>> Hey, welcome back already, Jeffrey. Here with the Cube, we are high top San Francisco in the Salesforce Tower in the newest center offices. It's really beautiful and is part of that. They have their San Francisco innovation hubs, so it's five floors of maker's labs and three D printing and all kinds of test facilities and best practices Innovation theater and in this studio, which is really fun to be at. So we're talking about hybrid cloud in the development of cloud and multi cloud. And, you know, we're, you know, continuing on this path. Not only your customers on this path, but everyone's kind of on this path is the same kind of evolved and transformed. We're excited. Have a couple experts in the field. We got Larry Soccer. He's the global managing director of Intelligent Cloud Infrastructure Service's growth and strategy at a center. Very good to see you again. Great to be here. And the Jay Patel. He's the senior vice president and general manager, cloud provider, software business unit, being where enemies of the people are nice. Well, so, uh so first off, how you like the digs appear >> beautiful place and the fact we're part of the innovation team. Thank you for that. It's so let's just >> dive into it. So a lot of crazy stuff happening in the market place a lot of conversations about hybrid cloud, multi cloud, different cloud, public cloud movement of Back and forth from Cloud. Just wanted. Get your perspective a day. You guys have been in the Middle East for a while. Where are we in this kind of evolution? It still kind of feeling themselves out. Is it? We're kind of past the first inning, so now things are settling down. How do you kind of you. Evolution is a great >> question, and I think that was a really nice job of defining the two definitions. What's hybrid worse is multi and simply put hybrid. We look at hybrid as when you have consistent infrastructure. It's the same infrastructure, regardless of location. Multi is when you have disparate infrastructure. We're using them in a collective. So just from a level setting perspective, the taxonomy starting to get standardized industry starting to recognize hybrid is a reality. It's not a step in the long journey. It is an operating model that's gonna be exists for a long time, so it's no longer about location. It's a lot harder. You operate in a multi cloud and a hybrid cloud world and together, right extension BM would have a unique opportunity. Also, the technology provider Accenture, as a top leader in helping customers figure out where best to land their workload in this hybrid multicolored world, because workloads are driving decisions right and one of the year in this hybrid medical world for many years to come. But >> do I need another layer of abstraction? Cause I probably have some stuff that's in hybrid. I probably have some stuff in multi, right, because those were probably not much in >> the way we talked a lot about this, and Larry and I were >> chatting as well about this. And the reality is, the reason you choose a specific cloud is for those native different share capability. Abstraction should be just enough so you can make were close portable, really use the caper berry natively as possible right, and by fact, that we now with being where have a native VM we're running on every major hyper scaler, right? And on. Prem gives you that flexibility. You want off not having to abstract away the goodness off the cloud while having a common and consistent infrastructure. What tapping into the innovations that the public cloud brings. So it is a evolution of what we've been doing together from a private cloud perspective to extend that beyond the data center to really make it operating model. That's independent location, right? >> Solarium cures your perspective. When you work with customers, how do you help them frame this? I mean, I always feel so sorry for corporate CEOs. I mean, they got >> complexities on the doors are already going on >> like crazy that GDP are now, I think, right, The California regs. That'll probably go national. They have so many things to be worried about. They got to keep up on the latest technology. What's happening in containers away. I thought it was Dr Knight. Tell me it's kubernetes. I mean, it's really tough. So how >> do you help them? Kind of. It's got a shot with the foundation. >> I mean, you look at cloud, you look at infrastructure more broadly. I mean, it's there to serve the applications, and it's the applications that really drive business value. So I think the starting point has to be application lead. So we start off. We have are intelligent. Engineering guys are platform guys. You really come in and look And do you know an application modernisation strategy? So they'll do an assessment. You know, most of our clients, given their scale and complexity, usually have from 520,000 applications, very large estates, and they got to start to freak out. Okay, what's my current application's? You know, you're a lot of times I use the six R's methodology, and they say, OK, what is it that I I'm gonna retire. This I'm no longer needed no longer is business value, or I'm gonna, you know, replace this with sass. Well, you know, Yeah, if I move it to sales force, for example, or service now mattress. Ah, and then they're gonna start to look at their their workloads and say OK, you know, I don't need to re factor reform at this, you know, re hosted. You know, when one and things obviously be Emily has done a fantastic job is allowing you to re hosted using their softer to find a data center in the hyper scale er's environments >> that we called it just, you know, my great and then modernized. But >> the modern eyes can't be missed. I think that's where a lot of times you see clients kind of getting the trap Hammer's gonna migrate and then figure it out. You need to start tohave a modernisation strategy and then because that's ultimately going to dictate your multi and your hybrid cloud approaches, is how they're zaps evolve and, you know, they know the dispositions of those abs to figure out How do they get replaced? What data sets need to be adjacent to each other? So >> right, so a j you know, we were there when when Pat was with Andy and talking about, you know, Veum, Where on AWS. And then, you know, Sanjay has shown up, but everybody else's conferences a Google cloud talking about you know, Veum. Where? On Google Cloud. I'm sure there was a Microsoft show I probably missed. You guys were probably there to know it. It's kind of interesting, right from the outside looking in You guys are not a public cloud per se. And yet you've come up with this great strategy to give customers the options to adopt being We're in a public hot. And then now we're seeing where even the public cloud providers are saying here, stick this box in your data center and Frank, this little it's like a little piece of our cloud of floating around in your data center. So talk about the evolution of the strategy is kind of what you guys are thinking about because you know, you're cleared in a leadership position, making a lot of interesting acquisitions. How are you guys see this evolving? And how are you placing your bets? >> You know, that has been always consistent about this. Annie. Any strategy, whether it's any cloud, was any device, you know, any workload if you will, or application. And as we started to think about it, right, one of the big things be focused on was meeting the customer where he's out on its journey. Depending on the customer, let me simply be trying to figure out looking at the data center all the way to how the drive in digital transformation effort in a partner like Accenture, who has the breadth and depth and something, the vertical expertise and the insight. That's what customers looking for. Help me figure out in my journey. First tell me where, Matt, Where am I going and how I make that happen? And what we've done in a clever way, in many ways is we've created the market. We've demonstrated that VM where's the omen? Consistent infrastructure that you can bet on and leverage the benefits of the private or public cloud. And I You know, I often say hybrids a two way street. Now, which is you're bringing Maur more hybrid Cloud service is on Prem. And where is he? On Premise now the edge. I was talking to the centering folks and they were saying the mitral edge. So you're starting to see the workloads, And I think you said almost 40 plus percent off future workers that are gonna be in the central cloud. >> Yeah, actually, is an interesting stat out there. 20 years 2020 to 70% of data will be produced and processed outside the cloud. So I mean, the the edges about, you know, as we were on the tipping point of, you know, I ot finally taking off beyond, you know, smart meters. You know, we're gonna see a huge amount of data proliferate out there. So, I mean, the lines between public and private income literary output you look at, you know, Anthony, you know, as your staff for ages. So you know, And that's where you know, I think I am where strategy is coming to fruition >> sometime. It's great, >> you know, when you have a point of view and you stick with it >> against a conventional wisdom, suddenly end up together and then all of a sudden everyone's falling to hurt and you're like, This is great, but I >> hit on the point about the vertical ization. Every one of our client wth e different industries have very different has there and to the meeting that you know the customer, you know, where they're on their journey. I mean, if you talk to a pharmaceutical, you know, geekspeak compliance. Big private cloud started to dip their toes into public. You know, you go to minds and they're being very aggressive public. So >> every manufacturing with EJ boat back in >> the back, coming to it really varies by industry. >> And that's, you know, that's a very interesting here. Like if you look at all the ot environment. So the manufacturing we started see a lot of end of life of environment. So what's that? Next generation, you know, of control system's gonna run on >> interesting on the edge >> because and you've brought of networking a couple times where we've been talking it, you know, and as as, ah, potential gate right when I was still in the gates. But we're seeing Maura where we're at a cool event Churchill Club, when they had Xilinx micron and arm talking about, you know, shifting Maur that compute and store on these edge devices ti to accommodate, which you said, you know, how much of that stuff can you do at the adverse is putting in. But what I think is interesting is how are you going to manage that? There is a whole different level of management complexity when now you've got this different level of you're looting and security times many, many thousands of these devices all over the place. >> You might have heard >> recent announcements from being where around the carbon black acquisition right that combined with our work space one and the pulse I ot well, >> I'm now >> giving you a management framework with It's what people for things or devices and that consistency. Security on the client tied with the network security with NSX all the way to the data center, security were signed. A look at what we call intrinsic security. How do we bake and securing the platform and start solving these end to end and have a park. My rec center helped design these next generation application architectures are distributed by design. Where >> do you put a fence? You're you could put a fence around your data center, >> but your APP is using service now. Another SAS service is so hard to talk to an application boundary in the sea security model around that. It's a very interesting time. >> You hear a lot of you hear a >> lot about a partnership around softer to find data center on networking with Bello and NSX. But we're actually been spending a lot of time with the i o. T. Team and really looking at and a lot of our vision, the lines. I mean, you actually looked that they've been work similarly, agent technology with Leo where you know, ultimately the edge computing for io ti is gonna have to be containerized because you can need multiple middleware stacks supporting different vertical applications, right? We're actually you know what we're working with with one mind where we started off doing video analytics for predictive, you know, maintenance on tires for tractors, which are really expensive. The shovels, It's after we started pushing the data stream up it with a video stream up into azure. But the network became a bottleneck looking into fidelity. So we gotta process there. They're not looking autonomous vehicles which need eight megabits low laden C band with, you know, sitting at the the edge. Those two applications will need to co exist. And you know why we may have as your edge running, you know, in a container down, you know, doing the video analytics. If Caterpillar chooses, you know, Green Grass or Jasper that's going to co exist. So you see how the whole container ization that were started seeing the data center push out there on the other side of the pulse of the management of the edge is gonna be very difficult. I >> need a whole new frontier, absolutely >> moving forward. And with five g and telco. And they're trying to provide evaluated service is So what does that mean from an infrastructure perspective. Right? Right, Right. When do you stay on the five g radio network? Worse is jumping on the back line. And when do you move data? Where's his process? On the edge. Those all business decisions that need to be doing to some framework. >> You guys were going, >> we could go on. Go on, go. But I want to Don't fall upon your Segway from containers because containers were such an important part of this story and an enabler to the story. And, you know, you guys been aggressive. Move with hefty Oh, we've had Craig McCloskey, honor. He was still at Google and Dan great guys, but it's kind of funny, right? Cause three years ago, everyone's going to Dr Khan, right? I was like that were about shows that was hot show. Now doctors kind of faded and and kubernetes has really taken off. Why, for people that aren't familiar with kubernetes, they probably here to cocktail parties. If they live in the Bay Area, why's containers such an important enabler? And what's so special about Coburn? 80 specifically. >> Do you wanna go >> on the way? Don't talk about my products. I mean, if you >> look at the world is getting much more dynamics on the, you know, particularly you start to get more digitally to couple applications you started. You know, we've gone from a world where a virtual machine might have been up for months or years. Toe, You know, obviously you have containers that are much more dynamic, allowed to scale quickly, and then they need to be orchestrated. That's essential. Kubernetes does is just really starts to orchestrate that. And as we get more distributed workloads, you need to coordinate them. You need to be able to scale up as you need it for performance, etcetera. So kubernetes an incredible technology that allows you really to optimize, you know, the placement of that. So just like the virtual machine changed, how we compute containers now gives us a much more flexible portable. You know that, you know you can run on anything infrastructure, any location, you know, closer to the data, et cetera. To do that. And I >> think the bold movie >> made is, you know, we finally, after working with customers and partners like century, we have a very comprehensive strategy. We announced Project Enzo, a philosophy in world and Project tansy really focused on three aspects of containers. How do you build applications, which is pivotal in that mansion? People's driven around. How do we run these arm? A robust enterprise class run time. And what if you could take every V sphere SX out there and make it a container platform? Now we have half a million customers. 70 million be EMS, all of sudden that run time. We're continue enabling with the Project Pacific Soviets. Year seven becomes a commonplace for running containers, and I am so that debate of'em czar containers done gone well, one place or just spin up containers and resource is. And then the more important part is How do I manage this? You said, becoming more of a platform not just an orchestration technology, but a platform for how do I manage applications where I deploy them where it makes most sense, right? Have decoupled. My application needs from the resource is, and Coburn is becoming the platform that allows me to port of Lee. I'm the old job Web logic guy, right? >> So this is like distributed Rabb logic job on steroids, running across clouds. Pretty exciting for a middle where guy This is the next generation and the way you just said, >> And two, that's the enabling infrastructure that will allow it to roll into future things like devices. Because now you've got that connection >> with the fabric, and that's working. Becomes a key part of one of the key >> things, and this is gonna be the hard part is optimization. So how do we optimize across particularly performance, but even costs? >> You're rewiring secure, exact unavailability, >> Right? So still, I think my all time favorite business book is Clayton Christians. An innovator's dilemma. And in one of the most important lessons in that book is What are you optimizing four. And by rule, you can't optimize for everything equally you have to you have to rank order. But what I find really interesting in this conversation in where we're going in the complexity of the throughput, the complexity of the size of the data sets the complexity of what am I optimizing for now? Just begs for applied a I or this is not This is not a people problem to solve. This is this >> is gonna be all right. So you look at >> that, you know, kind of opportunity to now apply A I over the top of this thing opens up tremendous opportunity. >> Standardize infrastructural auditory allows you to >> get more metrics that allows you to build models to optimize infrastructure over time. >> And humans >> just can't get their head around me because you do have to optimize across multiple mentions. His performances cost, but then that performances gets compute. It's the network, I mean. In fact, the network's always gonna be the bottlenecks. You look at it even with five G, which is an order of magnitude, more bandwidth from throughput, the network will still lag. I mean, you go back to Moore's Law, right? It's Ah, even though it's extended to 24 months, price performance doubles. The amount of data potentially can kick in and you know exponentially grow on. Networks don't keep pays, so that optimization is constantly going to be tuned. And as we get even with increases in network, we have to keep balancing that right. >> But it's also the business >> optimization beyond the infrastructure optimization. For instance, if you're running a big power generation field of a bunch of turbines, right, you may wanna optimize for maintenance because things were running at some steady state. But maybe there's oil crisis or this or that. Suddenly the price, right? You're like, forget the maintenance. Right now we've got you know, we >> got a radio controlled you start about other >> than a dynamic industry. How do I really time change the behavior, right? Right. And more and more policy driven. Where the infrastructure smart enough to react based on the policy change you made. >> That's the world we >> want to get to. And we're far away from that, right? >> Yeah. I mean, I think so. Ultimately, I think the Cuban honeys controller gets an A I overlay and the operators of the future of tuning the Aye aye engines that optimizing, >> right? Right. And then we run into the whole thing, which we've talked about many times in this building with Dr Room, A child re from a center. Then you got the whole ethics overlay on top of the thing. That's a whole different conversation from their day. So before we wrap kind of just want to give you kind of last thoughts. Um, as you know, customers Aaron, all different stages of their journey. Hopefully, most of them are at least at least off the first square, I would imagine on the monopoly board What does you know, kind of just top level things that you would tell people that they really need just to keep always at the top is they're starting to make these considerations, starting to make these investments starting to move workloads around that they should always have kind of top >> of mind. For me, it's very simple. It's really about focused on the business outcome. Leverage the best resource for the right need and design. Architectures are flexible that give you a choice. You're not locked in and look for strategic partners with this technology partners or service's partners that alive you to guide because the complexities too high the number of choices that too high. You need someone with the breath in depth to give you that platform in which you can operate on. So we want to be the digital kind of the ubiquitous platform. From a software perspective, Neck Centuries wants to be that single partner who can help them guide on the journey. So I think that would be my ask. It's not thinking about who are your strategic partners. What is your architecture and the choices you're making that gave you that flexibility to evolve. Because this is a dynamic market. What should make decisions today? I mean, I'll be the one you need >> six months even. Yeah. And And it's And that that dynamic that dynamics is, um is accelerating if you look at it. I mean, we've all seen change in the industry of decades in the industry, but the rate of change now the pace, you know, things are moving so quickly. >> I mean, little >> respond competitive or business or in our industry regulations, right. You have to be prepared for >> Yeah. Well, gentlemen, thanks for taking a few minutes and ah, great conversation. Clearly, you're in a very good space because it's not getting any less complicated in >> Thank you. Thank you. All right. Thanks, Larry. Ajay, I'm Jeff. You're watching the Cube. >> We are top of San Francisco in the Salesforce Tower at the center Innovation hub. Thanks for watching. We'll see next time. Quick

(upbeat tech music) >> Announcer: Live from Las Vegas, it's the Cube! covering, AWS re:Invent 2017 presented by AWS, intel, and our ecosystem of partners. >> Well, welcome back to the sands, we're here in Las Vegas, just off the strip, as the re:Invent show continues here with a really exciting day one. You talk about buzz on the show for it, the place has been jampacked since they opened the doors at 11 o'clock our time this morning, and continues to do so, and I imagine for the next two or three days, you're going to see a lot of people. 50,000 plus. A lot of exhibitors, a lot of people, a lot of buzz, a lot of excitement here around the AWS community. We have with us now, Chris Wahl, who is the chief technologist at Rubrik, and he knows so much about this space, it takes three hosts to surround him. >> It does, to talk to him. >> John Wahl is here, Lisa Martin to my left, Justin Warren on the far right. You're surrounded. >> I am, you've ganged up on me. >> Yeah right, and rightly so. >> Yeah, yeah. >> Justin can't wait. >> He's got the evil eye from Justin. >> Chris: This feels like a trap. >> There's some good history here going on, so we'll find out a little bit later on. First off, Chris, do welcome. Well, welcome the Cube. Tell us a little bit about Rubrik, and your place here and your feelings about the show. >> Yeah, yeah. So, Rubrik, about two and a half years in the market, about three and a half years old as a company. Really focused on solving the conundrum around, there's all this public cloud stuff out there, and everyone's kind of feeling the elephant with the blindfold on, describing it differently. And we're trying to figure out, how can we take that cloud type architecture that's out there in the world, and combine that with an almost 50 billion dollar TAM that is data protection, back up recovery archive. Put those two together to solve challenges within the enterprise is really struggling with. Onboarding into the cloud, and using those resources, as well as making sure their assets, be it the application, the data itself, or, a physical server, be protected and available for recovery in a really, really quick way. So that's kind of the high level pitch of Rubrik, around the last ten major releases of the product, and it's been a rocket ship, I've really enjoyed it. >> John: Great. >> So bigger focus in enterprise, or are you also playing with the startups and also helping the transition? >> That's a good question, I mean, originally we were kind of looking mid market, you know, like, let's kind of go for that sweet spot, but very early on, a lot of large enterprise customers came up and said, wow, you're fully restful API compliant, the full stack is distributed and scaled out, and really solves their problems, so they kind of pulled us into that space, and ever since, we've really embraced the large enterprise globally. It doesn't really matter where you are in the world, those are challenges that are kind of ubiquitous across verticals and the market. >> So, I've got a good storage and backup background, as you well know. >> Okay. >> There has been such a big shift in data storage and backup, and data protection in the last, say, three or five years. What do you think is driving that, because really, like backup recovery was always a hygiene function, it was boring, no one really wanted to spend any money on it, but now you're part of this guard of brand new ways of doing things, that has that part of the market being kind of exciting again. >> I almost feel like we got used to the horrible nature of that business. Because, as a technologist, I was a customer for about 13 years, I was in the channel for about five. And it was always, well, this is just the way it is, and you've got to put up with slower stores that were clunky, it was seen as an insurance policy. I think as the enterprise matured to the point, where everything else was amazing and hyper converged, and driven by APIs, and cloud is starting to eat up part of the data center, we finally saw, okay, this isn't going to stand, we can't operate in a model where an RTO is days, or even many hours, and it's really heavy lift, and I needed a full team of people to manage this stuff. So I think as the technology advanced, as well as kind of outpaced all of the data protection, software, and solutions are out there, it just kind of had to happen. And another thing, as cloud and object store also permeated the market, it really gave us a great opportunity to use that for long term retention. Beyond just the old tape and things like that. >> Yeah, okay. >> Yeah, that sounds fair. >> And what do you think has bene the biggest cultural change? Because, there's a lot of technology that goes into that, but you're talking about having whole teams of people who have to herd this stuff around with small little toothbrushes and stuff just to keep the thing running, whereas now, you can pretty much run it with one or two guys just sitting there, and go yeah, it just works. >> Well, it's similar to, remember when we went through virtualization, and it used to be whole armies of people managing all these pizza boxes, and tube servers, and there was just a lot of infrastructure and operation people necessary to run the data center, and then we virtualized, and I know my personal story was there was two of us managing 1,300 virtual machines. >> Wow! >> Right? So that scale is astronomical compared to what we're used to, we'll then apply that kind of mentality to data protection and it's yeah, it's a few minutes from one person, or distributed team that spends a few minutes a week, maybe a month, something like that, managing things more at the policy and the tag, and the meta data layer, and it's that journey all over again. So the nice part is we've done this before, we know it can be done, but kind of the hard part is, people are always the hardest part of the equation, and sometimes it's tough to put your hands off the handlebars of the bike and just say, I trust an intelligent system to manage this part of the stack, and I'm gonna go focus on where are we trying to go. >> Justin: Yeah, you know. >> Speaking of trust, you know, you talked about how your enterprise customers had pulled you into or up the chain there, a lot of what Andy Jussy said recently to John Furrier is, 18 billion dollar run rate, growing up 42% a year ... They haven't gotten this big with just startups alone. So he's talking about enterprise as really being on the precipice of this mass migration. How does being a young company, how does your relationship with AWS help give more credibility to Rubrik as a trusted advisor to these enterprises? >> Yeah, I'll kind of start at the end and work my way backwards. So we recently hit the advanced tier partner status with Amazon. And part of that I would site a couple of public references with Castalia schools, as well as Fuji Rabio, are two different companies in different parts of the market, but they're very much focused on, we need a partner that can bring us into the cloud, kind of on board us into that environment. AWS was the specific cloud provider they were looking to get into, without kind of operationally. That's a scary thing, you know? It's tough as an infrastructure, or as an operations focused engineer, or even as a developer I think sometimes, to say, I wanna take this data, and it represents my apps, and my servers, and my solution stack, and put that into public cloud. Either for archive and retention, or potentially to use our cloud instantiation solution that was recently renounced, where they can start building workloads into public cloud. So I think that's why, kind of at that point, we work backwards a little bit to say, as we work with the customers that we're looking to do that, before it was, well, you have to learn all this stuff, and really become super technically deep on it, and I love that article by the way, I thought it was really deep, but if you looked at this week in AWS, that by Quinny Pig on Twitter, he's always pointing out every week, the S3 bucket failure, because it's hard, cloud is really, really hard. So if you have that kind of abstraction layer that can make it really simple for customers, to on board in there. It's simple, but it's also abstracted from the nuances across multiple public cloud providers, including AWS. I think that's the magic sauce that really gets people excited about it. >> That abstraction also probably gives them a little bite more comfort, right? >> Exactly. >> Some of the sausage making, they don't have to see. >> Exactly, cuz part of our secret sauce, is as the data is entering into that environment, we're not just saying okay, it's there, done, it's now your problem. Part of our cloud data management story is that the data enters that environment, but we're constantly checking it, making sure it's valid, making sure that it's secure. We handle all of the encryption. The data efficiency. The whole end to end life cycle of the data is respected, whereas traditionally, it was, you just kind of scrape data out of the data center, you drop it off into an S3 bucket, you pray that it's going to be there when you need it, and who knows? Now it's IT offices problem. We don't just do the hand off and say good luck, we handle it from cradle to grave for all the data. >> Now, you mentioned a little bit ago, you were talking to Justin, you talked about the horrible nature of things four or five years ago, right? So, no matter what time you're in, there's always a horrible nature of things. There's always a problem, so now that whatever was the issue then, what is the issue now? As you, new capabilities will develop, it will open up a whole new Pandora's box of challenges and problems. You have unforeseen issues, so what do you guys, when you're looking at your headlights, twelve, eighteen months down the road, you say, oh yeah, this is our next one we've got to tackle, this is the baby we've got to get our arms around? >> For me more near term, it's around the transition from trusting infrastructure, to provide high availability and disaster recovery, and moving that more towards the application and the stack itself. So, holistically, in the past, you'd have two data centers, they'd replicate, one's for DR, one's not. The cloud wasn't really in that equation, and all of the redundancies was handled at the infrastructure layer. Well, okay, now, if we can kind of surround meta data around the application, provide instant search, global availability, replication, the ability to actually stand up those applications in a public cloud? Well now the question is, do I really need that infrastructure layer anymore? Do I need the second data center? Can't I just use public cloud, or an MSP, or someone that's providing Rubrik as a service as an example of a service to provide that for me? More long term, I tend to look at, kind of in the discussion that I saw between John and Andy Jassy, was around the part where I get really nerdy is around like server lists, and the ability to provide functions kind of in the data path. And now I start to imagine, okay, we're putting a lot of data for customers into public cloud and even into private object store resources, and there's the ability, I think there was Green Grass as an example, where you can kind of put that shim layer into the edge to do the function as the data's going in there. There's a lot of interesting opportunities that I'm looking forward to in the next year where, well, we already have an index of the data, we are already very cognizant and content aware when it comes to what we're protecting. Wouldn't it be cool if we could do more interesting things with the data in flight, as well as where it's ultimately resting, kind of like with the announcements with the media and the trans-coding and the video services that I think came out rather recently. So that's kind of the two stage answer to that question that I have. >> So Chris, one of the ways that AWS has succeeded, is by appealing to developers. And you're talking there about things that are in the application layer, that have nothing to do with infrastructure, and developers hate infrastructure. So what are some of the things that you're doing, that Rubrik is doing to appeal to developers specifically in being able to access their data and not have to manage it, as you say, the way we used to do it, which was, the very infrastructure centric problem? What are you using to expose the data and to manage it as a data problem, rather than an infrastructure problem? >> Well, I think that goes back to traditionally how we managed infrastructure. Especially on Prim, and it was all very manual, very imperative, meaning you're pulling the lever, and you're telling the system ... It's a dumb system that you're the intelligent layer of it and you have to control it. And that, it doesn't work in the cloud model at all, and it really, I don't think it works long term in the data center model. Because then I need, I always have to scale literally people to data. And that doesn't work. >> Yeah, humans don't scale. >> Right, we can't just get magically more of us. So what we've done differently from day one was designed a system where every component within the stack, even internal communications, are calling restful APIs, and the whole system is distributed. So there's no controller that you have to deal with, you don't have to become, you don't have to know anything about storage to use the product. It's not infrastructure bound, so you're able to control it completely through restful APIs, or through configuration management tools, cloud management platforms, etc. So if you're a developer looking to, alright, I have an application, I want to make sure that it's automatically protected as part of that process, and sent to AWS, and automatically build me a cloud instantiation, and EC2 is an instance ... Great, make one, two, maybe three API calls, you don't have to know anything about infrastructure, which is the panacea, no developer wants to like, dig into V lands and things like that. That's really cool, and it solves a very valid business case in that if one person can write the code, and it works, just repeat that process, and it scales infinitely. I don't need extra developers for that. >> So to be able to do that, I need to understand that that API exists, so what are you doing to actually show developers that hey, this thing is here and this is how it works? Here's something that you actually know how to do! How are you exposing that idea to the developers? >> Well, very early on, we worked with swagger, which ultimately has become the open API spec to that O, and so every node within our distributed system actually surfaces the entire API suite, in two formats. One, is like a playground, so you can, even if you're newer to APIs, maybe on the infrastructure side, you can kind of do a, try it now button, you can kind of say, what would happen? And it surfaces what the call would look like, and how to structure properly, and what the return codes are, but more importantly, there's also the why and the how of the API in a different kind of documentation suite, using redoc, where you can go in and literally see, okay, what's the mindset here? What's the use case? What's the example? And I feel like that's typically what's missing in a lot of these equations, where it's just, here is the nuts and bolts, here is the tactical information, here is, push this button, things happen. It's more like, here is why you would use it, here's an example, and a lot of the code to do that has already been created by our ranger team internally and made either exposed publicly as open source as privately as something that we share with out customer base. >> Cool. >> Well, Chris, you described, like you said, a rocket ship, right? You've been on for two and a half years, I think you better fasten that seatbelt, it's not going to slow down for you, I don't think. >> Chris: (laughing) I appreciate that. >> Which is a good thing, right? >> Chris: Yeah, yeah. >> Yeah, it's all good. >> Chris: Yeah, I know. >> John: I hope you didn't feel ganged up on either, right? You came out here, it was okay? >> It's a pretty friendly crowd, I appreciate that. >> I think so. Chris Wahl from Rubrik joining us here as we continue our coverage live here on the Cube, we're at AWS's re:Invent, live in Las Vegas, back with more in just a bit. (soft tech music)

(energetic music playing) >> Hi, I'm Stu Miniman here at the Silicon Ango Media Office in Palo Alto, happy to welcome back to the program Kiran Bhageshpur, who is the CEO of Igneous Systems. Kiron, great to see you. >> Great to see you again, Stu. >> Alright, so we've been really busy at theCUBE looking at so many big trends, and of course, really looking at kind of massively scalable distributed type of architectures are something we've been looking at, and something I know Igneous has been doing since the earliest days. But, the exact focus of what you've been working on, I think's changed a little bit since you first came out of Stealth and we've been looking at what your doing. So, why don't you bring our audience up to speed. >> Love to do that. It's not changed so much as expanded if you will. We launched, I believe I was here last, in October of last year, just as we were getting ready to launch. And, at that time, we launched the company and the platform, which the beginning services was object of the service, televert as a service and the enterprise data center. And, that was just the beginning. We've gone on since then, expanded the number of native services available, but really what we have done is built applications on top of that. So, the first application that we have developed and deployed at customers is backup and archive for massive file systems. So, we are talking about people who have terabytes of data, billions of files, spread across hundreds of systems. So, that's kind of been a pretty exciting thing, and it's a very unique set of challenges both for customers and for us to go forward. >> So, it's interesting, just step back for a second, object storage is something. If you talk to anybody that's a storage technologist they're like absolutely the way we need to architect things. But, usually we tend to get away from talking about object storage itself, and truly what do I do with it, what are those applications, what are those use cases. So, there's still object underneath it if I understand it right, it's just you're getting closer, moving up the stack a little bit, and getting closer to what your customers were asking for. >> Absolutely. The underlying infrastructure is still a collection of cloud services, not just object and S3, but a bunch of other services, which are very API compatible with the cloud, but, really, that doesn't matter because those are just tools. What matters is what are you doing with that, and what we are doing to begin with is really backup, archive, and discovery of massive files inside the enterprise. >> Alright, so there're some backup we've been doing for a long time, but backup has been broken. We were at the VM world show, there was a lot of buzz around some of the new companies, sometimes they called them secondary storage; you know, Rubric, Cohesity, Veem who everybody knows from the virtualization world, why don't you tell us are you part of kind of a similar wave? How do you kind of compare and contrast that to some of those other players? >> Great question. It's similar, but quite different. So, if you look at Rubric or Veem, for example, Veem really came about by doing tight integration with Veemware and doing a Veemware specific backup, which was the right technology, the right time for VMS and virtualization. Similarly, Rubric, and for that matter Cohesity, are really re-imagining data protection primarily for structured workflows, databases, physical servers, VMS, tightly integrating it and re-imagining how that feels from an experience point of view. We are really looking explicitly at unstructured data. This is data which lives on network devices from a net-app or a deliMC or a whole bunch of others and the content is really digital assets. It's data that could be media data, it could be microscopy imaging, it could be design data for a variety of work flows and this stuff continues to grow. It is monotonically increasing in every place, whether it is on premises or on the cloud or the edge, and protecting and managing this data is really a challenge and getting worse for customers. >> Yeah, the word that keeps coming up a lot is data. And, one of the things I know we've been excited about storage use to be about storing it. Now when we're talking about data, how do I leverage it? How do I get get value out of it? How do I discover different pieces of it? How have you been seeing these changes, your background you worked on some of the scale-out NASA solutions in the past, so how do we see kind of, unlocking the value of data? >> Yeah, you are absolutely right. If you go back 10 years ago, the real problem with how do I store all of this data, today there are plenty of solutions for ways you store data, especially on the primary teir, right? The challenge is really getting data from where it lives to where it's needed, whether it is backing it up or archiving it into the cloud. Being able to automatically discover things about it. Simple things like how is it growing, who is using it, how big is it, how much of it is what size of data? What about things you can infer about it by looking at the type of data it is. This is what now becomes valuable because if you look at the data sets and sizes, even modest size businesses today will have para bytes of data, billions of files, and that's challenging for any system system to go, sort of understand, unless you build it as a part of the platform. >> Okay, how about organizationally? Yah know, one of the other shifts we've seen is, you know, it used to be the storage administrator. How do I, how do I grow, how do I manage it, how do I have all of my protections and things set? A lot of the types of applications you are using are closer to the business, this is what runs the business. The business user needs to be involved. How are you setting your solution up to, you know, do what the business user needs? >> Great, yeah that's a good question. Today if you look at this data sets, this is not stuff that is an IT application. It's an end-user business focused application where they research in a life sciences world, or its designed in an electronic design world, right? And in all of these cases, essentially the end-user cares, because this data is critical to their daily working, working experience. Now, IT is clearly involved; it's a clear sort of partner of the business unit and actually operationalizing this data and making it easier to go consume. But now, it's really a joint thing, the final decision maker is always the end-user. In fact, we find ourselves in multiple places where we talk to IT, and talk to the IT teams. They get excited, but very quickly they bring in the end-users to make certain, whether the end-users are researchers or software developers, or even (mumbles) to make it so that they're comfortable with what we're talking about and they get really excited and that's sort of the starting point for our deployments. >> Yeah, we saw a similar dynamic between the business and the IT when we talked about cloud. And when I talked cloud I specifically mean public cloud and your customers, I have to imagine, they're all using public cloud in one way or another. Maybe, explain that dynamic how public cloud fits in with what your doing and how some of those IT and business people. >> Right. Look, cloud is simply the most disruptive trend in the last 10 years. In fact, you have to go back to Veemware, and Veemware's virtualization to see another trend of that magnitude. And all of our customers are embracing the cloud. They are wanting to go adopt cloud patterns, if you will. But the 180 over there massively challenged is around large data sets. Think about it, if you have terabytes of data that continues to grow, it's billion of files, it's spread across multiple geographies and dozens to hundreds of systems, it's a challenge to go leverage this in the cloud. So they're looking to ask, to be able to go chart the journey from all on premise, to a true hybrid world where they can use those cloud patterns much more effectively. >> Yah know I'm curious, and maybe it doesn't fit exactly for what Igneous is doing today. But, we've been talking about the data center versus the public cloud and a lot of those environments. I talked to some companies, that, you know, when I'm building those data legs, I'm doing that in the public cloud too. Then the discussion that's come up a lot in the past year, is Edge; so, IOT applications, we know we're going to have orders of magnitude more devices, and there's going to be a lot of data but the requirement for the data center versus the public cloud versus the Edge are very different. How does Igneous look at that? How are you having those discussions? Customers, how do they get their arms around all the various places of data?-- >> Right. You're absolutely right. The requirements are different, as in the public cloud is this massive hyper-scale, always available. The enterprise is a smaller version of that. And the Edge has a very different physical characteristics. But, what we believe is important is the same patterns, the same API's are available everywhere. And if you look at what the big public cloud providers are doing, Amazon with, you know, Snowball, and Green Grass, they're trying to go move their API's out and we completely embrace that trend. And, that's one of the reasons we built our platform to be API compatible with the cloud, with a variety of the cloud services. Because that means the services we run can run in the enterprise data center or in the public cloud or on the Edge all on a platform which is appropriate for the three. >> Yeah, and, to drill down to specifically, you say API compatible, that's S3, that's fully compatible. And do we have an API creep every cloud seems to have not only one API but many API's especially our friends at Amazon, what are you seeing out there, and what is the breath of offering they have today? >> Yeah, so, its SS3 is a constant storage leg is the obvious one, but the ones we did not talk about the last time were things like index store. So this is the equal of Amazon's dynamoDB, or Azure's table store the ability to go store a massive amount of index. But it's not just that. It's also the ability to go around compute, close to the data, which boils down to Cubanaties and containers. So all these three are part of our on the line platform. We don't talk about that to customers except after they become customers; we really focus on the application which is back up, archive, and discovery of all of their file data. >> Yeah, Kiran, take me inside the customers you are talking to; a lot of times we're like, I hear this term secondary storage out there and I worked on converge and hyper-converge stuff, you know, those terms are something that customers hear about after awhile, but they don't solve the problem. What, can you help translate for us, what's going on in your customers and why is secondary storage important to them? What's different than traditional back up, and how do you fit in? >> Right, so if you look at all of these guys, the data, the fundamental truth is data sets are growing and they are growing monotonically. Every year it is more. We've talked to folks where in the two years that we've spent as we were growing up as a company, they've sort of essentially had a 40 percent growth in their on search data sets, right? So then, the question is a couple of things. One, they clearly realize that not all of that stuff needs to live, or should live, on high performance, relatively expensive primary tiers. Right? That's the first set of piece. But the question is, how do you find out, what is active what is not active and how do you move it to the appropriate place; so this is sort of trend line and this is the patterns that they are living with. What we do is go in, very simply start off by saying, lets go find all of your filers, you know some of them, some of them you may not even know about, and let's go automatically back-up all of the data, and give you intelligence about all that. What is sort of simple intelligence. The intelligence could be how infrequently are these data sets changing, how frequently are parts of this data being accessed or modified by your applications. So that's sort of first part of this. And when this drives to is, not only does this reduce the cost of backup, which is really an insurance policy, it makes possible a bunch of intelligence about the data itself which is the beginnings of, sort of appropriately staging data on the right infrastructure. >> Alright. Kiran, you've had a number of customers since the early days talk to us a little bit about the journey you've been going on with them. How many of them have been pulling you towards the direction you are now going? What's their response been? To I guess what you call it, kind of storage as a service? >> Yeah, you know people love the whole concept of our offering as a service; initially when we talked of customers they kind of a little skeptical of our ability to go do this but they very quickly fall in love with that. It's pretty amazing. What's not to like about infrastructure that is inside your data center but that you do not have to manage at all? And when I say do not manage, people don't even look at things like drives or CPUs or network. That's not the world they live in. They live in the world of what's logically important to them, which if my backup's running, is my data being archived, how quickly is my data growing, who is accessing this data? And so on, and it goes to the next level, which is they don't have to go to manage things like software updates, just like you don't know what version of Gmail you're running or you do not know what version of S3 is being used in the cloud. Our customers don't know what version it is. Is it API level compatible or is it guarantee the services are not interrupted; and they absolutely love that aspect once they get used to it. We tell our customers, "You don't call us, we call you if there is an issue." And we're living up to that and they are pretty jazzed about that. >> Yeah, I love that. Kind of the version control thing is something we said is something, is cloud experience is actually what we want. (Mumbles) when we wrote true private cloud is exactly that; you don't know or care what version of Azure you're running, you assume that they're going to test that out and do that. Can you give us any kind of concrete examples, customers, love if you can share any names, but a lot of your customers are quite big, but what are the concrete results? What are they seeing, any good stories you can share? >> Yeah! So I give you an example of one of our largest customers, can't mention the name, but it is a large tech company in California. There's a lot of large tech companies in California-- (giggles) >> There's a bunch, yeah. >> Well, lets go through the South in California. And, these folks had an enormous amount of data. We started off by telling them, "Hey give us your most "complex systems, the ones that you are not able "to go back up today." And we started with their file systems, which were literally had this thing called file density, which is an enormous number of files in a relatively small amount of storage. So you're talking about a billion plus files and terabytes of data, and this is things that they had never been able to back up and we go off and we were able to go back it up and completely system protect. So, that's an example of a used case where we can go to a customer and allow them to accomplish what they cannot do today just from a basic back-up point of view. And, take it to the next level. In fact they did this great demo for their internal teams where they showed how easy it is to search through this data and essentially accomplish in seconds what typically, in their current world, takes hours to do. >> Okay, yeah, that's great. Yeah, sounds like you have some really good interesting, large companies there. Is that, what's the typical profile you see? Is it really companies that have specific challenges because they've got the massive scale? How far down does this scale? >> So. Uh, that's a common question that comes along. And the way I like to answer that is we are applicable to people with lots of data. It turns out it could be much smaller companies with lots of data, so we've got customers who are in the hundreds of people only world-wide, maybe two or three locations, but they are really looking at a multi-terabyte sized data problem. Similar data density problem. In fact, another one that we are working with has got 300 million files and a terabyte of data. How do you back it up? How do you go discover information about that? That's what we solve, and for these smaller companies which still have the problem, they are actually starting to find out about us and come to us. Which is really gratifying. >> Okay, well you seem pretty excited about it, about the space, what's exciting you the most about where we are today with the technology. >> The really sure is, people talk about data and they immediately go to databases, they talk about virtualization and physical servers. But that's not where the data lives. The data hasn't lived there for over a decade. And more and more of the data lives outside in files and object and there is this sort of ability to go understand that better, manage that better, protect that better and last but not least, provide intelligence to users because this data is something they care about. People are not keeping this because somebody else told them to; it is their life blood. It is their sort of livlihood, if you will, from a company point of view, and helping customers be able to go take that to the next level will bring this sort of cloud patterns to these used cases. That's pretty exciting. >> Yeah, absolutely! Want to sort of give you the final word. I hear this and I think about, you know, the whole wave of big data, what we're starting to talk about, you know, continuously with AI and ML really it is about unlocking data, so huge opportunities going forward. Any of the other trends outside what we've discussed already that you want to give us for a final word? >> You know, the last thing that I say is it is about data. It is about complete automation all across the, across the sky, weather it is storing, managing, or deriving intelligence and the reason you want to go automate all that stuff using intelligence in the software systems itself is simply because it's too large. There's no other way to go do it. And last, but not the least, all of the stuff has to be offered as a service because the cloud has gotten people really hooked on this sort of, comparatively, easy world of not having to go managing infrastructure. And I think those are the three things we should, we hold by. >> Alright, Kiran Bhageshpur, I really appreciate the update on Igneus systems. Absolutely customers dealing with massive amounts of data, how do I unlock the value of that without having to be down in the guts which has really been the history of storage. I'm Stu Miniman, thanks so much for watching theCUBE. (energetic music playing)

>> Announcer: Live from San Jose, California, it's the CUBE, covering Big Data Silicon Valley 2017. (upbeat synthesizer music) >> Live with the CUBE coverage of Big Data Silicon Valley or Big Data SV, #BigDataSV in conjunction with Strata + Hadoop. I'm John Furrier with the CUBE and my co-host George Gilbert, analyst at Wikibon. I'm excited to have our next guest, Yaron Haviv, who's the founder and CTO of iguazio, just wrote a post up on SiliconANGLE, check it out. Welcome to the CUBE. >> Thanks, John. >> Great to see you. You're in a guest blog this week on SiliconANGLE, and always great on Twitter, cause Dave Alante always liked to bring you into the contentious conversations. >> Yaron: I like the controversial ones, yes. (laughter) >> And you add a lot of good color on that. So let's just get right into it. So your company's doing some really innovative things. We were just talking before we came on camera here, about some of the amazing performance improvements you guys have on many different levels. But first take a step back, and let's talk about what this continuous analytics platform is, because it's unique, it's different, and it's got impact. Take a minute to explain. >> Sure, so first a few words on iguazio. We're developing a data platform which is unified, so basically it can ingest data through many different APIs, and it's more like a cloud service. It is for on-prem and edge locations and co-location, but it's managed more like a cloud platform so very similar experience to Amazon. >> John: It's software? >> It's software. We do integrate a lot with hardware in order to achieve our performance, which is really about 10 to 100 times faster than what exists today. We've talked to a lot of customers and what we really want to focus with customers in solving business problems, Because I think a lot of the Hadoop camp started with more solving IT problems. So IT is going kicking tires, and eventually failing based on your statistics and Gardner statistics. So what we really wanted to solve is big business problems. We figured out that this notion of pipeline architecture, where you ingest data, and then curate it, and fix it, et cetera, which was very good for the early days of Hadoop, if you think about how Hadoop started, was page ranking from Google. There was no time sensitivity. You could take days to calculate it and recalibrate your search engine. Based on new research, everyone is now looking for real time insights. So there is sensory data from (mumbles), there's stock data from exchanges, there is fraud data from banks, and you need to act very quickly. So this notion of and I can give you examples from customers, this notion of taking data, creating Parquet file and log files, and storing them in S3 and then taking Redshift and analyzing them, and then maybe a few hours later having an insight, this is not going to work. And what you need to fix is, you have to put some structure into the data. Because if you need to update a single record, you cannot just create a huge file of 10 gigabyte and then analyze it. So what we did is, basically, a mechanism where you ingest data. As you ingest the data, you can run multiple different processes on the same thing. And you can also serve the data immediately, okay? And two examples that we demonstrate here in the show, one is video surveillance, very nice movie-style example, that you, basically, ingest pictures for S3 API, for object API, you analyze the picture to detect faces, to detect scenery, to extract geolocation from pictures and all that, all those through different processes. TensorFlow doing one, serverless functions that we have, do other simpler tasks. And in the same time, you can have dashboards that just show everything. And you can have Spark, that basically does queries of where was this guys last seen? Or who was he with, you know, or think about the Boston Bomber example. You could just do it in real time. Because you don't need this notion of pipeline. And this solves very hard business problems for some of the customers we work with. >> So that's the key innovation, there's no pipe lining. And what's the secret sauce? >> So first, our system does about a couple of million of transactions per second. And we are a multi-modal database. So, basically, you can ingest data as a stream, exactly the same data could be read by Spark as a table. So you could, basically, issue a query on the same data. Give me everything that has a certain pattern or something, and could also be served immediately through RESTful APIs to a dashboard running AngularJS or something like that. So that's the secret sauce, is by having this integration, and this unique data model, it allows you all those things to work together. There are other aspects, like we have transactional semantics. One of the challenges is how do you make sure that a bunch of processes don't collide when they update the same data. So first you need a very low ground alert. 'cause each one may update to different field. Like this example that I gave with GeoData, the serverless function that does the GeoData extraction only updates the GeoData fields within the records. And maybe TensorFlow updates information about the image in a different location in the record or, potentially, a different record. So you have to have that, along with transaction safety, along with security. We have very tight security at the field level, identity level. So that's re-thinking the entire architecture. And I think what many of the companies you'll see at the show, they'll say, okay, Hadoop is given, let's build some sort of convenience tools around it, let's do some scripting, let's do automation. But serve the underlying thing, I won't use dirty words, but is not well-equipped to the new challenges of real time. We basically restructured everything, we took the notions of cloud-native architectures, we took the notions of Flash and latest Flash technologies, a lot of parallelism on CPUs. We didn't take anything for granted on the underlying architecture. >> So when you found the company, take a personal story here. What was the itch you were scratching, why did you get into this? Obviously, you have a huge tech advantage, which is, will double-down with the research piece and George will have some questions. What got you going with the company? You got a unique approach, people would love to do away with the pipeline, that sounds great. And the performance, you said about 100x. So how did you get here? (laughs) Tell the story. >> So if you know my background, I ran all the data center activities in Mellanox, and you know Mellanox, I know Kevin was here. And my role was to take Mellanox technology, which is 100 gig networking and silicon, and fit it into the different applications. So I worked with SAP HANA, I worked with Teradata, I worked on Oracle Exadata, I work with all the cloud service providers on building their own object storage and NoSQL and other solutions. I also owned all the open source activities around Hadoop and Saf and all those projects, and my role was to fix many of those. If a customer says I don't need 100 gig, it's too fast for me, how do I? And my role was to convince him that yes, I can open up all the bottleneck all the way up to your stack so you can leverage those new technologies. And for that we basically sowed inefficiencies in those stacks. >> So you had a good purview of the marketplace. >> Yaron: Yes. >> You had open source on one hand, and then all the-- >> All the storage players, >> vendors, network. >> all the database players and all the cloud service providers were my customers. So you're a very unique point where you see the trajectory of cloud. Doing things totally different, and sometimes I see the trajectory of enterprise storage, SAN, NAS, you know, all Flash, all that, legacy technologies where cloud providers are all about object, key value, NoSQL. And you're trying to convince those guys that maybe they were going the wrong way. But it's pretty hard. >> Are they going the wrong way? >> I think they are going the wrong way. Everyone, for example, is running to do NVMe over Fabric now that's the new fashion. Okay, I did the first implementation of NVMe over Fabric, in my team at Mellanox. And I really loved it, at that time, but databases cannot run on top of storage area networks. Because there are serialization problems. Okay, if you use a storage area network, that mean that every node in the cluster have to go and serialize an operation against the shared media. And that's not how Google and Amazon works. >> There's a lot more databases out there too, and a lot more data sources. You've got the Edge. >> Yeah, but all the new databases, all the modern databases, they basically shared the data across the different nodes so there are no serialization problems. So that's why Oracle doesn't scale, or scale to 10 nodes at best, with a lot of RDMA as a back plane, to allow that. And that's why Amazon can scale to a thousand nodes, or Google-- >> That's the horizontally-scalable piece that's happening. >> Yeah, because, basically, the distribution has to move into the higher layers of the data, and not the lower layers of the data. And that's really the trajectory where the traditional legacy storage and system vendors are going, and we sort of followed the way the cloud guys went, just with our knowledge of the infrastructure, we sort of did it better than what the cloud guys did. 'Cause the cloud guys focused more on the higher levels of the implementation, the algorithms, the Paxos, and all that. Their implementation is not that efficient. And we did both sides extremely efficient. >> How about the Edge? 'Cause Edge is now part of cloud, and you got cloud has got the compute, all the benefits, you were saying, and still they have their own consumption opportunities and challenges that everyone else does. But Edge is now exploding. The combination of those things coming together, at the intersection of that is deep learning, machine learning, which is powering the AI hype. So how is the Edge factoring into your plan and overall architectures for the cloud? >> Yeah, so I wrote a bunch of posts that are not published yet about the Edge, But my analysis along with your analysis and Pierre Levin's analysis, is that cloud have to start distribute more. Because if you're looking at the trends. Five gig, 5G Wi-Fi in wireless networking is going to be gigabit traffic. Gigabit to the homes, they're going to buy Google, 70 bucks a month. It's going to push a lot more bend with the Edge. On the same time, a cloud provider, is in order to lower costs and deal with energy problems they're going to rural areas. The traditional way we solve cloud problems was to put CDNs, so every time you download a picture or video, you got to a CDN. When you go to Netflix, you don't really go to Amazon, you got to a Netflix pop, one of 250 locations. The new work loads are different because they're no longer pictures that need to be cashed. First, there are a lot of data going up. Sensory data, upload files, et cetera. Data is becoming a lot more structured. Censored data is structured. All this car information will be structured. And you want to (mumbles) digest or summarize the data. So you need technologies like machine learning, NNI and all those things. You need something which is like CDNs. Just mini version of cloud that sits somewhere in between the Edge and the cloud. And this is our approach. And now because we can string grab the mini cloud, the mini Amazon in a way more dense approach, then this is a play that we're going to take. We have a very good partnership with Equinox. Which has 170 something locations with very good relations. >> So you're, essentially, going to disrupt the CDN. It's something that I've been writing about and tweeting about. CDNs were based on the old Yahoo days. Cashing images, you mentioned, give me 1999 back, please. That's old school, today's standards. So it's a whole new architecture because of how things are stored. >> You have to be a lot more distributive. >> What is the architecture? >> In our innovation, we have two layers of innovation. One is on the lower layers of, we, actually, have three main innovations. One is on the lower layers of what we discussed. The other one is the security layer, where we classify everything. Layer seven at 100 gig graphic rates. And the third one is all this notion of distributed system. We can, actually, run multiple systems in multiple locations and manage them as one logical entity through high level semantics, high level policies. >> Okay, so when we take the CUBE global, we're going to have you guys on every pop. This is a legit question. >> No it's going to take time for us. We're not going to do everything in one day and we're starting with the local problems. >> Yeah but this is digital transmissions. Stay with me for a second. Stay with this scenario. So video like Netflix is, pretty much, one dimension, it's video. They use CDNs now but when you start thinking in different content types. So, I'm going to have a video with, maybe, just CGI overlayed or social graph data coming in from tweets at the same time with Instagram pictures. I might be accessing multiple data everywhere to watch a movie or something. That would require beyond a CDN thinking. >> And you have to run continuous analytics because it can not afford batch. It can not afford a pipeline. Because you ingest picture data, you may need to add some subtext with the data and feed it, directly, to the consumer. So you have to move to those two elements of moving more stuff into the Edge and running into continuous analytics versus a batch on pipeline. >> So you think, based on that scenario I just said, that there's going to be an opportunity for somebody to take over the media landscape for sure? >> Yeah, I think if you're also looking at the statistics. I seen a nice article. I told George about it. That analyzing the Intel cheap distribution. What you see is that there is a 30% growth on Intel's cheap Intel Cloud which is faster than what most analysts anticipate in terms of cloud growth. That means, actually, that cloud is going to cannibalize Enterprise faster than what most think. Enterprise is shrinking about 7%. There is another place which is growing. It's Telcos. It's not growing like cloud but part of it is because of this move towards the Edge and the move of Telcos buying white boxes. >> And 5G and access over the top too. >> Yeah but that's server chips. >> Okay. >> There's going to be more and more computation in the different Telco locations. >> John: Oh you're talking about computer, okay. >> This is an opportunity that we can capitalize on if we run fast enough. >> It sounds as though because you've implemented these industry standard APIs that come from the, largely, the open source ecosystem, that you can propagate those to areas on the network that the vendors, who are behind those APIs can't, necessarily, do. Into the Telcos, towards the Edge. And, I assume, part of that is cause of the density and the simplicity. So, essentially, your footprint's smaller in terms of hardware and the operational simplicity is greater. Is that a fair assessment? >> Yes and also, we support a lot of Amazon compatible APIs which are RESTful, typically, HTTP based. Very convenient to work with in a cloud environment. Another thing is, because we're taking all the state on ourself, the different forms of states whether it's a message queue or a table or an object, et cetera, that makes the computation layer very simple. So one of the things that we are, also, demonstrating is the integration we have with Kubernetes that, basically, now simplifies Kubernetes. Cause you don't have to build all those different data services for cloud native infrastructure. You just run Kubernetes. We're the volume driver, we're the database, we're the message queues, we're everything underneath Kubernetes and then, you just run Spark or TensorFlow or a serverless function as a Kubernetes micro service. That allows you now, elastically, to increase the number of Spark jobs that you need or, maybe, you have another tenant. You just spun a Spark job. YARN has some of those attributes but YARN is very limited, very confined to the Hadoop Ecosystem. TensorFlow is not a Hadoop player and a bunch of those new tools are not in Hadoop players and everyone is now adopting a new way of doing streaming and they just call it serverless. serverless and streaming are very similar technologies. The advantage of serverless is all this pre-packaging and all this automation of the CICD. The continuous integration, the continuous development. So we're thinking, in order to simplify the developer in an operation aspects, we're trying to integrate more and more with cloud native approach around CICD and integration with Kubernetes and cloud native technologies. >> Would it be fair to say that from a developer or admin point of view, you're pushing out from the cloud towards the Edge faster than if the existing implementations say, the Apache Ecosystem or the AWS Ecosystem where AWS has something on the edge. I forgot whether it's Snowball or Green Grass or whatever. Where they at least get the lambda function. >> They're field by the way and it's interesting to see. One of the things they allowed lambda functions in their CDS which is going the direction I mentioned just for a minimal functionality. Another thing is they have those boxes where they have a single VM and they can run lambda function as well. But I think their ability to run computation is very limited and also, their focus is on shipping the boxes through mail and we want it to be always connected. >> Our final question for you, just to get your thoughts. Great save up, by the way. This is very informative. Maybe be should do a follow up on Skype in our studio for Silocon Friday show. Google Next was interesting. They're serious about the Enterprise but you can see that they're not yet there. What is the Enterprise readiness from your perspective? Cause Google has the tech and they try to flaunt the tech. We're great, we're Google, look at us, therefore, you should buy us. It's not that easy in the Enterprise. How would you size up the different players? Because they're all not like Amazon although Amazon is winning. You got Amazon, Azure and Google. Your thoughts on the cloud players. >> The way we attack Enterprise, we don't attack it from an Enterprise perspective or IT perspective, we take it from a business use case perspective. Especially, because we're small and we have to run fast. You need to identify a real critical business problem. We're working with stock exchanges and they have a lot of issues around monitoring the daily trade activities in real time. If you compare what we do with them on this continuous analytics notion to how they work with Excel's and Hadoops, it's totally different and now, they could do things which are way different. I think that one of the things that Hadook's customer, if Google wants to succeed against Amazon, they have to find the way of how to approach those business owners and say here's a problem Mr. Customer, here's a business challenge, here's what I'm going to solve. If they're just going to say, you know what? My VM's are cheaper than Amazon, it's not going to be a-- >> Also, they're doing the whole, they're calling lift and shift which is code word for rip and replace in the Enterprise. So that's, essentially, I guess, a good opportunity if you can get people to do that but not everyone's ripping and replacing and lifting and shifting. >> But a lot of Google advantages around areas of AI and things like that. So they should try and leverage, if you think about Amazon approach to AI, this fund the university to build a project and then set it's hours where Google created TensorFlow and created a lot of other IPs and Dataflow and all those solutions and consumered it to the community. I really love Google's approach of contributing Kubernetes, to contributing TensorFlow. And this way, they're planting the seeds so the new generation this is going to work with Kubernetes and TensorFlow who are going to say, "You know what?" "Why would I mess with this thing on (mumbles) just go and. >> Regular cloud, do multi-cloud. >> Right to the cloud. But I think a lot of criticism about Google is that they're too research oriented. They don't know how to monetize and approach the-- >> Enterprise is just a whole different drum beat and I think that's the only thing on my complaint with them, they got to get that knowledge and/or buy companies. Have a quick final point on Spanner or any analysis of Spanner that went from paper, pretty quickly, from paper to product. >> So before we started iguazio, I started Spanner quite a bit. All the publication was there and all the other things like Spanner. Spanner has the underlying layer called Colossus. And our data layer is very similar to how Colossus works. So we're very familiar. We took a lot of concepts from Spanner on our platform. >> And you like Spanner, it's legit? >> Yes, again. >> Cause you copied it. (laughs) >> Yaron: We haven't copied-- >> You borrowed some best practices. >> I think I cited about 300 research papers before we did the architecture. But we, basically, took the best of each one of them. Cause there's still a lot of issues. Most of those technologies, by the way, are designed for mechanical disks and we can talk about it in a different-- >> And you have Flash. Alright, Yaron, we have gone over here. Great segment. We're here, live in Silicon Valley, breakin it down, getting under the hood. Looking a 10X, 100X performance advantages. Keep an eye on iguazio, they're looking like they got some great products. Check them out. This is the CUBE. I'm John Furrier with George Gilbert. We'll be back with more after this short break. (upbeat synthesizer music)