>>The cube presents HPE discover 2022 brought to you by HPE. >>Hi everybody. Welcome back to the Cube's coverage of HPE. Discover 2022 from the Venetian convention center, formerly the sand convention center in Las Vegas, Dave ante, with John furrier. We're here with Brad parks. Who's the chief product officer at morphia data and Brian Thompson. Who's the vice president of GreenLake cloud product management at Hewlett Packard enterprise gentlemen. Great to see you first time on the queue first time. Wow. I just assumed we've known each other for, so >>We've been around a long time now. I'm happy to be here and thanks for, thanks for making the >>Time. Yeah, you've put a lot of people on the queue, but Morpheus data, when we, you know, we first met, I mean, with your new role here several years ago, tell, give us the update what's Morpheus do, why are you, so why does people, why do people need Morpheus? Think >>People need Morpheus, cuz it is messy, right? Technology promise, you know, simple, better, faster, but it's only gotten more complex, more heterogeneous over the last decade. We are a unified orchestration and automation platform that makes kind of the, the messy labyrinth that is enterprise. It kind of simpler to navigate primary use case. Self-service for developers who wanna push a button, get a database and an abstract deployed into their on-prem or their public cloud without having to wait on it. >>So you've, you've, you've been through the hyper-converged world. You've seen all that hardware come together. The VMware Nutanix of the world's kind of hardware. Now you got this software abstraction where you got operations, you've got AI, you got all kinds of ops AI ops dev ops data ops ops machine, >>You >>Know, they're all there. And so you got developer environments, you got operating environments. It's just getting more complicated at scale. Yep. This is a huge challenge. You guys are tackling this and then by the way, throw in automation in there too. Right? So, so all that's kind of coming together. How does self-service work put all that complication? >>Well, so I was just talking about Robert Christiansen. I know he's probably think he's been on the Q he's on S team and the ven diagram that we see in hundreds of enterprises we talk to is there's a need for central platform engineering at an enterprise to enable developers, to hit a button, get their database, run an I API line, you know, get their app stack deployed. They also wanna do the same thing with Kubernetes, right? Micro clusters deployed, you know, at a service, same thing with Terraform and Ansible. And they're just there aren't enough skilled operators who have moved up that stack. So you have to automate and canonize that knowledge and, and make it easy. >>Brian, one of the sort of pillars of GreenLake is, and as a service is data and we see a change in the way data is data platforms are being architected, data organizations. And one of the things that is a critical principle of sort of what we see as the new data era is self-service infrastructure where the operation of the technical details are an operational detail, not the be all end, all, you have to go beg and get data out. Okay. So you guys are building out, I think, consistent with that principle self-service infrastructure. That's right. So where does Morpheus fit in, in terms of that objective, what's your relationship like and, and help us understand >>That. Yeah. Within GreenLake, specifically think of this as a broad portfolio of different as a service offerings. Part of that key is meeting customers where they are and where they want to be. So we have that array of things which are fully self-service if you will, but serving an it admin type of persona. So it's where as a enterprise, I still have those resources. I want that granular of control all the way through, how do we deliver some of our more advanced cloud services, really trying to serve the end user to your point, how do I empower application owners, developers to, to bring in and, and work with those services? This is key in, in some of those cloud services, we're delivering more of VSC is a key component that we work as we bring to again, provide those interfaces. How do I provide everything from API CLI through a gooey experience that can span across multiple form factors, bring together that more of a homogenous experience? >>What, what options are out there to solve this problem today? I mean, what are the best practices? Is it do it yourself? Is it, you know, a little bit of VMware here, a little bit of, you know, other tooling there, what, what do you see out there in the marketplace? >>I'll give kinda my perspective kind of yeah. Outside the, the tools that we see when we walk into an enterprise, you've got a company that's got a lot of VMware, maybe a little Nutanix, we've got some AWS, they wanna use OpenShift for their clusters. They got Terraform Ansible, and they got service now. And there's a, there's a poor it ops team in the middle, trying to wire all that together. And each of those domains have tried to go up this hill, right. VMware's done with vRealize automation, you know? Yeah. OpenShift will say no where the way, and you use cube vert to >>Do your virtual service now will say the same thing. Right? >>So our goal is, you know, we started in the middle right. Middle out, right. We started unifying that for self-service for developers and finance teams. And we're we're agnostic. We don't have a dog in the fight, right. We don't have a hypervisor business, a hardware business, an ITSM business. We're all about bringing the pieces together. But that said, we work with partners like HP, you have a footprint of thousands of customers who are solving that same problem and need to need to move up stack. So it's been a good win-win. So >>You're not trying to be the cloud operating system per se. I mean, right. The way, the way a VMware wants to be, or you could even argue, well, I guess open, you >>Got, you got the hyperscalers coming down, you got VMware moving up. But again, they all at the end of the day are trying to control their cash cow, right. Their hardcore business. We wanna make them all transparent. So >>Your bet is it's gonna be all of the above. Yeah. That's not gonna change. Right. That's the complexity is, is that right? Or do you think they're gonna consolidate? >>No, I think there's definitely something to that. I also think there's enough. Disparate. Technology's not gonna be one size fits all or one to rule them all. In fact, I think that's part of the examples in the past, like private cloud is we announced yesterday private cloud for enterprise. It's not a new term. People are doing that for quite a while now, but they are typically fairly brittle hand rolled disparate technologies, some poor it team trying to hold it together. So where we can provide that kind of life cycle management in a cloud operating model, remove that complexity and provide that stability. And in that experience across what will be interchangeable parts at times, I think that's really that direction in, >>Yeah. You guys talk about this whole starting in the middle. I like that because there's a skills gap as well. Right. Not only is there for a challenge on it that transforms, there's not enough. People actually know how to manage a Kubernetes cluster spin one up. Yeah. So there's been a rise of managed services. We're seeing come outta the woodwork almost in all areas where it's complex. Yeah. How does that fit into the makeup of as customers, engineer or rearchitect or, or just evolve to edge on premises and public cloud? Yeah. In a cloud operating way, because if I got managed service, do they just plug in, I mean, new orchestrating services, managed services all the above, take us through this dynamic because we're seeing more and more customers saying, just gimme the service. Yeah. >>I, I know manage perspective. This, this kinda goes back to that portfolio of meeting customers where they are. There are some that, that have that expertise in house they're opinionated. They just want a different consumption model. But on the other side of that, it's difficult to attract and retain that type of talent. And if I have limited resources, am I gonna focus on the care and feeding of that underlying infrastructure? Or am I gonna try to up level and focus on things more strategic to the business? So that's where we've certainly been focusing. And I think this type of management capability is what feeds into that. Right? >>Talk about the trust aspect, because if I'm gonna go manage service, it better work. I need to trust it. It's not a zero trust environment. It's actually a trust and verified, but you're seeing the software supply chain is a big discussion point. Developers don't wanna have to get back off their CDC pipeline to go in and manage stuff. So a managed service has to be verified. Yeah. There's a huge trust factor in there. How does what's the status of this now? Is it real? >>I think one of the, one of the pieces we see in terms of trust organizationally, I mean, people in process is always harder than the tech usually. And, and a lot of the trust is just internal. You get, you know, developers don't trust the ops team, right? Security doesn't trust anybody, you know, finance doesn't trust, you know, who's billing them. Part of what we do as a stack is we give each of those stakeholder groups, the ability to get their core needs met without getting each other's way. And from a delivery perspective where we partner with HPE is we are, you know, we're a platform framework, we're a technology provider we're inside, you know, products like the private cloud. We work their GMs team, the manage services team. If they wanna take on more of that operational concern, right. They use us or if the customer wants to manage it themselves. So we we're all about enabling them at the end of the day. And, and HP brings >>And how hard bread is it to unify? UN unification is a great word. I love let's unify everybody. Right. So how, how hard is that? Can you scope that problem statement for us? What does that mean? >>I'll separate it from a technology perspective and then the people process. So a lot of the traditional people that have played in that space that do it yourself, you mention right. Scripting it all together is hard, right? And if you change from cloud a to cloud B, you're set back six months, like why we exist is we wanna very quickly pull the pieces together. We can usually get a POC up and running in about two hours, right? That's a, self-service VMware private cloud, right? That doesn't mean you've solved the organizational inertia. You know, that's, that takes time, weeks, months. And that's where people are like Accenture GreenLake, other SI other channel partners bring that together to, to help make that change happen. >>How mature is the platform? Where are you in terms of determining product market fit? Are you, are you scaling at this point? >>Well, the, the great part about our origin story, right? We got our start as an internal tool set inside a two and a half billion dollar private equity firm that was transforming it at dozens of companies. So we were built for the use case product market fit happened, cuz a bunch of guys needed to get their jobs done. So we've been an outbound since 2015, right? We were top of the stack ranking, you know, all the MQs, all the quadrants, all the analysis. So we think we're their product market fit. The nice thing is customers have actually moved to where we are. Right? Five years ago, cloud management meant cleaning up the lift and shift mess. Now it's automation platform engineering. So it it's a fun time. >>It's it's operational. Yeah. It's they're operationalizing it. >>What's your go to market model. Maybe you could double click on those through >>Partners. So honestly through HP is a big one. We're small, right? We want to be the best unified platform we can be. Our go to market is via technology partners like HPE, right? The other systems integrators, other channel partners globally. So, so yeah. It's >>So then you've got kind of a tiger team overly. Yep. Salesforce is that, that >>Yeah, we've got teams globally. So we've got about 700,000 workloads under management around the world. About 70% of those are OnPrem VMware Nutanix. The rest are up in the public cloud. So we work with partners, solution providers, services, engines to, to help deliver that to >>Customers. What do you make of the 61 billion acquisition of VMware from Broadcom? >>We're, you know, I think your analysis was spot on. It is gonna be a, a war of, you know, what is the, the most profitable to that new Broadcom business and things like vRealize automation, some of these fringe products that are core at a customer use cases, but may not be driving a lot of bottom revenue for VMware, I think are gonna be gonna be on the bubble. And we've seen more interest in the last few weeks from people who just want to hedge their bets. Right. They want to be able to switch from hypervisor a to hypervisor B or cloud a to cloud B without being locked into anyone's stack. And that is, that is why we exist. Mm. >>You wanna comment on that? >>I mean, it's, you know, for HP and from a GreenLake and even just historically, right. It's about customer choice. Mm. We have a strong relationship with VMware. Sure. We have, I don't know how many bajillions of servers out there running VMware that we, we support with. So, you know, it's, it's, it's all just looking at that ecosystem and helping deliver those customer solutions and outcomes is our focus. Yeah. >>Thank >>You. Brian. Talk about the GreenLake success with partners. We're seeing ecosystem is a big part of that and we know the formula for ecosystems create value. What is the pitch that green lakes making to the marketplace right now to attract more folks to build and or integrate into the >>Platform? Yeah. I mean, GreenLake started with a, a vehicle of how do I start to deliver an OPEX model, a consumption model for traditional infrastructure that we've been providing more and more as the services and solutions really have emerged and evolved. It's gone from, how do I just give you kit and a consumption model for it to now looking at embedded solutions with third party ISV software building or wrapping those services around it, really delivering outcomes and solutions you're seeing. And hopefully you'll solve just from announcement more and more of that, where we have kind of turnkey solutions with key partners, how do we bring a marketplace ecosystem together? How do we help enable those kind of full solutions? Because we're not gonna build it all ourselves, right. We wanna make sure that we can deliver those outcomes. >>So marketing is often and should be ahead of the actual product, early days of GreenLake. It was really a, you know, financial model. Sure. Right. Where do, where do you see GreenLake today? How far is it matured? We saw some of the, the announcements yesterday. We saw some demos. Where are we at? >>Yeah. So this actually, I think really the exciting part is you might have heard Antonio refer to as that journey to one each of our different businesses within green or within HPE, they've all been building these cloud services in GreenLake enabled services. But as you saw Alma share the path to the HPE GreenLake cloud platform that really is bringing these services together into a functional platform, right? Common identity, common telemetry services, bringing these together as now, integrable interoperable services. Like you're starting to see that come together and you can really see the Chrome trail of, of where we're going with a very powerful hybrid cloud experience, right? Spanning private public on-prem colo and a, and a full solution set within there. So it that's, that's the exciting part >>For me and Brad Morpheus will be a capability inside of GreenLake that a customer can consume. Do you have to write to GreenLake APIs to enable that? Or is it, is it more just certify that you work inside a GreenLake? What has to get done? I'll say a lot >>Of what they've done is actually written into, into our APIs. Like we've normalized hybrid it. We have a, a database model of every load balance or a cloud endpoint automation tool. So we are, we're all about making it easier to consume it. And the vision that Alma and HP has around GreenLake fits very well with why we exist. So they're able to extract metering data from our, you know, from our API, we know who provisioned what, where how much they spent. So we're a good repository and platform partner for them to, to build on. It's >>Great for that console that you guys have. Yeah. >>You got the, you got the open APIs, you publish those, you guys take advantage of 'em and then sure. Boom. Then you can consume. Got it. All right, guys. Hey, great to see you again, red. Thanks for, for >>Coming on. Thanks. Thanks for having us on >>Our pleasure. Great stuff. Congratulations. Okay. Keep it right there. This is Dave Valante for John furrier. Are you watching the cubes coverage of HPE discover 2022 from Las Vegas? We'll be right back.

>> Okay, welcome back everyone to our special Silicon angle presentation with Druva's Live Event here in Palo Alto. I'm John Furrier. Our next segment is about the 80-million dollars in funding topic and I'm here with Mahesh Patel, CFO of Druva and Jeff Parks, general co-founder and partner of Riverwood Capital, the investor who put it all together with Mahesh. Thank you for coming on, appreciate it. Let's talk about the 80 million dollars, a lot of cabbage, as we say. (chuckling) So talk about what's going on with the funding. >> Well we're a late-stage company. We have established product market fit, over 4,000 customers, 40 petabytes managed in the Cloud, but a real differentiated product, the way we attack the market and the data-protection landscape is evolving fast and we believe we have the right products out there to go capitalize on. >> Just to get the numbers down, what round is this and how much total raise have you guys done? >> We really call it late-stage round because just the nomenclature around (cross-talk) what round >> John: A-B-C -- >> Changes, what round -- >> John: So it's a growth round? >> It's a growth round for us, so it's what we believe, very late-stage round for us, we believe it's the right amount of capital to kind of carry us forward. It's really about the opportunities that are ahead of us. When we think about what we've executed on thus far, we've had an amazing journey but our best is ahead of us so we have the tipping point with Clouded Option, especially in infrastructure management and back-up and we play really well and our new product sets are showing very strong progress so, really, our best is ahead of us with this capital, we're going to be able to capitalize on some market. >> So were you guys looking for capital? Or did this just kind of come together opportunistically? How did, this validation market, with the new next generation Druva Cloud Platform, also you got to do some more development but were you guys seeking financing or how did this all come together? >> Well it really validates, again, it validates what we've been building and we really think of this as opportunistic for us because we see, we see this as a great market for us. We see, executed at, accelerating revenue, accelerated billing growth, with the Clouded Option, we're seeing it continue to evolve but we recognize at this point, at this journey, the additional capital allows us to get to the next stage, the next level and really, with a partner like Riverwood to join us, not only with the capital but with the domain experience they bring to the table, it was the right partnership to carry us forward. >> Jeff, what did you see in Druva? Obviously, you do a lot of investing. You've invested in companies that have been over a billion dollars in valuation. You've got a great pedigree of deals that you guys are involved in. What, about Druva, was attractive to you from a financial investment standpoint? >> Yeah, thanks for the compliment, by the way. I would probably remark on three things, John. The first thing we look for in companies like this is really a very large market opportunity and when you think about the secondary storage market, I think it's actually one of the larger markets that is yet to be really, truly disrupted by the Cloud and Druva, certainly at the forefront of that, and so with that leadership position and that sort of obvious megatrend, I think there's a great growth opportunity for the company. The second thing I'd say is part of our due diligence, we really check out an extensive customer call background check and what we found there is really to a T that every customer is not only happy with what Druva's providing but really, I think, have a significant expansion opportunity there, and that's a great validation. And, really, lastly is the management team. We certainly look to partner with these companies and really with the teams that we're backing and between Jaspreet and Mahesh and the team, I think we're really quite excited about partnering with the Druva team. >> Interesting you mention about grilling the customers for the references as your due diligence, which is what all investors do, you guys are hard-core, it sounds like you're disciplined to go see that all the way through. On the customer journey segment, we had the customers drilling Druva pretty hard, so -- (cross-talk) some, all this due diligence, it's a new space. >> Jeff: Sure! Of course. >> You're not new to this area. You're also on the board of Nutanix, they do primary storage, you mentioned secondary storage, was there help there having a visibility into what Nutanix was doing and then visibility into secondary storage, and the difference in-between them? >> Well, certain, we've been long-time investors in the infrastructure space, generally, and certainly our involvement in Nutanix has given us a great front row seat at some of the major enterprise infrastructure transitions and that certainly informs our view about the secondary storage market and the opportunity that Druva has. >> What's next for Druva? What are you guys going to do with the cash? Are you going to do an exit? Is that being discussed? I mean, take us through how you see Druva unfolding. >> Well, it's been a fantastic journey so far. I'd say we executed amazingly over the last few years and continue to execute with accelerating revenue growth, as well. We built out a global sales team servicing over 20 countries at this point. We are near cashflow-positive at this point and frankly, we have an amazing set of customers to capitalize on further. Really, what we've done with the new Druva Cloud Platform bring it to the table, this additional capital allows us to really take this forward and we really think we're in the early stages of a long game ahead of us so we're really excited about where this capital is going to take us. >> And you're watching the cash, making sure it's going to the right spot? You got your investor there, you got to keep him happy. >> Mahesh: Absolutely, absolutely. >> Congratulations, guys. It's been great exposure to the data transformation, as you put it, appreciate it. Great disruption in data protection, secondary storage, great market opportunity. The stakes are high and the best product, great management team and the ones who have the technology chops for centralizing the Cloud operation will make it happen. Thanks to you guys for participating in this special event. Thanks for watching, everyone.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Okay, >>we're now going to explore the vision of the future of cloud computing From the perspective of one of the leaders in the field, J G >>Share >>a pure off is the vice president of As Your Data ai and Edge at Microsoft G. Welcome to the Cuban cloud. Thanks so much for participating. >>Well, thank you, Dave, and it's a real pleasure to be here with you. And I just wanna welcome the audience as well. >>Well, jg judging from your title, we have a lot of ground to cover, and our audience is definitely interested in all the topics that are implied there. So let's get right into it. You know, we've said many times in the Cube that the new innovation cocktail comprises machine intelligence or a I applied to troves of data. With the scale of the cloud. It's it's no longer, you know, we're driven by Moore's law. It's really those three factors, and those ingredients are gonna power the next wave of value creation and the economy. So, first, do you buy into that premise? >>Yes, absolutely. we do buy into it. And I think, you know, one of the reasons why we put Data Analytics and Ai together is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from a I as being on that continues off, having started off with really things like analytics and proceeding toe. You know, machine learning and the use of data. Interesting breaks. Yes. >>I'd like to get some more thoughts around a data and how you see the future data and the role of cloud and maybe how >>Microsoft, you >>know, strategy fits in there. I mean, you, your portfolio, you got you got sequel server, Azure, Azure sequel. You got arc, which is kinda azure everywhere for people that aren't familiar with that. You've got synapse. Which course that's all the integration a data warehouse, and get things ready for B I and consumption by the business and and the whole data pipeline and a lot of other services as your data bricks you got You got cosmos in their, uh, Blockchain. You've got open source services like Post Dress and my sequel. So lots of choices there. And I'm wondering, you know, how do you think about the future of Of of Cloud data platforms? It looks like your strategies, right tool for the right job? Is that fair? >>It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today is that customer was the Sikh really a comprehensive proposition? And when I say a comprehensive proposition, it is sometimes not just about saying that. Hey, listen way No, you're a sequel server company. We absolutely trust that you have the best Azure sequel database in the cloud, but tell us more. We've got data that's sitting in her group systems. We've got data that's sitting in Post Press in things like mongo DB, right? So that open source proposition today and data and data management and database management has become front and center, so are really sort of push. There is when it comes to migration management, modernization of data to present the broadest possible choice to our customers so we can meet them where they are. However, when it comes to analytics. One of the things they asked for is give us a lot more convergence use. You know it, really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like synapse Fitzer, where in just land any kind of data in the leg and then use any compute engine on top of it to drive insights from it. So, fundamentally, you know, it is that flexibility that we really sort of focus on to meet our customers where they are and really not pushing our dogma and our beliefs on it. But to meet our customers according to the way they have deployed stuff like this. >>So that's great. I want to stick on this for a minute because, you know, I know when when I have guests on like yourself, do you never want to talk about you know, the competition? But that's all we ever talk about. That's all your customers ever talk about, because because the counter to that right tool for the right job and that I would say, is really kind of Amazon's approach is is that you got the single unified data platform, the mega database that does it all. And that's kind of Oracle's approach. It sounds like you wanna have your cake and eat it, too, so you you got the right tool for the right job approach. But you've got an integration layer that allows you to have that converge database. I wonder if you could add color to that and you confirm or deny what I just said. >>No, that's a That's a very fair observation, but I I say there's a nuance in what I sort of describe when it comes to data management. When it comes to APS, we have them customers with the broadest choice. Even in that, even in that perspective, we also offer convergence. So, case in point, when you think about Cosmos TV under that one sort of service, you get multiple engines, but with the same properties, right global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native AB toe, adopt cosmos Davey and adopted in a way that it's and choose an engine that is most flexible. Tow them, however you know when it comes to say, you know, writing a sequel server, for example from organizing it you know you want. Sometimes you just want to lift and shift it into things. Like I asked In other cases, you want to completely rewrite it, so you need to have the flexibility of choice there that is presented by a legacy off What's its on premises? When it moved into things like analytics, we absolutely believe in convergence, right? So we don't believe that look, you need to have a relation of data warehouse that is separate from a loop system that is separate from, say, a B I system. That is just, you know, it's a bolt on for us. We love the proposition off, really building things that are so integrated that once you land data, once you prep it inside the lake, you can use it for analytics. You can use it for being. You can use it for machine learning. So I think you know, are sort of differentiated. Approach speaks for itself there. Well, >>that's that's interesting, because essentially, again, you're not saying it's an either or, and you're seeing a lot of that in the marketplace. You got some companies say no, it's the Data Lake and others saying No, no put in the data warehouse and that causes confusion and complexity around the data pipeline and a lot of calls. And I'd love to get your thoughts on this. Ah, lot of customers struggled to get value out of data and and specifically data product builders of frustrated that it takes too long to go from. You know, this idea of Hey, I have an idea for a data service and it could drive monetization, but to get there, you gotta go through this complex data lifecycle on pipeline and beg people to add new data sources. And do you do you feel like we have to rethink the way that we approach data architectures? >>Look, I think we do in the cloud, and I think what's happening today and I think the place where I see the most amount of rethink the most amount of push from our customers to really rethink is the area of analytics in a I. It's almost as if what worked in the past will not work going forward. Right? So when you think about analytics on in the Enterprise today, you have relational systems, you have produced systems. You've got data marts. You've got data warehouses. You've got enterprise data warehouses. You know, those large honking databases that you use, uh, to close your books with right? But when you start to modernize it, what deep you are saying is that we don't want to simply take all of that complexity that we've built over say, you know, 34 decades and simply migrated on mass exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see shape or form inside the lake. Once you landed inside the lake, you can essentially use a synapse studio toe. Prep it in the way that you like, use any compute engine of your choice and and operate on this data in any way that you see fit. So, case in point, if you want to hydrate relation all data warehouse, you can do so if you want to do ad hoc analytics using something like spark. You can do so if you want to invoke power. Bi I on that data or b i on that data you can do so if you want to bring in a machine learning model on this breath data you can do so, so inherently. So when customers buy into this proposition, what it solves for them and what it gives them is complete simplicity, right? One way to land the data, multiple ways to use it. And it's all eso. >>Should we think of synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way toe? Think about it. >>Yeah, you can think of it that way. It abstracts away, Dave a couple of things. It takes away the type of data, you know, sort of the complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data and fundamentally puts it in a place where it can be now consumed by any sort of entity inside the actual proposition. And by that token, even data breaks. You know, you can, in fact, use data breaks in in sort off an integrated way with a synapse, Right, >>Well, so that leads me to this notion of and then wonder if you buy into it s Oh, my inference is that a data warehouse or a data lake >>could >>just be a node in inside of a global data >>mesh on. >>Then it's synapses sort of managing, uh, that technology on top. Do you buy into that that global data mesh concept >>we do. And we actually do see our customers using synapse and the value proposition that it brings together in that way. Now it's not where they start. Often times when a customer comes and says, Look, I've got an enterprise data warehouse, I want to migrate it or I have a group system. I want to migrate it. But from there, the evolution is absolutely interesting to see. I give you an example. You know, one of the customers that we're very proud off his FedEx And what FedEx is doing is it's completely reimagining its's logistics system that basically the system that delivers What is it? The three million packages a day on in doing so in this covert times, with the view of basically delivering our covert vaccines. One of the ways they're doing it is basically using synapse. Synapse is essentially that analytic hub where they can get complete view into their logistic processes. Way things are moving, understand things like delays and really put all that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, you know, is one of my favorite, uh, we see once customers buy into it, they essentially can do other things with it. So an example of this is, uh is really my favorite story is Peace Parks Initiative. It is the premier Air White Rhino Conservancy in the world. They essentially are using data that has landed in azure images in particular. So, basically, you know, use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue so that this part with about 200 rangers can scramble surgically versus having to read range across the last area that they cover. So What do you see here is you know, the importance is really getting your data in order. Landed consistently. Whatever the kind of data ideas build the right pipelines and then the possibilities of transformation are just endless. >>Yeah, that's very nice how you worked in some of the customer examples. I appreciate that. I wanna ask you, though, that that some people might say that putting in that layer while it clearly adds simplification and e think a great thing that they're begins over time to be be a gap, if you will, between the ability of that layer to integrate all the primitives and all the peace parts on that, that you lose some of that fine grain control and it slows you down. What would you say to that? >>Look, I think that's what we excel at, and that's what we completely sort of buy into on. It's our job to basically provide that level off integration that granularity in the way that so it's an art, absolutely admit it's an art. There are areas where people create simplicity and not a lot of you know, sort of knobs and dials and things like that. But there are areas where customers want flexibility, right? So I think just to give you an example of both of them in landing the data inconsistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. Just 100 to do it. When it comes to computing and reducing this data analyzing this data, they want flexibility. This is one of the reasons why we say, Hey, listen, you want to use data breaks? If you're you're buying into that proposition and you're absolutely happy with them, you can plug plug it into it. You want to use B I and no, essentially do a small data mart. You can use B I If you say that. Look, I've landed in the lake. I really only want to use em melt, bringing your animal models and party on. So that's where the flexibility comes in. So that's sort of really sort of think about it. Well, >>I like the strategy because, you know, my one of our guest, Jim Octagon, e E. I think one of the foremost thinkers on this notion of off the data mesh and her premises that that that data builders, data product and service builders air frustrated because the the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system, you could get products to market much, much, much faster. So but that seems to be your philosophy. But I'm gonna jump ahead to do my ecosystem question. You've mentioned data breaks a couple of times. There's another partner that you have, which is snowflake. They're kind of trying to build out their own, uh, data cloud, if you will, on global mesh in and the one hand, their partner. On the other hand, there are competitors. How do you sort of balance and square that circle? >>Look, when I see snowflake, I actually see a partner. You know that when we essentially you know, we are. When you think about as you know, this is where I sort of step back and look at Azure as a whole and in azure as a whole. Companies like snowflakes are vital in our ecosystem, right? I mean, there are places we compete, but you know, effectively by helping them build the best snowflake service on Asia. We essentially are able toe, you know, differentiate and offer a differentiated value proposition compared to, say, a Google or on AWS. In fact, that's being our approach with data breaks as well, where you know they are effectively on multiple club, and our opportunity with data breaks is toe essentially integrate them in a way where we offer the best experience. The best integrations on Azure Barna That's always been a focus. >>That's hard to argue with. Strategy. Our data with our data partner eat er, shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I wanna come back thio ai a little bit. It's obviously one of the fastest growing areas in our in our survey data. As I said, clearly, Microsoft is a leader in this space. What's your what's your vision of the future of machine intelligence and how Microsoft will will participate in that opportunity? >>Yeah, so fundamentally, you know, we've built on decades of research around, you know, around, you know, essentially, you know, vision, speech and language that's being the three core building blocks and for the for a really focused period of time we focused on essentially ensuring human parody. So if you ever wondered what the keys to the kingdom are it, czar, it's the most we built in ensuring that the research posture that we've taken there, what we then done is essentially a couple of things we focused on, essentially looking at the spectrum. That is a I both from saying that, Hollis and you know it's gotta work for data. Analysts were looking toe basically use machine learning techniques, toe developers who are essentially, you know, coding and building a machine learning models from scratch. So for that select proposition manifesto us, as you know, really a. I focused on all skill levels. The other court thing we've done is that we've also said, Look, it will only work as long as people trust their data and they can trust their AI models. So there's a tremendous body of work and research we do in things like responsibility. So if you ask me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum off a I, and you can sort of come together for any skill level, and we keep that responsibly. I proposition. Absolutely strong now against that canvas, Dave. I'll also tell you that you know, as edge devices get way more capable, right where they can input on the edge, see a camera or a mike or something like that, you will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality. But the core really is all skill levels and that responsible denia. >>Yeah, So that that brings me to this notion of wanna bring an edge and and hybrid cloud Understand how you're thinking about hybrid cloud multi cloud. Obviously one of your competitors, Amazon won't even say the word multi cloud you guys have, Ah, you know, different approach there. But what's the strategy with regard? Toe, toe hybrid. You know, Do you see the cloud you bringing azure to the edge? Maybe you could talk about that and talk about how you're different from the competition. >>Yeah, I think in the edge from Annette, you know, I live in I'll be the first one to say that the word nge itself is conflated. Okay, It's, uh but I will tell you, just focusing on hybrid. This is one of the places where you know I would say the 2020 if I would have looked back from a corporate perspective. In particular, it has Bean the most informative because we absolutely saw customers digitizing moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became really from a cloud computing perspective and an example of this is we understood it's not all or nothing. So sometimes customers want azure consistency in their data centers. This is where things like Azure stack comes in. Sometimes they basically come to us and say, We want the flexibility of adopting flexible pattern, you know, platforms like, say, containers orchestra, Cuban Pettis, so that we can essentially deployed wherever you want. And so when we design things like art, it was built for that flexibility in mind. So here is the beauty of what's something like our can do for you. If you have a kubernetes endpoint anywhere we can deploy and as your service onto it, that is the promise, which means if for some reason, the customer says that. Hey, I've got this kubernetes endpoint in AWS and I love as your sequel. You will be able to run as your sequel inside AWS. There's nothing that stops you from doing it so inherently you remember. Our first principle is always to meet our customers where they are. So from that perspective, multi cloud is here to stay. You know, we're never going to be the people that says, I'm sorry, we will never see a But it is a reality for our customers. >>So I wonder if we could close. Thank you for that by looking, looking back and then and then ahead. And I wanna e wanna put forth. Maybe it's, Ah criticism, but maybe not. Maybe it's an art of Microsoft, but But first you know, you get Microsoft an incredible job of transitioning. It's business as your nominee president Azzawi said. Our data shows that so two part question First, Microsoft got there by investing in the cloud, really changing its mind set, I think, in leveraging its huge software state and customer base to put Azure at the center of its strategy, and many have said me included that you got there by creating products that air Good enough. You know, we do a 1.0, it's not that great. And the two Dato, and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expanding market. >>How >>do you respond to that? Is that is that a fair comment? Ume or than good enough? I wonder if you could share your >>thoughts, gave you? You hurt my feelings with that question. I don't hate me, g getting >>it out there. >>So there was. First of all, thank you for asking me that. You know, I am absolutely the biggest cheerleader. You'll find a Microsoft. I absolutely believe you know that I represent the work off almost 9000 engineers and we wake up every day worrying about our customer and worrying about the customer condition and toe. Absolutely. Make sure we deliver the best in the first time that we do. So when you take the platter off products we've delivered in nausea, be it as your sequel, be it as your cosmos TV synapse as your data breaks, which we did in partnership with data breaks, a za machine learning and recently when we prevail, we sort off, you know, sort of offered the world's first comprehensive data government solution in azure purview. I would humbly submit to you that we're leading the way and we're essentially showing how the future off data ai and the actual work in the cloud. >>I'd be disappointed if you if you had If you didn't, if you capitulated in any way J g So so thank you for that. And the kind of last question is, is looking forward and how you're thinking about the future of cloud last decade. A lot about your cloud migration simplifying infrastructure management, deployment SAS if eyeing my enterprise, lot of simplification and cost savings. And, of course, the redeployment of resource is toward digital transformation. Other other other valuable activities. How >>do >>you think this coming decade will will be defined? Will it be sort of more of the same? Or is there Is there something else out there? >>I think I think that the coming decade will be one where customers start one law outside value out of this. You know what happened in the last decade when people leave the foundation and people essentially looked at the world and said, Look, we've got to make the move, you know, the largely hybrid, but we're going to start making steps to basically digitize and modernize our platforms. I would tell you that with the amount of data that people are moving to the cloud just as an example, you're going to see use of analytics ai for business outcomes explode. You're also going to see a huge sort of focus on things like governance. You know, people need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all other privacy and compliance regulations out there. Essentially, they're complying this posture. So I think the unlocking of outcomes versus simply Hey, I've saved money Second, really putting this comprehensive sort off, you know, governance, regime in place. And then, finally, security and trust. It's going to be more paramount than ever before. Yeah, >>nobody's gonna use the data if they don't trust it. I'm glad you brought up your security. It's It's a topic that hits number one on the CEO list. J G. Great conversation. Obviously the strategy is working, and thanks so much for participating in Cuba on cloud. >>Thank you. Thank you, David. I appreciate it and thank you to. Everybody was tuning in today. >>All right? And keep it right there. I'll be back with our next guest right after this short break.

>> Okay, we're now going to explore the vision of the future of cloud computing from the perspective of one of the leaders in the field, JG Chirapurath is the Vice President of Azure Data AI and Edge at Microsoft. JG, welcome to theCUBE on Cloud, thanks so much for participating. >> Well, thank you, Dave. And it's a real pleasure to be here with you and just want to welcome the audience as well. >> Well, JG, judging from your title, we have a lot of ground to cover and our audience is definitely interested in all the topics that are implied there. So let's get right into it. We've said many times in theCUBE that the new innovation cocktail comprises machine intelligence or AI applied to troves of data with the scale of the cloud. It's no longer we're driven by Moore's law. It's really those three factors and those ingredients are going to power the next wave of value creation in the economy. So first, do you buy into that premise? >> Yes, absolutely. We do buy into it and I think one of the reasons why we put data analytics and AI together, is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from AI as being on that continuum of having started off with really things like analytics and proceeding to be machine learning and the use of data in interesting ways. >> Yes, I'd like to get some more thoughts around data and how you see the future of data and the role of cloud and maybe how Microsoft strategy fits in there. I mean, your portfolio, you've got SQL Server, Azure SQL, you got Arc which is kind of Azure everywhere for people that aren't familiar with that you got Synapse which course does all the integration, the data warehouse and it gets things ready for BI and consumption by the business and the whole data pipeline. And then all the other services, Azure Databricks, you got you got Cosmos in there, you got Blockchain, you've got Open Source services like PostgreSQL and MySQL. So lots of choices there. And I'm wondering, how do you think about the future of cloud data platforms? It looks like your strategy is right tool for the right job. Is that fair? >> It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today, is that customers they seek really a comprehensive proposition. And when I say a comprehensive proposition it is sometimes not just about saying that, "Hey, listen "we know you're a sequence of a company, "we absolutely trust that you have the best "Azure SQL database in the cloud. "But tell us more." We've got data that is sitting in Hadoop systems. We've got data that is sitting in PostgreSQL, in things like MongoDB. So that open source proposition today in data and data management and database management has become front and center. So our real sort of push there is when it comes to migration management modernization of data to present the broadest possible choice to our customers, so we can meet them where they are. However, when it comes to analytics, one of the things they ask for is give us lot more convergence use. It really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like Synapse fits in where you can just land any kind of data in the lake and then use any compute engine on top of it to drive insights from it. So fundamentally, it is that flexibility that we really sort of focus on to meet our customers where they are. And really not pushing our dogma and our beliefs on it but to meet our customers according to the way they've deployed stuff like this. >> So that's great. I want to stick on this for a minute because when I have guests on like yourself they never want to talk about the competition but that's all we ever talk about. And that's all your customers ever talk about. Because the counter to that right tool for the right job and that I would say is really kind of Amazon's approach is that you got the single unified data platform, the mega database. So it does it all. And that's kind of Oracle's approach. It sounds like you want to have your cake and eat it too. So you got the right tool with the right job approach but you've got an integration layer that allows you to have that converged database. I wonder if you could add color to that and confirm or deny what I just said. >> No, that's a very fair observation but I'd say there's a nuance in what I sort of described. When it comes to data management, when it comes to apps, we have then customers with the broadest choice. Even in that perspective, we also offer convergence. So case in point, when you think about cosmos DB under that one sort of service, you get multiple engines but with the same properties. Right, global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native app to adopt cosmos DB and adopt it in a way that is an choose an engine that is most flexible to them. However, when it comes to say, writing a SequenceServer for example, if modernizing it, you want sometimes, you just want to lift and shift it into things like IS. In other cases, you want to completely rewrite it. So you need to have the flexibility of choice there that is presented by a legacy of what sits on premises. When you move into things like analytics, we absolutely believe in convergence. So we don't believe that look, you need to have a relational data warehouse that is separate from a Hadoop system that is separate from say a BI system that is just, it's a bolt-on. For us, we love the proposition of really building things that are so integrated that once you land data, once you prep it inside the Lake you can use it for analytics, you can use it for BI, you can use it for machine learning. So I think, our sort of differentiated approach speaks for itself there. >> Well, that's interesting because essentially again you're not saying it's an either or, and you see a lot of that in the marketplace. You got some companies you say, "No, it's the data lake." And others say "No, no, put it in the data warehouse." And that causes confusion and complexity around the data pipeline and a lot of cutting. And I'd love to get your thoughts on this. A lot of customers struggle to get value out of data and specifically data product builders are frustrated that it takes them too long to go from, this idea of, hey, I have an idea for a data service and it can drive monetization, but to get there you got to go through this complex data life cycle and pipeline and beg people to add new data sources and do you feel like we have to rethink the way that we approach data architecture? >> Look, I think we do in the cloud. And I think what's happening today and I think the place where I see the most amount of rethink and the most amount of push from our customers to really rethink is the area of analytics and AI. It's almost as if what worked in the past will not work going forward. So when you think about analytics only in the enterprise today, you have relational systems, you have Hadoop systems, you've got data marts, you've got data warehouses you've got enterprise data warehouse. So those large honking databases that you use to close your books with. But when you start to modernize it, what people are saying is that we don't want to simply take all of that complexity that we've built over, say three, four decades and simply migrate it en masse exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like Synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see, shape or form inside the lake. Once you landed inside the lake, you can essentially use a Synapse Studio to prep it in the way that you like. Use any compute engine of your choice and operate on this data in any way that you see fit. So case in point, if you want to hydrate a relational data warehouse, you can do so. If you want to do ad hoc analytics using something like Spark, you can do so. If you want to invoke Power BI on that data or BI on that data, you can do so. If you want to bring in a machine learning model on this prep data, you can do so. So inherently, so when customers buy into this proposition, what it solves for them and what it gives to them is complete simplicity. One way to land the data multiple ways to use it. And it's all integrated. >> So should we think of Synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way to think about it? >> Yeah, you can think of it that way. It abstracts away Dave, a couple of things. It takes away that type of data. Sort of complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data. And fundamentally puts it in a place where it can be now consumed by any sort of entity inside the Azure proposition. And by that token, even Databricks. You can in fact use Databricks in sort of an integrated way with the Azure Synapse >> Right, well, so that leads me to this notion of and I wonder if you buy into it. So my inference is that a data warehouse or a data lake could just be a node inside of a global data mesh. And then it's Synapse is sort of managing that technology on top. Do you buy into that? That global data mesh concept? >> We do and we actually do see our customers using Synapse and the value proposition that it brings together in that way. Now it's not where they start, oftentimes when a customer comes and says, "Look, I've got an enterprise data warehouse, "I want to migrate it." Or "I have a Hadoop system, I want to migrate it." But from there, the evolution is absolutely interesting to see. I'll give you an example. One of the customers that we're very proud of is FedEx. And what FedEx is doing is it's completely re-imagining its logistics system. That basically the system that delivers, what is it? The 3 million packages a day. And in doing so, in this COVID times, with the view of basically delivering on COVID vaccines. One of the ways they're doing it, is basically using Synapse. Synapse is essentially that analytic hub where they can get complete view into the logistic processes, way things are moving, understand things like delays and really put all of that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, it's one of my favorite. We see once customers buy into it, they essentially can do other things with it. So an example of this is really my favorite story is Peace Parks initiative. It is the premier of white rhino conservancy in the world. They essentially are using data that has landed in Azure, images in particular to basically use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue. So that this part with about 200 radios can scramble surgically versus having to range across the vast area that they cover. So, what you see here is, the importance is really getting your data in order, landing consistently whatever the kind of data it is, build the right pipelines, and then the possibilities of transformation are just endless. >> Yeah, that's very nice how you worked in some of the customer examples and I appreciate that. I want to ask you though that some people might say that putting in that layer while you clearly add simplification and is I think a great thing that there begins over time to be a gap, if you will, between the ability of that layer to integrate all the primitives and all the piece parts, and that you lose some of that fine grain control and it slows you down. What would you say to that? >> Look, I think that's what we excel at and that's what we completely sort of buy into. And it's our job to basically provide that level of integration and that granularity in the way that it's an art. I absolutely admit it's an art. There are areas where people crave simplicity and not a lot of sort of knobs and dials and things like that. But there are areas where customers want flexibility. And so I think just to give you an example of both of them, in landing the data, in consistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. There's one way to do it. When it comes to computing and reducing this data, analyzing this data, they want flexibility. This is one of the reasons why we say, "Hey, listen you want to use Databricks. "If you're buying into that proposition. "And you're absolutely happy with them, "you can plug it into it." You want to use BI and essentially do a small data model, you can use BI. If you say that, "Look, I've landed into the lake, "I really only want to use ML." Bring in your ML models and party on. So that's where the flexibility comes in. So that's sort of that we sort of think about it. >> Well, I like the strategy because one of our guests, Jumark Dehghani is I think one of the foremost thinkers on this notion of of the data mesh And her premise is that the data builders, data product and service builders are frustrated because the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system you can get products to market much, much, much faster. So, and that seems to be your philosophy but I'm going to jump ahead to my ecosystem question. You've mentioned Databricks a couple of times. There's another partner that you have, which is Snowflake. They're kind of trying to build out their own DataCloud, if you will and GlobalMesh, and the one hand they're a partner on the other hand they're a competitor. How do you sort of balance and square that circle? >> Look, when I see Snowflake, I actually see a partner. When we see essentially we are when you think about Azure now this is where I sort of step back and look at Azure as a whole. And in Azure as a whole, companies like Snowflake are vital in our ecosystem. I mean, there are places we compete, but effectively by helping them build the best Snowflake service on Azure, we essentially are able to differentiate and offer a differentiated value proposition compared to say a Google or an AWS. In fact, that's been our approach with Databricks as well. Where they are effectively on multiple clouds and our opportunity with Databricks is to essentially integrate them in a way where we offer the best experience the best integrations on Azure Berna. That's always been our focus. >> Yeah, it's hard to argue with the strategy or data with our data partner and ETR shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I want to come back to AI a little bit. It's obviously one of the fastest growing areas in our survey data. As I said, clearly Microsoft is a leader in this space. What's your vision of the future of machine intelligence and how Microsoft will participate in that opportunity? >> Yeah, so fundamentally, we've built on decades of research around essentially vision, speech and language. That's been the three core building blocks and for a really focused period of time, we focused on essentially ensuring human parity. So if you ever wonder what the keys to the kingdom are, it's the boat we built in ensuring that the research or posture that we've taken there. What we've then done is essentially a couple of things. We've focused on essentially looking at the spectrum that is AI. Both from saying that, "Hey, listen, "it's got to work for data analysts." We're looking to basically use machine learning techniques to developers who are essentially, coding and building machine learning models from scratch. So for that select proposition manifest to us as really AI focused on all skill levels. The other core thing we've done is that we've also said, "Look, it'll only work as long "as people trust their data "and they can trust their AI models." So there's a tremendous body of work and research we do and things like responsible AI. So if you asked me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum of AI can sort of come together for any skill level. And we keep that responsible AI proposition absolutely strong. Now against that canvas Dave, I'll also tell you that as Edge devices get way more capable, where they can input on the Edge, say a camera or a mic or something like that. You will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality but the core really is all skill levels and that responsibility in AI. >> Yeah, so that brings me to this notion of, I want to bring an Edge and hybrid cloud, understand how you're thinking about hybrid cloud, multicloud obviously one of your competitors Amazon won't even say the word multicloud. You guys have a different approach there but what's the strategy with regard to hybrid? Do you see the cloud, you're bringing Azure to the edge maybe you could talk about that and talk about how you're different from the competition. >> Yeah, I think in the Edge from an Edge and I even I'll be the first one to say that the word Edge itself is conflated. Okay, a little bit it's but I will tell you just focusing on hybrid, this is one of the places where, I would say 2020 if I were to look back from a COVID perspective in particular, it has been the most informative. Because we absolutely saw customers digitizing, moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became real from a cloud computing perspective. And an example of this is we understood that it's not all or nothing. So sometimes customers want Azure consistency in their data centers. This is where things like Azure Stack comes in. Sometimes they basically come to us and say, "We want the flexibility of adopting "flexible button of platforms let's say containers, "orchestrating Kubernetes "so that we can essentially deploy it wherever you want." And so when we designed things like Arc, it was built for that flexibility in mind. So, here's the beauty of what something like Arc can do for you. If you have a Kubernetes endpoint anywhere, we can deploy an Azure service onto it. That is the promise. Which means, if for some reason the customer says that, "Hey, I've got "this Kubernetes endpoint in AWS. And I love Azure SQL. You will be able to run Azure SQL inside AWS. There's nothing that stops you from doing it. So inherently, remember our first principle is always to meet our customers where they are. So from that perspective, multicloud is here to stay. We are never going to be the people that says, "I'm sorry." We will never say (speaks indistinctly) multicloud but it is a reality for our customers. >> So I wonder if we could close, thank you for that. By looking back and then ahead and I want to put forth, maybe it's a criticism, but maybe not. Maybe it's an art of Microsoft. But first, you did Microsoft an incredible job at transitioning its business. Azure is omnipresent, as we said our data shows that. So two-part question first, Microsoft got there by investing in the cloud, really changing its mindset, I think and leveraging its huge software estate and customer base to put Azure at the center of it's strategy. And many have said, me included, that you got there by creating products that are good enough. We do a one Datto, it's still not that great, then a two Datto and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expand your market. How do you respond to that? Is that a fair comment? Are you more than good enough? I wonder if you could share your thoughts. >> Dave, you hurt my feelings with that question. >> Don't hate me JG. (both laugh) We're getting it out there all right, so. >> First of all, thank you for asking me that. I am absolutely the biggest cheerleader you'll find at Microsoft. I absolutely believe that I represent the work of almost 9,000 engineers. And we wake up every day worrying about our customer and worrying about the customer condition and to absolutely make sure we deliver the best in the first attempt that we do. So when you take the plethora of products we deliver in Azure, be it Azure SQL, be it Azure Cosmos DB, Synapse, Azure Databricks, which we did in partnership with Databricks, Azure Machine Learning. And recently when we premiered, we sort of offered the world's first comprehensive data governance solution in Azure Purview. I would humbly submit it to you that we are leading the way and we're essentially showing how the future of data, AI and the Edge should work in the cloud. >> Yeah, I'd be disappointed if you capitulated in any way, JG. So, thank you for that. And that's kind of last question is looking forward and how you're thinking about the future of cloud. Last decade, a lot about cloud migration, simplifying infrastructure to management and deployment. SaaSifying My Enterprise, a lot of simplification and cost savings and of course redeployment of resources toward digital transformation, other valuable activities. How do you think this coming decade will be defined? Will it be sort of more of the same or is there something else out there? >> I think that the coming decade will be one where customers start to unlock outsize value out of this. What happened to the last decade where people laid the foundation? And people essentially looked at the world and said, "Look, we've got to make a move. "They're largely hybrid, but you're going to start making "steps to basically digitize and modernize our platforms. I will tell you that with the amount of data that people are moving to the cloud, just as an example, you're going to see use of analytics, AI or business outcomes explode. You're also going to see a huge sort of focus on things like governance. People need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all of the privacy and compliance regulations out there essentially their compliance posture. So I think the unlocking of outcomes versus simply, Hey, I've saved money. Second, really putting this comprehensive sort of governance regime in place and then finally security and trust. It's going to be more paramount than ever before. >> Yeah, nobody's going to use the data if they don't trust it, I'm glad you brought up security. It's a topic that is at number one on the CIO list. JG, great conversation. Obviously the strategy is working and thanks so much for participating in Cube on Cloud. >> Thank you, thank you, Dave and I appreciate it and thank you to everybody who's tuning into today. >> All right then keep it right there, I'll be back with our next guest right after this short break.

>> Live from San Jose, in the heart of Silicone Valley, it's theCUBE. Covering Datawork Summit 2018. Brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of Dataworks Summit here in San Jose, California. I'm your host, Rebecca Knight, along with my cohost James Kobielus. We're joined by Scott Gnau, he is the chief technology officer at Hortonworks. Welcome back to theCUBE, Scott. >> Great to be here. >> It's always fun to have you on the show. So, you have really spent your entire career in the data industry. I want to start off at 10,000 feet, and just have you talk about where we are now, in terms of customer attitudes, in terms of the industry, in terms of where customers feel, how they're dealing with their data and how they're thinking about their approach in their business strategy. >> Well I have to say, 30 plus years ago starting in the data field, it wasn't as exciting as it is today. Of course, I always found it very exciting. >> Exciting means nerve-wracking. Keep going. >> Or nerve-wracking. But you know, we've been predicting it. I remember even you know, 10, 15 years ago before big data was a thing, it's like oh all this data's going to come, and it's going to be you know 10x what it is. And we were wrong. It was like 5000x, you know what it is. And I think the really exciting part is that data really used to be relegated frankly, to big companies as a derivative work of ERP systems, and so on and so forth. And while that's very interesting, and certainly enabled a whole level of productivity for industry, when you compare that to all of the data flying around everywhere today, whether it be Twitter feeds and even doing live polls, like we did in the opening session today. Data is just being created everywhere. And the same thing applies to that data that applied to the ERP data of old. And that is being able to harness, manage and understand that data is a new business creating opportunity. And you know, we were with some analysts the other day, and I think one of the more quoted things that came out of that when I was speaking with them, was really, like railroads and shipping in the 1800s and oil in the 1900s, data really is the wealth creator of this century. And so that creates a very nerve-wracking environment. It also creates an environment, a very agile and very important technological breakthroughs that enable those things to be turned into wealth. >> So thinking about that, in terms of where we are at this point in time and on the main stage this morning someone had likened it to the interstate highway system, that really revolutionized transportation, but also commerce. >> I love that actually. I may steal it in some of my future presentations. >> That's good but we'll know where you pilfered it. >> Well perhaps if data is oil the edge, in containerized applications and piping data, you know, microbursts of data across the internet of things, is sort of like the new fracking. You know, you're being able to extract more of this precious resource from the territory. >> Hopefully not quite as damaging to the environment. >> Maybe not. I'm sorry for environmentalist if I just offended you, I apologize. >> But I think you know, all of those analogies are very true, and I particularly like the interstate one this morning. Because when I think about what we've done in our core http platform, and I know Arun was here talking about all the great advances that we built into this, the kind of the core hadoop platform. Very traditional. Store data, analyze data but also bring in new kinds of algorithms, rapid innovation and so on. That's really great but that's kind of half of the story. In a device connected world, in a consumer centric world, capturing data at the edge, moving and processing data at the edge is the new normal, right? And so just like the interstate highway system actually created new ways of commerce because we could move people and things more efficiently, moving data and processing data more efficiently is kind of the second part of the opportunity that we have in this new deluge of data. And that's really where we've been with our Hortonworks data flow. And really saying that the complete package of managing data from origination at the edge all the way through analytic to decision that's triggered back at the edge is like the holy grail, right? And building a technology for that footprint, is why I'm certainly excited today. It's not the caffeine, it's just the opportunity of making all of that work. >> You know, one of the, I think the key announcement for me at this show, that you guys made on HDP 3.0 was containerization of more of the capabilities of your distributed environment so that these capabilities, in terms of processing. First of all, capturing and analyzing an moving that data, can be pushed closer to the end points. Can you speak a bit Scott, about this new capability or this containerization support? Within HDP 3.0 but really in your broader portfolio and where you're going with that in terms of addressing edge applications perhaps, autonomous vehicles or you know, whatever you might put into a new smart phone or whatever you put at the edge. Describe the potential containerizations to sort of break this ecosystem wide open. >> Yeah, I think there are a couple of aspects to containerization and by the way, we're like so excited about kind of the cloud first, containerized HDP 3.0 that we launched here today. There's a lot of great tech that our customers have been clamoring for that they can take advantage of. And it's really just the beginning, which again is part of the excitement of being in the technology space and certainly being part of Hortonworks. So containerization affords a couple of things. Certainly, agility. Agility in deploying applications. So, you know for 30 years we've built these enterprise software stacks that were very integrated, hugely complicated systems that could bring together multiple different applications, different workloads and manage all that in a multi-tendency kind of environment. And that was because we had to do that, right? Servers were getting bigger, they were more powerful but not particularly well distributed. Obviously in a containerized world, you now turn that whole paradigm on its head and you say, you know what? I'm just going to collect these three microservices that I need to do this job. I can isolate them. I can have them run in a server-less technology. I can actually allocate in the cloud servers to go run, and when they're done they go away. And I don't pay for them anymore. So thinking about kind of that from a software development deployment implementation perspective, there huge implications but the real value for customers is agility, right? I don't have to wait until next year to upgrade my enterprise software stack to take advantage of this new algorithm. I can simply isolate it inside of a container, have it run, and have it go away. And get the answer, right? And so when I think about, and a number of our keynotes this morning were talking about just kind of the exponential rate of change, this is really the net new norm. Because the only way we can do things faster, is in fact to be able to provide this. >> And it's not just microservices. Also orchestrating them through Kubernetes, and so forth, so they can be. >> Sure. That's the how versus yeah. >> Quickly deployed as an ensemble and then quickly de-provisioned when you don't need them anymore. >> Yeah so then there's obviously the cost aspect, right? >> Yeah. >> So if you're going to run a whole bunch of stuff or even if you have something as mundane as a really big merge join inside of hive. Let me spin up a thousand extra containers to go do that big thing, and then have them go away when it's done. >> And oh, by the way, you'll be deployed on. >> And only pay for it while I'm using it. >> And then you can possibly distribute those containers across different public clouds depending on what's most cost effective at any point in time Azure or AWS or whatever it might be. >> And I tease with Arun, you know the only thing that we haven't solved is for the speed of light, but we're working on it. >> In talking about how this warp speed change, being the new norm, can you talk about some of the most exciting use cases you've seen in terms of the customers and clients that are using Hortonworks in the coolest ways. >> Well I mean obviously autonomous vehicles is one that we all captured all of our imagination. 'Cause we understand how that works. But it's a perfect use case for this kind of technology. But the technology also applies in fraud detection and prevention. It applies in healthcare management, in proactive personalized medicine delivery, and in generating better outcomes for treatment. So, you know, all across. >> It will bind us in every aspect of our lives including the consumer realm increasingly, yeah. >> Yeah, all across the board. And you know one of the things that really changed, right, is well a couple things. A lot of bandwidth so you can start to connect these things. The devices themselves are particularly smart, so you don't any longer have to transfer all the data to a mainframe and then wait three weeks, sorry, wait three weeks for your answer and then come back. You can have analytic models running on and edge device. And think about, you know, that is really real time. And that actually kind of solves for the speed of light. 'Cause you're not waiting for those things to go back and forth. So there are a lot of new opportunities and those architectures really depend on some of the core tenets of ultimately containerization stateless application deployment and delivery. And they also depend on the ability to create feedback loops to do point-to-point and peer kinds of communication between devices. This is a whole new world of how data get moved and how the decisions around date movement get made. And certainly that's what we're excited about, building with the core components. The other implication of all of this, and we've know each other for a long time. Data has gravity. Data movements expensive. It takes time, frankly, you have to pay for the bandwidth and all that kind of stuff. So being able to play the data where it lies becomes a lot more interesting from an application portability perspective and with all of these new sensors, devices and applications out there, a lot more data is living its entire lifecycle in the cloud. And so being able to create that connective tissue. >> Or as being as terralexical on the edge. >> And even on the edge. >> In with machine learn, let me just say, butt in a second. One of the areas that we're focusing on increasingly in Wikibot in terms of our focus on machine learning at the edge, is more and more machine learning frameworks are coming into the browser world. Javascript for the most like tenser flow JS, you know more of this inferencing and training is going to happen inside your browser. That blows a lot of people's minds. It may not be heavy hitting machine learning, but it'll be good enough for a lot of things that people do in their normal life. Where you don't want to round trip back to the cloud. It's all happening right there, in you know, Chrome or whatever you happen to be using. >> Yeah and so the point being now, you know when I think about the early days, talking about scalability, I remember ship being my first one terabyte database. And then the first 10 terabyte database. Yeah, it doesn't sound very exciting. When I think about scalability of the future, it's really going to, scalability is not going to be defined as petabytes or exabytes under management. It's really going to be defined as petabytes or exabytes affected across a grid of storage and processing devices. And that's a whole new technology paradigm, and really that's kind of the driving force behind what we've been building and what we've been talking about at this conference. >> Excellent. >> So when you're talking about these things. I mean how much, are the companies themselves prepared, and do they have the right kind of talent to use the kinds of insights that you're able to extract? And then act on them in the real time. 'Cause you're talking about how this is saving a lot of the waiting around time. So is this really changing the way business gets done, and do companies have the talent to execute? >> Sure. I mean it's changing the way business gets done. We showed a quote on stage this morning from the CEO of Marriott, right? So, I think there a couple of pieces. One is business are increasingly data driven and business strategy is increasingly the data strategy. And so it starts from the top, kind of setting that strategy and understanding the value of that asset and how that needs to be leveraged to drive new business. So that's kind of one piece. And you know, obviously there are more and more folks kind of coming to the realization that that is important. The other thing that's been helpful is, you know, as with any new technology there's always kind of the startup shortage of resource and people start to spool up and learn. You know the really good news, and for the past 10 years I've been working with a number of different university groups. Parents are actually going to universities and demanding that the curriculum include data, and processing and big data and all of these technologies. Because they know that their children educated in that kind of a world, number one, they're going to have a fun job to go to everyday. 'Cause it's going to be something different everyday. But number two they're going to be employed for life. (laughing) >> Yeah. >> They will be solvent. >> Frankly the demand has actually created a catch up in supply that we're seeing. And of course, you know, as tools start to get more mature and more integrated, they also become a little bit easier to use. You know, less, there's a little bit easier deployment and so on. So a combination of, I'm seeing a really good supply, there really, obviously we invest in education through the community. And then frankly, the education system itself, and folks saying this is really the hot job of the next century. You know, I can be the new oil barren. Or I can be the new railroad captain. It's actually creating more supply which is also very helpful. >> Data's the heart of what I call the new stem cell. It's science, technology, engineering, mathematics that you want to implant in the brains of the young as soon as possible. I hear ya. >> Yeah, absolutely. >> Well Scott thanks so much for coming on. But I want to first also, we can't let you go without the fashion statement. You arrived on set wearing it. >> The elephants. >> I mean it was quite a look. >> Well I did it because then you couldn't see I was sweating on my brow. >> Oh please, no, no, no. >> 'Cause I was worried about this tough interview. >> You know one of the things I love about your logo, and I'll just you know, sounds like I'm fawning. The elephant is a very intelligent animal. >> It is indeed. >> My wife's from Indonesia. I remember going back one time they had Asian elephants at a one of these safari parks. And watching it perform, and then my son was very little then. The elephant is a very sensitive, intelligent animal. You don't realize 'till you're up close. They pick up all manner of social cues. I think it's an awesome symbol for a company that's all about data driven intelligence. >> The elephant never forgets. >> Yeah. >> That's what we know. >> That's right we never forget. >> Him forget 'cause he's got a brain. Or she, I'm sorry. He or she has a brain. >> And it's data driven. >> Yeah. >> Thanks very much. >> Great. Well thanks for coming on theCUBE. I'm Rebecca Knight for James Kobielus. We will have more coming up from Dataworks just after this. (upbeat music)