(upbeat electronic music plays) >> Hey everyone, welcome back to theCube's coverage of .NEXT 2021 Virtual. I'm John Furrier, hosts of theCube. We have two great guests, Rajiv Mirani, who's the Chief Technology Officer, and Thomas Cornely, SVP of Product Management. Day Two keynote product, the platform, announcements, news. A lot of people, Rajiv, are super excited about the, the platform, uh, moving to a subscription model. Everything's kind of coming into place. How are the customers, uh, seeing this? How they adopted hybrid cloud as a hybrid, hybrid, hybrid, data, data, data? Those are the, those are the, that's the, that's where the puck is right now. You guys are there. How are customers seeing this? >> Mirani: Um, um, great question, John, by the way, great to be back here on theCube again this year. So when we talk to our customers, pretty much, all of them agreed that for them, the ideal state that they want to be in is a hybrid world, right? That they want to essentially be able to run both of those, both on the private data center and the public cloud, and sort of have a common platform, common experience, common, uh, skillset, same people managing, managing workloads across both locations. And unfortunately, most of them don't have that that tooling available today to do so, right. And that's where the platform, the Nutanix platform's come a long way. We've always been great at running in the data center, running every single workload, we continue to make great strides on our core with the increased performance for, for the most demanding, uh, workloads out there. But what we have done in the last couple of years has also extended this platform to run in the public cloud and essentially provide the same capabilities, the same operational behavior across locations. And that's when you're seeing a lot of excitement from our customers because they really want to be in that state, for it to have the common tooling across work locations, as you can imagine, we're getting traction. Customers who want to move workloads to public cloud, they don't want to spend the effort to refactor them. Or for customers who really want to operate in a hybrid mode with things like disaster recovery, cloud bursting, workloads like that. So, you know, I think we've made a great step in that direction. And we look forward to doing more with our customers. >> Furrier: What is the big challenge that you're seeing with this hybrid transition from your customers and how are you solving that specifically? >> Mirani: Yeah. If you look at how public and private operate today, they're very different in the kind of technologies used. And most customers today will have two separate teams, like one for their on-prem workloads, using a certain set of tooling, a second completely different team, managing a completely different set of workloads, but with different technologies. And that's not an ideal state in some senses, that's not true hybrid, right? It's like creating two new silos, if anything. And our vision is that you get to a point where both of these operate in the same manner, you've got the same people managing all of them, the same workloads anyway, but similar performance, similar SaaS. So they're going to literally get to point where applications and data can move back and forth. And that's, that's, that's where I think the real future is for hybrid >> Furrier: I have to ask you a personal question. As the CTO, you've got be excited with the architecture that's evolving with hybrid and multi-cloud, I mean, I mean, it's pretty, pretty exciting from a tech standpoint, what is your reaction to that? >> Mirani: %100 and it's been a long time coming, right? We have been building pieces of this over years. And if you look at all the product announcements, Nutanix has made over the last few years and the acquisitions that made them and so on, there's been a purpose behind them. That's been a purpose to get to this model where we can operate a customer's workloads in a hybrid environment. So really, really happy to see all of that come together. Years and years of work finally finally bearing fruit. >> Furrier: Well, we've had many conversations in the past, but it congratulates a lot more to do with so much more action happening. Thomas, you get the keys to the kingdom, okay, and the product management you've got to prioritize, you've got to put it together. What are the key components of this Nutanix cloud platform? The hybrid cloud, multi-cloud strategy that's in place, because there's a lot of headroom there, but take us through the key components today and then how that translates into hybrid multi-cloud for the future. >> Cornely: Certainly, John, thank you again and great to be here, and kind of, Rajiv, you said really nicely here. If you look at our portfolio at Nutanix, what we have is great technologies. They've been sold as a lot of different products in the past, right. And what we've done last few months is we kind of bring things together, simplify and streamline, and we align everything around a cloud platform, right? And this is really the messaging that we're going after is look, it's not about the price of our solutions, but business outcomes for customers. And so are we focusing on pushing the cloud platform, which we encompasses five key areas for us, which we refer to as cloud infrastructure, no deficiencies running your workloads. Cloud management, which is how you're going to go and actually manage, operate, automate, and get governance. And then services on top that started on all around data, right? So we have unified storage, finding the objects, data services. We have database services. Now we have outset of desktop services, which is for EMC. So all of this, the big change for us is this is something that, you know, you can consume in terms of solutions and consume on premises. As Rajiv discussed, you know, we can take the same platform and deploy it in public cloud regions now, right? So you can now get no seamless hybrid cloud, same operating model. But increasingly what we're doing is taking your solutions and re-targeting issues and problems at workers running native public clouds. So think of this as going, after automating more governance, security, you know, finding objects, database services, wherever you're workload is running. So this is taking this portfolio and reapplying it, and targeting on prem at the edge in hybrid and in christening public cloud in ATV. >> Furrier: That's awesome. I've been watching some of the footage and I was noticing quite a lot of innovation around virtualized, networking, disaster, recovery security, and data services. It's all good. You guys were, and this is in your wheelhouse. I know you guys are doing this for many, many years. I want to dive deeper into that because the theme right now that we've been reporting on, you guys are hitting right here what the keynote is cloud scale is about faster development, right? Cloud native is about speed, it's about not waiting for these old departments, IT or security to get back to them in days or weeks and responding to either policy or some changes, you got to move faster. And data, data is critical in all of this. So we'll start with virtualized networking because networking again is a key part of it. The developers want to go faster. They're shifting left, take us through the virtualization piece of how important that is. >> Mirani: Yeah, that's actually a great question as well. So if you think about it, virtual networking is the first step towards building a real cloud like infrastructure on premises that extends out to include networking as well. So one of the key components of any cloud is automation. Another key component is self service and with the API, is it bigger on virtual networking All of that becomes much simpler, much more possible than having to, you know, qualify it, work with someone there to reconfigure physical networks and slots. We can, we can do that in a self service way, much more automated way. But beyond that, the, the, the notion of watching networks is really powerful because it helps us to now essentially extend networks and, and replicate networks anywhere on the private data center, but in the public cloud as well. So now when customers move their workloads, we'd already made that very simple with our clusters offering. But if you're only peek behind the layers a little bit, it's like, well, yea, but the network's not the same on the side. So now it, now it means that a go re IP, my workloads create new subnets and all of that. So there was a little bit of complication left in that process. So to actual network that goes away also. So essentially you can repeat the same network in both locations. You can literally move your workloads, no redesign of your network acquired and still get that self service and automation capabilities of which cookies so great step forward, it really helps us complete the infrastructure as a service stack. We had great storage capabilities before, we create compute capabilities before, and sort of networking the third leg and all of that. >> Furrier: Talk about the complexity here, because I think a lot of people will look at dev ops movement and say, infrastructure is code when you go to one cloud, it's okay. You can, you can, you know, make things easier. Programmable. When, when you start getting into data center, private data centers, or essentially edges now, cause if it's distributed cloud environment or cloud operations, it's essentially one big cloud operation. So the networks are different. As you said, this is a big deal. Okay. This is sort of make infrastructure as code happen in multiple environments across multiple clouds is not trivial. Could you talk about the main trends and how you guys see this evolving and how you solve that? >> Mirani: Yeah. Well, the beauty here is that we are actually creating the same environment everywhere, right? From, from, from point of view of networking, compute, and storage, but also things like security. So when you move workloads, things with security, posture also moves, which is also super important. It's a really hard problem, and something a lot of CIO's struggle with, but having the same security posture in public and private clouds reporting as well. So with this, with this clusters offering and our on-prem offering competing with the infrastructure service stack, you may not have this capability where your operations really are unified across multicloud hybrid cloud in any way you run. >> Furrier: Okay, so if I have multiple cloud vendors, there are different vendors. You guys are creating a connection unifying those three. Is that right? >> Mirani: Essentially, yes, so we're running the same stack on all of them and abstracting away the differences between the clouds that you can run operations. >> Furrier: And when the benefits, the benefits of the customers are what? What's the main, what's the main benefit there? >> Mirani: Essentially. They don't have to worry about, about where their workloads are running. Then they can pick the best cloud for their workloads. It can seamlessly move them between Cloud. They can move their data over easily, and essentially stop worrying about getting locked into a single, into a single cloud either in a multi-cloud scenario or in a hybrid cloud scenario, right. There many, many companies now were started on a cloud first mandate, but over time realized that they want to move workloads back to on-prem or the other way around. They have traditional workloads that they started on prem and want to move them to public cloud now. And we make that really simple. >> Furrier: Yeah. It's kind of a trick question. I wanted to tee that up for Thomas, because I love that kind of that horizontal scales, what the cloud's all about, but when you factor data into it, this is the sweet spot, because this is where, you know, I think it gets really exciting and complicated too, because, you know, data's got, can get unwieldy pretty quickly. You got state got multiple applications, Thomas, what's your, what can you share the data aspect of this? This is super, super important. >> Absolutely. It's, you know, it's really our core source of differentiation, when you think about it. That's what makes Nutanix special right? In, in the market. When we talk about cloud, right. Actually, if you've been following Nutanix for years, you know, we've been talking a lot about making infrastructure invisible, right? The new way for us to talk about what we're doing, with our vision is, is to make clouds invisible so that in the end, you can focus on your own business, right? So how do you make Cloud invisible? Lots of technology is at the application layer to go and containerize applications, you know, make them portable, modernize them, make them cloud native. That's all fine when you're not talking of state class containers, that the simplest thing to move around. Right. But as we all know, you know, applications end of the day, rely on data and measure the data across all of these different locations. I'm not even going to go seconds. Cause that's almost a given, you're talking about attribution. You can go straight from edge to on-prem to hybrid, to different public cloud regions. You know, how do you go into the key control of that and get consistency of all of this, right? So that's part of it is being aware of where your data is, right? But the other part is that inconsistency of set up data services regardless of where you're running. And so this is something that we look at the cloud platform, where we provide you the cloud infrastructure go and run the applications. But we also built into the cloud platform. You get all of your core data services, whether you have to consume file services, object services, or database services to really support your application. And that will move with your application, that is the key thing here by bringing everything onto the same platform. You now can see all operations, regardless of where you're running the application. The last thing that we're adding, and this is a new offering that we're just launching, which is a service, it's called, delete the dead ends. Which is a solution that gives you visibility and allow you to go and get better governance around all your data, wherever it may live, across on-prem edge and public clouds. That's a big deal again, because to manage it, you first have to make sense of it and get control over it. And that's what data answer's is going to be all about. >> Furrier: You know, one of the things we've we've been reporting on is data is now a competitive advantage, especially when you have workflows involved, um, super important. Um, how do you see customers going to the edge? Because if you have this environment, how does the data equation, Thomas, go to the edge? How do you see that evolving? >> Cornely: So it's yeah. I mean, edge is not one thing. And that's actually the biggest part of the challenge of defining what the edge is depending on the customer that you're working with. But in many cases you get data ingesting or being treated at the edge that you then have to go move to either your private cloud or your public cloud environment to go and basically aggregate it, analyze it and get insights from it. Right? So this is where a lot of our technologies, whether it's, I think the object's offering built in, we'll ask you to go and make the ingest over great distances over the network, right? And then have your common data to actually do an ethics audit over our own object store. Right? Again, announcements, we brought into our storage solutions here, we want to then actually organize it then actually organize it directly onto the objects store solution. Nope. Using things, things like or SG select built into our protocols. So again, make it easy for you to go in ingest anywhere, consolidate your data, and then get value out of it. Using some of the latest announcements on the API forms. >> Furrier: Rajiv databases are still the heart of most applications in the enterprise these days, but databases are not just the data is a lot of different data. Moving around. You have a lot a new data engineering platforms coming in. A lot of customers are scratching their head and, and they want to kind of be, be ready and be ready today. Talk about your view of the database services space and what you guys are doing to help enterprise, operate, manage their databases. >> Mirani: Yeah, it's a super important area, right? I mean, databases are probably the most important workload customers run on premises and pretty close on the public cloud as well. And if you look at it recently, the tooling that's available on premises, fairly traditional, but the clouds, when we integrate innovation, we're going to be looking at things like Amazon's relational database service makes it an order of magnitude simpler for our customers to manage the database. At the same time, also a proliferation of databases and we have the traditional Oracle and SQL server. But if you have open source Mongo, DB, and my SQL, and a lot of post-grads, it's a lot of different kinds of databases that people have to manage. And now it just becomes this cable. I have the spoke tooling for each one of them. So with our Arab product, what we're doing is essentially creating a data management layer, a database management layer that unifies operations across your databases and across locations, public cloud and private clouds. So all the operations that you need, you do, which are very complicated in, in, in, in with traditional tooling now, provisioning of databases backing up and restoring them providing a true time machine capabilities, so you can pull back transactions. We can copy data management for your data first. All of that has been tested in Era for a wide variety of database engines, your choice of database engine at the back end. And so the new capabilities are adding sort of extend that lead that we have in that space. Right? So, so one of the things we announced at .Next is, is, is, is one-click storage scaling. So one of the common problems with databases is as they grow over time, it's not running out of storage capacity. Now re-provisions to storage for a database, migrate all the data where it's weeks and months of look, right? Well, guess what? With Era, you can do that in one click, it uses the underlying AOS scale-out architecture to provision more storage and it does it have zero downtime. So on the fly, you can resize your databases that speed, you're adding some security capabilities. You're adding some capabilities around resilience. Era continues to be a very exciting product for us. And one of the things, one of the real things that we are really excited about is that it can really unify database operations between private and public. So in the future, we can also offer an aversion of Era, which operates on native public cloud instances and really excited about that. >> Furrier: Yeah. And you guys got that two X performance on scaling up databases and analytics. Now the big part point there, since you brought up security, I got to ask you, how are you guys talking about security? Obviously it's embedded in from the beginning. I know you guys continue to talk about that, but talk about, Rajiv, the security on, on that's on everyone's mind. Okay. It goes evolving. You seeing ransomware are continuing to happen more and more and more, and that's just the tip of the iceberg. What do you guys, how are you guys helping customers stay secure? >> Mirani: Security is something that you always have to think about as a defense in depth when it comes to security, right? There's no one product that, that's going to do everything for you. That said, what we are trying to do is to essentially go with the gamut of detection, prevention, and response with our security, and ransom ware is a great example of that, right. We've partnered with Qualys to essentially be able to do a risk assessment of your workloads, to basically be able to look into your workloads, see whether they have been bashed, whether they have any known vulnerabilities and so on. To try and prevent malware from infecting your workloads in the first place, right? So that's, that's the first line of defense. Now not systems will be perfect. Some, some, some, some malware will probably get in anyway But then you detect it, right. We have a database of all the 4,000 ransomware signatures that you can use to prevent ransomware from, uh, detecting ransom ware if it does infect the system. And if that happens, we can prevent it from doing any damage by putting your fire systems and storage into read-only mode, right. We can also prevent lateral spread of, of your ransomware through micro-segmentation. And finally, if you were, if you were to invade, all those defenses that you were actually able to encrypt data on, on, on a filer, we have immutable snapshots, they can recover from those kinds of attacks. So it's really a defense in depth approach. And in keeping with that, you know, we also have a rich ecosystem of partners while this is one of them, but older networks market sector that we work with closely to make sure that our customers have the best tooling around and the simplest way to manage security of their infrastructure. >> Furrier: Well, I got to say, I'm very impressed guys, by the announcements from the team I've been, we've been following Nutanix in the beginning, as you know, and now it's back in the next phase of the inflection point. I mean, looking at my notebook here from the announcements, the VPC virtual networking, DR Observability, zero trust security, workload governance, performance expanded availability, and AWS elastic DR. Okay, we'll get to that in a second, clusters on Azure preview cloud native ecosystem, cloud control plane. I mean, besides all the buzzword bingo, that's going on there, this is cloud, this is a cloud native story. This is distributed computing. This is virtualization, containers, cloud native, kind of all coming together around data. >> Cornely: What you see here is, I mean, it is clear that it is about modern applications, right? And this is about shifting strategy in terms of focusing on the pieces where we're going to be great at. And a lot of these are around data, giving you data services, data governance, not having giving you an invisible platform that can be running in any cloud. And then partnering, right. And this is just recognizing what's going on in the world, right? People want options, customers and options. When it comes to cloud, they want options to where they're running the reports, what options in terms of, whether it be using to build the modern applications. Right? So our big thing here is providing and being the best platform to go and actually support for Devers to come in and build and run their new and modern applications. That means that for us supporting a broad ecosystem of partners, entrepreneur platform, you know, we announced our partnership with Red Hat a couple of months ago, right? And this is going to be a big deal for us because again, we're bringing two leaders in the industry that are eminently complimentary when it comes to providing you a complete stack to go and build, run, and manage your client's applications. When you do that on premises, utilizing like the preferred ATI environment to do that. Using the Red Hat Open Shift, or, you're doing this open to public cloud and again, making it seamless and easy, to move the applications and their supporting data services around, around them that support them, whether they're running on prem in hybrid winter mechanic. So client activity is a big deal, but when it comes to client activity, the way we look at this, it's all about giving customers choice, choice of that from services and choice of infrastructure service. >> Furrier: Yeah. Let's talk to the red hat folks, Rajiv, it's you know, it's, they're an operating system thinking company. You know, you look at the internet now in the cloud and edge, and on-premise, it's essentially an operating system. you need your backup and recovery needs to disaster recovery. You need to have the HCI, you need to have all of these elements part of the system. It's, it's, it's, it's building on top of the existing Nutanix legacy, then the roots and the ecosystem with new stuff. >> Mirani: Right? I mean, it's, in fact, the Red Hat part is a great example of, you know, the perfect marriage, if you will, right? It's, it's, it's the best in class platform for running the cloud-native workloads and the best in class platform with a service offering in there. So two really great companies coming together. So, so really happy that we could get that done. You know, the, the point here is that cloud native applications still need infrastructure to run off, right? And then that infrastructure, if anything, the demands on that and growing it since it's no longer that hail of, I have some box storage, I have some filers and, you know, just don't excite them, set. People are using things like object stores, they're using databases increasingly. They're using the Kafka and Map Reduce and all kinds of data stores out there. And back haul must be great at supporting all of that. And that's where, as Thomas said, earlier, data services, data storage, those are our strengths. So that's certainly a building from platform to platform. And then from there onwards platform services, great to have right out of the pocket. >> Furrier: People still forget this, you know, still hardware and software working together behind the scenes. The old joke we have here on the cube is server less is running on a bunch of servers. So, you know, this is the way that is going. It's really the innovation. This is the infrastructure as code truly. This is what's what's happened is super exciting. Rajiv, Thomas, thank you guys for coming on. Always great to talk to you guys. Congratulations on an amazing platform. You guys are developing. Looks really strong. People are giving it rave reviews and congratulations on, on, on your keynotes. >> Cornely: Thank you for having us >> Okay. This is theCube's coverage of.next global virtual 2021 cube coverage day two keynote review. I'm John Furrier Furrier with the cube. Thanks for watching.

(techy music) >> Hi, everybody, welcome to this special Cube conversation, my name is Dave Vellante. I'm very excited to be here in our Palo Alto studios in the heart of Silicon Valley, the innovation hub of technology. In 2015 we introduced a company to your community called Datrium and one of the co-founders, Brian Biles at the time, came on as one of our segments and shared with us a little bit about what they were doing. Well, several years on, three years on, this company Datrium is exploding and we're really excited to have Brian Biles back, who's the co-founder and chief product officer at Datrium and he's joined by Sazzala Reddy, who's the CTO and another co-founder. One of the, two of the five co-founders here, so gentlemen, great to see you again, thanks for coming on. >> Good to see you, Dave. >> Yeah, so Brian, I remember that interview and I remember, you know, trying to get out of you what that secret sauce was, exactly what you were doing. There were a lot of other start ups, you know, at that time and several have gone by the wayside. You guys are exploding, so I want to help people understand why you're being so successful. Now, I want to start with the two co-founders. Why did you and your other co-founders start the company? >> You know, we started the company... We hired our first people in 2013, and at that time, there were really two separate worlds. There was a cloud world and there was an on-prem world that was sort of dominated by VMware. So, there were these two evolving discussions about how each one was going to grow in it's own way but kind of within its sphere. We thought there was an opportunity to bridge the two, and to do that, you know, ultimately it becomes a questions of how to run sort of coordinating applications on public clouds and deal with data on public clouds but also to have a capable version of the same infrastructure on private clouds. So, our sort of job one was to build up, to sort of import cloud technology onto prem. We currently have, if you want an Amazon-like version of infrastructure on-prem, we're still the best place to go because we have a two layer model, where there's, you know, compute with fast flash, talking to a separate durability layer very much like EC to an S3. You want to do that, we're still the way to go. But the long term story is also developing. We have a footprint on cloud with a backup store on S3 that coordinates all our data services for global deduping security and so on in a very cost effective, simple SAS way, and that part is growing significantly over the next couple of years. So we're, you know, through with sort of phase one. It'll keep, you know, evolving but phase two is really just getting going. >> So Sazzala, as the chief technologist you had to think about the architecture of where the industry was going and the architecture that would fit that. And you know, that people talk about future proofing so if you think back to the original sort of founding premise, what were some of the challenges that you were trying to solve? >> Right, so there's a business use cases and then there's technology use cases. And as a CTO you have to think of both of them, not just technologies, so if you look at technology point of view, you know, in 2000, back in 2000, Google published a paper called Map Reduce that said hey, this is all we can do at large scale. It was the beginning of how to build large scale distributed systems. But it was built for one use case for surge. But if you look at, we started in a time when Google was already there and they built a system for multiple, unpredictable use cases. So you think differently how the problem is whereas Google start from, though. Some of the CI vendors, they've done good things. They kind of evolved in that direction. We have evolved in a new direction. To the technology point of view, that's kind of what we thought about. But from a business perspective, what do people want? You know, if you look at the next generation, the millennials, and look beyond that they're used to iPhone experience. They don't want, if you tell them about LUNs, they don't re-phone LUNs, they're going to just say what is this and why do you have this stuff, right? So you have to evolve away from that. So, it's the CIA wants to think about how do I make my idea the service? How do I consume, you know, how do I make it a consumption model, how do I make my IT not a cost center but a friendly way to you know, grow my business? And the developers want a platform they can develop things faster, they can adapt to newer, kind of newer technologies coming in, there's Mesos, there's Docker container, there's Kubernetes, thus things change rapidly. So that's going to build a framework in how we wanted to start the company. Basically build a cloud-like experience simple as a SAS, simple as a click and then just make that work. >> The thing that's interesting to me about Datrium is you know, the simplicity, like open. You know, I remember when Unix was considered open and then obviously the definition changes, simplicity has changed. I remember when converged infrastructure, bolting together, compute storage and networking, simplified things. Hyperconverge took that to another level. You guys are going beyond that taking it to yet another level of simplicity, so I wonder if you could talk about that-- >> Yeah, so-- Specifically in terms of the problems that you're solving today for customers. >> So if you look at the V block, I guess the VCE was the first, I guess that they made a successful convergence. So they did hardware convergence-- >> Right. which is a useful thing to do. Same thing with your head CI, the traditional vendors, they do hardware convergence, if you look at Hecht, probably stands for hardware convergence, maybe. But we took a little bigger step in the sense that what you really want us to think about is data convergence. Not, hyperconvergence is useful, but you also think operating about data convergence. What's the point of building your on-prem cloud- like experience when you still have to do backups and some other you know, some other boxes you are to buy. That's not a really good experience, but you need is this whole new hardware convergence, we also need data convergence to get that experience of like cloud-like simplicity in your on-prem. >> Right, in the cloud you don't think of backing it up, right, it's self protecting. That's just the nature of how you should be thinking about on-prem as well. So, when we imported that technology to be a two-layer approach, we built that stuff in so you don't have to think about it. It's kind of like there's no SQL or we're sort of like no backup. >> Yeah, we're going to talk some more about that but that's an important point is you get backup and data protection, you know, full capability, it's just there. I always use the example of Netflix or Spotify. I don't have to call up a salesperson or the billing department or the customer service department, it's just there and I deal with it. >> Right and it gives you, you know, this combination of, in the two layers, the ability to run multiple workloads at big scale, which is otherwise hard in some of these more historical approaches, with great performance that you know is off the charts. But it also means you don't have to move data around as much. So you restore, you restart, you don't restore. You don't copy stuff in and out. >> Yeah. >> That data mobility efficiency it turns out, is also super critical when you think about multi-cloud behavior. >> You have to be in the business to actually feel like you talk to backup admins and life is hell. It is really painful and it's also very fearful if you have a problem, you have to restore and everybody's watching you when you're restoring. So we try to eliminate all those problems, right? Make it, just, why worry about all these things? We are living in a new world, let's adapt to it. >> I think I've, tongue-in-cheek I think about the show Silicon Valley and you guys didn't start out to build a box. >> No. >> No. >> You settled this off some problems and so what you have is a set of best of breed storage services that are running the cloud, called multi-cloud, meaning on-prem or in the cloud so I want to try to juxtapose that to sort of the traditional storage model or even some of these emerging storage models of some of the very successful companies. So, how do you guys differentiate, help us understand what's different about Datrium from the classical storage model and even some of these emerging storage models? >> I'll kick it off and Sazzala can expand on it. You know, first we're bringing a cloud experience to on-prem, so it's not a storage system that you, like a SAN. We, you know, offer compute as well and a way to make that whole operation simple around you know, standard and emerging standard coordination frameworks like VMware and Red Hat and Docker. It includes these really powerful data services to make life simple so you don't have to add on a lot of different control panes and spots of data storage and so on. By getting that right, it makes multi-cloud coordination a lot easier because the hardest problem getting started in that, aside from, you know, just doing SAS applications to run it and so on, is getting data back and forth. Making it efficient and cost effective to move it. So, you want to expand? >> Yeah, so you know, I think you give examples of like maybe there are some successful companies in the market today. There is old school array market and there's the new school head CI markets. So, the old school array market, I mean, if some people are still comfortable with that model, I think they just because the flash array market has some performance characteristics but still it's, again, going back to that rotary phone landing, it doesn't map your, the lands don't map your business. It's just a very old school way of thinking about it. Those will probably vanish at some point because it makes no sense to have them around. And yes, they do provide higher performance but they're still, you know, it's still not providing you that level of ideal service. From a developer point of view, I can make my application life easier, I can do things like test and dev. Test and dev, simple thing like test and dev requires you to clone your application so they can run test and dev on them. It's a very powerful use case, it's a very common use case for most companies, including ours. So, you can't do any of that stuff with that old school style of array. And the new school style, they are making progress in terms of making that developer life a little bit more easier but they haven't thought deeply about data services. Like they built a nice packaging and like some UI frameworks but ultimately, data needs to be like stable. They didn't, you think about data in a how do you make it compressed, efficient and cost effective and make it so that it is easy to move data around. And you're think about the backup and DR. Because if we look at application, you've run it, you have to back it up and you have to do archiving for it. You have to think up the entire lifecycle of it. Which is kind of what most people are not doing, thinking of the entire lifecycle. They're solving a small piece of the puzzle but not the entire thing. >> I'll give you another example of that. In you know, to the operator of a private cloud, you're thinking about workloads, you're thinking about relationships between VMs you know, how to get them to the right place, copy them at the right rate, secure them in the right way. In a sort of old style, that kind of thinking about say protection, you might have a catalog in a backup software but you have volumes of VMs in a SAN. Those are completely different mindsets, we've merged them. So we have a completely scalable catalog you know and detailed validation, verification information about every scrap of data on the system that we can test everything four times a day for test restores. All that kind of stuff is organically in a single user interface that's VM focused, so you don't have to think about these different mindsets. >> But it's SAS really for data services. >> For data services, yeah. >> I mean is that a fair way to think about this? >> Yeah, I think so because what's better than one click? Zero clicks, so lot of people are aiming for one click. We are aiming for zero clicks. That's actually a harder problem to do. It's actually hard to actually think about how do I automate everything so they have to do nothing? That's kind of where we have really, really tried hard is that, as little clicks as possible. Aim for zero as much as possible. That's our goal, in the internal company engineers are told you must aim for zero clicks, actually a harder problem. >> Right so, when you think about how to then expand that to managing multiple sort of availability zones across multiple clouds there are additional problems. But starting from these capabilities, starting from great indexing of data, great cataloging of relationships between things, everything's workload specific and great data mobility infrastructure with data reduction and encryption and so on. As we forecast where we can go with that, it's profound. You can start to imagine some context for how to deal with information across clouds and how to both run and protect it in a way that's really just never been in the market. >> So I want to talk about that vision but before we do, before we leave sort of the differences let's take two examples. Two very successful companies, Nutanix and Pure, so how are you different from, let's start with Nutanix, for example. >> I think that there's some good things, I think they're moved the industry forward quite a bit. I think they've brought some new ideas to the market, they made it VM-centric, they said no LUNs. They've made quite some improvements, and then they're a successful company, but ultimately I think their focus tends to be mostly on how to make the UI shiny and how to kind of think about the hypervisor, which is kind of where they're going to. They don't hypervise in the world today, we don't want to go invent another hypervisor. >> Mm-hmm. >> There are so many other options and the world is changing a lot. Like you said, Kubernetes is coming, Mesos is coming, so we want to adapt to those newer ways or style of doing it, and we don't want to invest in making or building a new hypervisor, and we're good partners with VMware, so that's one angle to it. If you look at, you know, how... Because if you're going to go to large enterprises, they want to consolidate the workloads. They want large scale, they want exabyte scale, so you meet customers now who have exabyte scale data, they think they're the cloud. They're not thinking of any other cloud, they think they're the cloud, so how do you make them successful? So, you have to think about exabyte scale systems where basically you can operate it as a cloud internally, so we build those kinds of infrastructures and those kinds of tools to make that exabyte scale successful, and we probably are the fastest system on the planet. Right, so that's kind of where we come from is that we not only say that we scale, we actually prove that we scale. It's not just enough to say we have Google style and the scale, so it's actually you have to prove it, so we actually have tests where we can, we actually have run with other people that it actually works as we say it does. So, I think it's important that you have to speak, you have to not only produce a product which is useful from a UI point of view, that's useful, but also it has to actually work at scale, and we make it more resilient. We have a lot of features built in to make it more resilient and at scale, like what does a tier one mean, what is mission critical apps, how do you make sure that we don't lose data, for example. It runs at the highest performance possible at a price which is reasonable. >> Okay, and I guess the other difference is you're a pure SAS model in that you're responsible for (chuckles) the data services, right, and-- >> Yeah, that's right. >> Yeah, we've pulled a lot more into the data services in our cloud approach. >> Mm-hmm. >> And we've separated from the performance elements, so they're these two layers, so it's both self-protecting in a way that's independently provisioned if you want to expand capacity for backup retention, that's a standard thing. If you want to expand performance or workloads you do that independently on stateless hosts. >> Mm-hmm. >> An example of where this pays off is just the resilience of the system. In a standard hyperconverged model a good case is like what's the crater size when a, or the risk, you know, profile when a single component fails. So, if a motherboard fails in a sort of hyperconverged model that's standard, you know, a single layer thing, then all the data on that system has to be rebuilt. That puts enormous pressure on the network, and you know, some of these systems can have 80, 160 terabytes of data on a single node, that's like a crazy week, and if two of them go down then the whole thing stops. In our model the hosts are stateless, if any number of them go down for any reason the data's still safe, separate-- >> Mm-hmm, right. >> You know, in a hyperconverged model you can't really integrate backup well because when primary goes down back up goes down, too, then what? >> Okay, so that's, I think, clear how you differentiate from hyperconverged. Did you have another-- >> Yeah, I have one more point, it's about the data services you mentioned. We have, again, going back to zero-click, we built all our features into the system. For example, you know, there are a lot things like deduplication, compression, image recording, those are like, I mean, they're not like details, but ultimately they do bring the cost down quite a bit, like by 10 X to five X, right, that's a big difference. >> So, those are services that are inherent. >> That are inherent in the system. >> Yeah, okay. >> Either you can have check boxes, you can say one click and have to like check box, all that. I mean, you have to go and click it, but to click it then you must read a manual, you must do the manual, so then what is this right click and what happens to me, why isn't not on by default. >> Yeah. >> So, those are the problems, I think the differences between them, I think Nutanix and us, is that we kind of made it all, like, be seamless and all built in. >> Yeah, and when we, you know, if you have to, if it's an option that you ask for later that means it probably has some impact on the system that you have to decide about. In our case you can't turn it off, it's always there and we do all our benchmarking with all that stuff turned on, including software-based encryption. It's just a standard thing, and we still are like the fastest thing on the planet. >> Yeah. >> And let's talk about Pure a little bit, because they don't have-- >> Yeah. >> The networking component and then the compute component, it's, you know, flash array, so how would you position relative to Pure? >> Okay, so again, going back to that SAN array was built before the internet, it is just the same. It is just the same, it's just to deport SSDs behind those controllers in central hard drives. It is likely faster, but ultimately the bottleneck is those controllers, those two controllers they have, that's what it is. No matter how many, how awesome your... You put envy in drive, it doesn't matter. It's going to be as much as speed as your network pipe is going to be, and as much faster as your controllers are going to be. Ultimately, the latency, you cannot, like, basically it's over the wire. It will always be slower than what kind of having... >> So, the big thing here is-- >> Yeah, and it's not a private cloud. You know, that kind of model is for someone who's assembling a lot of parts to create a cloud. >> Yep. >> You know, we're integrating these parts, so it's a much simpler deployment of a cloud experience and you're not integrating all these double parts. >> I'm getting a cloud, I'm buying a cloud experience from you guys with the sets of services, let's talk about those services. So, mobility, discovery, analytics. >> Yeah. >> Governance, talked about the... >> Encryption, yeah. >> The other data reduction services, encryption... >> Right, the cataloging and indexing of the data so you can, you know, restart from old data. >> And I can run this on any cloud, including my on-prem cloud, correct? >> Well, that's the direction, we have some parts now and you know, you... (laughs) Sorry, Sazzala can talk about where we're going. >> So, architecturally it's designed to run on... >> Yeah, because I think fundamentally we chose that design philosophy that it has to be two-layer, right, that's a fundamental decision we made long ago, and it's a detail but it's a fundamental decision we made long ago that because if you go to Amazon it is two-layer. You cannot make one-layer work there. Like, you know, compute and storage has to be split to through that part, but they must work together in a nice way, and also S3's very weird. I don't know if you know about S3. S3's very weird behavior, it does not like random writes, it has to be all sequential writes, and that also happens to be how we built it. The way our system works is that we only do sequential writes to any device. It works beautifully in S3 with EC2, so just to step back a little bit, taking big picture, like so, we wanted a cloud-like experience for your on-prem, right. That's kind of what we built, we built a Datrium cloud on-prem, and then we, as of beginning of this year, we started offering services, multi-cloud services and started with Amazon first. The first service we enabled was backup and archiving, that's our first service. A lot of people like it and you have some stats from that, like from last quarter, like how people like it, because people like it because you don't have to have another on-prem infrastructure. You can just consume it as a SAS model, it's very convenient and it's as easy as an iPhone backup. I don't know if you use iPhone backup, it's like a click. >> Yeah. >> Okay, unfortunately it's a click. We have tried to avoid the clicks, but we can't really avoid it all the way, so you have to click it so that you can then start doing backups into the cloud and then can retrieve them in a very simple single pane of glass. It's very cost-effective because we do dedupe on the cloud and we dedupe over the wire, but dedupe over the wire, by the way, it's actually a very unique feature. Not many companies have it, like Nutanix and Pure you mentioned, they don't have it, so you know, so that's one of the things where I think we differentiate because data has gravity, right, so to move it somewhere you need an antigravity device. So, you need something to actually move this data faster, how to defeat speed of light. You have a pipe, you have a VAN network, so how do you defeat the speed of light, so what we have built is a feature, it's called Global Dedupe, is that you can move data in a much more efficient way across the cloud. So, now you may question, "Hey, I'm moving my data "from here to another place," obviously we have these cloud services... The question you may ask is, "Okay, how do "I know I get guaranteed security? "How do I know that it's going to be correct, "that I moved all these places," right? So, we do multiple things, one is that we have built in encryption. It's going to be globally encrypted, it's like an encryption across the whole thing, we call it blanket encryption. >> Mm-hmm. >> The other one is that we have blockchain-like features that are built into the systems so that if you move an object, like an app or whatever, you're going to move from one place to the other, it's built in kind of blockchain features where you cannot move something to another place and get it wrong. It's fundamentally going to be correct for you, so those are the kind of things we thought about, like never to worry about it again. It's going to guarantee the data's correct and it's moved in the most efficient way, so that's our first landing thing we've done is that we wanted to build an experience which is like on-prem cloud, I mean, onto also the cloud. Right, what other experience people are... People like simplicity, people want the SAS-like experience. They don't want to manage it, they don't want to think about it. They just consume the services, so the first service we have in Amazon is what we chose, is backup and DR. The next thing we are going to be shipping soon, announcing soon, and we'll have a demo in the VM World is something we call Cloud Shift. It's an app mobility orchestration framework where you can just click and move your workload to somewhere else, to Amazon, and you can run, so it's not just a backup thing, it'll also become you can run your workloads in Amazon and get a consistent experience from your on-prem and the cloud. So, one of the challenges is that if you move to another place, is it different tool sets, I have to change my whole lifestyle, no. >> Mm-hmm. >> We want to provide that seamless operational consistency that-- >> That's the key, right. >> That's the key. >> Whether it's on-prem or it's in the cloud it operates the same way. I'm accessing those sets of data services and-- >> Yeah. >> I don't really care where it is, is that-- >> That's right. >> The vision? >> Yeah, that's right. >> Exactly. >> That's right, so if it turns out that there's a cost advantage in moving from, you know, A to B, we make it super easy and the control panel from our standpoint is consistent, and it's... So, all of our control orientation moving forward will literally be SAS. It'll be running on a cloud even if you're managing on-prem stuff, because that way, assuming you're multi-cloud, you need a control plane to be dealing with the cloud stuff anyway, and it just sort of neutralizes the experience so that in a multi-cloud way it's always consistent, it's always simple, and the nice thing about sort of true SAS is you don't have to upgrade software parts. We do that for you in the background. >> Mm-hmm. >> So, it's just always up to date. >> So, I was saying before, Datrium takes care of everything. >> Yeah. >> And it's the true cloud experience. >> Just consume it. >> Right. >> Okay, I want to talk about, end on the two other areas: the operational impact and the developer impact. So, when you think of operations, we've talked about LUNs before. I've always said if you're in the business of managing LUNs you really want to think about, you know, updating your skill sets (chuckles) because that capability is not really going to be viewed as valuable. It isn't today and certainly in the future, so the operational impact, the degrees of automation that IT operations are driving is going through the roof. Cloud-like, we've talked about that, and the other is developer productivity. People are using containers, you know, Kubernetes... >> Yeah. >> And new styles of writing software-- >> Yeah. >> As everybody becomes a software company. So, can you talk about those two aspects? >> And ultimately there's going to be serverless. >> Right. >> Right. >> As we think about if you take a leap, in another 10 years I think serverless will probably be one of the important ways, because why do you even care how it runs. You just write some software and like, you know, we can run it. It should be that way, but I think we're not there completely yet, I think, so we want to adopt a methodology where we provide the framework where we don't dictate what apps, how we write your apps. That's, I think, very powerful because that's actually evolving faster as we move forward, because serverless is a new app framework. >> Mm-hmm. >> You cannot anticipate this, right, you cannot anticipate on building everything but what you can anticipate is services we can provide for the developers, which is, you know, no matter... Because it's the granularity of it. We can map their application granularity into our system, we have that fine level granularity, so that kind of was what you want to provide as a primitive. LUNs don't have that primitive, right, so we provide that level of primitive that whatever apps you have will have that level of primitives to global data services for you, and once you have the data services like that we'll guarantee that it's highest performance, which is what app developers want. Like, I get the highest performance, I can easily... And then we will also provide a way to clone those things easily, those apps, because sometimes you're at an app, you want to test it, too. Like a hundred times, you want to just... If you can copy all the data a hundred times or you can just, say, you know what, clone this thing a hundred times in a millisecond and run my tests fast and then okay, I'm done with my test, it looks good, I'll deploy it. >> Mm-hmm. >> That's kind of what developers really want is that they are able to run, write faster, develop faster, because tests on dev cycles are important. A lot of people think that hey, I can put my test on dev in some old box over there, but that's really bad because from business perspective testing does, engineering's expensive. Their test cycles have to be fast so that they can e-trade faster and kind of produce faster. The harder you make it to test your system, this is like, this is what happens in our company today. The harder it is to test your logic and your code, the longer it takes to, like, do e-trade. >> In some ways test and dev is becoming more strategic than the production system, I mean, really-- >> Well, it-- >> (chuckles) Because of speed. >> Yeah, I mean, it can take immediate advantage of some of these improvements in, you know, stacks. Like if, you know, if Kubernetes is better just, you know, go quickly to it. The things that these new stacks assume, though, is that it's, you know, a server-based data, so on-site you can accelerate mobility significantly by, you know, when people ask to copy things from here to there, clone it, you know, start another instance, we can help them do that by just, you know, faking it out with metadata-- >> Mm-hmm. >> And deduplication, and so we tried this with Jenkins just in our own development, moved to that model and you know, everything was suddenly twice as fast in development. To do a build all of a sudden you didn't have to copy data here to there. You were cloning, you know, with metadata. The way to do it across clouds is, again, kind of dedupe focused. If you have to actually move the data it takes a long time and it's expensive, especially for egress costs. If you can just, you know, validate which elements of the data are new versus old on either site you can move a lot less. >> Hmm... >> It might be, you know, six times less, and then the costs go down, the speed goes up, you defeat data gravity. >> Yeah, so-- >> Excellent, all right, we have to leave it there. >> Okay. >> Out of time, thanks so much, you guys, for helping us better understand, you know, Datrium. Congratulations on your success so far and all the great innovations that you've achieved. >> Okay, thank you. >> Okay, thanks for watching, everybody, this special CUBE conversation. This is Dave Vellante, see you next time. (techy music)

>> Live from Boston, Massachusetts, this is the CUBE, covering Spark Summit East 2017 brought to you by databricks. Now, here are your hosts Dave Valante and George Gilbert. >> Welcome back to Boston everyone. It's snowing like crazy outside, it's a cold mid-winter day here in Boston but we're here with the CUBE, the world-wide leader in tech coverage. We are live covering Spark Summit. This is wall to wall coverage, this is our second day here. John Landry with us, he's the distinguished technologist for HP's personal systems data science group within Hewlett Packard. John, welcome. >> Thank you very much for having me here. >> So I was saying, I was joking, we do a lot of shows with HPE, it's nice to have HP back on the CUBE, it's been awhile. But I want to start there. The company split up just over a year ago and it's seemingly been successful for both sides but you were describing to us that you've gone through an IT transformation of sorts within HP. Can you describe that? >> In the past, we were basically a data warehousing type of approach with reporting and what have you coming out of data warehouses, using Vertica, but recently, we made an investment into more of a programming platform for analytics and so where transformation to the cloud is about that where we're basically instead of investing into our own data centers because really, with the split, our data centers went with Hewlett Packard Enterprise, is that we're building our software platform in the cloud and that software platform includes analytics and in this case, we're building big data on top of Spark and so that transformation is huge for us, but it's also enabled us to move a lot faster, the velocity of our business and to be able to match up to that better. Like I said, it's mainly around the software development really more than anything else. >> Describe your role in a little bit more detail inside of HP. >> My role is I'm the leader in our big data investments and so I've been leading teams internally and also collaborating across HP with our print group and what we've done is we've managed to put together a strategy around our cloud-based solution to that. One of the things that was important was we had a common platform because when you put a program platform in place, if it's not common, then we can't collaborate. Our investment could be fractured, we could have a lot of side little efforts going on and what have you so my role is to pry the leadership in the direction for that and also one of the reasons I'm here today is to get involved in the Spark community because our investment is in Spark so that's another part of my role is to get involved with the industry and to be able to connect with the experts in the industry so we can leverage off of that because we don't have that expertise internally. >> What are the strategic and tactical objectives of your analytics initiatives? Is it to get better predictive maintenance on your devices? Is it to create new services for customers? Can you describe that? >> It's two-fold, internal and external so internally, we got millions of dollars of opportunity to better our products with cost, also to optimize our business models and the way we can do that is by using the data that comes back from our products, our services, our customers, combining that together and creating models around that that are then automated and can be turned into apps that can be used internally by our organizations. The second part is to take the same approach, same data, but apply that back towards our customers and so with the split, our enterprise services group also went with Hewlett Packard Enterprise and so now, we have a dedicated effort towards creating manage services for the commercial environment. And that's both on the print size and on the personal system side so to basically fuel that, analytics is a big part of the story. So we've had different things that you'll see out there like touch point manager is one of our services we're delivering in personal systems. >> Dave: What is that? >> Touch point manager is aimed at providing management services for SMB and for commercial environments. So for instance, in touch point manager, we can provide predictive type of capabilities for support. A number of different services that companies are looking for when they buy our products. Another thing we're going after too is device as a service. So there's another thing that we've announced recently that basically we're invested into there and so this is obviously if you're delivering devices as a service, you want to do that as optimal as possible. Well, being able to understand the devices, what's happening with them, been able to predictive support on them, been able to optimize the usage of those devices, that's all important. >> Dave: A lot of data. >> The data really helps us out, right? So the data that we can collect back from our devices and to be able to take that and turn that around into applications that are delivering information inside or outside is huge for us, a huge opportunity. >> It's interesting where you talk about internal initiatives and manage services, which sound like they're most external, but on the internal ones, you were talking about taking customer data and internal data and turning those into live models. Can you elaborate on that? >> Sure, I can give you a great example is on our mobile products, they all have batteries. All of our batteries are instrumented as smart batteries and that's an industry standard but HP actually goes a step further on that, it's the information that we put into our batteries. So by monitoring those batteries and the usage in the field is we can tell how optimally they're performing, but also how they're being used and how we can better design batteries going forward. So in addition, we can actually provide information back into our supply chain. For instance, there's a cell supplier for the battery, there's a pack supplier, there's our unit manufacturer for the product, and so a lot of things that we've been able to uncover is that we can go and improve process. And so improving process alone helps to improve the quality of what we deliver and the quality of the experience to our customers. So that's one example of just using the data, turning that around into a model. >> Is there an advantage to having such high volume, such market share in getting not just more data, but sort of more of the bell curve, so you get the edge conditions? >> Absolutely, it's really interesting because when we started out on this, everybody's used to doing reporting which is absolute numbers and how much did you shift and all that kind of stuff. But, we're doing big data, right? So in big data, you just need a good sample population. Turn the data scientist into that and they've got their statistical algorithms against that. They give you the confidence factor based upon the data that you have so it's absolutely a good factor for us because we don't have to see all the platforms out there. Then, the other thing is, when you look at populations, we see variances in different customers so we're looking at, like one of our populations that's very valuable to us is our own, so we take the 60 thousand units that we have internally at HP and that's one of our sample populations. What a better way to get information on your own products? But, you take that and you take it to one of our other customers and their population's going to look slight different. Why? Because they use the products differently. So one of the things is just usage of the products, the environment they're used in, how they use them. Our sample populations are great in that respect. Of course, the other thing is, very important to point out, we only collect data under the rules and regulations that are out there, so we absolutely follow that and we absolutely keep our data secure and we absolutely keep everything and that's important. Sometimes, today they get a little bit spooked sometimes around that, but the case is that our services are provided based on customers signing up for them. >> I'm guessing you don't collect more data than Google. >> No, we're nowhere near Google. >> So, if you're not spooked at Google - >> That's what I tell people. I say if you got a smartphone, you're giving up a lot more data than we're collecting. >> Buy something from Amazon. Spark, where does Spark fit into all of this? >> Spark is great because we needed a programming platform that could scale in our data centers and in our previous approaches, we didn't have a programming platform. We started with a Hadoop, the Hadoop was very complex though. It really gets down to the hardware and you're programming and trying to distribute that load and getting clusters and you pick up Spark and immediately abstraction. The other thing is it allows me to hire people that can actually program on top of it. I don't have to get someone that knows Map Reduce. I can sit there and it's like what do you know? You know R, Scala, you know Python, it doesn't matter. I can run all of that on top of it. So that's huge for us. The other thing is flat out the speed because as you start getting going with this, we get this pull all of a sudden. It's like well I only need the data like once a month, it's like I need it once a week, I need it once a day, I need the output of this by the hour now. So, the scale and the speed of that is huge and then when you put that on the cloud platform, you know, Spark on a cloud platform like Amazon, now I've got access to all the compute instances. I can scale that, I can optimize it because I don't always need all the power. The flexibility of Spark and being able to deliver that is huge for our success. >> So, I've got to ask some columbo questions and George, maybe you can help me sort of frame it. So you mentioned you were using Hadoop. Like a lot of Hadoop practitioners, you found it very complex. Now, Hewlett Packard has resources. Many companies don't but so you mentioned people out doing Python and R and Scale and Map Reduce, are you basically saying okay, we're going to unify portions of our Hadoop complexity with Spark and that's going to simplify our efforts? >> No, what we actually did was we started on the Hadoop side of it. The first thing we did was try to move from a data warehouse to more of a data lake approach or repository and that was internal, right? >> Dave: And that was a cost reduction? >> That was a cost reduction but also, data accessibility. >> Dave: Yeah, okay. >> The other thing we did was ingesting the data. When you're starting to bring data in from millions of devices, we had a problem coming through the firewall type approach and you got to have something in front of that like a Kafka or something in front of it that can handle it. So when we moved to the cloud, we didn't even try to put up our own, we just used Kinesis and that we didn't have to spend any resources to go solve that problem. Well, the next thing was, when we got the data, you need to ingest the data in and our data's coming in, we want to split it out, we needed to clean it and what you, we actually started out running Java and then we ran Java on top of Hadoop, but then we came across Spark and we said that's it. For us to go to the next step of actually really get into Hadoop, we were going to have to get some more skills and to find the skills to actually program in Hadoop was going to be complex. And to train them organically was going to be complex. We got a lot of smart people, but- >> Dave: You got a lot of stuff to do, too. >> That's the thing, we wanted to spend more time getting information out of the data as opposed to the framework of getting it to run and everything. >> Dave: Okay, so there's a lot of questions coming out. You mentioned Kinesis, so you've replaced that? >> Yeah, when we went to the cloud, we used as many Amazon services as we can as opposed to growing something for ourselves so when we get onto Amazon, you know, getting data into an S3 bucket through Kineses was a no-brainer. When we transferred over to the cloud, it took us less than 30 days to point our devices at Kinesis and we had all our data flowing into S3. So that was like wow, let's go do something else. >> So I got to ask you something else. Again, I love when practitioners come on. So, one of the complaints that I hear sometimes from AWS users and I wonder if you see this is the data pipeline is getting more and more complex. I got an API for Kinesis, one for S3, one for DynamoDB, one for Elastic Plus. There must be 15 proprietary APIs that are primitive, and again, it gets complicated and sometimes it's hard to even figure out what's the right cost model to use. Is that increasingly becoming more complex or is it just so much simpler than what you had before and you're in nirvana right now? >> When you mentioned costs, just the cost of moving to the cloud was a major cost reduction for us. >> Reduction? >> So now it's - >> You had that HP corporate tax on you before - >> Yeah, now we're going from data centers and software licenses. >> So that was a big win for you? >> Yeah, huge, and that released us up to go spend dollars on resources to focus on the data science aspect. So when we start looking at it, we continually optimized, don't get me wrong. But, the point is, if we can bring it up real quickly, that's going to save us a lot of money even if you don't have to maintain it. So we want to focus on creating the code inside of Spark that's actually doing the real work as opposed to the infrastructure. So that cost savings was huge. Now, when you look at it over time, we could've over analyzed that and everything else, but what we did was we used a rapid prototyping approach and then from there, we continued to optimize. So what's really good about the cloud is you can predict the cost and with internal data centers and software licenses and everything else, you can't predict the cost because everybody's trying to figure out who's paying for what. But in the case of the cloud, it's all pretty much you get your bill and you understand what you're paying. So anyway - >> And then you can adjust accordingly? >> We continue to optimize so we use the services but if we have for some reason, it's going to deliver us an advantage, we'll go develop it. But right now, our advantage is we got umteen opportunities to create AI type code and applications to basically automate these services, we don't even have enough resources to do it right now. But, the common programming platform's going to help us. >> Can you drill into those umpteen examples? Just some of them because - >> I mentioned the battery one for instance. So take that across the whole system so now you've got your storage devices, you've got your software that's running on there, we've got built into our system security monitoring at the firmware level just basically connecting into that and adding AI around that is huge because now we can see a tax that may be happening upon your fleet and we can create services out of that. Anything that you can automate around that is money in our pocket or money in our customers' pocket so if we can save them money with these new services, they're going to be more willing to come to HP for products. >> It's actually more than just automation because it's the stuff you couldn't do with 1,000 monkeys trying to write Shakespeare. You have data that you could not get before. >> You're right, what we're doing, the automation is helping us uncover things that we would've never seen and you're right, the whole gorilla walking through the room, I could sit there and I could show you tons of examples of where we're missing the boat. Even when we brought up our first data sets, we started looking at them and some of the stuff we looked at, we thought this is just bad data and actually it wasn't, it was bad product. >> People talk about dark data - >> We had no data models, we had no data model to say is it good or bad? And now we have data models and we're continuing to create those data models around, you create the data model and then you can continue to teach it and that's where we create the apps around it. Our primitives are the data models that we're creating from the device data that we have. >> Are there some of these apps where some of the intelligence lives on the device and it can, like in a security attack, it's a big surface area, you want to lock it down right away. >> We do. The good example on the security is we built something into our products called Sure Start. What essentially it is is we have ability to monitor the firmware layer and so there's a local process that's running independent of everything else that's running that's monitoring what's happening at that firmware level. Well, if there's an attack, it's going to immediately prevent the attack or recover from the attack. Well, that's built into the product. >> But it has to have a model of what this anomalous behavior is. >> Well in our case, we're monitoring what the firmware should look like and if we see that the firmware, you know you take check sums from the firmware or the pattern - >> So the firmware does not change? >> Well basically we can take the characteristics of the firmware and monitor it. If we see that changing, then we know something's wrong. Now it can get corrupt through hardware failure maybe because glitches can happen maybe. I mean solar flares can cause problems sometimes. So, the point is we've found that customers had problems sometimes where basically their firmware would get corrupted and they couldn't start their system. So we're like are we getting attacked? Is this a hardware issue? Could it be bad Flash devices? There's always all kinds of things that could cause that. Well now we monitor it and we know what's going on. Now, the other cool thing is we create logs from that so when those events occur, we can collect those logs and we're monitoring those events so now we can have something monitor the logs that are monitoring all the units. So, if you've got millions of units out there, how are you going to do that manually? You can't and that's where the automation comes in. >> So the logs give you the ability up in the cloud or at HP to look at the ecosystem of devices, but there is intelligence down on the - >> There's intelligence to protect the device in an auto recover which is really cool. So in the past, you had to get your repair. Imagine if someone attacked your fleet of notebooks. Say you got 10 thousand of them and basically it brought every single one of them down one day. What would you do? >> Dave: Freak. >> And everything you got to replace. It was just an attack and it could happen so we basically protect against that with our products and at the same time, we can see that may be a current and then from the footprints of it, we can then do analysis on it and determine was that malicious, is this happening because of a hardware issue, is this happening because maybe we tried to update the firmware and something happened there? What caused that to happen? And so that's where collecting the data from the population then helps us do that and then mix that with other things like service events. Are we seeing service events being driven by this? Thermal, we can look at the thermal data. Maybe there's some kind of heat issue that's causing this to happen. So we starting mixing that. >> Did Samsung come calling to buy this? >> Well, actually what's funny is Samsung is actually a supplier of ours, is a battery supplier of ours. So, by monitoring the batteries, what's interesting is we're helping them out because we go back to them. One of the things I'm working on, is we want to create apps that can go back to them and they can see the performance of their product that they're delivering to us. So instead of us having to call a meeting and saying hey guys let's talk about this, we've got some problems here. Imagine how much time that takes. But if they can self-monitor, then they're going to want to keep supplying to us, then they're going to better their product. >> That's huge. What a productivity boost because you're like hey, we got a problem, let's meet and talk about it and then you take an action to go and figure out what it is. Now if you need a meeting, it's like let's look at the data. >> Yeah, you don't have enough people. >> But there's also potentially a shift in pricing power. I would imagine it shifts a little more in your favor if you have all the data that indicates the quality of their product. >> That's an interesting thing. I don't know that we've reached that point. I think that in the future, it would be something that could be included in the contracts. The fact that the world is the way it is today and data is a big part of that to where going forward, absolutely, the fact that you have that data helps you to better have a relationship with your suppliers. >> And your customers, I mean it used to be that the brand used to have all the information. The internet obviously changed all that, but this whole digital transformation and IOT and all those log data, that sort of levels the playing field back to the brand. >> John: It actually changes it. >> You can now add value for the consumer that you couldn't before. >> And that's what HP's trying to do. We're invested to exactly do that is to really improve or increase the value of our brand. We have a strong brand today but - >> What do you guys do with - we got to wrap - but what do you do with databricks? What's the relationship there? >> Databricks, again we decided that we didn't want to be the experts on managing the whole Spark thing. The other part was that we're going to be involved with Spark and help them drive the direction as far as our use cases and what have you. Databricks and Spark go hand in hand. They got the experts there and it's been huge, our relationship, being able to work with these guys. But I recognize the fact that, and going back to software development and everything else, we don't want to spare resources on that. We got too many other things to do and the less that I have to worry about my Spark code running and scaling and the cost of it and being able to put code in production, the better and so, having that layer there is saving us a ton of money and resources and a ton of time. Just imagine time to market, it's just huge. >> Alright, John, sorry we got to wrap. Awesome having you on, thanks for sharing your story. >> It's great to talk to you guys. >> Alright, keep it right there everybody. We'll be back with our next guest. This is the CUBE live from Spark Summit East, we'll be right back.

(mellow electronic music) >> Welcome back, we're here live in Las Vegas. This is SiliconANGLE and Wikibon's theCUBE, our flagship program. We go out to the events, extract the signal from the noise. We are live in Las Vegas at Amazon Web Services re:Invent conference, about developers, large-scale cloud, big data, the future. I'm John Furrier, the founder of SiliconANGLE. I'm joined by co-host, Dave Vellante, co-founder of Wikibon.org, and our guest is James Hamilton, VP and Distinguished Engineer at Amazon Web Services. Welcome to theCUBE. >> Well thank you very much. >> You're a tech athlete, certainly in our book, is a term we coined, because we love to use sports analogies You're kind of the cutting edge. You've been the business and technology innovating for many years going back to the database days at IBM, Microsoft, and now Amazon. You gave a great presentation at the analyst briefing. Very impressive. So I got to ask you the first question, when did you first get addicted to the notion of what Amazon could be? When did you first taste the Cool-Aide? >> Super good question. Couple different instances. One is I was general manager of exchange hosts and services and we were doing a decent job, but what I noticed was customers were loving it, we're expanding like mad, and I saw opportunity to improve by at least a factor of two I'm sorry, 10, it's just amazing. So that was a first hint that this is really important for customers. The second one was S3 was announced, and the storage price pretty much froze the whole industry. I've worked in storage all my life, I think I know what's possible in storage, and S3 was not possible. It was just like, what is this? And so, I started writing apps against it, I was just blown away. Super reliable. Unbelievably priced. I wrote a fairly substantial app, I got a bill for $7. Wow. So that's really the beginnings of where I knew this was going to change the world, and I've been, as you said, addicted to it since. >> So you also mentioned some stats there. We'll break it down, 'cause we love to talk about the software defined data center, which is basically not even at the hype stage yet. It's just like, it's still undefined, but software virtualization, network virtualization really is pushing that movement of the software focus, and that's essentially you guys are doing. You're talking about notifications and basically it's a large-scale systems problem. That you guys are building a global operating system as Andy Jassy would say. Well, he didn't say that directly, he said internet operating system, but if you believe that APIs are critical services. So I got to ask you that question around this notion of a data center, I mean come on, nobody's really going to give up their data center. It might change significantly, but you pointed out the data center costs are in the top three order, servers, power circulation systems, or cooling circulation, and then actual power itself. Is that right, did I get that right? >> Pretty close, pretty close. Servers dominate, and then after servers if you look at data centers together, that's power, cooling, and the building and facility itself. That is the number two cost, and the actual power itself is number three. >> So that's a huge issue. When we talk like CIOs, it's like can you please take the facility's budget off my back? For many reasons, one, it's going to be written off soon maybe. All kinds of financial issues around-- >> A lot of them don't see it, though, which is a problem. >> That is a problem, that is a problem. Real estate season, and then, yes. >> And then they go, "Ah, it's not my problem" so money just flies out the window. >> So it's obviously a cost improvement for you. So what are you guys doing in that area and what's your big ah-ha for the customers that you walk in the door and say, look, we have this cloud, we have this system and all those headaches can be, not shifted, or relieved if you will, some big asprin for them. What's the communication like? What do you talk to them about? >> Really it depends an awful lot on who it is. I mean, different people care about different things. What gets me excited is I know that this is the dominate cost of offering a service is all of this muck. It's all of this complexity, it's all of this high, high capital cost up front. Facility will run 200 million before there's servers in it. This is big money, and so from my perspective, taking that way from most companies is one contribution. Second contribution is, if you build a lot of data centers you get good at it, and so as a consequence of that I think we're building very good facilities. They're very reliable, and the costs are plummeting fast. That's a second contribution. Third contribution is because... because we're making capacity available to customers it means they don't have to predict two years in advance what they're going to need, and that means there's less wastage, and that's just good for the industry as a whole. >> So we're getting some questions on our crowd chat application. If you want to ask a question, ask him anything. It's kind of like Reddit. Go to crowdchat.net/reinvent. The first question came in was, "James, when do you think ARM will be in the data center?" >> Ah ha, that's a great question. Well, many people know that I'm super excited about ARM. It's early days, the reason why I'm excited is partly because I love seeing lots of players. I love seeing lots of innovation. I think that's what's making our industry so exciting right now. So that's one contribution that ARM brings. Another is if you look at the history of server-side computing, most of the innovation comes from the volume-driven, usually on clients first. The reason why X86 ended up in such a strong position is so many desktops we running X86 processors and as a consequence it became a great server processor. High R&D flow into it. ARM is in just about every device that everyone's carrying around. It's almost every disk drive, it's just super broadly deployed. And whenever you see a broadly deployed processor it means there's an opportunity to do something special for customers. I think it's good for the industry. But in a precise answer to your question, I really don't have one right now. It's something that we're deeply interested in and investigating deeply, but at this point it hasn't happened yet, but I'm excited by it. >> Do you think that... Two lines of questioning here. One is things that are applicable to AWS, other's just your knowledge of the industry and what you think. We talked about that yesterday with OCP, right? >> Yep. >> Not a right fit for us, but you applaud the effort. We should talk about that, too, but does splitting workloads up into little itty, bitty processors change the utilization factor and change the need for things like virtualization, you know? What do you think? >> Yeah, it's a good question. I first got excited about the price performance of micro-servers back in 2007. And at that time it was pretty easy to produce a win by going to a lower-powered processor. At that point memory bandwidth wasn't as good as it could be. It was actually hard on some workloads to fully use a processor. Intel's a very smart company, they've done great work on improving the memory bandwidth, and so today it's actually harder to produce a win, and so you kind of have workloads in classes. At the very, very high end we've got database workloads. They really love single-threaded performance, and performance really is king, but there are lots of highly parallel workloads where there's an opportunity for a big gain. I still think virtualization is probably something where the industry's going to want to be there, just because it brings so many operational advantages. >> So I got to ask the question. Yesterday we had Jason Stowe on, CEO of Cycle Computing, and he had an amazing thing that he did, sorry, trumping it out kids say, but it's not new to you, but it's new to us. He basically created a supercomputer and spun up hundreds of thousands of cores in 30 minutes, which is like insane, but he did it for like 30 grand. Which would've cost, if you try to provision it to the TUCO calculator or whatever your model, it'd be months and years, maybe, and years. But the thing that he said I want to get your point on and I'm going to ask you questions specifically on is, Spot instances were critical for him to do that, and the creativity of his solutions, so I got to ask you, did you see Spot pricing instances being a big deal, and what impact has that done to AWS' vision of large scale? >> I'm super excited by Spot. In fact, it's one of the reasons I joined Amazon. I went through a day of interviews, I met a bunch of really smart people doing interesting work. Someone probably shouldn't have talked to me about Spot because it hadn't been announced yet, and I just went, "This is brilliant! "This is absolutely brilliant!" It's taking the ideas from financial markets, where you've got high-value assets, and saying why don't we actually sell it off, make a market on the basis of that and sell it off? So two things happen that make Spot interesting. The first is an observation up front that poor utilization is basically the elephant in the room. Most folks can't use more than 12% to 15% of their overall server capacity, and so all the rest ends up being wasted. >> You said yesterday 30% is outstanding. It's like have a party. >> 30% probably means you're not measuring it well. >> Yeah, you're lying. >> It's real good, yeah, basically. So that means 70% or more is wasted, it's a crime. And so the first thing that says is, that one of the most powerful advertisements for cloud computing is if you bring a large number of non-correlated workloads together, what happens is when you're supporting a workload you've got to have enough capacity to support the peak, but you only get to monetize the average. And so as the peak to average gets further apart, you're wasting more. So when you bring a large number of non-correlated workloads together what happens is it flattens out just by itself. Without doing anything it flattens out, but there's still some ups and downs. And the Spot market is a way of filling in those ups and downs so we get as close to 100%. >> Is there certain workloads that fit the spot, obviously certain workloads might fit it, but what workloads don't fit the Spot price, because, I mean, it makes total sense and it's an arbitrage opportunity for excess capacity laying around, and it's price based on usage. So is there a workload, 'cause it'll be torrent up, torrent down, I mean, what's the use cases there? >> Workloads that don't operate well in an interrupted environment, that are very time-critical, those workloads shouldn't be run in Spot. It's just not what the resource is designed for. But workloads like the one that we were talking to with Cycle Computing are awesome, where you need large numbers of resources. If the workload needs to restart, that's absolutely fine, and price is really the focus. >> Okay, and question from crowd chat. "Ask James what are his thoughts "on commodity networking and merchant silicon." >> I think an awful lot about that. >> This guy knows you. (both laughing) >> Who's that from? >> It's your family. >> Yeah, exactly! >> They're watching. >> No, network commoditization is a phenomenal thing that the whole industry's needed that for 15 years. We've got a vertical ecosystem that's kind of frozen in time. Vertically-integrated ecosystem kind of frozen in time. Costs everywhere are falling except in networking. We just got to do something, and so it's happening. I'm real excited by that. It's really changing the Amazon business and what we can do for customers. >> Let's talk a little bit about server design, because I was fascinated yesterday listening to you talk how you've come full circle. Over the last decade, right, you started with what's got to be stripped down, basic commodity and now you're of a different mindset. So describe that, and then I have some follow-up questions for you. >> Yeah, I know what you're alluding to. Is years ago I used to argue you don't want hardware specialization, it's crazy. It's the magic's in software. You want to specialize software running on general-purpose processors, and that's because there was a very small number of servers out there, and I felt like it was the most nimble way to run. However today, in AWS when we're running ten of thousands of copies of a single type of server, hardware optimizations are absolutely vital. You end up getting a power-performance advantage at 10X. You can get a price-performance advantage that's substantial and so I've kind of gone full circle where now we're pulling more and more down into the hardware, and starting to do hardware optimizations for our customers. >> So heat density is a huge problem in data centers and server design. You showed a picture of a Quanta package yesterday. You didn't show us your server, said "I can't you ours," but you said, "but we blow this away, "and this is really good." But you describe that you're able to get around a lot of those problems because of the way you design data centers. >> Yep. >> Could you talk about that a little bit? >> Sure, sure, sure. One of the problems when you're building a server it could end up anywhere. It could end up in a beautiful data center that's super well engineered. It could end up on the end of a row on a very badly run data center. >> Or in a closet. >> Or in a closet. The air is recirculating, and so the servers have to be designed with huge headroom on cooling requirements, and they have to be able to operate in any of those environments without driving warranty costs for the vendors. We take a different approach. We say we're not going to build terrible data centers. We're going to build really good data centers and we're going to build servers that exploit the fact those data centers are good, and what happens is more value. We don't have to waste as much because we know that we don't have to operate in the closet. >> We got some more questions coming here by the way. This is awesome. This ask me anything crowd chat thing is going great. We got someone, he's from Nutanix, so he's a geek. He's been following your career for many years. I got to ask you about kind of the future of large-scale. So Spot, in his comment, David's comment, Spot instances prove that solutions like WMare's distributed power management are not valuable. Don't power off the most expensive asset. So, okay, that brings up an interesting point. I don't want to slam on BMWare right now, but I just wanted to bring to the next logical question which is this is a paradigm shift. That's a buzz word, but really a lot's happening that's new and innovative. And you guys are doing it and leading. What's next in the large-scale paradigm of computing and computer science? On the science-side you mentioned merchant silicon. Obviously that's, the genie's out of the bottle there, but what's around the corner? Is it the notifications at the scheduling? Was it virtualization, is it compiler design? What are some of the things that you see out on the horizon that you got your eyes on? >> That's interesting, I mean. I've got, if you name your area, and I'll you some interesting things happening in the area, and it's one of the cool things of being in the industry right now. Is that 10 years ago we had a relatively static, kind of slow-pace. You really didn't have to look that far ahead, because of anything was coming you'd see it coming for five years. Now if you ask me about power distribution, we've got tons of work going on in power distribution. We're researching different power distribution topologies. We're researching higher voltage distribution, direct current distribution. Haven't taken any of those steps yet, but we're were working in that. We've got a ton going on in networking. You'll see an announcement tomorrow of a new instance type that is got some interesting characteristics from a networking perspective. There's a lot going on. >> Let's pre-announce, no. >> Gary's over there like-- >> How 'about database, how 'about database? I mean, 10 years ago, John always says database was kind of boring. You go to a party say, oh welcome to database business, oh yeah, see ya. 25 years ago it was really interesting. >> Now you go to a party is like, hey ah! Have a drink! >> It a whole new ballgame, you guys are participating. Google Spanner is this crazy thing, right? So what are your thoughts on the state of the database business today, in memory, I mean. >> No, it's beautiful. I did a keynote at SIGMOD a few years ago and what I said is that 10 years ago Bruce Linsey, I used to work with him in the database world, Bruce Linsey called it polishing the round ball. It's just we're making everything a little, tiny bit better, and now it's fundamentally different. I mean what's happening right now is the database world, every year, if you stepped out for a year, you wouldn't recognize it. It's just, yeah, it's amazing. >> And DynamoDB has had rapid success. You know, we're big users of that. We actually built this app, crowd chat app that people are using on Hadoop and Hbase, and we immediately moved that to DynamoDB and your stack was just so much faster and scalable. So I got to ask you the-- >> And less labor. >> Yeah, yeah. So it's just been very reliable and all the other goodness of the elastic B socket and SQS, all that other good stuff we're working with node, et cetera So I got to ask you, the area that I want your opinion around the corner is versioning control. So at large-scale one of the challenges that we have is as we're pushin' new code, making sure that the integrated stack is completely updated and synchronized with open-source projects. So where does that fit into the scaling up? 'Cause at large scale, versioning control used to be easy to manage, but downloading software and putting in patches, but now you guys handle all that at scale. So that, I'm assuming there's some automation involved, some real tech involved, but how are you guys handling the future of making sure the code is all updated in the stack? >> It's a great question. It's super important from a security perspective that the code be up to date and current. It's super important from a customer perspective and you need to make sure that these upgrades are just non-disruptive. One customer, best answer I heard was yesterday from a customer was on a panel, they were asked how did they deal with Amazon's upgrades, and what she said is, "I didn't even know when they were happening. "I can't tell when they're happening." Exactly the right answer. That's exactly our goal. We monitor the heck out of all of our systems, and our goal, and boy we take it seriously, is we need to know any issue before a customer knows it. And if you fail on that promise, you'll meet Andy really quick. >> So some other paradigm questions coming in. Floyd asks, "Ask James what his opinion of cloud brokerage "companies such as Jamcracker or Graviton. "Do they have a place, or is it wrong thinking?" (James laughs) >> From my perspective, the bigger and richer the ecosystem, the happier our customers all are. It's all goodness. >> It's Darwinism, that's the answer. You know, the fit shall survive. No, but I think that brings up this new marketplace that Spot pricing came out of the woodwork. It's a paradigm that exists in other industries, apply it to cloud. So brokering of cloud might be something, especially with regional and geographical focuses. You can imagine a world of brokering. I mean, I don't know, I'm not qualified to answer that. >> Our goal, honestly, is to provide enough diversity of services that we completely satisfy customer's requirements, and that's what we intend to do. >> How do you guys think about the make versus buy? Are you at a point now where you say, you know what, we can make this stuff for our specific requirements better than we can get it off the shelf, or is that not the case? >> It changes every few minutes. It really does. >> So what are the parameters? >> Years ago when I joined the company we were buying servers from OEM suppliers, and they were doing some tailoring for our uses. It's gotten to the point now where that's not the right model and we have our own custom designs that are being built. We've now gotten to the point where some of the components in servers are being customized for us, partly because we're driving sufficient volume that it's justified, and partly because the partners that the component suppliers are happy to work with us directly and they want input from us. And so it's every year it's a little bit more specialized and that line's moving, so it's shifting towards specialization pretty quickly. >> So now I'm going to be replaced by the crowd, gettin' great questions, I'm going to be obsolete! No earbud, I got it right here. So the question's more of a fun one probably for you to answer, or just kind of lean back and kind of pull your hair out, but how the heck does AWS add so much infrastructure per day? How do you do it? >> It's a really interesting question. I kind of know how much infrastructure, I know abstractly how much infrastructure we put out every day, but when you actually think about this number in context, it's mind boggling. So here's the number. Here's the number. Every day, we deploy enough servers to support Amazon when it was a seven billion dollar company. You think of how many servers a seven billion dollar e-commerce company would actually require? Every day we deploy that many servers, and it's just shocking to me to think that the servers are in the logistics chain, they're being built, they're delivered to the appropriate data centers, there's back positions there, there's networking there, there's power there. I'm actually, every day I'm amazed to be quite honest with you. >> It's mind-boggling. And then for a while I was there, okay, wait a minute. Would that be Moors' Law? Uh no, not even in particular. 'Cause you said every day. Not every year, every day. >> Yeah, it really is. It's a shocking number and one, my definition of scale changes almost every day, where if you look at the number of customers that are trusting with their workloads today, that's what's driving that growth, it's phenomenal! >> We got to get wrapped up, but I got to ask the Hadoob World SQL over Hadoob question solutions. Obviously Hadoob is great, great for storing stuff, but now you're seeing hybrids come out. Again this comes back down to the, you can recognize the database world anymore if you were asleep for a year. So what's your take on that ecosystem? You guys have a lasting map or a decent a bunch of other things. There's some big data stuff going on. How do you, from a database perspective, how do you look at Hadoob and SQL over Hadoob? >> I personally love 'em both, and I love the diversity that's happening in the database world. There's some people that kind of have a religion and think it's crazy to do anything else. I think it's a good thing. Map reduce is particularly, I think, is a good thing, because it takes... First time I saw map reduce being used was actually a Google advertising engineer. And what I loved about his, I was actually talking to him about it, and what I loved is he had no idea how many servers he was using. If you ask me or anyone in the technology how many servers they're using, they know. And the beautiful thing is he's running multi-thousand node applications and he doesn't know. He doesn't care, he's solving advertising problems. And so I think it's good. I think there's a place for everything. >> Well my final question is asking guests this show. Put the bumper sticker on the car leaving re:Invent this year. What's it say? What does the bumper sticker say on the car? Summarize for the folks, what is the tagline this year? The vibe, and the focus? >> Yeah, for me this was the year. I mean, the business has been growing but this is the year where suddenly I'm seeing huge companies 100% dependent upon AWS or on track to be 100% dependent upon AWS. This is no longer an experiment, something people want to learn about. This is real, and this is happening. This is running real businesses. So it's real, baby! >> It's real baby, I like, that's the best bumper... James, distinguished guest now CUBE alum for us, thanks for coming on, you're a tech athlete. Great to have you, great success. Sounds like you got a lot of exciting things you're working on and that's always fun. And obviously Amazon is killing it, as we say in Silicon Valley. You guys are doing great, we love the product. We've been using it for crowd chats. Great stuff, thanks for coming on theCUBE. >> Thank you. >> We'll be right back with our next guest after this short break. This is live, exclusive coverage with siliconANGLE theCUBE. We'll be right back.