a data value perspective, you talked about in Redshift from an expense perspective. Do you also help customers gain abstraction by helping them evaluate value of data and where they ought to keep it, and then you give them access to it? Or is that something that they need to do, kind of bring to the table? >> We don't really care, necessarily, about the source of the data, as long as it can be expressed in a way that can be accessed by whatever engine it is. Lift and shift is an example. There's a big move to move from Teradata or from Netezza into a Cloud-based offering. People want to lift it and shift it. It's the easiest way to do this. Same table definitions, but that's not optimized necessarily for the underlying data store. Take BigQuery for example, BigQuery's an amazing piece of technology. I think there's nothing like it out there in the market today, but if you really want BigQuery to be cost-effective, and perform and scale up to concurrency of... one of our customers is going to roll out about 8,000 users on this. You have to do things in BigQuery that are BigQuery-friendly. The data structures, the way that you store the data, repeated values, those sorts of things need to be taken into consideration when you build your schema out for consumption. With AtScale they don't need to think about that, they don't need to worry about it, we do it for them. They drop the schema in the same way that it exists on their current technology, and then behind the scenes, what we're doing is we're looking at signals, we're looking at queries, we're looking at all the different ways that people access the data naturally, and then we restructure those summary tables using algorithms and statistics, and I think people would broadly call it ML type approaches, to build out something that answers those questions, and adapts over time to new questions, and new use cases. So it's really about, imagine you had the best data engineering team in the world, in a box, they're never tired, they never stop, and they're always interacting with what the customers really want, which is "Now I want to look at the data this way". >> It's sounds actually like what your talking about is you have a whole set of sources, and targets, and you understand how they operate, but why I say you, I mean your software. And so that you can take data from wherever it's coming in, and then you apply, if it's machine learning or whatever other capabilities to learn from the access methods, how to optimize that data for that engine. >> Matthew: Exactly. >> And then the end users have an optimal experience and it's almost like the data migration service that Amazon has, it's like, you give us your Postgres or Oracle database, and we'll migrate it to the cloud. It sounds like you add a lot of intelligence to that process for decision support workloads. >> Yes. >> And figure out, so now you're going to... It's not Postgres to Postgres, but it might be Teradata to Redshift, or S3, that's going to be accessed by Athena or Redshift, and then let's put that in the right format. >> I think you sort of hit something that we've noticed is very powerful, which is if you can set up, and we've done this with a number of customers, if you can set up at the abstraction layer that is AtScale, on your on-prem data, literally in, say hours, you can move it into the Cloud, obviously you have to write the detail to move it into the Cloud, but once it's in the Cloud you take the same AtScale instance, you re-point it at that new data source, and it works. We've done that with multiple customers, and it's fast and effective, and it let's you actually try out things that you may not have the agility to do before because there's differences in how the SQL dialects work, there's differences in, potentially, how the schema might be built. >> So a couple things I'm interested in, I'm hearing two A-words, that abstraction that we've talked about a number of times, you also mention adaptability. So when you're talking with customers, what are some of the key business outcomes they need to drive, where adaptability and abstraction are concerned, in terms of like cost reduction, revenue generation. What are some of those see-swee business objectives that AtScale can help companies achieve? >> So looking at, say, a customer, a large retailer on the East Coast, everybody knows the stores, they're everywhere, they sell hardware. they have a 20-terabyte cube that they use for day-to-day revenue analytics. So they do period over period analysis. When they're looking at stores, they're looking at things like, we just tried out a new marketing approach... I was talking to somebody there last week about how they have these special stores where they completely redo one area and just see how that works. They have to be able to look at those analytics, and they run those for a short amount of time. So if you're window for getting data, refreshing data, building cubes, which in the old world could take a week, you know my co-founder at Yahoo, he had a week and a half build time. That data is now two weeks old, maybe three weeks old. There might be bugs in it-- >> And the relevance might be, pshh... >> And the relevance goes down, or you can't react as fast. I've been at companies where... Speed is so important these days, and the new companies that are grasping data aggressively, putting it somewhere where they can make decisions on it on a day-to-day basis, they're winning. And they're spending... I was at a company that was spending three million dollars on pay-per-click data, a month. If you can't get data everyday, you're on the wrong campaigns, and everything goes off the rails, and you only learn about it a week later, that's 25% of your spend, right there, gone. >> So the biggest thing, sorry George, it really sounds to me like what AtScale can facilitate for probably customers in any industry is the ability to truly make data-driven business decisions that can really directly affect revenue and profit. >> Yes, and in an agile format. So, you can build-- >> That's the third A; agile, adaptability, abstraction. >> There ya go, the three A's. (Lisa laughs) We had the three V's, now we have the three A's. >> Yes. >> The fact that you're building a curated model, so in retail the calendars are complex. I'm sure everybody that uses Tableau is good at analyzing data, but they might not know what your rules are around your financial calendar, or around the hierarchies of your product. There's a lot of things that happen where you want an enterprise group of data modelers to build it, bless it, and roll it out, but then you're a user, and you say, wait, you forgot x, y, and z, I don't want to wait a week, I don't want to wait two weeks, three weeks, a month, maybe more. I want that data to be available in the model an hour later 'cause that's what I get with Tableau today. And that's where we've taken the two approaches of enterprise analytics and self-service, and tried to create a scenario where you get the best of both worlds. >> So, we know that an implication of what you're telling us is that insights are perishable, and latency is becoming more and more critical. How do you plan to work with streaming data where you've got a historical archive, but you've got fresh data coming in? But fresh could mean a variety of things. Tell us what some of those scenarios look like. >> Absolutely, I think there's two approaches to this problem, and I'm seeing both used in practice, and I'm not exactly sure, although I have some theories on which one's going to win. In one case, you are streaming everything into, sort of a... like I talked about, this data lake, S3, and you're putting it in a format like ParK, and then people are accessing it. The other way is access the data where it is. Maybe it's already in, this is a common BI scenario, you have a big data store, and then you have a dimensional data store, like Oracle has your customers, Hadoop has machine data about those customers accessing on their mobile devices or something. If there was some way to access those data without having to move the Oracle stuff into the big data store, that's a Federation story that I think we've talked about in the Bay Area for a long time, or around the world for a long time. I think we're getting closer to understanding how we can do that in practice, and have it be tenable. You don't move the big data around, you move the small data around. For data coming in from outside sources it's probably a little bit more difficult, but it is kind of a degenerate version of the same story. I would say that streaming is gaining a lot of momentum, and with what we do, we're always mapping, because of the governance piece that we've built into the product, we're always mapping where did the data come from, where did it land, and how did we use it to build summary tables. So if we build five summary tables, 'cause we're answering different types of questions, we still need to know that it goes back to this piece of data, which has these security constraints, and these audit requirements, and we always track it back to that, and we always apply those to our derived data. So when you're accessing this automatically ETLed summary tables, it just works the way it is. So I think that there are two ways that this is going to expand and I'm excited about Federation because I think the time has come. I'm also excited about streaming. I think they can serve two different use cases, and I don't actually know what the answer will be, because I've seen both in customers, it's some of the biggest customers we have. >> Well Matthew thank you so much for stopping by, and four A's, AtScale can facilitate abstraction, adaptability, and agility. >> Yes. Hashtag four A's. >> There we go. I don't even want credit for that. (laughs) >> Oh wow, I'm going to get five more followers, I know it! (George laughs) >> There ya go! >> We want to thank you for watching theCUBE, I am Lisa Martin, we are live in San Jose, at our event Big Data SV, I'm with George Gilbert. Stick around, we'll be back with our next guest after a short break. (techno music)