>> John: Hello and welcome to this cube conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We got a great conversation on making machine learning easier and more affordable in an era where everybody wants more machine learning and AI. We're featuring Neural Magic with the CEO is also Cube alumni, Brian Steve. CEO, Great to see you Brian. Thanks for coming on this cube conversation. Talk about machine learning. >> Brian: Hey John, happy to be here again. >> John: What a buzz that's going on right now? Machine learning, one of the hottest topics, AI front and center, kind of going mainstream. We're seeing the success of the, of the kind of NextGen capabilities in the enterprise and in apps. It's a really exciting time. So perfect timing. Great, great to have this conversation. Let's start with taking a minute to explain what you guys are doing over there at Neural Magic. I know there's some history there, neural networks, MIT. But the, the convergence of what's going on, this big wave hitting, it's an exciting time for you guys. Take a minute to explain the company and your mission. >> Brian: Sure, sure, sure. So, as you said, the company's Neural Magic and spun out at MIT four plus years ago, along with some people and, and some intellectual property. And you summarize it better than I can cause you said, we're just trying to make, you know, AI that much easier. And so, but like another level of specificity around it is. You know, in the world you have a lot of like data scientists really focusing on making AI work for whatever their use case is. And then the next phase of that, then they're looking at optimizing the models that they built. And then it's not good enough just to work on models. You got to put 'em into production. So, what we do is we make it easier to optimize the models that have been developed and trained and then trying to make it super simple when it comes time to deploying those in production and managing them. >> Brian: You know, we've seen this movie before with the cloud. You start to see abstractions come out. Data science we saw like was like the, the secret art of being like a data scientist now democratization of data. You're kind of seeing a similar wave with machine learning models, foundational models, some call it developers are getting involved. Model complexity's still there, but, but it's getting easier. There's almost like the democratization happening. You got complexity, you got deployment, it's challenges, cost, you got developers involved. So it's like how do you grow it? How do you get more horsepower? And then how do you make developers productive, right? So like, this seems to be the thread. So, so where, where do you see this going? Because there's going to be a massive demand for, I want to do more with my machine learning. But what's the data source? What's the formatting? This kind of a stack develop, what, what are you guys doing to address this? Can you take us through and demystify this, this wave that's hitting, that everyone's seeing? >> Brian: Yeah. Now like you said, like, you know, the democratization of all of it. And that brings me all the way back to like the roots of open source, right? When you think about like, like back in the day you had to build your own tech stack yourself. A lot of people probably probably don't remember that. And then you went, you're building, you're always starting on a body of code or a module that was out there with open source. And I think that's what I equate to where AI has gotten to with what you were talking about the foundational models that didn't really exist years ago. So you really were like putting the layers of your models together in the formulas and it was a lot of heavy lifting. And so there was so much time spent on development. With far too few success cases, you know, to get into production to solve like a business stereo technical need. But as these, what's happening is as these models are becoming foundational. It's meaning people don't have to start from scratch. They're actually able to, you know, the avant-garde now is start with existing model that almost does what you want, but then applying your data set to it. So it's, you know, it's really the industry moving forward. And then we, you know, and, and the best thing about it is open source plays a new dimension, but this time, you know, in the, in the realm of AI. And so to us though, like, you know, I've been like, I spent a career focusing on, I think on like the, not just the technical side, but the consumption of the technology and how it's still way too hard for somebody to actually like, operationalize technology that all those vendors throw at them. So I've always been like empathetic the user around like, you know what their job is once you give them great technology. And so it's still too difficult even with the foundational models because what happens is there's really this impedance mismatch between the development of the model and then where, where the model has to live and run and be deployed and the life cycle of the model, if you will. And so what we've done in our research is we've developed techniques to introduce what's known as sparsity into a machine learning model. It's already been developed and trained. And what that sparsity does is that unlocks by making that model so much smaller. So in many cases we can make a model 90 to 95% smaller, even smaller than that in research. So, and, and so by doing that, we do that in a way that preserves all the accuracy out of the foundational model as you talked about. So now all of a sudden you get this much smaller model just as accurate. And then the even more exciting part about it is we developed a software-based engine called Deep Source. And what that, what the Inference Runtime does is takes that now sparsified model and it runs it, but because you sparsified it, it only needs a fraction of the compute that it, that it would've needed otherwise. So what we've done is make these models much faster, much smaller, and then by pairing that with an inference runtime, you now can actually deploy that model anywhere you want on commodity hardware, right? So X 86 in the cloud, X 86 in the data center arm at the edge, it's like this massive unlock that happens because you get the, the state-of-the-art models, but you get 'em, you know, on the IT assets and the commodity infrastructure. That is where all the applications are running today. >> John: I want to get into the inference piece and the deep sparse you mentioned, but I first have to ask, you mentioned open source, Dave and I with some fellow cube alumnis. We're having a chat about, you know, the iPhone and Android moment where you got proprietary versus open source. You got a similar thing happening with some of these machine learning modules where there's a lot of proprietary things happening and there's open source movement is growing. So is there a balance there? Are they all trying to do the same thing? Is it more like a chip, you know, silicons involved, all kinds of things going on that are really fascinating from a science. What's your, what's your reaction to that? >> Brian: I think it's like anything that, you know, the way we talk about AI you think had been around for decades, but the reality is it's been some of the deep learning models. When we first, when we first started taking models that the brain team was working on at Google and billing APIs around them on Google Cloud where the first cloud to even have AI services was 2015, 2016. So when you think about it, it's really been what, 6 years since like this thing is even getting lift off. So I think with that, everybody's throwing everything at it. You know, there's tons of funded hardware thrown at specialty for training or inference new companies. There's legacy companies that are getting into like AI now and whether it's a, you know, a CPU company that's now building specialized ASEX for training. There's new tech stacks proprietary software and there's a ton of asset service. So it really is, you know, what's gone from nascent 8 years ago is the wild, wild west out there. So there's a, there's a little bit of everything right now and I think that makes sense because at the early part of any industry it really becomes really specialized. And that's the, you know, showing my age of like, you know, the early pilot of the two thousands, you know, red Hat people weren't running X 86 in enterprise back then and they thought it was a toy and they certainly weren't running open source, but you really, and it made sense that they weren't because it didn't deliver what they needed to at that time. So they needed specialty stacks, they needed expensive, they needed expensive hardware that did what an Oracle database needed to do. They needed proprietary software. But what happens is that commoditizes through both hardware and through open source and the same thing's really just starting with with AI. >> John: Yeah. And I think that's a great point before we to call that out because in any industry timing's everything, right? I mean I remember back in the 80s, late 80s and 90s, AI, you know, stuff was going on and it just wasn't, there wasn't enough horsepower, there wasn't enough tech. >> Brian: Yep. >> John: You mentioned some of the processing. So AI is this industry that has all these experts who have been itch scratching that itch for decades. And now with cloud and custom silicon. The tech fundamental at the lower end of the stack, if you will, on the performance side is significantly more performant. It's there you got more capabilities. >> Brian: Yeah. >> John: Now you're kicking into more software, faster software. So it just seems like we're at a tipping point where finally it's here, like that AI moment or machine learning and now data is, is involved. So this is where organizations I see really jumping in with the CEO mandate. Hey team, make ML work for us. Go figure it out. It's got to be an advantage for us. >> Brian: Yeah. >> John: So now they go, okay boss, we will. So what, what do they do? What's the steps does an enterprise take to get machine learning into their organizations? Cause you know, it's coming down from the boards, you know, how does this work for rob? >> Brian: Yeah. Like the, you know, the, what we're seeing is it's like anything, like it's, whether that was source adoption or whether that was cloud adoption, it always starts usually with one person. And increasingly it is the CEO, which realizes they're getting further behind the competition because they're not leaning in, you know, faster. But typically it really comes down to like a really strong practitioner that's inside the organization, right? And, that realizes that the number one goal isn't doing more and just training more models and and necessarily being proprietary about it. It's really around understanding the art of the possible. Something that's grounded in the art of the possible, what, what deep learning can do today and what business outcomes you can deliver, you know, if you can employ. And then there's well proven paths through that. It's just that because of where it's been, it's not that industrialized today. It's very much, you know, you see ML project by ML project is very snowflakey, right? And that was kind of the early days of open source as well. And so, we're just starting to get to the point where it's getting easier, it's getting more industrialized, there's less steps, there's less burdensome on developers, there's less burdensome on, on the deployment side. And we're trying to bring that, that whole last mile by saying, you know what? Deploying deep learning and AI models should be as easy as the as to deploy your application, right? You shouldn't have to take an extra step to deploy an AI model. It shouldn't have to require a new hardware, it shouldn't require a new process, a new DevOps model. It should be as simple as what you're already doing. >> John: What is the best practice for companies to effectively bring an acceptable level of machine learning and performance into their organizations? >> Brian: Yeah, I think like the, the number one start is like what you hinted at before is they, they have to know the use case. They have to, in most cases, you're going to find across every industry you know, that that problem's been tackled by some company, right? And then you have to have the best practice around fine-tuning the models already exist. So fine tuning that existing model. That foundational model on your unique dataset. You, you know, if you are in medical instruments, it's not good enough to identify that it's a medical instrument in the picture. You got to know what type of medical instrument. So there's always a fine tuning step. And so we've created open source tools that make it easy for you to do two things at once. You can fine tune that existing foundational model, whether that's in the language space or whether that's in the vision space. You can fine tune that on your dataset. And at the same time you get an optimized model that comes out the other end. So you get kind of both things. So you, you no longer have to worry about you're, we're freeing you from worrying about the complexity of that transfer learning, if you will. And we're freeing you from worrying about, well where am I going to deploy the model? Where does it need to be? Does it need to be on a device, an edge, a data center, a cloud edge? What kind of hardware is it? Is there enough hardware there? We're liberating you from all of that. Because what you want, what you can count on is there'll always be commodity capability, commodity CPUs where you want to deploy in abundance cause that's where your application is. And so all of a sudden we're just freeing you of that, of that whole step. >> John: Okay. Let's get into deep sparse because you mentioned that earlier. What inspired the creation of deep sparse and how does it differ from any other solutions in the market that are out there? >> Brian: Sure. So, so where unique is it? It starts by, by two things. One is what the industry's pretty good at from the optimization side is they're good at like this thing called quantization, which turns like, you know, big numbers into small numbers, lower precision. So a 32 bit representation of a, of AI weight into a bit. And they're good at like cutting out layers, which also takes away accuracy. What we've figured out is to take those, the industry techniques for those that are best practice, but we combined it with unstructured varsity. So by reducing that model by 90 to 95% in size, that's great because it's made it smaller. But we've taken that when it's the deep sparse engine, when you deploy it that looks at that model and says, because it's so much smaller, I no longer have to run the part of the model that's been essentially sparsified. So what that's done is, it's meant that you no longer need a supercomputer to run models because there's not nearly as much math and processing as there was before the model was optimized. So now what happens is, every CPU platform out there has, has an enormous amount of compute because we've sparsified the rest of it away. So you can pick a, you can pick your, your laptop and you have enough compute to run state-of-the-art models. The second thing that, and you need a software engine to do that cause it ignores the parts of the models. It doesn't need to run, which is what like specialized hardware can't do. The second part is it's then turned into a memory efficiency problem. So it's really around just getting memory, getting the models loaded into the cash of the computer and keeping it there. Never having to go back out to memory. So, so our techniques are both, we reduce the model size and then we only run the part of the model that matters and then we keep it all in cash. And so what that does is it gets us to like these, these low, low latency faster and we're able to increase, you know, the CPU processing by an order magnitude. >> John: Yeah. That low latency is key. And you got developers, you know, co coding super fast. We'll get to the developer angle in a second. I want to just follow up on this, this motivation behind the, the deep sparse because you know, as we were talking earlier before we came on camera about the old days, I mean, not too long ago, virtualization and VMware abstracted away the os from, from the hardware rights and the server virtualization changed the game. >> Brian: Yeah. >> John: And that basically invented cloud computing as we know it today. So, so we see that abstraction. >> Brian: Yeah. >> John: There seems to be a motivation behind abstracting the way the machine learning models away from the hardware. And that seems to be bringing advantages to the AI growth. Can you elaborate on, is that true? And it's, what's your comment? >> Brian: It's true. I think it's true for us. I don't think the industry's there yet, honestly. Cause I think the industry still is of that mindset that if I took, if it took these expensive GPUs to train my model, then I want to run my model on those same expensive GPUs. Because there's often like not a separation between the people that are developing AI and the people that have to manage and deploy at where you need it. So the reality is, is that that's everything that we're after. Like, do we decrease the cost? Yes. Do we make the models smaller? Yes. Do we make them faster? A yes. But I think the most amazing power is that we've turned AI into a docker based microservice. And so like who in the industry wants to deploy their apps the old way on a os without virtualization, without docker, without Kubernetes, without microservices, without service mesh without serverless. You want all those tools for your apps by converting AI models. So they can be run inside a docker container with no apologies around latency and performance cause it's faster. You get the best of that whole world that you just talked about, which is, you know, what we're calling, you know, software delivered AI. So now the AI lives in the same world. Organizations that have gone through that digital cloud transformation with their app infrastructure. AI fits into that world. >> John: And this is where the abstraction concepts matter. When you have these inflection points, the convergence of compute data, machine learning that powers AI, it really becomes a developer opportunity. Because now applications and businesses, when they actually go through the digital transformation, their businesses are completely transformed. There is no IT. Developers are the application. They are the company, right? So AI will be part of whatever business or app will be out there. So there is a application developer angle here. Brian, can you explain >> Brian: Oh completely. >> John: how they're going to use this? Because you mentioned docker container microservice, I mean this really is an insane flipping of the script for developers. >> Brian: Yeah. >> John: So what's that look like? >> Brian: Well speak, it's because like AI's kind of, I mean, again, like it's come so fast. So you figure there's my app team and here's my AI team, right? And they're in different places and the AI team is dragging in specialized infrastructure in support of that as well. And that's not how app developers think. Like they've ran on fungible infrastructure that subtracted and virtualized forever, right? And so what we've done is we've, in addition to fitting into that world that they, that they like, we've also made it simple for them for they don't have to be a machine learning engineer to be able to experiment with these foundational models and transfer learning 'em. We've done that. So they can do that in a couple of commands and it has a simple API that they can either link to their application directly as a library to make difference calls or they can stand it up as a standalone, you know, scale up, scale out inference server. They get two choices. But it really fits into that, you know, you know that world that the modern developer, whether they're just using Python or C or otherwise, we made it just simple. So as opposed to like Go learn something else, they kind of don't have to. So in a way though, it's made it. It's almost made it hard because people expect when we talk to 'em for the first time to be the old way. Like, how do you look like a piece of hardware? Are you compatible with my existing hardware that runs ML? Like, no, we're, we're not. Because you don't need that stack anymore. All you need is a library called to make your prediction and that's it. That's it. >> John: Well, I mean, we were joking on Twitter the other day with someone saying, is AI a pet or a cattle? Right? Because they love their, their AI bots right now. So, so I'd say pet there. But you look at a lot of, there's going to be a lot of AI. So on a more serious note, you mentioned in microservices, will deep sparse have an API for developers? And how does that look like? What do I do? >> Brian: Yeah. >> John: tell me what my, as a developer, what's the roadmap look like? What's the >> Brian: Yeah, it, it really looks, it really can go in both modes. It can go in a standalone server mode where it handles, you know, rest API and it can scale out with ES as the workload comes up and scale back and like try to make hardware do that. Hardware may scale back, but it's just sitting there dormant, you know, so with this, it scales the same way your application needs to. And then for a developer, they basically just, they just, the PIP install de sparse, you know, has one commanded to do an install, and then they do two calls, really. The first call is a library call that the app makes to create the model. And models really already trained, but they, it's called a model create call. And the second command they do is they make a call to do a prediction. And it's as simple as that. So it's, it's AI's as simple as using any other library that the developers are already using, which I, which sounds hard to fathom because it is just so simplified. >> John: Software delivered AI. Okay, that's a cool thing. I believe in it personally. I think that's the way to go. I think there's going to be plenty of hardware options if you look at the advances of cloud players that got more silicon coming out. Yeah. More GPU. I mean, there's more instance, I mean, everything's out there right now. So the question is how does that evolve in your mind? Because that's seems to be key. You have open source projects emerging. What, what path does this take? Is there a parallel mental model that you see, Brian, that is similar? You mentioned open source earlier. Is it more like a VMware virtualization thing or is it more of a cloud thing? Is there Yeah. Is it going to evolve in a, in a trajectory that looks similar to what we might've seen in the past? >> Brian: Yeah, we're, you know, when I, when when I got involved with the company, what I, when I thought about it and I was reasoning about it, like, do you, you know, you want to, like, we all do when you want to join something full-time. I thought about it and said, where will the industry eventually get to? Right? To fully realize the value of, of deep learning and what's plausible as it evolves. And to me, like I, I know it's the old adage of, you know, you know, software, its hardware, cloudy software. But it truly was like, you know, we can solve these problems in software. Like there's nothing special that's happening at the hardware layer and the processing AI. The reality is that it's just early in the industry. So the view that that we had was like, this is eventually the best place where the industry will be, is the liberation of being able to run AI anywhere. Like you're really not democratizing, you democratize the model. But if you can't run the model anywhere you want because these models are getting bigger and bigger with these large language models, then you're kind of not democratizing. And if you got to go and like by a cluster to run this thing on. So the democratization comes by if all of a sudden that model can be consumed anywhere on demand without planning, without provisioning, wherever infrastructure is. And so I think that's with or without Neural Magic, that's where the industry will go and will get to. I think we're the leaders, leaders in getting it there. It's right because we're more advanced on these techniques. >> John: Yeah. And your background too. You've seen OpenStack, pre-cloud, you saw open source grow and still exponentially growing. And so you have the same similar dynamic with machine learning models growing. And they're also segmenting into almost a, an ML stack or foundational model as we talk about. So you're starting to see the formation of tooling inference. So a lot of components coming. It's almost a stack, it's almost a, it literally is like an operating system problem space, you know? How do you run things, how do you link things? How do you bring things together? Is that what's going on here? Is this like a data modeling operating environment kind of red hat type thing going on? Like. >> Brian: Yeah. Yeah. Like I think there is, you know, I thought about that too. And I think there is the role of like distribution, because the industrialization not happening fast enough of this. Like, can I go back to like every customers, every, every user does it in their own kind of way. Like it's not, everyone's a little bit of a snowflake. And I think that's okay. There's definitely plenty of companies that want to come in and say, well, this is the way it's going to be and we industrialize it as long as you do it our way. The reality is technology doesn't get industrialized by one company just saying, do it our way. And so that's why like we've taken the approach through open source by saying like, Hey, you haven't really industrialized it if you said. We made it simple, but you always got to run AI here. Yeah, right. You only like really industrialize it if you break it down into components that are simple to use and they work integrated in the stack the way you want them to. And so to me, that first principles was getting thing into microservices and dockers that could be run on VMware, OpenShare on the cloud in the edge. And so that's the, that's the real part that we're happening with. The other part, like I do agree, like I think it's going to quickly move into less about the model. Less about the training of the model and the transfer learning, you know, the data set of the model. We're taking away the complexity of optimization. Giving liberating deployment to be anywhere. And I think the last mile, John is going to be around the ML ops around that. Because it's easy to think of like soft now that it's just a software problem, we've turned it into a software problem. So it's easy to think of software as like kind of a point release, but that's not the reality, right? It's a life cycle. And it's, and so I think ML very much brings in the what is the lifecycle of that deployment? And, you know, you get into more interesting conversations, to be honest than like, once you've deployed in a docking container is around like model drift and accuracy and the dataset changes and the user changes is how do you become from an ML perspective of where of that sending signal back retraining. And, and that's where I think a lot of the, in more of the innovation's going to start to move there. >> John: Yeah. And software also, the software problem, the software opportunity as well is developer focused. And if you look at the cloud native landscape now, similar stacks developing a lot of components. A lot of things to, to stitch together a lot of things that are automating under the hood. A lot of developer productivity conversations. I think this is going to go down that same road. I want to get your thoughts because developers will set the pace. And this is something that's clear in this next wave developer productivity. They're the defacto standards bodies. They will decide what microservices check, API check. Now, skill gap is going to be a problem because it's relatively new. So model sprawl, model sizes, proprietary versus open. There has to be a way to kind of crunch that down into a, like a DevOps, like just make it, get the developer out of the, the muck. So what's your view? Are we early days like that? Or what's the young kid in college studying CS or whatever degree who comes into this with, with both feet? What are they doing? >> Brian: I'll probably say like the, the non-popular answer to that. A little bit is it's happening so fast that it's going to get kind of boring fast. Meaning like, yeah, you could go to school and go to MIT, right? Sorry. Like, and you could get a hold through end like becoming a model architect, like inventing the next model, right? And the layers and combining 'em and et cetera, et cetera. And then what operators and, and building a model that's bigger than the last one and trains faster, right? And there will be those people, right? That actually, like they're building the engines the same way. You know, I grew up as an infrastructure software developer. There's not a lot of companies that hire those anymore because they're all sitting inside of three big clouds. Yeah. Right? So you better be a good app developer, but I think what you're going to see is before you had to be everything, you had to be the, if you were going to use infrastructure, you had to know how to build infrastructure. And I think the same thing's true around is quickly exiting ML is to be able to use ML in your company, you better be like, great at every aspect of ML, including every intricacy inside of the model and every operation's doing, that's quickly changing. Like, you're going to start with a starting point. You know, in the future you're not going to be like cracking open these GPT models, you're going to just be pulling them off the shelf, fine tuning 'em and go. You don't have to invent it. You don't have to understand it. And I think that's going to be a pivot point, you know, in the industry between, you know, what's the future? What's, what's the future of a, a data scientist? ML engineer researcher look like? >> John: I think that's, the outcome's going to be determined. I mean, you mentioned, you know, doing it yourself what an SRE is for a Google with the servers scale's huge. So yeah, it might have to, at the beginning get boring, you get obsolete quickly, but that means it's progressing. So, The scale becomes huge. And that's where I think it's going to be interesting when we see that scale. >> Brian: Yep. Yeah, I think that's right. I think that's right. And we always, and, and what I've always said, and much the, again, the distribute into my ML team is that I want every developer to be as adept at being able take advantage of ML as non ML engineer, right? It's got to be that simple. And I think, I think it's getting there. I really do. >> John: Well, Brian, great, great to have you on theCUBE here on this cube conversation. As part of the startup showcase that's coming up. You're going to be featured. Or your company would featured on the upcoming ABRA startup showcase on making machine learning easier and more affordable as more machine learning models come in. You guys got deep sparse and some great technology. We're going to dig into that next time. I'll give you the final word right now. What do you see for the company? What are you guys looking for? Give a plug for the company right now. >> Brian: Oh, give a plug that I haven't already doubled in as the plug. >> John: You're hiring engineers, I assume from MIT and other places. >> Brian: Yep. I think like the, the biggest thing is like, like we're on the developer side. We're here to make this easy. The majority of inference today is, is on CPUs already, believe it or not, as much as kind of, we like to talk about hardware and specialized hardware. The majority is already on CPUs. We're basically bringing 95% cost savings to CPUs through this acceleration. So, but we're trying to do it in a way that makes it community first. So I think the, the shout out would be come find the Neural Magic community and engage with us and you'll find, you know, a thousand other like-minded people in Slack that are willing to help you as well as our engineers. And, and let's, let's go take on some successful AI deployments. >> John: Exciting times. This is, I think one of the pivotal moments, NextGen data, machine learning, and now starting to see AI not be that chat bot, just, you know, customer support or some basic natural language processing thing. You're starting to see real innovation. Brian Stevens, CEO of Neural Magic, bringing the magic here. Thanks for the time. Great conversation. >> Brian: Thanks John. >> John: Thanks for joining me. >> Brian: Cheers. Thank you. >> John: Okay. I'm John Furrier, host of theCUBE here in Palo Alto, California for this cube conversation with Brian Stevens. Thanks for watching.

(upbeat music) >> Live from San Francisco, it's theCUBE covering RSA Conference 2019. Brought to you by Forescout. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at the RSA North American conference in Moscone. They finally finished the remodel. We're excited to be here. We're in the Forescout Booth and our next guest is here. He's Scott Stevens, the SVP Global Systems Engineering for Palo Alto Networks. How're you doing? >> I'm doing well. How you doing? >> Good, so first impressions of the show. I mean, it always amazes me when we come to RSA. We go to a lot of shows but just the size and the scale and the buzz and the activity here is second to none. >> It's incredibly crowded. I've been trying to walk the halls here, is a bit of a mess, so yes. (both laughing) >> Well plus nobody can find their way through the new Moscone. Small detail. >> Well they're connected different now so it's pretty confusing. >> Right, all right, let's jump into it. As I look over your shoulder I see zero trust, I see zero trust. Everybody's about zero trust. We had Chason from Forescout last year. He was talking about zero trust. >> Yep. You guys are talking about zero trust. What is exactly is zero trust? And how should people be thinking about zero trust? >> Yeah it's kind of, it's become buzzword bingo along the way, hasn't it? >> Right, right, it has. >> Yeah, so yeah we've been working with Forescout here for about six years now looking at zero trust architectures. The way, I think the fundamental way you look at zero trust is it's an architectural approach to how do you secure your network focused on what's most important and so you focus on the data that's most, that's key to your business, and you build your security framework from the data out. And so there's all kinds of buzzword bingo we can play about what zero trust means, but what it allows us to do is to create the right segmentation strategy starting in the data center of the cloud and moving back towards those accessing the data and how do you segment and control that traffic 'cause fundamentally what we're dealing with in security is two basic problems that we have to there's many problems but two big problems we have to deal with. >> Right, right. First is credential based attacks and so do we have somebody with stolen credential in the network stealing our data? Or do we have an insider who has credentials but they're malicious, they're actually stealing content from the company. The second big problem is software based attacks. Malware, exploits, scripts right? And so how do we segment the network where we can enforce user behavior and we can watch for malicious software so we can prevent both of those occurrences through one architectural framework and I think zero trust gives us that template building block absent of the buzzword, on how we build out those networks 'cause everybody's enterprise network is a little bit different. >> Right, so it really goes back to kind of roles and access and those types of things 'cause the first one you describe a credential one if it's somebody in there they have every right to be there but they're doing behavior that's not necessarily what you expect them to do, what you want them to do is atypical, right? >> Right. >> So it's a kind of identity and rights management or is this a different approach or the most sophisticated approach? How's it been different before? >> No that's a great question. And we have to build those things together. So on the Palo Alto Networks side what we do is we do enforcement. Layer 7 enforcement based on identity. So based on who the user is and what their rights are we are able to control what they're allowed access to or what they're not allowed access to and of course if you've got a malicious insider. Or somebody that's logged in with stolen credentials we can prevent them from doing what they're not allowed to do. And working here with Forescout, we've done a lot of really good integration with them on that identity mapping constructs. So how do they help us understand all the identities and all the devices in the network so we can then map that to that user posture and control at Layer 7 what they're allowed to do or not allowed to do. >> Right, and then on the micro-segmentation, it's always a, how far you segment? You can segment to one that doesn't really do you much good right? (Scott laughing) It's just one. So what are some of the things people should think about in their segmentation strategy? >> Well again I think you need to start with what's most important and so if I take a cloud or a data center, clouds and data centers as a starting point or generally all the same. (Jeff laughing) Well and how we segment is actually the same. And so we have this, sometimes we think that clouds are more difficult to secure than data centers, they are the same basically we've got north-south traffic, or east-west traffic, how do we, how do we inspect them how do we, how do we segment that? But if you start with what's most important and work your way. If you tell somebody that you need to micro-segment their network they're going to be done in 14 years, alright? So how do we focus on what's the most important, critical data to their business? And if we stratify their datasets and their applications that access that data and then move down, we may have 50% of the applications in their cloud or data center that we don't micro-segment at all because they're not critical to the business. They're useful to the employees, but if something goes wrong there, no big deal. >> Right. No impact to the business. >> Right. And so micro-segmentation isn't just a conversation of where we have to do things, but it's a conversation contextually in terms of what's relevant, where it is important to do that. >> Right. And then where do we, where do you do a much less robust job. >> Right. You always have to have inspection and visibility but there are parts of your network where you're going to be somewhat passive about it. But there're parts of your network you're going to be very aggressive, multi-factor authentication, tight user identity mapping, all of the different aspects. How do we watch for malware? How do we watch for exploits? >> Curious on doing that segmentation on the value of the dataset 'cause there's some obvious ones that jumps to the top of the list but I'm just curious if customers get into a situation where they really haven't thought about it once you get ten steps down the list from the top ones or if you do a force priority? >> Yep. >> And then the other thing I just think is really interesting the time we live today is that a lot of the hackers are not necessarily motivated by personal information or trying to suck a little bit of money out of your bank account, but other types of data that they want to use for other types of actions like we saw in the election and some of these other >> Right. >> kind of, I want to say softer, kind of softer uses of softer data for different types of activity than the traditional ransomware or malware. And how does that map back to, oh I didn't necessarily think that was an important piece of data but that's a shifting landscape in that part of organization . >> Certainly, yeah you need to take a look at what's most important. You can stratify into a couple tiers so you're going to have the top ten applications and datasets that are critical to the business. And we know if something happens there we have to publicly announce. Okay there, that you're going to do a really nice segmentation strategy and implement a full zero trust where we're controlling user access, doing full malware inspection, everything there. You're going to have a second tier of data which kind of gets into your soft target conversation where maybe we're a little less robust with some of the user segmentation and the application controls but we're as aggressively robust on the malware and software based threats. And frankly being able to inspect and control, find malware, find commander control, find exploits in, going in or out of those parts of the network, that is very simple to do and zero trust helps us to find where are those locations on the data center cloud side but also throughout the enterprise and where should we have those sensors that are enforcing that behavior. >> Right, just traffic is exploding right? Everything's connected. Billions of billions of devices, et cetera, et cetera. We don't need to go through the numbers It's big. So clearly automation is more and more important as we go forward. Lot of buzz about machine learning artificial intelligence applying it. Both the bad guys have it and the good guys have it. A lot of interesting kind of subtopics in terms of training models and how do you train models and the other right type of data. But as you kind of sit where you're sitting and net, net is just a lot more traffic going through the network >> Yep. >> whether it's good, bad, or otherwise. How do you guys kind of look at automation? How are you kind of looking forward for using artificial intelligence and some of these newer techniques to help just basically get through, get through the mass if you will? >> So I think there's two ways to think about artificial intelligence, machine learning, big data analytics, All those, >> All those good ones. >> Now we run another buzzword bingo right? >> Right, right (laughs) >> But the first is if we're looking at how are we dealing with malware and finding undone malware in blocking it, we've been doing that for years. And so the platform we have uses big data analytics and machine learning in the cloud to process and find all of the unknown malware, make it known and be able to block it. So we find 20 to 30 thousand brand new pieces of malware every day and within five minutes of finding them, >> finding 30,000 >> every day. So analyzing millions and millions of files every day to figure out which ones are malicious. And once we know within five minutes, we're updating the security posture for all of our connected security devices globally. So whether it's endpoint software or it's our inline next gen firewalls, we're updating all of our, all of our signatures so that the unknown is now known and the known can be blocked. And so that's whether we're watching the block the malware coming in, or the command-and-control it's using via DNS and URL to communicate and start whatever it's going to do, and you mentioned crypto lockers and all kinds of things that can happen. And so that's one vector of using ML, AI and ML, to prevent the ability for these attacks to succeed. Now the other side of it I think you're alluding to a little bit more is how do we then take some of the knowledge and the lessons we've learned for what we've been doing now for many years in discovering malware and apply that same AI and ML locally to that customer so that they can detect very creative attacks. Very evasive attacks. Or that insider threat, that employee who's behaving inappropriately but quietly. And so we've announced over the last week what we call the Cortex XDR set of offerings that involves allowing the customer to build an aggregated data lake which uses the zero trust framework which tells us how to segment, also put sensors and all the places of the network both network sensors and endpoint as we look at how do you secure the endpoint as well as how do you secure the network links, and using those together we're able to stitch those logs together in the data lake. That machine learning can now be applied to on a customer by customer basis, to find maybe somebody was able to evade 'cause they're very creative, or that insider threat again, who isn't breaking security rules but they're being evasive? We can now find them through machine learning. >> Right. >> And the cool thing about zero trust is the prevention architecture that we needed for zero trust becomes the sensor architecture for this machine learning engine. You get dual purpose use out of the architecture of zero trust to solve both the inline prevention and their response architecture that you need. >> Right. >> It's a long answer, I know. >> It's a crazy space, I mean, it's just fast. I mean the numbers in the mass of just throughput in this area is just fascinating. >> Yes. >> And so we're here in the Forescout booth and they've got a unique take on all the objects and everything is connected to the networks. We've heard from people earlier today is 50, 60, 70% more things connected than they ever even, than they ever even thought. Most of them not malicious but just people plug it in at various remote offices and that and that. >> Yeah, well IoT, the next buzzword bingo >> Right, right, right, there you go. We'll hit them all. (both laughing) what are we missing? So how are you guys working with Forescout, how do the two solutions work together to get a one plus one makes three? >> Yeah, as we were talking a little bit before getting that concept of what are all these connected devices. What is the device itself and who are the users attached to those devices? Forescout has that insight. So we don't do, I always look at that is identity assertion. Device aware identity assertion so how do we define what they are and who they are. What we do then is in working with Forescout we take that knowledge that they have and that turns into identity and device enforcement. And that's how we enforce those postures so that I know employee A isn't allowed to the intellectual property datasets. Employee B is. Well in the old world of security you just have a rule for how do you get to that. In what we do now with layers with user based and application controls, I can, on a user by user basis determine what they're allowed to do, and not allowed to do. Forescout gives us that insight so that we are able to enforce. They handle making sure they know exactly who it is so we enforce it properly. >> Right, and for the devices, right? 'cause you basically assigned almost like an identity and a role to a device. >> Exactly, and then you don't end up with this weird spaghetti network topology where okay, we have to put all of our IoT devices on these 14 VLANs and we're going to extend them all across our enterprise not, all that goes away. >> All kinds of natural acts. >> Right. All right, so Scott, I'll give you the last word before you sign off. As we look forward to 2019, and I can't believe it's March already, (Scott laughing) Scary. What's some of your priorities? What are you working on? What's the rest of the year look like for you? >> I think, you're back to buzzword bingo, we're spending a lot of time right now looking at how do we help our customers with that generating that data lake so they can help figure out what's happening within their infrastructure. And as you pivot from the security posture which of course is where we're always going to pay attention and you help them think about operationalizing that. And how do we help the Sec Ops, or the SOC, figure out what's going on in their network. The data they're dealing with is massive. And so they're looking at haystacks and haystacks and haystacks. >> Right. >> And part of the goal of what we're trying to do is help them burn down those haystacks and hand them needles 'cause in the end all they care about is the needles. The hay is getting in the way. And so there's a lot of work that we're doing around machine learning, around optimizing workloads and automation so that we can reduce that complexity. We've been doing it for the last 10 years for network security. How do we take the complexity of all the things we used to do separate and simplify them and automate so we've automated the feedback loops for network security, for the next gen firewall. We've simplified what you can do on the endpoint for traps and how we protect that. We've done with the integration with Forescout we're simplifying how you map that identity back and forth. And I think for the rest of the year it's really about simplifying operations and helping quickly determine when something is wrong in the network so you can fix it fast. >> Right. >> Before you're dealing with an exfiltration problem. >> Not 150 days or whatever the >> Way too long. >> crazy average stat is. >> |How about four hours. What if we try for four hours? >> Yeah that's better. more better, more better. (laughing) All right, Scott, thanks for sharing the insight. >> Thanks for your time. >> Let's go burn some haystacks. He's Scott, I'm Jeff. You're watching theCUBE. We're at RSA 2019 in San Francisco. Thanks for watching. We'll be right back. (upbeat music)

>> Announcer: Live from New Orleans, Louisiana, it's theCUBE covering .NEXT conference, 2018. Brought to you by Nutanix. >> Welcome back I'm Stu Miniman with my co-host Keith Townsend, and you're watching theCUBE, the leader in live tech coverage. We're at Nutanix NEXT 2018, happy to welcome to the program Brian Stevens, who's the CTO of Google Cloud, had on the program many times. Brian, always a pleasure to catch up with you. >> Thanks, glad to be here. >> Stu: And have a first time guest, Ricardo Jenez, who's the SVP of Development at Nutanix. Thank you so much for joining us. >> Well, thank you for being, thanks for being here. >> Alright, so Ricardo you've only been with Nutanix for three months. I believe this is probably your first .NEXT? >> Ricardo: Yes, it is. >> So give us a little bit about your role and what brings you to us today. >> So I'm responsible for some of the core data path and per some products. So, you know, a lot of it has to do with how do we end up delivering value to our customers and actually end up having predictable, scalable HCI solutions. So, that's really what I'm focused on and focusing on sort of improving our ability to deliver products more quickly. >> So Brian, last year Diane Greene was up on stage talking about the partnership and what was happening here, see Google at the show, obviously a tighter partnership for Nutanix, but give us the update on-- >> We're downgrading. >> Yeah? >> Slumming it. >> Not at all. Not at all. I wish we had enough time to get into the weeds on some of the stuff you're working on, but tell us what brings you here and what kind of stuff you're poking at these days. >> Geez, I think I met Sunil Potti a couple years ago, just at the very beginning of trying to find sort of the intersection between Google Cloud and Nutanix. I mean, Nutanix is largely redefining what IT looks like on premise. We believe we're doing that in cloud, and you really just want to eliminate the impedance between on-premise and public cloud, and so the work with Nutanix is all like what can we do to actually make it more seamless for users that want to use core cloud technology. >> Yeah Brian, you're one of those people that we would say have enterprise DNA in what they've done in their background. People on the outside will always say, Well you know, it's Google, it's Google-y. It's too smart for us. >> Brian: Enterprise DNA is still sexy. >> Yeah, I mean look, there's a lot of enterprises out there, and while yes, the other startups. Maybe we talk a little bit about what that means inside Google. >> Oh my gosh, yeah it was quite a pivot for Google, you know. It was amazing technology, but the customer that you were serving with Google Cloud was already inside of Google. You were serving Surge and YouTube and ads. So you end being up, a really technically, but close relationship. And so what enterprise is, a couple things, it's been a cultural transformation inside of Google, it's been obviously working with enterprise customers globally and building that go to market model and motion that you can sell, but we want a really technical engineered partnership with our customers. So it's not a vendor relationship. So building all that out, we thing we're unique with that. And then the other part I think you were alluding to early before we went on mic, was just around enterprise has a increased set of requirements on what we deliver them from a capabilities perspective, from a security aspect, from a telemetry aspect. And then it's all like how do we actually slipstream into their process, rather than just redefine everything. So to us, that's a big part of what our enterprise pivot's been, for the last three, four years. >> So Ricardo, you have some background at Google. What brought you to Nutanix? What appealed to you? >> Well you know, more than anything else, I think Nutanix has set themselves up, to basically take that experience it has in enterprise, and translate that into the cloud. So when I was at Google, I actually worked on the Google search appliance, which was Google's first-- >> I remember that. >> You remember that. >> I had that one. >> Little yellow boxes, sometimes blue boxes, and that was a great experience. So I'm really happy to hear that Brain talk about the transformation that has happened within Google. But you know, being at Nutanix, the ability to take that experience very close with the work loads that customers are running, and then being able to work with a partner like Google and actually be able to have hybrid clouds where internal private cloud plus having public cloud providers, that really ends up changing the game for a lot of enterprises. >> Yep. Brain, One of the things we've been struggling with as an industry is, you know, it's application mobility. Data, where it goes. Nutanix has been talking about really hybrid cloud from their standpoint. We've talked with you before about where Kubernetes fits into this. Application portability, you just made an acquisition, today was announced, Velostrata. Give us your state on where those things added, it's a big gnarly topic. >> It was just more friction, like public cloud offers great capability that's going to be used not necessarily completely instead of, but in companion to, you know, application services. But there was still that friction around in the early incarnation, it was like it's VMware in this environment or KVM in this environment, and it's a whole nother AMI kind of model here. So the ability to use it, there was a tax. And then there also wasn't that portability and that lightweight aspect that you'd want from an application containerization. I mean, you want what you have on your phone. You want that ability to install apps anywhere. And cloud and IT infrastructure should be exactly the same way. So that's a big part of our investment in containerization. You know Google, back when I was at Red Hat, was investing in cgroups back when there was a kernel, way back then to kind of build that first incarnation of containers in Linux. Along comes Docker to standardize that. I mean, it's an amazing gift to the world. And then Kubernetes is, we're just moving up the stack, on how do you orchestrate it. So sure, companies like Velostrata are really interesting because you have, you know, beyond having Kubernetes platform everywhere, yeah we'll say it's the de facto, but that doesn't mean everybody's running it. And so you're still running on existing systems, you know, largely kind of virtualized. And Velostrata is a technology leader in being virtualization of this type to Google Cloud or other clouds. And then even more so, the technology they have to bring that to containers. So they help you do that migration, transformation process. And I think that's really important for IT organizations. >> Ricardo, you want to comment on some of the hybrid cloud migration stuff? >> So we have our com product, which allows us to actually end up taking workload and moving it to, for instance, Google Cloud or eventually sciCloud and then moving that workload back. So having that sort of Nutanix inside and outside gives it maximum flexibility, and that's a lot of power for IT to have, right? Deciding where it's best to actually run their workloads and be as efficient as possible. >> So as we look at the com, we look at Google Cloud, just the overall pictures, if you're enterprise, you're looking at Google and you're saying man, Google runs at two different speeds. One is 12 factor, micro services, Kubernetes, functions. And then the other side is that, some people just want a VM. They just want a cloud instance and how to make that simple. So let's talk about this relationship. How does Nutanix come together with Google who runs at two different speeds, to make Google Cloud more consumable to the average enterprise? >> Well we're going to talk a little bit more about it later, but the fact that basically we're going to be able to deploy Xi within Google Cloud with nested AHV, and then allow our customers, that'll basically be doing standard workloads to migrate their jobs over to a Google Cloud offering. And as Brian will point out, that basically creates opportunities for them to be able to avail themselves of other capabilities that Google has. So it's not altogether an instant moving path to rewrite, reorient all your apps. It's an ability to kind of do that school migration, if you want to. But you have that capability of being able to go back and forth, in terms of what your workloads are. >> Yeah. >> Brian, want to get your viewpoint on just some of the changing roles that are happening in our industry. We were talking that some of the interviews we've been doing today, it's people talking about infrastructure and code. There was a big hackathon at this event for the first time in, they sold out with over 14 groups, and everything like that. This is a show that started out with people talking about storage, and now we're talking about individual data centers and clouds and all of those things. What are you seeing out in the marketplace? What are some of the challenges and opportunities you're hearing from customers these days? >> I mean it depends on which customers, right? Which region of the world and what their business looks like and I think we all know the holy grail. Infrastructure, as code, is an implementation, but I think what we know that what you really desire is the ability for reproduce ability. The ability to sort of not have state in the IT process. You want to be able to recreate things anywhere. Recreate a whole application, blueprint internally, on public cloud. Tear it down, recreate it. There's no other way to do that without code. So what sort of comes from that SRE model that Google invented, is that what it you didn't have an IT department? And what if you had software engineers that were responsible for IT function? What would that look like? And that's where all of the sudden you realize, everything's APIs and code. So I think that's interesting, and that's sort of where you want to get to, but it's then like, how do you bridge that because a lot of people aren't software developers in IT departments. >> So here's my follow up, 'cause when I go to the Kubernetes show and I talk to users there, 95% of them-- >> They're way over there. >> Had built their own stack, and why do they do that? Because they were ahead of all the platforms. And then I come to the Nutanix show and they're like oh, tensorflow and functions and all that stuff. We're going to put an easy button, and make it easy. I need to take all of these tools and open source and put it together, versus the platform and the easy button. Is this just the early adopters and the majority? >> I think that's okay. That's the open source world, right? I mean think about what's great about open source, is not just creating sort of a venue for collaboration and developers, it's creating access for end users. And so some of the best companies in the world have been built on a DIY model of people just taking open source and integrating it and making the recipe that they want. And so I think you get that whole sort of spectrum and you aren't forced down this model of, here's a COTS product, oh and it happens to be based on open source, but you always have to use technology this way. Open source gives them the freedom to do it as they want. We just need to make sure that we bridge it, so that there's not anybody left behind. That everybody should be able to use the power of Kubernetes, and that means making things super easy to use, and the integration with Nutanix we think is a huge part of making you use that technology stack in a way that's seamlessly operated for an audience like this. >> So a lot of the debate and questions around Kubernetes is how far should it go? Should it go as far as being an opinionated pass? Should it just be a container platform? Where does it start and end? >> Brian: You want my opinion? >> Yeah, opinion that would be awesome. >> Yeah, that was it. Well I think the way the industry started was obviously, there were no PASes, and then we built OpenShift to Red Hat and Google app engine in Roku. And what happened is, those are interesting, but you're right, they are overly opinionated. So you were left either picking a PAS, and you got to change everything to do it this way, and it's great because it delivers value of managed service, but not everything fit in that model. Or you got next to nothing. >> Keith: Right. >> You got a straight IS platform, and then you got to do all the rest. So what we've been doing at Google is tearing that apart and building that architecture from the ground up where you opt into the level that you want. If you want to be able to use IS and the features of IS you use that. If you want to step up and just use containers and IS, you can use containers and IS. If you want to step up and use Kubernetes orchestration, you can do that. If you want to step up and run managing everything in services, than that stacks on top of Kubernetes with STO. If you want to be full on and put in a developer workflow that always has you do deploys this way, then that stacks on top. So I think you're going to get away from this false dichotomy of a choice over here or here, and you're going to all of a sudden get this architectural layering cake that lets you opt into what you want and have IT consistency all the way through it. >> I mean, I used to have a startup that was focused on Hatuputu service, and you know one of the things was basically you didn't have this layering, right? It was, you take the whole stack or you take nothing. And I think the strategy that Google has employed with Kubernetes is just brilliant, to kind of work you way up and basically get people at different levels to be involved. You know, there is a do-it-yourself folks, and they should be allowed to and empowered to do the things that they want to do. And then there are other people who want to have more composed environment. And so we can actually bring that to them as well. And I think that's brilliant. Basically very early on, while Google used a lot of open source internally, it wasn't a strong sort of part of the open source environment. And so I've just enjoyed watching the evolution of Google, sort of leading the open source movement. So, it's been fantastic. I'm right there with you, you know, give them at every level. >> Ricardo, one of the questions coming into this week, people want to know the update of what's happening with Xi. Can you speak about where we are with that and the relationship with Google? What should we be looking for for the rest of this year? >> Well I can't really talk about that, but you know, we are working very closely with Google. And we'll talk a little bit about that at our talk later today. But I won't comment on anything to do with Xi. >> So that gives me the opportunity to ask another controversial question about Kubernetes and getting both of your opinions on it. There's religions and open sourced as religions, enterprise IT, one of which is DevOps. And you look at what companies like Netflix have done with containerizing Java applications and running those legacy Java applications in their container platform. Enterprises are looking at that stuff and thinking, you know what, can I containerize my monolithic application, put it on top of Kubernetes, and drive more efficiency out of my operations from portability to being able to stack up applications in public cloud, general things. Monolithic applications, is that a good thing, bad thing, indifferent? Wrong plate, wrong tool, wrong-- >> No, I think it's just that there's no like one size even for what a monolithic app looks like. Like we don't really have a really proper definition of what it is, but I think people do feel that all of a sudden Kubernetes needs a rewrite and containers needs a rewrite, and actually it doesn't. Because apps are usually sort of separated from the OS already. And so what they're doing is marrying the libraries of the OS, and containers allows them to do that, but just get a higher degree of portability and then with Kubernetes orchestration. So it really depends more around what's the machine resources that that monolithic app needs and are those machine resources still available in a containerized environment. In most cases, the answer is yes. Now the most interesting thing is, what's the escape hatch? Because you can't have a monolithic app that your company, say it's on Mainframe, say it's in the case of something that will not containerize and shouldn't because it's working as designed and there's no use touching it. But that should still participate in the application architecture of the future. And that's why SEO and services are so important. So even if you can't change your runtime stack, you still need to be able to put a services layer in an API in front of that monolithic service, and you'll have a visibility of a service mesh inside of that environment. So now IT sees it just looks like a black box IT service. It doesn't really matter to them that it's not running on the next generation stack because they can still depend on its' services. >> Yeah, I mean I would agree. I look at what Kubernetes offers and containers as sort of an on ramp to creating services, the on ramp to actually taking that monolithic application, assuming that they're resources, and take a step up in terms of the architectures that you can build around it and then be able to break apart that monolithic application. It doesn't have to happen all at once. It's sort of the stepping stones that you can take. So it's a very powerful model for enablement for people who have stuff that they haven't been able to make the most value out of because maybe the application's been around for a while. Now they can actually end up putting it in an environment where they can actually make the most of it and then work on how they're going to end up slowly pulling it apart and making it more service oriented. >> Alright, Ricardo and Brian, thank you so much for joining us. Appreciate the update and look forward to seeing more throughout the show and further in the year. Be sure to check out theCUBE.net where you'll not only find all of this information, but theCUBE is really excited to say that we will be at the Google Cloud show in July. So for Keith Townsend, and I'm Stu Miniman, getting towards the end of day one of two days of live coverage. Thanks so much for watching theCUBE. (upbeat music)

>> Narrator: Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2017, presented by AWS, Intel, and our ecosystem of partners. >> Hey, welcome back everyone, this is theCUBE's exclusive live coverage here in Las Vegas for AWS Amazon web services re:Invent 2017. I'm John Furrier with Keith Townsend. Our next guest is Laura Stevens, data scientist at the American Heart Association, an AWS customer, welcome to theCUBE. >> Hi, it's nice to be here. >> So, the new architecture, we're seeing all this great stuff, but one of the things that they mention is data is the killer app, that's my word, Verna didn't say that, but essentially saying that. You guys are doing some good work with AWS and precision medicine, what's the story? How does this all work, what are you working with them on? >> Yeah, so the American Heart Association was founded in 1924, and it is the oldest and largest voluntary organization dedicated to curing heart disease and stroke, and I think in the past few years what the American Heart Association has realized is that the potential of technology and data can really help us create innovative ways and really launch precision medicine in a fashion that hasn't been capable to do before. >> What are you guys doing with AWS? What's that, what's the solution? >> Yeah so the HA has strategically partnered with Amazon Web Services to basically use technology as a way to power precision medicine, and so when I say precision medicine, I mean identifying individual treatments, based on one's genetics, their environmental factors, their life factors, that then results in preventative and treatment that's catered to you as an individual rather than kind of a one size fits all approach that is currently happening. >> So more tailored? >> Yeah, specifically tailored to you as an individual. >> What do I do, get a genome sequence? I walk in, they throw a high force computing, sequence my genomes, maybe edit some genes while they're at it, I mean what's going on. There's some cutting edge conversations out there we see in some of the academic areas, course per that was me just throwing that in for fun, but data has to be there. What kind of data do you guys look at? Is it personal data, is it like how big is the data? Give us a sense of some of the data science work that you're doing? >> Yeah so the American Heart Association has launched the Institute for Precision Cardiovascular Medicine, and as a result, with Amazon, they created the precision medicine platform, which is a data marketplace that houses and provides analytic tools that enable high performance computing and data sharing for all sorts of different types of data, whether it be personal data, clinical trial data, pharmaceutical data, other data that's collected in different industries, hospital data, so a variety of data. >> So Laura, there's a lot of think fud out there around the ability to store data in a cloud, but there's also some valid concerns. A lot of individual researchers, I would imagine, don't have the skillset to properly protect data. What is the Heart Association doing with the framework to help your customers protect data? >> Yeah so the I guess security of data, the security of the individual, and the privacy of the individual is at the heart of the AHA, and it's their number one concern, and making anything that they provide that a number one priority, and the way that we do that in partnering with AWS is with this cloud environment we've been able to create even if you have data that you'd like to use sort of a walled garden behind your data so that it's not accessible to people who don't have access to the data, and it's also HIPAA compliant, it meets the standards that the utmost secure standards of health care today. >> So I want to make sure we're clear on this, the Heart Association doesn't collect data themselves. Are you guys creating a platform for your members to leverage this technology? >> So there's, I would so maybe both actually. The American Heart Association does have data that it is associated with, with its volunteers and the hospitals that it's associated with, and then on top of that, we've actually just launched My Research Legacy, which allows individuals of the community to, who want to share their data, whether you're healthy or just sick, either one, they want to share their data and help in aiding to cure heart disease and stroke, and so they can share their own data, and then on top of that, anybody, we are committed to strategically partnering with anybody who's involved and wants to share their data and make their data accessible. >> So I can share my data? >> Yes, you can share your data. >> Wow, so what type of tools do you guys use against that data set and what are some of the outcomes? >> Yeah so I think the foundation is the cloud, and that's where the data is stored and housed, and then from there, we have a variety of different tools that enable researchers to kind of custom build data sets that they want to answer the specific research questions they have, and so some of those tools, they range from common tools that are already in use today on your personal computer, such as Python or R Bioconductor, and then they have more high performance computing tools, such as Hal or any kind of s3 environment, or Amazon services, and then on top of that I think what is so awesome about the platform is that it's very dynamic, so a tool that's needed to use for high performance computing or a tool that's needed even just as a on a smaller data set, that can easily be installed and may be available to researchers, and so that they can use it for their research. >> So kind of data as a service. I would love to know about the community itself. How are you guys sharing the results of kind of oh this process worked great for this type of analysis amongst your members? >> Yeah so I think that there's kind of two different targets in that sense that you can think of is that there's the researchers and the researchers that come to the platform and then there's actually the patient itself, and ultimately the HA's goal is to make, to use the data and use the researcher for patient centered care, so with the researchers specifically, we have a variety of tutorials available so that researchers can one, learn how to perform high performance computing analysis, see what other people have done. We have a forum where researchers can log on and enable, I guess access other researchers and talk to them about different analysis, and then additionally we have My Research Legacy, which is patient centered, so it's this is what's been found and this is what we can give back to you as the patient about your specific individualized treatment. >> What do you do on a daily basis? Take us through your job, are you writing code, are you slinging API's around? What are some of the things that you're doing? >> I think I might say all of the above. I think right now my main effort is focused on one, conducting research using the platform, so I do use the platform to answer my own research questions, and those we have presented at different conferences, for example the American Heart Association, we had a talk here about the precision medicine platform, and then two, I'm focused on strategically making the precision medicine platform better by getting more data, adding data to the platform, improving the way that data is harmonized in the platform, and improving the amount of data that we have, and the diversity, and the variety. >> Alright, we'll help you with that, so let's help you get some people recruited, so what do they got to do to volunteer, volunteer their data, because I think this is one of those things where you know people do want to help. So, how do they, how you onboard? You use the website, is it easy, one click? Do they have to wear an iWatch, I mean what I mean? >> Yeah. >> What's the deal? What do I got to do? >> So I think I would encourage researchers and scientists and anybody who is data centric to go to precision.heart.org, and they can just sign up for an account, they can contact us through that, there's plenty of different ways to get in touch with us and plenty of ways to help. >> Precision.heart.org. >> Yup, precision.heart.org. >> Stu: Register now. >> Register now click, >> Powered by AWS. >> Yup. >> Alright so I gotta ask you as an AWS customer, okay take your customer hat off, put your citizen's hat on, what is Amazon mean to you, I mean is it, how do you describe it to people who don't use it? >> Okay yeah, so I think... the HA's ultimate mission right, is to provide individualized treatment and cures for cardiovascular disease and stroke. Amazon is a way to enable that and make that actually happen so that we can mine extremely large data sets, identify those individualized patterns. It allows us to store data in a fashion where we can provide a market place where there's extremely large amounts of data, extremely diverse amounts of data, and data that can be processed effectively, so that it can be directly used for research. >> What's your favorite tool or product or service within Amazon? >> That's a good question. I think, I mean the cloud and s3 buckets are definitely in a sense they're my favorites because there's so much that can be stored right there, Athena I think is also pretty awesome, and then the EMR clusters with Spark. >> The list is too long. >> My jam. >> It is. (laughs) >> So, one of the interesting things that I love is a lot of my friends are in non-profits, fundraising is a big, big challenge, grants are again, a big challenge, have you guys seen any new opportunities as a result of the results of the research coming out of HA and AWS in the cloud? >> Yeah so I think one of the coolest things about the HA is that they have this Institute for Precision Cardiovascular Medicine, and the strategic partnership between the HA and AWS, even just this year we've launched 13 new grants, where the HA kind of backs the research behind, and the AWS provides credit so that people can come to the cloud and use the cloud and use the tools available on a grant funded basis. >> So tell me a little bit more about that program. Anybody specifically that you, kind of like saying, seeing that's used these credits from AWS to do some cool research? >> Yeah definitely, so I think specifically we have one grantee right now that is really focused on identifying outcomes across multiple clinical trials, so currently clinical trials take 20 years, and there's a large variety of them. I don't know if any of you are familiar with the Framingham heart study, the Dallas heart study, the Jackson heart study, and trying to determine how those trials compare, and what outcomes we can generate, and research insights we can generate across multiple data sets is something that's been challenging due to the ability to not being able to necessarily access that data, all of those different data sets together, and then two, trying to find ways to actually compare them, and so with the precision medicine platform, we have a grantee at the University of Colorado-Denver, who has been able to find those synchronicities across data sets and has actually created kind of a framework that then can be implemented in the precision medicine platform. >> Well I just registered, it takes really two seconds to register, that's cool. Thanks so much for pointing out precision.heart.org. Final question, you said EMR's your jam. (laughing) >> Why, why is it? Why do you like it so much, is it fast, is it easy to use? >> I think the speed is one of the things. When it comes to using genetic data and multiple biological levels of data, whether it be your genetics, your lifestyle, your environment factors, there's... it just ends up being extremely large amounts of data, and to be able to implement things like server-less AI, and artificial intelligence, and machine learning on that data set is time consuming, and having the power of an EMR cluster that is scalable makes that so much faster so that we can then answer our research questions faster and identify those insights and get them to out in the world. >> Gotta love the new services they're launching, too. It just builds on top of it. Doesn't it? >> Yes. >> Yeah, soon everyone's gonna be jamming on AWS in our opinion. Thanks so much for coming on, appreciate the stories and commentary. >> Yeah. >> Precision.heart.org, you want to volunteer if you're a researcher or a user, want to share your data, they've got a lot of data science mojo going on over there, so check it out. It's theCUBE bringing a lot of data here, tons of data from the show, three days of wall to wall coverage, we'll be back with more live coverage after this short break. (upbeat music)

>> Narrator: Live from Boston, Massachusets. It's theCUBE, covering OpenStack Summit 2017. Brought to you by the OpenStack Foundation, Red Hat and additional ecosystem and support. >> Hi, welcome back, I'm Stu Miniman, joined by my cohost John Troyer and happy to welcome back to the program Brian Stevens who's the CTO of Google Cloud. Brian, thanks for joining us. >> I'm glad to, it's been a few years. >> All right, I wanted to bounce something off you. We always talk about, you know, it's like open source. You worked for in the past what is most considered the most successful open source company for monetizing open source, which is Red Hat. We have posited at Wikibon that it's not necessarily the company, it's not only the companies that sell a product or a solution that make money off it, but I said, if it wasn't for things like Linux in general and open source, we wouldn't have a company like Google. Do you agree with that, you look at the market cap of a Google, I said if we didn't have Linux and we didn't have open source, Google probably couldn't exist today. >> Yeah, I don't think any of the hyper scale cloud companies would exist without open source and Linux and Intel. I think it's a big part of the stack, absolutely. >> All right. You made a comment at the beginning about what it means to be an open source person working at Google. The joke we all used to make was the rest of us are using what Google did 10 years ago, it eventually goes from that whitepaper all the way down to some product that you used internally and then maybe gets spun off. We wouldn't have Hadoop if it wasn't for Google. Just some of the amazing things that have come out of those people at Google. But what does it mean to be open source at Google and with Google? >> You get both, right? 'Cause I think that's the fun part is I don't think a week goes by where I don't get to discover something coming out of a resource group somewhere. Now the latest is machine learning, you know, Spanner because they'd learned how do to distributed time synchronization across geo data centers, like who does that, right? But Google has both the people and the desire and the ability to invest in on the research side. And then you marry that innovation with everything that's happening in open source. It's a really perfect combination. And so instead of building these proprietary systems, it's all about how do we actually not just contribute to open source, but how do we actually build that interoperability framework, because you don't want cloud to be an island, you want it to be really integrated into developer tools, databases, infrastructure, et cetera. >> And a lot of that sounds like it plays into the Kubernetes story, 'cause, you know, Kubernetes is a piece that allows some similarities between wherever you place your data. Maybe give us a little bit more about what Google, you know, how do you decide what's internal, I think about like the Spanner program, which there's some other open source pieces coming up, looks like they read the whitepaper and they're trying to do some pieces. You said less whitepapers, more code coming out of people, what does that means? >> It's not that we'll do less whitepapers. 'Cause whitepapers are great for research, and Google's definitely a research strong academic oriented company. It's just that you need to go further as well. So that was, you know, what I was talking about like with GRPC, creating an Apache project I think was the first time for streaming analytics, right, was the first time that I think Google's done that. Obviously, been involved for years at the Linux kernel, compilers, et cetera. I think it's more around what do developers need, where can we actually contribute to areas, because what you don't want, what we don't want is you're on premise and you're using one type of system, then you move to Google Cloud and it feels like there's impedance. You're really trying to get rid of the impedance mismatch all the way across the stack, and one of the best ways you can do that is by contributing new system designs. There's a little bit less of that happening in the analytics space now though, I think the new ground for that is everything that's happening in machine learning with Tensor Flow et cetera. >> Yeah, absolutely. There was some mention in the keynote this morning, all of the AI and ML, I mean, Google with Tensor Flow, even Amazon themselves getting involved more with open source. You said you couldn't build the hyper scales without them, but is that the, do they start with open source, do you see, or? >> Well, I think that most people are running on a Linux backplane. It's a little bit different in Google 'cause we got an underlying provisioning system called the Borg. And that just works, so some things work, don't change them. Here is where you really want to be open source first are areas that are just under active evolution, because then you actually can join that movement of active evolution. Developer tools are kind of like that. Even machine learning. Machine learning's super strategic to just about every company out there. But what Google did by actually open sourcing Tensor Flow is now they created a canvas, that community, we talk about that here, but for data scientists to collaborate, and these are people that didn't do much in open source prior, but you've given that ability to sort of come up with the best ideas and to innovate in code. >> I wanted to ask a little bit about the enterprise, right. We can all make jokes about enterprising is what everybody should've been doing 10 years ago, and they're finally getting to. But on the other hand, Red Hat, very enterprise focused company. OpenStack, service provider and very enterprise focused. One of the things that Google Cloud is doing... Well, I guess the criticism has typically been how does Google as a company and as a culture and as a cloud focused on the enterprise, especially bringing advanced topics like machine learning and things like that, which to a traditional IT person are a little foreign. So I just am interested in kind of how you're viewing, how do we approach the needs of the enterprise, meet them where they are today, while yet giving them an access to a whole set of services and tools that are actually going to take them into a business transformation stance? >> Sure. And that's because you end up as a public cloud provider with the enterprise, you end up having multiple conversations. You certainly have one of your primary audiences, the IT team, right. And so you have to earn trust and help them understand the tools and your strategy and your commitment to enterprise. And then you have CSOs, right, and the CEO, that's worried about everything security and risk and compliance, so it's a little bit different than your IT department. And then what's happening with machine learning and some of the higher end services is now you're actually building solutions for lines of business. So you're not talking to the IT teams with machine learning and you're not talking to the CSOs, you're really talking around business transformation. And when you're actually, if you're going into healthcare, if you're going into financial, it's a whole different team when you're talking about machine learning. So what happens is Google's really got a segmented three sort of discreet conversations that happen at separate points of time, but all of which are enterprise focused, 'cause they all have to marry together. Even though there may be interest in machine learning, if you don't wrap that in an enterprise security model and a way that IT can sustain and enable and deal with identity and all the other aspects, then you'll come up short. >> Yeah. Building on that. One of the critiques of OpenStack for years has been it's tough. I think about one of the critiques of Google is like, oh well, Google build stuff for Google engineers, we're not Google engineers, you know, Google's got the smartest people and therefore we're not worthy to be able to handle some of that. What's your response to that? How do you put some of those together? >> Of course, Google's really smart, but there's smart people everywhere. And I don't think that's it. I think the issue is, you know, Google had to build it for themselves, right, they'd build it for search and build it for apps and build it for YouTube. And OpenStack's got a harder problem in a way, when you think about it, 'cause they're building it for everybody. And that was the Red Hat model as well, it's not just about building it for Goldman Sachs, it's building it for every vertical. And so it's supposed to be hard. This isn't just about building a technology stack and saying we're done, we're going to move on. This community has to make sure that it works across the industry. And that doesn't happen in six years, it takes a longer period of time to do that, and it just means keeping your focus on it. And then you deal with all the use cases over time and then you build, that's what getting to a unified commoditized platform delivers. >> I love that, absolutely. We tend to oversimplify things and, right, building from the ground up some infrastructure stack that can live in any data center is a big challenge. I wrote an article years ago about Amazon hyperoptimizes. They only have to build for one data center, it's theirs. At Google, you understand what set of applications you're going to be running, you build your applications and the infrastructure supports it underneath that. What are some of the big challenges you're working on, some of the meaty things that are exciting you in the technology space today? >> In a way, it's similar. In a way, it's similar, it's just that at least our stack's our stack, but what happens is then we have to marry that into the operational environments, not just for a niche of customers, but for every enterprise segment that's out there. What you end up realizing is that it ends up becoming more of a competency challenge than a technology issue because cloud is still, you know, public cloud is still really new. It's consolidating but it's still relatively new when you start to think about these journeys that happen in the IT world. So a lot of it for us is really that technical enablement of customers that want to get to Google Cloud, but how do you actually help them? And so it's really a people and processes kind of conversation over how fast is your virtual machine. >> One of the things I think is interesting about that Google Cloud that has developed is the role of the SRE. And Google has been, has invented that, wrote the book on it, literally, is training others, has partnerships to help train others with their SREs and the CRE program. So much of the people formerly known as sysadmins, in this new cloud world, some of them are architects, but some of them will end up being operators and SREs. How do you see the balance in this upscaling of kind of the architecture and the traditional infrastructure and capacities and app dev versus operations, how important is operations in our new world? >> It's everything. And that's why I think people, you know... What's funny is that if you do this code handoff where the software developers build code and then they hand it to a team to run and deploy. Developers never become great at building systems that can be operationally managed and maintained. And so I think that was sort of the aha moment, as the best I understand the SRE model at Google is that until you can actually deliver code that can be maintained or alive, well then the software developer owns that problem. The SRE organization only comes in at that point in time where they hand up their, and they're software developers. They're every bit as skilled software developers as the engineers are that are building the code, it's just that's the problem they want to decode, which I think is actually a harder problem than writing the code. 'Cause when you think about it for a public cloud, its like, how do you actually make change, right, but keep the plane flying? And to make sure that it works with everything in an ecosystem. At a period of time where you never really had a validation stage, because in the land of delivering ISV software, you always have the six month, nine month evaluation phase to bring in a new operating system or something else, or all the ecosystem tests around that. Cloud's harder, the magic of cloud is you don't have that window, but you still have to guarantee the same results. One of the things that we did around that was we took the page out of the SRE playbook, which is how does Google do it, and what we realized is that, even though public cloud's moved the layers up, enterprises still have the same issue. Because they're deploying critical applications and workloads on top. How do they do that and how do they keep those workloads running and what are their mechanisms for managing availability, service level objectives, share a few dashboards, and that's why we created the CRE team, which is customer reliability engineering, which is a playbook of SRE, but they work directly with end users. And that's part of the how do we help them get to Google Cloud, part of it's like really understanding their application stacks and helping them build those operational procedures, so they become SREs if you will. >> Brian, one of the things I, if you look at OpenStack, it's really, it's the infrastructure layer that it handles, when I think about Google Cloud, the area that you're strongest and, you know, you're welcome to correct me, but it's really when we talk about data, how you use data, how analytics, your leadership you're taking in the machine learning space. Is it okay for OpenStack to just handle those lower levels and let other projects sit on top of it? And curious as to the developing or where Google Cloud sits. >> I think that was a lower level aha moment for me, even prior to Google, was it was, I did have a lens and it was all about infrastructure. And I think the infrastructure is every bit as important as it ever was. But the fact that some of these services that don't exist in the on-premise world that live in Google Cloud are the ones that are transformative change, as opposed to just giving you operational, easing the operational burden, easing the security burden. But it's some of these add-on services that are the ones that really changed here, bring around business transformation. The reason we have been moving away from Hadoop as an example, not entirely but just because Hadoop's a batch oriented application. >> Could go to Spark, Flink, everything beyond that. >> Sure, and also now when you get to real time and streaming image, you can have adjusted data pipelines, data come from multiple sources. But then you can action on that data instantly, and a lot of businesses require, or ours certainly does and I think a lot of our customers' businesses do, the time to action really matters, and those are the types of services that, at least at scale, don't really exist anywhere else and machine learning, the ability of our custom ASICs to support machine learning. But I don't think it's a one versus the other, I think that brings about how do you allow enterprises to have both. And not have to choose between public cloud and on premise, or doing (mumbles) services or (mumbles) services, because if you ask them, the best thing they can have is actually how do you marry the two environments together so they don't look, again, back to that impedance differences. >> Yeah, and I think that's a great point, we've talked OpenStack is fitting into that hybrid or multi-cloud world a bunch. The challenge I guess we look at is some of those really cool features that are game changers that I have in public cloud that I can't do in my own data center, how do we bridge that? Started to see the reach or the APIs that do that, but how do you see that playing out? >> Because you don't have to bring them in. Because if you think about the fabric of IT, the fabric of IT is that Google's data center in that way just becomes an extension of the data center that a large enterprise is already using anyway. So it's through us. So they aren't going to the lines of distinction, only we and sort of the IT side see that. There isn't going to be seen, as long as they have an existing platform and they can take advantage of those services, and it doesn't mean that their workload has to be portable and the services have to exist in both places, it's just a data extension with some pretty compelling services. >> I think back, you know, Hadoop was let me bring the compute to the data 'cause the data's big and can't be moved. Look at edge computing now, I'm not going to be able to move all that data from the edge, I don't have the networking connectivity. There's certain pieces which we'll come back to, you know, a core public cloud, but I wonder if you can comment on some of those edge pieces, how you see that fitting in? We've talked a little bit about it here at OpenStack, but 'cause you're Google. I think it's the evolution. When we look at, we just even see the edge of our network, the edge of our network is in, it's 173 countries and regions globally. And so that edge of the network is full compute and cashing. And so even for us, we're looking at what sort of compute services do you bring to the edge of the network. We're like, low latency really matters and proximity matters. The easiest obvious examples are gaming, but there's other ones as well, trading. But still though, if you want to take advantage of that foundation, it shouldn't be one that you have to dive into the specificities of a single provider, you'd really want that abstraction layer across the edge, whether that's Docker and a defined set of APIs around data management and delivery and security, that probably gives you that edge computing sell, and then you really want to build around that on Google's edge, you want to build around that on a telco's edge. So I don't think it really becomes necessarily around whether it's centralized or it's the edge, it's really what's that architecture to deliver. >> All right. Brian, I want to give you the opportunity, final world, things either from OpenStack, retrospectively or Google looking forward that you'd like to leave our audience with. >> Wow, closing remarks. You know, I think the continuity here is open source. And I know the backdrop of this is OpenStack, but it's really around open source is the accepted foundation and substrate for IT computing up the stack, so I think that's not changing, the faces may change and what we call these projects may change, but that's the evolution and I think there's really no turning back on that now. >> Brian Stevens, always a pleasure to catch up with you, we'll be back with lots more coverage here with theCUBE, thanks for watching. (energetic music)

>>Mhm. Mhm. >>We're here with the Cube covering Calm Vault Connections 21. We're gonna look at the data protection space and how cloud computing has advanced the way we think about backup recovery and protecting our most critical data. Ranga Rajagopalan, who is the vice president of products at Con vault and Stephen Orban, who's the General manager of AWS marketplace and control services gents. Welcome to the cube. Good to see you. >>Thank you. Always A pleasure to see you here >>steve. Thanks for having us. Very >>welcome, Stephen, let's start with you. Look the cloud has become a staple of digital infrastructure. I don't know where we'd be right now without being able to access enterprise services I. T. Services remotely. Um But specifically how our customers looking at backup and recovery in the cloud, is it a kind of a replacement for existing strategies? Is it another layer of protection? How are they thinking about that? >>Yeah. Great question. David again, thank thanks for having me. And I think you know, look if you look back to 15 years ago when the founders of AWS had the hypothesis that many enterprises governments and developers we're gonna want access to on demand pay as you go I. T. Resources in the cloud. Uh None of us would have been able to predict that it would have Matured and um you know become the staple that it has today over the last 15 years. But the reality is that a lot of these enterprise customers, many of whom have been doing their own IT infrastructure for the last 10, 20 or multiple decades do have to kind of figure out how they deal with the change management of moving to the cloud. And while a lot of our customers um will initially come to us because they're looking to save money or costs, almost all of them decided to stay and go big because of the speed at which they are able to innovate on behalf of their customers and when it comes to storage and backup, that just plays right into where they're headed. And there's a variety of different techniques that customers use, whether it be, you know, a lift and shift for a particular set of applications or a data center where they do very much. Look at how can they replace the backup and recovery that they have on premises in the cloud using solutions like, but we're partnering with console to do or completely reimagining their architecture for net new developments that they can really move quickly for their customers. Um and and completely developing something brand new, where it is really a, you know, a brand new replacement and innovation for for for what they've done in the past. >>Great, thank you, Stephen Rachael, I want to ask you about the d were digital. Look, if you're not a digital business today, you're basically out of business. So, my question to you is how have you seen customers change the way they think about data protection during what I call the forced March to digital over the last 18, 19 months or customers, you know, thinking about data protection differently today >>definitely Dave and and thank you for having me and steven. Pleasure to join you on this cube interview first going back to stevens comments can't agree more. Almost every business that we talked with today has a cloud first strategy, a cloud transformation mandate and you know, the reality is back to your digital comment. There are many different paths to the hybrid multi cloud and different customers. You know, there are different parts of the journey. So I still was saying most often customers at least in the data protection perspective start the conversation by thinking here have all these tips. Can I start using cloud as my air gap long term retention target and before they realized they start moving their workloads into the cloud and none of the backup and record yesterday's are going to change. So you need to continue protecting the clothes, which is where the cloud native data protection comes in and then they start innovating around er, can I use cloud as media sites so that you know, I don't need to meet in the other side. So this year is all around us. Cloud transformation is all around us and and the real essence of this partnership between AWS and calm vault is essentially to dr and simplify all the paths to the club regardless of whether you're going to use it as a storage started or you know, your production data center, all your dear disaster recovery site. >>Yeah, it really is about providing that optionality for customers. I talked to a lot of customers and said, hey, our business resilience strategy was really too focused on D. R. I've talked to all the customers at the other end of the spectrum said we don't even have a D. R. Strategy now, we're using the cloud for that. So it's really all over the map and you want that optionality. So steven and then go ahead. >>Please, ransomware plays a big role in many of these considerations that greatly. It's unfortunately not a question of whether you're going to be hit by ransomware, it's almost we can like, what do you do when you're hit by ransomware and the ability to use the clothes scaled immediately, bring up the resources, use the cloud backups has become a very popular choice simply because of the speed with which you can bring the business back to normal our patients. The agility and the power that cloud brings to the table. >>Yeah, ransomware is scary. You don't, you don't even need a high school diploma to be a ransomware ist you can just go on the dark web and by ransomware as a service and do bad things and hopefully you'll end up in jail. Uh Stephen we know about the success of the AWS marketplace, uh you guys are partnering here. I'm interested in how that partnership, you know, kind of where it started and how it's evolving. >>Yeah, happy to highlight on that. So, look when >>we when we started >>Aws or when the founders of aws started aws, as I said 15 years ago we we realized very early on that while we were going to be able to provide a number of tools for customers to have on demand access to compute storage, networking databases that many, particularly enterprise and government government customers still use a wide range of tools and solutions from hundreds, if not in some cases thousands of different partners. I mean I talked to enterprises who literally use thousands of of different vendors to help them deliver their solutions for their customers. So almost 10 years ago, we're almost at our 10 year anniversary for AWS marketplace, we launched the first substantiation of AWS marketplace which allowed builders and customers to find try buy and then deploy third party software solutions running on amazon machine instances also noticed as armies natively right in their AWS and cloud accounts to complement what they were doing in the cloud. And over the last nearly 10 years we've evolved quite a bit to the point where we support software and multiple different packaging types, whether it be amazon machine instances, containers, machine learning models and of course SAS and the rise of software as a service. So customers don't have to manage the software themselves. But we also support data products through the AWS Data exchange and professional services for customers who want to get services to help them integrate the software into their environments. And we now do that across a wide range of procurement options. So what used to be pay as you go amazon machine instances now includes multiple different ways to contract directly, customer can do that directly with the vendor with their channel partner or using kind of our public e commerce capabilities. And we're super excited, um, over the last couple of months we've been partnering with calm vault to get their industry leading backup and recovery solutions listed on AWS marketplace, which is available for our collective customers now. So not only do they have access to convulse awesome solutions to help them protect against ransomware as we talked about and to manage their backup and recovery environments, but they can find and deploy that directly in one click right into their AWS accounts and consolidate their building relationship right on the AWS and voice. And it's been awesome to work with with Rhonda and the product teams and convo to really, um, expose those capabilities where converts using a lot of different AWS services to provide a really great native experience for our collective customers as they migrate to the cloud. >>Yeah, the marketplace has been amazing. We've watched it evolve over the past decade and, and, and it's a, it's a key characteristic of everybody has a cloud today. We're a cloud to butt marketplaces unique uh, in that it's the power of the ecosystem versus the resources of one and Ringo. I wonder from, from your perspective, if you could talk about the partnership with AWS from your view and then specifically you've got some hard news, I wonder if you could talk about that as well. >>Absolute. So the partnership has been extended for more than 12 years. Right. So aws and Commonwealth have been bringing together solutions that help customers solve the data management challenges and everything that we've been doing has been driven by the customer demand that we seek. Right customers are moving their workloads in the cloud. They're finding new ways of deploying their workloads and protecting them. Um, you know, earlier we introduced cloud native integration with the EBS API which has driven almost 70% performance improvements in backup and restores. And when you look at huge customers like coca cola who have standardized on AWS um, combo. That is the scale that they want to operate in. You manage around 1 50,000 snapshots 1200 ec, two instances across six regions. But with just one resource dedicated for the data management strategy. Right? So that's where the real built in integration comes into play and we've been extending it to make use of the cloud efficiencies like our management and auto scale and so on. Another aspect is our commitment to a radically simple customer experience and that's, you know, I'm sure Stephen would agree it's a big month for at AWS as well. That's really together with the customer demand which brought us together to introduce com ball into the AWS marketplace exactly the way Stephen described it. Now the heart announcement is coming back up and recovery is available in native this marketplace. So the exact four steps that Stephen mentioned, find, try buy and deploy everything simplified through the marketplace So that our aws customers can start using far more back of software in less than 20 minutes. A 60 year trial version is included in the product through marketplace and you know, it's a single click buy, we use the cloud formation templates to deploy. So it becomes a super simple approach to protect the AWS workloads and we protect a lot of them. Starting from easy to rds dynamodb document DB um, you know, the containers, the list just keeps going on. So it becomes a very natural extension for our customers to make it super simple to start using convert data protection for the w >>well the con vault stack is very robust. You have extremely mature stack. I want, I'm curious as to how this sort of came about and it had to be customer driven. I'm sure where your customers saying, hey, we're moving to the cloud, we had a lot of workloads in the cloud, we're calm vault customer. That intersection between calm vault and AWS customers. So again, I presume this was customer driven. but maybe you can give us a little insight and add some color to that. >>Everything in this collaboration has been customer driven. We were earlier talking about the multiple paths to chlorine vapor example and still might probably add more color from his own experience at our jones. But I'll bring it to reference Parsons who's a civil engineering leader. They started with the cloud first mandate saying we need to start moving all our backups to the cloud but we have wanted that bad actors might find it easy to go and access the backups edible is um, Conwell came together with the security features and com well brought in its own authorization controls and now we have moved more than 14 petabytes of backup data into the club and it's so robust that not even the backup administrator and go and touch the backups without multiple levels of authorization. Right. So the customer needs, whether it is from a security perspective performance perspective or in this case from a simplicity perspective is really what is driving this. And and the need came exactly like that. There are many customers who have no standardized on it because they want to find everything through the AWS marketplace. They want to use their existing, you know, the AWS contracts and also bring data strategy as part of that so that that's the real um, driver behind this. Um, Stephen and I hope actually announced some of the customers that I actively started using it. You know, many notable customers have been behind this uh, innovation, don't even, I don't know, I wanted to add more to that. >>I would just, I would, I would just add Dave, you know, look if I look back before I joined a W S seven years ago, I was the C I O at dow jones and I was leading a a fairly big cloud migration there over a number of years. And one of the impetus is for us moving to the cloud in the first place was when Hurricane Sandy hit, we had a real disaster recovery scenario in one of our New Jersey data centers um, and we had to act pretty quickly convert was, was part of that solution. And I remember very clearly Even back then, back in 2013, they're being options available to help us accelerate are moved to the cloud and just to reiterate some of the stuff that Rhonda was talking about consoles, done a great job over the last more than a decade, taking features from things like EBS and S three and EC two and some of our networking capabilities and embedding them directly into their services so that customers are able to more quickly move their backup and recovery workloads to the cloud. So each and every one of those features was as a result of, I'm sure combo working backwards from their customer needs just as we do at >>AWS >>and we're super excited to take that to the next level to give customers the option to then also by that right on their AWS invoice on AWS marketplace. >>Yeah, I mean, we're gonna have to leave it there steven, you've mentioned several times the sort of the early days of back then we were talking about gigabytes and terabytes and now we're talking about petabytes and beyond. Guys. Thanks so much. I really appreciate your time and sharing the news with us. >>Dave. Thanks for having us. >>All right. Keep it right there more from combat connections. 21. You're watching the >>cube. Mm hmm.

>> Narrator: From theCUBE Studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> The next 10 years of cloud, they're going to differ dramatically from the past decade. The early days of cloud, deployed virtualization of standard off-the-shelf components, X86 microprocessors, disk drives et cetera, to then scale out and build a large distributed system. The coming decade is going to see a much more data-centric, real-time, intelligent, call it even hyper-decentralized cloud that will comprise on-prem, hybrid, cross-cloud and edge workloads with a services layer that will obstruct the underlying complexity of the infrastructure which will also comprise much more custom and varied components. This was a key takeaway of the guests from theCUBE on Cloud, an event hosted by SiliconANGLE on theCUBE. Welcome to this week's Wikibon CUBE Insights Powered by ETR. In this episode, we'll summarize the findings of our recent event and extract the signal from our great guests with a couple of series and comments and clips from the show. CUBE on Cloud is our very first virtual editorial event. It was designed to bring together our community in an open forum. We ran the day on our 365 software platform and had a great lineup of CEOs, CIOs, data practitioners technologists. We had cloud experts, analysts and many opinion leaders all brought together in a day long series of sessions that we developed in order to unpack the future of cloud computing in the coming decade. Let me briefly frame up the conversation and then turn it over to some of our guests. First, we put forth our view of how modern cloud has evolved and where it's headed. This graphic that we're showing here, talks about the progression of cloud innovation over time. A cloud like many innovations, it started as a novelty. When AWS announced S3 in March of 2006, nobody in the vendor or user communities really even in the trade press really paid too much attention to it. Then later that year, Amazon announced EC2 and people started to think about a new model of computing. But it was largely tire kickers, bleeding-edge developers that took notice and really leaned in. Now the financial crisis of 2007 to 2009, really created what we call a cloud awakening and it put cloud on the radar of many CFOs. Shadow IT emerged within departments that wanted to take IT in bite-sized chunks and along with the CFO wanted to take it as OPEX versus CAPEX. And then I teach transformation that really took hold. We came out of the financial crisis and we've been on an 11-year cloud boom. And it doesn't look like it's going to stop anytime soon, cloud has really disrupted the on-prem model as we've reported and completely transformed IT. Ironically, the pandemic hit at the beginning of this decade, and created a mandate to go digital. And so it accelerated the industry transformation that we're highlighting here, which probably would have taken several more years to mature but overnight the forced March to digital happened. And it looks like it's here to stay. Now the next wave, we think we'll be much more about business or industry transformation. We're seeing the first glimpses of that. Holger Mueller of Constellation Research summed it up at our event very well I thought, he basically said the cloud is the big winner of COVID. Of course we know that now normally we talk about seven-year economic cycles. He said he was talking about for planning and investment cycles. Now we operate in seven-day cycles. The examples he gave where do we open or close the store? How do we pivot to support remote workers without the burden of CAPEX? And we think that the things listed on this chart are going to be front and center in the coming years, data AI, a fully digitized and intelligence stack that will support next gen disruptions in autos, manufacturing, finance, farming and virtually every industry where the system will expand to the edge. And the underlying infrastructure across physical locations will be hidden. Many issues remain, not the least of which is latency which we talked about at the event in quite some detail. So let's talk about how the Big 3 cloud players are going to participate in this next era. Well, in short, the consensus from the event was that the rich get richer. Let's take a look at some data. This chart shows our most recent estimates of IaaS and PaaS spending for the Big 3. And we're going to update this after earning season but there's a couple of points stand out. First, we want to make the point that combined the Big 3 now account for almost $80 billion of infrastructure spend last year. That $80 billion, was not all incremental (laughs) No it's caused consolidation and disruption in the on-prem data center business and within IT shops companies like Dell, HPE, IBM, Oracle many others have felt the heat and have had to respond with hybrid and cross cloud strategies. Second while it's true that Azure and GCP they appear to be growing faster than AWS. We don't know really the exact numbers, of course because only AWS provides a clean view of IaaS and passwords, Microsoft and Google. They kind of hide them all ball on their numbers which by the way, I don't blame them but they do leave breadcrumbs and clues on growth rates. And we have other means of estimating through surveys and the like, but it's undeniable Azure is closing the revenue gap on AWS. The third is that I like the fact that Azure and Google are growing faster than AWS. AWS is the only company by our estimates to grow its business sequentially last quarter. And in and of itself, that's not really enough important. What is significant is that because AWS is so large now at 45 billion, even at their slower growth rates it grows much more in absolute terms than its competitors. So we think AWS is going to keep its lead for some time. We think Microsoft and AWS will continue to lead the pack. You know, they might converge maybe it will be a 200 just race in terms of who's first who's second in terms of cloud revenue and how it's counted depending on what they count in their numbers. And Google look with its balance sheet and global network. It's going to play the long game and virtually everyone else with the exception of perhaps Alibaba is going to be secondary players on these platforms. Now this next graphic underscores that reality and kind of lays out the competitive landscape. What we're showing here is survey data from ETR of more than 1400 CIOs and IT buyers and on the vertical axis is Net Score which measures spending momentum on the horizontal axis is so-called Market Share which is a measure of pervasiveness in the data set. The key points are AWS and Microsoft look at it. They stand alone so far ahead of the pack. I mean, they really literally, it would have to fall down to lose their lead high spending velocity and large share of the market or the hallmarks of these two companies. And we don't think that's going to change anytime soon. Now, Google, even though it's far behind they have the financial strength to continue to position themselves as an alternative to AWS. And of course, an analytics specialist. So it will continue to grow, but it will be challenged. We think to catch up to the leaders. Now take a look at the hybrid zone where the field is playing. These are companies that have a large on-prem presence and have been forced to initiate a coherent cloud strategy. And of course, including multicloud. And we include Google in this so pack because they're behind and they have to take a differentiated approach relative to AWS, and maybe cozy up to some of these traditional enterprise vendors to help Google get to the enterprise. And you can see from the on-prem crowd, VMware Cloud on AWS is stands out as having some, some momentum as does Red Hat OpenShift, which is it's cloudy, but it's really sort of an ingredient it's not really broad IaaS specifically but it's a component of cloud VMware cloud which includes VCF or VMware Cloud Foundation. And even Dell's cloud. We would expect HPE with its GreenLake strategy. Its financials is shoring up, should be picking up momentum in the future in terms of what the customers of this survey consider cloud. And then of course you could see IBM and Oracle you're in the game, but they don't have the spending momentum and they don't have the CAPEX chops to compete with the hyperscalers IBM's cloud revenue actually dropped 7% last quarter. So that highlights the challenges that that company facing Oracle's cloud business is growing in the single digits. It's kind of up and down, but again underscores these two companies are really about migrating their software install basis to their captive clouds and as well for IBM, for example it's launched a financial cloud as a way to differentiate and not take AWS head-on an infrastructure as a service. The bottom line is that other than the Big 3 in Alibaba the rest of the pack will be plugging into hybridizing and cross-clouding those platforms. And there are definitely opportunities there specifically related to creating that abstraction layer that we talked about earlier and hiding that underlying complexity and importantly creating incremental value good examples, snowfallLike what snowflake is doing with its data cloud, what the data protection guys are doing. A company like Loomio is headed in that direction as are others. So, you keep an eye on that and think about where the white space is and where the value can be across-clouds. That's where the opportunity is. So let's see, what is this all going to look like? How does the cube community think it's going to unfold? Let's hear from theCUBE Guests and theCUBE on Cloud speakers and some of those highlights. Now, unfortunately we don't have time to show you clips from every speaker. We are like 10-plus hours of video content but we've tried to pull together some comments that summarize the sentiment from the community. So I'm going to have John Furrier briefly explain what theCUBE on Cloud is all about and then let the guests speak for themselves. After John, Pradeep Sindhu is going to give a nice technical overview of how the cloud was built out and what's changing in the future. I'll give you a hint it has to do with data. And then speaking of data, Mai-Lan Bukovec, who heads up AWS is storage portfolio. She'll explain how she views the coming changes in cloud and how they look at storage. Again, no surprise, it's all about data. Now, one of the themes that you'll hear from guests is the notion of a distributed cloud model. And Zhamak Deghani, he was a data architect. She'll explain her view of the future of data architectures. We also have thoughts from analysts like Zeus Karavalla and Maribel Lopez, and some comments from both Microsoft and Google to compliment AWS's view of the world. In fact, we asked JG Chirapurath from Microsoft to comment on the common narrative that Microsoft products are not best-to-breed. They put out a one dot O and then they get better, or sometimes people say, well, they're just good enough. So we'll see what his response is to that. And Paul Gillin asks, Amit Zavery of Google his thoughts on the cloud leaderboard and how Google thinks about their third-place position. Dheeraj Pandey gives his perspective on how technology has progressed and been miniaturized over time. And what's coming in the future. And then Simon Crosby gives us a framework to think about the edge as the most logical opportunity to process data not necessarily a physical place. And this was echoed by John Roese, and Chris Wolf to experience CTOs who went into some great depth on this topic. Unfortunately, I don't have the clips of those two but their comments can be found on the CTO power panel the technical edge it's called that's the segment at theCUBE on Cloud events site which we'll share the URL later. Now, the highlight reel ends with CEO Joni Klippert she talks about the changes in securing the cloud from a developer angle. And finally, we wrap up with a CIO perspective, Dan Sheehan. He provides some practical advice on building on his experience as a CIO, COO and CTO specifically how do you as a business technology leader deal with the rapid pace of change and still be able to drive business results? Okay, so let's now hear from the community please run the highlights. >> Well, I think one of the things we talked about COVID is the personal impact to me but other people as well one of the things that people are craving right now is information, factual information, truth, textures that we call it. But here this event for us Dave is our first inaugural editorial event. Rob, both Kristen Nicole the entire cube team, SiliconANGLE on theCUBE we're really trying to put together more of a cadence. We're going to do more of these events where we can put out and feature the best people in our community that have great fresh voices. You know, we do interview the big names Andy Jassy, Michael Dell, the billionaires of people making things happen, but it's often the people under them that are the real Newsmakers. >> If you look at the architecture of cloud data centers the single most important invention was scale-out. Scale-out of identical or near identical servers all connected to a standard IP ethernet network. That's the architecture. Now the building blocks of this architecture is ethernet switches which make up the network, IP ethernet switches. And then the server is all built using general purpose x86 CPU's with DRAM, with SSD, with hard drives all connected to inside the CPU. Now, the fact that you scale these server nodes as they're called out was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose compute but this architecture, Dave is a compute centric architecture. And the reason it's a compute centric architecture is if you open this, is server node. What you see is a connection to the network typically with a simple network interface card. And then you have CPU's which are in the middle of the action. Not only are the CPU's processing the application workload but they're processing all of the IO workload what we call data centric workload. And so when you connect SSDs and hard drives and GPU is everything to the CPU, as well as to the network you can now imagine that the CPU is doing two functions. It's running the applications but it's also playing traffic cop for the IO. So every IO has to go to the CPU and you're executing instructions typically in the operating system. And you're interrupting the CPU many many millions of times a second. Now general purpose CPU and the architecture of the CPU's was never designed to play traffic cop because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's critical that in this new architecture where does a lot of data, a lot of these stress traffic the percentage of workload, which is data centric has gone from maybe one to 2% to 30 to 40%. >> The path to innovation is paved by data. If you don't have data, you don't have machine learning you don't have the next generation of analytics applications that helps you chart a path forward into a world that seems to be changing every week. And so in order to have that insight in order to have that predictive forecasting that every company needs, regardless of what industry that you're in today, it all starts from data. And I think the key shift that I've seen is how customers are thinking about that data, about being instantly usable. Whereas in the past, it might've been a backup. Now it's part of a data Lake. And if you can bring that data into a data lake you can have not just analytics or machine learning or auditing applications it's really what does your application do for your business and how can it take advantage of that vast amount of shared data set in your business? >> We are actually moving towards decentralization if we think today, like if it let's move data aside if we said is the only way web would work the only way we get access to various applications on the web or pages to centralize it We would laugh at that idea. But for some reason we don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, embrace the distribution of sources of data that they're not just within one part of organization. They're not just within even bounds of organizations that are beyond the bounds of organization. And then look back and say, okay, if that's the trend of our industry in general, given the fabric of compensation and data that we put in, you know, globally in place then how the architecture and technology and organizational structure incentives need to move to embrace that complexity. And to me that requires a paradigm shift a full stack from how we organize our organizations how we organize our teams, how we put a technology in place to look at it from a decentralized angle. >> I actually think we're in the midst of the transition to what's called a distributed cloud, where if you look at modernized cloud apps today they're actually made up of services from different clouds. And also distributed edge locations. And that's going to have a pretty profound impact on the way we go vast. >> We wake up every day, worrying about our customer and worrying about the customer condition and to absolutely make sure we dealt with the best in the first attempt that we do. So when you take the plethora of products we've dealt with in Azure, be it Azure SQL be it Azure cosmos DB, Synapse, Azure Databricks, which we did in partnership with Databricks Azure machine learning. And recently when we sort of offered the world's first comprehensive data governance solution and Azure overview, I would, I would humbly submit to you that we are leading the way. >> How important are rankings within the Google cloud team or are you focused mainly more on growth and just consistency? >> No, I don't think again, I'm not worried about we are not focused on ranking or any of that stuff. Typically I think we are worried about making sure customers are satisfied and the adding more and more customers. So if you look at the volume of customers we are signing up a lot of the large deals we did doing. If you look at the announcement we've made over the last year has been tremendous momentum around that. >> The thing that is really interesting about where we have been versus where we're going is we spend a lot of time talking about virtualizing hardware and moving that around. And what does that look like? And creating that as more of a software paradigm. And the thing we're talking about now is what does cloud as an operating model look like? What is the manageability of that? What is the security of that? What, you know, we've talked a lot about containers and moving into different, DevSecOps and all those different trends that we've been talking about. Like now we're doing them. So we've only gotten to the first crank of that. And I think every technology vendor we talked to now has to address how are they are going to do a highly distributed management insecurity landscape? Like, what are they going to layer on top of that? Because it's not just about, oh, I've taken a rack of something, server storage, compute, and virtualized it. I know have to create a new operating model around it in a way we're almost redoing what the OSI stack looks like and what the software and solutions are for that. >> And the whole idea of we in every recession we make things smaller. You know, in 91 we said we're going to go away from mainframes into Unix servers. And we made the unit of compute smaller. Then in the year, 2000 windows the next bubble burst and the recession afterwards we moved from Unix servers to Wintel windows and Intel x86 and eventually Linux as well. Again, we made things smaller going from million dollar servers to $5,000 servers, shorter lib servers. And that's what we did in 2008, 2009. I said, look, we don't even need to buy servers. We can do things with virtual machines which are servers that are an incarnation in the digital world. There's nothing in the physical world that actually even lives but we made it even smaller. And now with cloud in the last three, four years and what will happen in this coming decade. They're going to make it even smaller not just in space, which is size, with functions and containers and virtual machines, but also in time. >> So I think the right way to think about edges where can you reasonably process the data? And it obviously makes sense to process data at the first opportunity you have but much data is encrypted between the original device say and the application. And so edge as a place doesn't make as much sense as edge as an opportunity to decrypt and analyze it in the care. >> When I think of Shift-left, I think of that Mobius that we all look at all of the time and how we deliver and like plan, write code, deliver software, and then manage it, monitor it, right like that entire DevOps workflow. And today, when we think about where security lives, it either is a blocker to deploying production or most commonly it lives long after code has been deployed to production. And there's a security team constantly playing catch up trying to ensure that the development team whose job is to deliver value to their customers quickly, right? Deploy as fast as we can as many great customer facing features. They're then looking at it months after software has been deployed and then hurrying and trying to assess where the bugs are and trying to get that information back to software developers so that they can fix those issues. Shifting left to me means software engineers are finding those bugs as they're writing code or in the CIC CD pipeline long before code has been deployed to production. >> During this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as they have the right requirements. So that goes back to people making sure we have the partnership that goes back to leadership and the people and then the change management aspects right out of the gate, you should be worrying about how this change is going to be how it's going to affect, and then the adoption and an engagement, because adoption is critical because you can go create the best thing you think from a technology perspective. But if it doesn't get used correctly, it's not worth the investment. So I agree, what is a digital transformation or innovation? It still comes down to understand the business model and injecting and utilizing technology to grow our reduce costs, grow the business or reduce costs. >> Okay, so look, there's so much other content on theCUBE on Cloud events site we'll put the link in the description below. We have other CEOs like Kathy Southwick and Ellen Nance. We have the CIO of UI path. Daniel Dienes talks about automation in the cloud and Appenzell from Anaplan. And a plan is not her company. By the way, Dave Humphrey from Bain also talks about his $750 million investment in Nutanix. Interesting, Rachel Stevens from red monk talks about the future of software development in the cloud and CTO, Hillary Hunter talks about the cloud going vertical into financial services. And of course, John Furrier and I along with special guests like Sergeant Joe Hall share our take on key trends, data and perspectives. So right here, you see the coupon cloud. There's a URL, check it out again. We'll, we'll pop this URL in the description of the video. So there's some great content there. I want to thank everybody who participated and thank you for watching this special episode of theCUBE Insights Powered by ETR. This is Dave Vellante and I'd appreciate any feedback you might have on how we can deliver better event content for you in the future. We'll be doing a number of these and we look forward to your participation and feedback. Thank you, all right, take care, we'll see you next time. (upbeat music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by Silicon Angle. Hi, I'm stupid, man. And welcome back to the Cube on Cloud. We're talking about developers. And while so many people remember the mean from 2010 of Steve Balmer jumping around on stage development developers and developers, uh, many people know what really important is really important about developers. They probably read the 2013 book called The New King Makers by Stephen O. Grady. And I'm really happy to welcome to the program. Rachel Stevens, who is an industry analyst with Red Monk who was co founded by the aforementioned Stephen O. Grady. Rachel, Great to see you. Thank you so much for joining us. >>Thank you so much for having me. I'm excited to be here. >>Well, I've had the opportunity, Thio read some of what you've done. We've interacted on social media. We've got to talk events back when we used to do those in people. And >>I'm so >>glad that you get to come on the program especially. You were the ones I reached out. When we have this developer track, um, if you could just give our audience a little bit about your background. You know, that developer cred that you have Because as I joke, I've got a closet full of hoodies. But, you know, I'm an infrastructure guy by training I've been learning about, you know, containers and serverless and all this stuff for years. But I'm not myself much of developer. I've touched a thing or two in the years. >>Yeah. So happy to be here. Red Monk has been around since 2002 and have kind of been beating that developer drum ever since then, kind of as the company, The founder, Stephen James, notice that the decision making that developers was really a driver for what was actually ending up in the Enterprise. And as even more true, as cloud came onto, the scene is open source exploded, and I think it's become a lot more of a common view now. But in those early days, it was probably a little bit more of a controversial opinion, but I have been with the firm for coming up on five years now. My work is an industry analyst. We kind of help people understand, bottoms up technology, adoption trends, so that that's where I spend my time focusing is what's getting used in the enterprise. Why, what kind of trends are happening? So, yeah, that's where we all come from. That's the history of Red Monk in 30 seconds. >>Awesome. Rachel, you talk about the enterprise and developers For the longest time. I just said there was this huge gap you talk about. Bottoms up. It's like, well, developers use the tools that they want If they don't have to, they don't pay for anything. And the general I t. And the business sides of the house were like, I don't know, We don't know what those people in the corner we're doing, you know, it's important and things like that. But today it feels like that that's closed a bunch. Where are we? In your estimation, you know, our developers do they have a clear seat at the table? The title we have for this is whether the Enterprise Developer is its enterprise development oxymoron. In 2020 and 2021 >>I think enterprise developers have a lot more practical authority than people give them credit for, especially if you're kind of looking at that old view of the world where everything is driven by a buyer decision or kind of this top down purchasing motion. And we've really seen that authority of what is getting used and why change a lot in the last year. In the last decade, even more of people who are able to choose the tools that meet the job bring in tools, regardless of whether they maybe have that official approval through the right channels because of the convenience of trying to get things up and running. We are asking developers to do so much right now and to go faster and thio shifting things left. And so the things that they are responsible for incorporating into the way they are building APS is growing. And so, as we are asking developers to do more and to do more quickly, um, the tools that they need to do those, um, tasks to get these APS built is that the decision making us fall into them? This is what I need. This is what needs to come in, and so we're seeing. Basically, the tools that enterprise is air using are the tools that developers want to be using, and they kind of just find their way into the enterprise. >>Now I want to key off what you were talking about. Just developers were being asked to do Mawr and Mawr. We've seen these pendulum swings in technology. There was a time where it was like, Well, I'll outsource it because that'll be easier and maybe it'll be less expensive. And number one we found it necessarily. It wasn't necessarily cheaper. And number two, I couldn't make changes, and I didn't understand what was happening. So when when I talked to Enterprises today, absolutely. I need to have skills that's internally. I need to be able to respond to things fast, and therefore I need skills that I need people that can build what they have. What what do you see? What are those skill sets that are so important today? Uh, you know, we've talked so many times over the years is to you know, there's there's the skills gap. We don't have enough data scientists. We don't have enough developers way. We don't have any of these things. So what do we have and where things trending? >>Yeah, it's It's one of those things for developers where they both have probably the most full tool set that we've seen in this industry in terms of things that are available to them. But it's also really hard because it also indicates that there is just this fragmentation at every level of the stack. And there's this explosion of choice and decisions that is happening up and down the stack of how are we going to build things? And so it's really tricky to be a developer these days and that you are making a lot of decisions and you are wiring a lot of things together and you have to be able to navigate a lot of things. E think. One of the things that is interesting here is that we have seen the phrase like Full stack developer really carried a lot of panache, maybe earlier this decade and has kind of fallen away. Just because we've realized that it's impossible for anybody to be ableto spanned this whole broad spectrum of all of the things we're asking people to dio. So we're seeing this explosion of choice, which is meaning that there is a little bit more focused and where developers are trying to actually figure out what is my niche. What is it that I'm supposed to focus on. And so it's really just this balancing of act of trying to see this big picture of how to get this all put together and also have this focused area realizing that you have to specialize at some point. >>Rachel is such a great point there. We've actually seen that Cambrian explosion of developer tools that are out there. If you go to the CFCF landscape and look at everything out there or goto any of your public cloud providers, there's no way that anybody even working for those companies no good portion of the tools that are out there so nobody could be a master of everything. How about from a cloud standpoint, you know, there is the discussion of, you know what do I shift? Left What? You know, Can I just say, Okay, this piece of it, it could be a manage service. I don't need to think about it versus what skills that I need to have in house. What is it that's important. And obviously, you know, a zoo analyst. We know it varies greatly across companies, but you know what? What are some of those top things that we need to make sure that enterprises have skill set and the tools in house that they should understand. And what can they push off to their platform of choice? >>Yeah, I think your comment about managed services is really pressing because one of the trends that we're watching closely, it's just this rise of manage services. And it kind of ties back into the concept you had before about like, what an I team. That's they have, like the Nicholas Carr. I t doesn't matter, and we're pushing this all the way. And then we realized, Oh, we've got to bring that all back. Um, but we also realize that we really want as enterprises want to be spending our time doing differentiated work and wiring together, your entire infrastructure isn't necessarily differentiated for a lot of companies. And so it's trying to find this mix of where can I push my abstraction higher or to find a manage service that can do something for me? And we're seeing that happen in all levels of the stack. And so what we're seeing is this rise of composite APS where we're going to say, Okay, I'm gonna pull in back end AP ice from a whole bunch of tools like twilio or stripe or all zero where algo Leah, all of those things are great tools that I can incorporate into my app. And I can have this great user, um, interface that I can use. And then I don't have to worry quite so much about building it all myself. But I am responsible for wiring at all together. So I think it's that wire together set of interest that is happening for developers as the tool set that they are spending a lot of time with. So we see the manage services being important. Um played an important role in how absent composed, and it's the composition of that APs that is happening internally. >>What one of the one of the regular research items that I see a red monk is you know what languages you know. Where are the trends going? There's been relative stability, but then something's changed. You know, I look at the tools that you mentioned Full stack developer. I talked to a full stack developer a couple of years ago, and he's like like like terror form is my life and I love everything and I've used it forever. And that was 18 months, Andi. I kind of laugh because it's like, OK, I managed. I measure a lot of the technology that I used in the decades. Um, not that await. This came out six months ago and it's kind of mature. And of course, you know, C I C d. Come on. If it's six weeks old, it's probably gone through a lot of generations. So what do you see? Do you have any research that you can share as to looking forward? What are the You know what the skill sets we need? How should we be training our force? What do >>we need to >>be looking at in this kind of next decade of cloud? >>Yeah. So when when you spoke about languages, we dio a semi annual review of language usage as a sign on get hub and in discussion as seen on stack overflow, which we fully recognize is not a perfect representation of how these languages are used in the broader world. But those air data sets that we have access to that are relatively large and open eso just before anyone writes me angry letters that that's not the way that we should be doing it, Um, but one of the things that we've seen over time is that there is a lot of relative stability in those top tier languages in terms of how they are used, and there's some movement at the bottom. But the trends we're seeing where the languages are moving is type safety and having a safer language and the communities that are building upon other communities. So things like, um, we're seeing Scotland that is able to kind of piggyback off of being a jvm based language and having that support from Google. Or we're seeing typescript where it can piggyback off of the breath of deployment of JavaScript, things like that. So those things where were combining together multiple trends that developers are interested in the same time combined with an ecosystem that's already rich and full. And so we're seeing that there's definitely still movement in languages that people are interested in, but also, language on its own is probably pretty stable. So, like as you start to make language choices as a developer, that's not where we're seeing a ton of like turnover language frameworks on the other hand, like if you're a JavaScript developer and all of a sudden there's just explosion of frameworks that you need to choose from, that may be a different story, a lot more turnover there and harder to predict. But language trends are a little bit more stable over >>time, changing over time. You know, Boy, I I got to dig into, you know, relatively Recently I went down like the jam stack. Uh, ecosystem. I've been digging into a serverless for a number of years. What's your take on that? There's certain people. I talked to him. They're like, I don't even need to be a code. Or I could be a marketing person. And I can get things done when I talked to some developers there like a citizen developers. They're not developers. Come on, you know, I really need to be able to do this, so I'll give you your choices, toe. You know, serverless and some of these trends to kind of ext fan. You know who can you know? Code and development. >>Yeah. So for both translate jam stack and serve Ellis, One of the things that we see kind of early in the iteration of a technology is that it is definitely not going to be the right tool for every app. And the number of APS that they approach will fit for will grow as the tool develops. And you add more functionality over time and all of these platforms expand the capability, but definitely not the correct tool choice in every case. That said, we do watch both of those areas with extreme interest in terms of what this next generation of APS can look like and probably will look like in a lot of cases. And I think that it is super interesting to think about who gets to build these APs, because I e. I think one of the things that we probably haven't landed on the right language yet is what that what we should call these people because I don't think anyone associates themselves as a low code person. Like if you're someone from marketing and all of a sudden you can build something technical, that's really cool, and you're excited about that. Nobody else on your team could build. You're not walking around saying I am a low code marketing person like that, that that's that's that's demeaning. Like you're like. No, I'm technical. I'm a technical market, or look what I just did. And if you're someone who codes professionally for a living like and you use a low code tool to get something out the door quickly and >>you don't >>wanna demean and said, Oh, that was I did a low code that just like everybody, is just trying to solve problems. And everybody, um, is trying to figure out how to do things in the most effective way possible and making trade offs all the time. And so I don't think that the language of low code really is anything that resonates with any of the actual users of low code tools. And so I think that's something that we as an industry need toe work on finding the correct language because it doesn't feel like we've landed there yet. >>Yeah, Rachel, what? Want to get your take on just careers for developers now to think about in 2020 everyone is distributed. Lots of conversations about where we work. Can we bring the remote? Many of the developers I talked to already were remote. I had the chance that interview that the head of remote. Forget lab. They're over 1000 people and they're fully remote. So, you know, remote. Absolutely a thing for developers. But if you talk about careers, it is no longer, you know. Oh, hey, here's my CV. It's I'm on git Hub. You can see the code I've done. We haven't talked about open source yet, so give us your take on kind of developers today. Career paths. Andi. Kind of the the online community there. >>Yeah, this could be a whole own conversation. We'll try to figure out my points. Um, so I think one of the things that we are trying to figure out in terms of balance is how much are we expecting people to have done on the side? It's like a side project Hustle versus doing, exclusively getting your job done and not worrying too much about how many green squares you have on your get hub profile. And I think it's a really emotional and fraught discussion and a lot of quarters because it can be exclusionary for people saying that you you need to be spending your time on the side working on this open source project because there are people who have very different life circumstances, like if you're someone who already has kids or you're doing elder care or you are working another job and trying to transition into becoming a developer, it's a lot to ask. These people toe also have a side hustle. That said, it is probably working on open source, having an understanding of how tools are done. Having this, um, this experience and skills that you can point to and contributions you can point Teoh is probably one of the cleaner ways that you can start to move in the industry and break through to the industry because you can show your skills two other employers you can kind of maybe make your way in is a junior developer because you worked on a project and you make those connections. And so it's really still again. It's one of those balancing act things where there's not a perfect answer because there really is to correct sides of this argument. And both of those things are true. At the same time where it's it's hard to figure out what that early career path maybe looks like, or even advancing in a career path If you're already a developer, it's It's tricky. >>Well, I want to get your take on something to you know, I think back to you know, I go back a decade or two I started working with about 20 years ago. Back in the crazy days were just Colonel Daughter Warg and, you know, patches everywhere and lots of different companies trying to figure out what they would be doing on most of the people contributing to the free software before we're calling it open source. Most of the time, it was their side Hustle was the thing they're doing. What was their passion? Project? I've seen some research in the last year or so that says the majority of people that are contributing to open source are doing it for their day job. Obviously, there's a lot of big companies. There's plenty of small companies. When I goto the Linux Foundation shows. I mean, you've got whole companies that are you know, that that's their whole business. So I want to get your take on, you know, you know, governance, you know, contribution from the individual versus companies. You know, there's a lot of change going on there. The public cloud their impact on what's happening open source. What are you seeing there? And you know what's good? What's bad? What do we need to do better as a community? >>Yeah. E think the governance of open source projects is definitely a live conversation that we're having right now about what does this need to look like? What role do companies need to be having and how things are put together is a contribution or leadership position in the name of the individual or the name of the company. Like all of these air live conversations that are ongoing and a lot of communities e think one of the things that is interesting overall, though, is just watching if you're if you're taking a really zoomed out view of what open source looks like where it was at one point, um, deemed a cancer by one of the vendors in the space, and now it is something that is just absolutely an inherent part of most well tech vendors and and users is an important part of how they are building and using software today, like open source is really an integral tool. And what is happening in the enterprise and what's being built in the enterprise. And so I think that it is a natural thing that this conversation is evolving in terms of what is the enterprises role here and how are we supposed to govern for that? And e don't think that we have landed on all the correct answers yet. But I think that just looking at that long view, it makes sense that this is an area where we are spending some time focusing >>So Rachel without giving away state secrets. We know read Monk, you do lots of consulting out there. What advice do you give to the industry? We said we're making progress. There's good things there. But if we say okay, I wanna at 2030 look back and say, Boy, this is wonderful for developers. You know, everything is going good. What things have we done along the way? Where have we made progress? >>Yeah, I think I think it kind of ties back to the earlier discussion we were having around composite APS and thinking about what that developer experience looks like. I think that right now it is incredibly difficult for developers to be wiring everything together and There's just so much for developers to dio to actually get all of these APs from source to production. So when we talk with our customers, a lot of our time is spent thinking, How can you not only solve this individual piece of the puzzle, but how can you figure out how to fit it into this broader picture of what it is the developers air trying to accomplish? How can you think about where your ATF, It's not on your tool or you your project? Whatever it is that you are working on, how does this fit? Not only in terms of your one unique problem space, but where does this problem space fit in the broader landscape? Because I think that's going to be a really key element of what the developer experience looks like in the next decade. Is trying to help people actually get everything wired together in a coherent way. >>Rachel. No shortage of work to do there really appreciate you joining us. Thrilled to have you finally as a cube. Alumni. Thanks so much for joining. >>Thank you for having me. I appreciate it. >>All right. Thank you for joining us. This is the developer content for the cube on cloud, I'm stew minimum, and as always, thank you for watching the Cube.

>> Hi, everyone; I am thrilled to personally invite you to a special event created and hosted by "theCUBE." On January 21st, we're holding "theCUBE on Cloud," our first editorial event of the year. We have lined up a fantastic guest list of experts in their respective fields, talking about CIOs, COOs, CEOs, and technologists, analysts, and practitioners. We're going to share their vision of Cloud in the coming decade. Of course, we also have guests from the big three Cloud companies, who are going to sit down with our hosts and have unscripted conversations that "theCUBE" is known for. For example, Mahlon Thompson Bukovec is the head of AWS's storage business, and she'll talk about the future of infrastructure in the Cloud. Amit Zavery is one of Thomas Kurian's lieutenants at Google, and he'll share a vision of the future of application development and how Google plans to compete in Cloud. And J.G. Chirapurath leads Microsoft's data and analytics business. He's going to address our questions about how Microsoft plans to simplify the complexity of tools in the Azure ecosystem and compete broadly with the other Cloud players. But this event, it's not just about the big three Cloud players. It's about how to take advantage of the biggest trends in Cloud, and, of course, data in the coming decade, because those two superpowers along with AI are going to create trillions of dollars in value, and not just for sellers, but for practitioners who apply technology to their businesses. For example, one of our guests, Zhamak Dehghani, lays out her vision of a new data architecture that breaks the decade-long failures of so-called big data architectures and data warehouse and data lakes. And she puts forth a model of a data mesh, not a centralized, monolithic data architecture, but a distributed data model. Now that dovetails into an interview we do with the CEO of Fungible, who will talk about the emergence of the DPU, the data processing unit, and that's a new class of alternative processors that's going to support these massively distributed systems. We also have a number of CXOs who are going to bring practical knowledge and experience to the program. Allen Nance, he led technology transformation for Phillips. Dan Sheehan is a CIO, COO, and CTO and has led teams at Dunkin' brands, Modell's Sporting Goods and other firms. Cathy Southwick has been a CIO at a large firm like AT&T and now is moving at the pace of Silicon Valley at Pure Storage. Automation in the Cloud is another theme we'll hit on with Daniel Dines, who founded and heads the top RPA company. And of course, we'll have a focus on developers in the Cloud with Rachel Stevens of RedMonk. That's a leading edge analyst firm focused exclusively on the developer community. And much more that I just don't have time to go into here, but rest assured, John Furrier and I will be bringing our thoughts, our hard-hitting opinions, along with some special guests that you don't want to miss. So click on the link below and register for this free event, "theCUBE on Cloud." Join us and join the conversation. We'll see you there.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Worldwide Public sector. >>Welcome to the Cube. Virtual. This is our coverage of aws reinvent 2020. Specialized programming for worldwide public sector. I'm Lisa Martin. Got a couple of guests here from No. Veta, please welcome Steven Adelman, principal computer scientists, and Kevin Healed, vice president of Information Exploitation. Gentlemen, welcome to the Cube. >>Thank you. >>Thank you for having us. >>Alright, guys. So? So, Kevin, we're going to start with you. Give our audience an introduction to Nevada. What do you What do you guys do? Who are you? How do you play in the public sector Government space, >>right? Yeah. Thank you, Lisa. Eso, Nevada Nevada is a technology services company focused on government solutions. So primarily national security solutions. Eso think customers such as Doody, the intelligence community, FBI, law enforcement and things like that about 13 1300 employees worldwide, primarily in our in our field. Clear resource is, um, that really focused on cloud for solutions for our customers. So solving the tough mission challenges our customers have, so that could be in technology solutions such as Data Analytics A I M L i O T. Secure Workloads, full spectrum cyber Cobb video processing. Really anything that's a high end technology solution or something we do for the government. We have been a privilege. We have. It's a privilege to be a partner with AWS for for some time now. In fact, I think the first reinvent we may have been to Stephen was six years ago. Five years ago, two >>1012 or 13 >>s So we've we've we've been around for a while, really kind of enjoying it and certainly sad that we're missing an in person reinvent this year, but looking forward to doing it virtually so, we're actually advanced your partner with AWS with a machine learning and government competency. Andi really kind of thio pump the m l side of that. That was one of our first companies with compasses with AWS and led by a center of excellence that I have in my division that really focuses on machine learning and how we applied for the Michigan. And so, um, really, we focus on protecting the nation and protecting our activities in the country >>and on behalf of the country. We thank you, Steven. Give me a little bit of information from a double click perspective as computer scientists. What are some of the key challenges that no, that helps its customers to solve. And how do you do that with a W s? >>Yeah, Thank you. So really as, ah, company, that is is data first. So our initial love and and still are kind of strongest competency is in applying solutions to large data sets. And as you can imagine, uh, the bigger the data set them or compute you need the the more resource is you need and the flexibility from those resource is is truly important, which led us very early, as especially in the government space and public sector space to be in early. A doctor of cloud resource is because of the fact that, you know, rather than standing up a 200 node cluster at at many millions of dollars, we could we could spend up a W s resource is process a big data set, and then and then get the answers an analyst or on operator needed and then spin down. Those resource is when When when that kind of compute wasn't needed. And that is really, uh, kind of informed how we do our work Azaz Nevadans that that cloud infrastructure and now pushing into the edge compute space. Still kind of keeping those cloud best practices in play to get access to more data. That the two, the two biggest, I think revolutions that we've seen with regards to using data to inform business processes and missions has been that that cloud resource that allows us to do so much with so less and so much more flexibly and then the idea of cheap compute making it to the edge and the ability to apply sensors thio places where you know it would been a would have been, you know, operational cost prohibitive to do that and then, ironically, those air to things that aren't necessarily data analytics or machine learning focused but man, did they make it easier to collect that data and process that data and then get the answers back out. So that really has has has kind of, uh, shaped a lot of the way Nevada has grown as a company and how we serve our customers. >>So coming back over to you lets. One of the things that we've been talking about almost all year is just the acceleration in digital transformation and how much faster organizations, private sector, public sector need to innovate to stay relevant, to stay competitive. How do you are you working with government customers to help them innovate so quickly? >>You know, we're very fortunate that a set of customers that focuses actually innovation it's focuses. I rad on. Do you know we can't do the cool things we do without those customer relationships that really encourage us to, um, to try new things out and, quite frankly, fail quickly when we need Thio. And so, by establishing that relationship, what we've been able to do is to blend agile development. Actual acquisition with government requirements process, right? If if you know the typical stereotype of government work is it's this very stovepiped hard core acquisition process, right? And so we have been fortunate to instead try quick win kind of projects. And so one of the biggest things we do is partner with our government customers and try to find it difficult, um, challenged to solve over 6 to 12 month time, right? So instead of making this long four or five year acquisition cycles like show me, right. How can we solve this problem? And then we partner with the mission partner show success in six months show that we can do it with a smaller part of money, and then as we're able to actually make that happen, it expands in something bigger, broader, and then we kind of bringing together a coalition of the willing, if you will in the government and saying, Okay, are there other stakeholders to care about this problem, bring them on, bring their problems and bringing together? You know, we can't do that with some of the passionate people we have, like Stevens. A perfect example. When we talk about a car in the projects we're doing here, Stevens passion for this technology partner with our customers having these challenges and try to enhance what they're doing is a powerful combination. And then the last thing that we're able to is a company is we actually spend a decent amount of our own dollar dollars on I rad S O. R and D that we fund ourselves. And so, while finding those problems and spending government dollars in doing that. We also have spent our own dollars on machine learning Coyote sensor next Gen five g and things like that and how those compartment together partner together to go back to the government. >>Yeah, yeah, So I would even say, You know, there's this. There's a conventional wisdom that government is slow in plotting and a little bit behind commercial best practices. But there are There are pockets in growing pockets across the government, Um, where they're really they're really jumping ahead of, ah, lot of processes and getting in front of this curve and actually are quite innovative. And and because they kind of started off from behind, they could jump over a lot of kind of middle ground legacy technologies. And they're really innovating. As Kevin said with With With the card platform, we're partnering with um P E O Digital in the Air Force in South C, D. M and Air Force security forces as that kind of trifecta of stakeholders who all want toe kind of saw a mission problem and wanted to move forward quickly and leave the legacy behind and and really take a quantum leap forward. And if anything, they're they're driving us Thio, Innovate Mawr Thio Introduce more of those kind of modern back practices on bond. Nevada as a company loves to find those spots in the government sector where we've got those great partners who love what we're doing. And it's this great feedback loop where, um, where we can solve hard technical problems but then see them deployed to some really important and really cool and impactful missions. And we tend to recruit that that set that kind of nexus of people who want to both solve a really difficult problem but want to see it executed in a really impactful way as well. I mean, that really grates a great bond for us, and and I'm really excited to say that that a lot of the government it is really taking a move forward in this this this realm. And I think it's it's just good for our country and good for the missions that they support. >>Absolutely. And it's also surprising because, as you both said, you know, there is this expectation that government processes or lengthy, you know, laborious, um, not able to be turned around quickly. But as Kevin, you just said, you know helping customers. Government agencies get impact within 6 to 12 months versus 4 to 5 years. So you talked about Picard? Interesting name. Kevin. Tell me a little bit more about that technology and what it is that you guys deliver. That's unique. >>Well, honestly, it's probably best to start with Stephen. I can give you the high level. This is Stevens vision. I have to give him credit for that. And I will say way have lots of fun. Acronym. So it isn't Actually, it isn't backward. Um, right. Stephen doesn't actually stand for something. >>It stands for Platform for Integrated, a C three and Responsive for defense on >>Guy. You know >>that the Star Trek theme is the leg up from the last set of programs I had, >>which were >>my little ponies. So >>Oh, wow. That's a definite stuff in a different direction. Like >>it? Part of the great thing about working in the government is you get to name things, cool things, so but t get to your question eso So Picard really sprung out of this idea that I had a few years ago that the world but for our spaces, the Department of defense and the federal government was going to see a massive influx of the desire to consume sensors from from areas of responsibility, from installations and, frankly, from battlefields. Um, but they were gonna have to do it. In a way, um, uh, that presented some real challenges that you couldn't just kind of throw compute editor, throw traditional I t processes at it. You know, we have legacy sensors that are 40 years old sitting on installations. You know, old program, a logical controllers or facilities control systems that were written in cobalt in the seventies, right in the world are not even I, p based, most of them bond. Then on the other end of the spectrum, you have seven figure sensors that air, you know, throwing out megabits of second of data that are mounted to the back of jeeps. Right, That that air bouncing through the desert today. But we'll be bouncing through the jungle tomorrow, and you have to find all of those kind of in combined all of those together, um, and kind of create a cohesive data center for data set set for you know, the mission for, um, you know what we call a user to find common operating picture for a person. Thio kind of combine all of those different resource is and make it work for them. And so we found a great partner with security forces. Um, they realized that they wanted Thio to make a quantum leap forward. They had this idea that the next defender So there are there, like a military police outfit that the next defender was going to be a data driven defender and they were gonna have to win the information war war as much as they had to kind of dominate physical space. And they immediately got what we were trying to achieve, and it was just just great synergy. And then we've piled on some other elements, and we're really moving that platform forward to to kind of take every little bit of information we can get from the areas of responsibility and get it into a you know, your modern Data Lake, where they can extract information from all that data. >>Kevin, as the VP of information exploitation, that's a very interesting title. How are you helping government organizations to win the war on information? Leverage that information to make a big impact fast. >>Yeah. I mean, I think a lot of it is is that we try to break down the barriers between systems on data so that we can actually enable that data to fuse together to find and get insights into it. You know, as ML and I have become trendy topics, you know, they're very data hungry operations. And I think what Steven has done with the card and his team is really we want to be able to make those sensors seamless from a plug and play perspective that Aiken plug in a new sensor. It's a standards based, uh, interface that sends that data back so that we can and take it back to the user to find Operation Picture and make some decisions based off of that data. Um, you know, what's more is that data could even refused with more than the data that Stevens collecting off the sensors. It could be commercial data, other government data and I think is Davis. As Stephen said earlier, you have to get it back. And as long as you've gotten back in Labour's share with some of our mission partners, then you can do amazing things with it. And, you know, Stephen, I know you have some pretty cool ideas and what we're gonna do on the edge, right? How do we do some of this work of the edge where a sensor doesn't allow us to pull out that data back? >>Yeah, and and Thio follow on to what you were kind of referring to with regards to thio handling heterogeneous data from different sensors. Um, one of the main things that our government customers and we have seen is that there are a lot of historically there are a lot of vertical solutions where you know, the sensor, the platform, and then the data Laker kind of all part of this proprietary stack. And we quickly realized that that just doesn't work. And so one of the major thrust of that card platform was to make sure that we had ah, platform by which we could consume data through adapters from essentially any sensor speaking. Any protocol with any style data object, Whether that was an industry standard or a proprietary protocol, we could quickly interested and bring it into our Data lake. And then to pile on to what Kevin was talking about with compute. Right? So you have, uh, like, almost like a mass locks hierarchy of needs when it comes to cyber data or thio this coyote data or kind of unified data, Um, you know, you wanna turn it into basic information, alerts alarms, then you want to do reporting on it, or analytics or some some higher level workflow function. And then finally, you probably want to perform some analytics or some trending or sort of anomaly detection on it. And and that gets more computational e intensive each step of the way. And so you gotta You gotta build a platform that allows you to to both take some of that high level compute down to the edge, but also then bring some of that data up into the clouds where you could do that processing, and you have to have kind of fun jubilate e between that and so that hard platform allows you to kind of bring GP use and high processing units down to the edge and and make that work. Um, but then also and then as maybe even a first passive to rule out some of the most you know, some of the boring gated in the video Analytics platform. We call it Blue Sky and Blue Ocean. Right, so you're recording lots of video. That's not that interesting. How do you filter that out? So you're only sending the information The interesting video up eso You're not wasting bandwidth on stuff that just doesn't matter on DSO. It's It's a lot of kind of tuning these knobs and having a flexible enough platform that you could bring Compute down when you need it. And you could bring data up to compute on Big Cloud while you need it, and just kind of finding a way to tune that that that really does. I mean it. You know, that's a lot of words about how you do that. But what that comes to is flexible hardware and being able to apply those dev ops and C I. C D platform characteristics to that edge hardware and having a unified platform that allows you to kind of orchestrate your applications in your services all the way up and down your stack, from micro controllers to a big cloud instant creation. >>You make it sound so easy. Steven Kevin. Let's wrap it up with you in terms of like making impacts and going forward. We know the edge has exploded, even mawr, during this very interesting year. And that's going to be something that's probably going to stay, um, stay as a permanent impact or effect. What are some of the things that we can expect in 2021 in terms of how you're able to help government organizations capitalize on that, find things faster, make impact faster? >>Yeah. I mean, I think the cool thing we're seeing is that there's a lot more commoditization of sensors. There's a lot more censored information. And so let's use lighters. Example. We you know, things were getting cheaper, and so we can all of a sudden doom or or more things at the edge, and we ever would have expected. Right when you know Steven's team is integrating camera data and fence data from 40 years ago, you know, it's just saying on off it's not do anything fancy. But now we you know, you know, Stephen, I camera whether Metro you gave him before was, but the cost of light are has dropped so significantly that we can now then deploy that we can actually roll it out there and not being locked in their proprietary, uh, system. Um, so I see that being very powerful, you know? Also, I can see where you start having sensors interact with each other, right? So one sensor finds one thing and then a good example that we've started thio experiment with. And I think Steve, you could touch on it is using triggering a sensor, triggers a drone to actually investigate what's going on and then therefore, hybrid video back and then automatically can investigate instead of having to deploy a defender to actually see what happened at that. At that end, Points dio e don't know. There's it's amore detail you can provide there. >>Yeah, No. So exactly that Kevin. So So the power of the sensor is is something something old that that gives you very uninteresting Data like a one or a zero on on or off can detect something very specific and then do something kind of high speed, like task a drone to give you a visual assessment and then run object detection or facial recognition on, you know, do object detection to find a person and do facial recognition on that person to find out if that's a patrol walking through a field or a bad guy trying Thio invade your space. Um and so it's really the confluence and the gestalt of all of these sensors in the analytics working together, Um, that really creates the power from very simple, simple delivery. I think, um, there's this, You know, this idea that you know, ah 100 bytes of data is not that important. But when you put a million sensors giving you 100 bytes of data, you can truly find something extremely powerful. And then when you kind of and you make those interactions sing, um, it's amazing. Tow us the productivity that we can produce and the kind of fidelity of response that we can give thio actors in the space whether that's a defender trying to defend the base or a maintenance person trying thio proactively replace the fan or clean the fan on an H vac system. So So you know, you know, there isn't a fire at a base or for, uh, interesting enough. One of the things that we we've been able to achieve is we've taken maintenance data for helicopter engines and And we've been able to proactively say, Hey, you need to You need to take care of this part of the helicopter engine. Um and it saves money. It saves downtimes. It keeps the birds in the air. And it's a relatively simple algorithm that we were able to achieve. And we were able to do that with the maintenance people, bring them along in this endeavor and create analytics that they understood and could trust on DSO. I think that's really the power of this base. >>Tremendous power. I wish we had more time to to dig into it. Guys, thank you so much for sharing. Not just your insights, what nobody is doing but your passion for what you're doing and how you're making such an impact. Your passion is definitely palpable. Steven. Kevin, Thank you for joining me today. >>Thank you >>for my guests. I'm Lisa Martin. You're watching the Cube? Virtual. Yeah,

>>live from Atlanta, Georgia. It's the Q covering Answerable Fest 2019. Brought to you by >>Red Hat. >>Okay, welcome back. It runs two cubes. Live coverage of Ansel Fest here in Atlanta, Georgia. I'm John for a host of the Cube with stewed Minutemen. Analysts were looking angle. The Cube are next to guest Tom Anderson and most product owner. Red Hat is part of the sensible platform automation properly announced. And Richard Henshaw, product manager. Guys, welcome to the Cube Way had all the execs on yesterday and some customers all pretty jazzed up about this year, mainly around just the timing of how automation is really hitting the scene and some of the scale that's going on. You guys had big news with the answerable automation platform. New addition to the portfolio. What's the feedback? >>So far, I think the feedback has been super positive. We have customers have come to us. A lot of the last little one said, Hey, we're maturing. We're moving along the automation maturity curve, right, and we have multiple teams coming to us and saying, Hey, can you help us connect this other team? We've had a lot of success doing cloud provisioning or doing network automation were doing security automation. What have you and they're coming to us and saying, Help us give us kind of the story if you will, to be able to connect these other teams in our organization. And so that way I kind of feel the pole for this thing to move from a tool that automates this or that. This task for that task. Too much more of a platform center. >>It seems to be scaling out in terms of what automation is touching these days. And look at the numbers six million plus activations on get Hub versus other projects. So activities high in the community. But this seems to be much more broader. Scope now. Bring more things together. What's the rationale behind? What's the reasoning? What's the strategy? But the main thing is, >>automation is got to that point where it's becoming the skill set that we do. So it was always the focus. You know, I'm a database administrator. I'm assists out, man. I'm a middle where I'm a nap deaf on those people, then would do task inside their job. But now we're going to the point off, actually, anybody that can see apiece. Technology can automate piece technology in the clouds have shown This is the way to go forward with the things what we had. We bring that not just in places where it's being created from scratch, a new How do you bring that into what's existing? Because a lot of our customers have 20 or 30 years like a heritage in the I T estate. How do you do with all of that? You can't just rebuild everything into new as well. So you gotta be ableto automate across both of those areas and try and keep. You know, we say it's administrative efficiency versus organization effectiveness. Now how do I get to the point of the organization? Could be effective, supposed just doing things that make my job easier. And that's what we're gonna bring with applying automation capability that anybody can take advantage of. >>Richard. I actually felt the keynote demo this morning did a nice job of that line that they set it up with is this is this is tools that that all the various roles and teams just get it, and it's not the old traditional okay, I do my piece and set it up and then throw it over the wall. There was that, you know? Oh, I've got the notification and then some feedback loops and, you know, we huddled for something and it gets done rather fast, not magic. It's still when I get a certain piece done. Okay, I need to wait for it's actually be up and running, but you know, you're getting everybody into really a enterprise collaboration, almost with the tool driving those activities together >>on that. And that's why yesterday said that focus on collaboration is the great thing. All teams need to do that to be more successful because you get Maur inclusivity, Maurin puts. But organizations also need to coordinate what activities they're doing because they have rules, regulations, structures and standards they have to apply. Make sure that those people can do things in a way that's guided for them so that they're they're effective at what they're trying to do. >>Okay, I think I'm going to explain what's in the platform first because an engine and tower and there, what else is in there, what's new? What's what our customers is going to see. That's new. That's different >>it's the new components are automation Hope Collections, which is a technology inside answer ball itself. On also Automation Analytics and the casing is that engine and terrorist of the beating heart of the platform. But it's about building the body around the outside. So automation is about discover abilities like, What can we find out? What automation can I do that I'm allowed to do? Um, and let six is about the post activity. So I've automated all these things. I've done all this work well, How did it go? Who did what, who did? How much of what? How well did it work? How much did it failed? Succeeds and then, once you build on that, you don't start to expand out into other areas. So what? KP eyes, How much of what I do is automated versus no automated? You can start to instigate other aspects of business change, then Gamification amongst teams. Who's the Who's the boat? The closest motive here into the strategy input source toe How? >>Find out what's working right, essentially and sharing mechanism to for other groups in terms of knowing what's happening >>and how is my platform performing which areas are performing well, which airs might not be performing well. And then, as we move down the road, kind of how my performing against my peers are other organizations that are automating using the ants will automation platform doing? And am I keeping up on my doing better? That kind of stuff. >>So, Tom, there's a robust community as we was talking about. Their platform feels like it builds on yet to change the dynamic a little bit. When you talk about the automation hub and collections, you've already got a long list of the ecosystem vendors that are participating here. Bring us two through a little bit. What led Thio. You know all these announcements and where you expect, you know, how would this change the dynamics of >>the body? And maybe we'll split up that question. I'll talk a little bit about partners because it's both partners and customers in community here that's been driving us this way. I'll talk a little bit about partners and Rich talk about the customer piece here, which is partners have been traditionally distributing their content there. Ansel automation content through our engine capability. So our engine release cycle, or cadence, has been sort of the limiting factor to how fast they can get content out to their users and what what the collections does is part of the platforms allows us to separate those things. Rich talked about it yesterday in his keynote, having that stable platform. But you having yet having content be able to read fast. And our partners love that idea because they can content. They can develop content, create content, get into their users hands faster. So partners like at five and Microsoft you've seen on stage here are both huge contributors. And they've been part of the pole for us to get to the platform >>from a customer perspective. And the thing I love most about doing this job with the gas of customers is because I was a customer on Guy was danceable customer, and then I came over to this side on Dhe. I now go and see customers. I see what they've done, and I know what that's what I want to do. Or that's what I was trying to do. And she started to see those what people wanted to achieve, and I was said yesterday it is moving away from should I automate. How would we automate Maura? What should I automate? And so we'll start to see how customers are building their capabilities. And there's no there's many different ways people do. This is about different customers, >>you know. What's interesting is you guys have such a great success formula first. Well, congratulations. It's great to see how this is turning into such a wider market, because is not just the niche configuration management. More automation become with cloud to point a whole new wider category. So congratulations. The formula we see with success is good product, community customers adopting and then ecosystem that seems to be the successful former in these kinds of growth growth waves you guys experiencing? What is the partnering with you mentioned? S five Microsoft? Because that, to me, is gonna be a tipping point in a tel sign for you guys because you got the community. You got the customers that check check ecosystem. What's the partner angle? How do they involve? Take us through that. What's going on? They're >>so you're absolutely so you know, kind of platform velocity will be driven by partner adoption and how many things customers can automate on that platform or through that platform and for us I mean, the example was in the demo this morning where they went to the automation hub and they pulled down the F five collection, plugged it into a workflow, and they were automating. What are partners? Experience through their customers is Look, if I'm a customer, I have a multi cloud environment or hybrid cloud environment. I've got automation from AWS. I've got azure automation via more automation. Five. Got Sisko. I've got Palo Alto. I've got all these different automation tools to try and string them together, and the customers are coming and telling those vendors Look, we don't want to use your automation to end this automation tooling that one we want to use Ansel is the common substrate if you will automation substrate across this platform. So that's motivating the partners to come to us and say, Hey, I had I was out five Aspire last week, and they're all in a natural. I mean, it's really impressive to see just how much there in unanswerable and how much they're being driven by their customers when they do Ansell workshops without five, they say the attendance is amazing so they're being pulled by their customers and therefore the partners are coming to us. And that's driving our platform kind of usability across the across the scale. >>Another angle we'll see when we talk to the engineers of the partners that are actually doing the work to work with danceable is that they're seeing is ah, change also in how they it's no longer like an individual customer side individual day center because everything is so much more open and so much more visible. You know there's value in there, making it appealing and easy for their customers to gain advantage of what they're doing. And also the fact that the scales across those customers as well because they have their internal team's doing it, saying the same things and so bringing them to an automation capable, like Ansel have to push. That means that they also gained some of the customers appreciation for them, making it easier to do their tasking collaboration with us and you know, the best collaborations. We've got some more partners, all initiated by customers, saying Hey, I want you to go and get danceable content, >>the customer driving a lot of behavior, the guest system. Correct. On the just another point, we've been hearing a lot of security side separate sector, but cyber security. A lot of customers are building teams internally, Dev teams building their own stacks and then telling the suppliers a support my AP eyes. So now you start to see more of a P I integration point. Is that something that is gonna be something that you guys gonna be doubling down on? What's that? What's the approach there? How does that partner connected scale with the customers? So we've >>been eso Ansel security automation, which is the automation connecting I. P. S. C. P. S that kind of stuff. It is almost a replay of what we did the network automation space. So we saw a need in the network automation space. We feel that we became a catalyst in the community with our partners and our customers and our and our contributors. And after about three years now, Ansel Network automation is a huge piece of our business and adoption curve. We're doing the exactly see the exact same thing in the security automation space compliance. The side over here, we're talking about kind of automating the connections between your firewalls, your threat detection systems and all that kind of stuff. So we're working with a set of partners, whether it's Cisco, whether it's Palo Alto, whether it's whether it's resilient by the EMS, resilient and being able to connect and automate the connections between the threat and the response and and all of that kind of >>the same trajectory as the network automation >>Zach. Same trajectory, just runnin the same play and it's working out right now. We're on that kind of early part of that curve, that adoption curve, and we have partners jumping in with us. >>You're talking to customers. We've heard certain stories. You know how I got, you know, 1000 hours of work down to a dozen hours of work there. Is there anything built into the tool today that allows them to kind of generate those those hero stats O. R. Any anything along those lines? >>Talk about analytic committee from yes, >>well, again without any analytic side. I mean, those things starts become possible that one of the things we've been doing is turning on Maur more metrics. And it's actually about mining the data for the customer because Tower gives this great focal point for all the automation that's going on. It's somewhere that everything comes through. So when we export that and then we can we can do that work for all the customers rather than have to duel themselves. Then you start to build those pictures and we start with a few different areas. But as we advance with those and start, see how people use them and start having that conversation customers about what data they want to use and how they want to use it, I think that's gonna be very possible. You know, it's so >>important. E think was laid out here nicely. That automation goes from a tactical solution to more strategic, but more and more how customers can leverage that data and be data driven. That's that's gonna drive them for it. And any good customer examples you have of the outcomes. No, you're talking to a lot of >>PS one from this morning. Yeah, >>so I mean, I'll be Esther up this morning, and I think that the numbers they used in the demo that she's like, you know, last year they did 100,000 from launch to the end of the year. 100,000 changes through their platform on this year so far that in a 1,000,000. So now you know, from my recollection, that's about the same time frame on either side of the year. So that's a pretty impressive acceleration. Side of things. We've had other ones where people have said, You know how many times you were telling some customers yesterday? What used to take eight hours to a D R test with 20 or 30 people in for the weekend now takes 12 minutes for two People on the base is just pushing a few buttons just as they go through and confirm everything worked that that type of you can't get away from that type of change. >>J. P. Morgan example yesterday was pretty compelling. I mean, time savings and people are, I mean, this legit times. I mean, we're talking serious order of magnitude, time savings. So that's awesome. Then I want to ask you guys, Next is we're seeing another pattern in the market where amongst your customer base, where it's the same problem being automated, allover the place so playbooks become kind of key as that starts to happen is that where the insights kind of comes in? Can you help us kind of tie that together? Because if I'm a large enterprise with its I'm decentralized or centralized, are organized problem getting more gear? I'm getting more clouds, game or operations. There's more surface area of stuff and certainly five g I ot is coming around the corner. Mention security. All this is expanding to be much more touchpoints. Automation seems to be the killer app for this automation, those mundane task, but also identifying new things, right? Can you guys comment on that? >>Yeah, so maybe I'll start rich. You could jump in, which is a little bit around, uh, particularly those large accounts where you have these different disparate teams taking a approach to automate something, using Ansel and then be able to repeat or reuse that somewhere else. The organization. So that idea of being for them to be able to curate they're automation content that they've created. Maybe they pulled something down from galaxy. Maybe they've got something from our automation husband. They've made it their own, and now they want to curate that and spread it across the organization to either obviously become more efficient, but also in four standards. That's where automation hub is going to come into play here. Not only will it be a repo for certify content from us and our partners, but it will also be an opportunity for them to curate their own content and share it across the organization. >>Yeah, I think when you tie those two things together and you've got that call discover abilities, I had away go and find what I want. And then the next day, the next day, after you've run the automation, you then got the nerve to say, Well, who's who's using the right corporate approved rolls? Who's using the same set of rolls from the team that builds the standards to make sure you're gonna compliant build again, showing the demo That's just admin has his way of doing it, puts the security baseline application on top and you go, Oh, okay, who's running that security baseline continuously every time. So you can both imposed the the security standards in the way the build works. But you can also validate that everybody is actually doing the security standards. >>You what I find fascinating about what you guys are doing, and I think this is came out clearly yesterday and you guys are talking about it. And some of the community conversations is a social construct here. Going on is that there's a cultural shift where the benefits that you guys are throwing off with the automation is creating a network effect within the companies. So it's not just having a slack channel on texting. The servers are up or down. It's much more of a tighter bond between the stakeholders inside the company's. Because you have people from different geography is you have champions driving change. And there's some solidarity happening between the groups of people, whether they're silo door decentralized. So there's a whole new social network, almost a cultural shift that's happening with the standardization of the substrate. Can you guys comment on this dynamic? Did you see this coming? You planning forward? Are you doubling down on it? >>I think so. And we talk about community right on how important that is. But how did you create that community internally and so ask balls like the catalyst so most teams don't actually need to understand in their current day jobs. Get on all the Dev ops, focus tools or the next generation. Then you bring answer because they want to automate, and suddenly they go. Okay, Now I need to understand source control, and it's honest and version. I need to understand how to get pulls a full request on this and so on and so forth on it changes that provides this off. The catalyst for them to focus on what changed they have to make about how they work, because what they wanted to do was something that requires them to do you no good disciplines and good behaviors that previously there was no motivation or need to do. I think >>Bart for Microsoft hit on that yesterday. You know, if you saw Bart Session but their network engineers having to get familiar with concepts of using automation almost like software development, life cycles right and starting to manage those things in repose. And think of it that way, which is intimidating at first for people who are not used to. But once they're over that kind of humping understand that the answer language itself is simple, and our operations person admin can use it. No problem, >>he said himself. Didn't my network engineers have become network developers. >>It's funny watching and talking to a bunch of customers. They all have their automation journey that they're going through. And I hear the Gamification I'm like, Okay, what if I have certain levels I have to reach in it unlocked capabilities, you know, in the community along the way. Maybe that could build a built in the future. >>Maybe it's swag based, you know, you >>get level C shows that nice work environment when you're not talking about the server's down on some slack channel when you're actually focusing on work. Yeah, so that mean that's the shift. That's what I'm saying, going >>firefighting to being able to >>do for throwing bombs. Yeah, wars. And the guy was going through this >>myself. Now you start a lot of the different team to the deaf teams and the ops teams. And I say it would be nice if these teams don't have to talk to complain about something that hadn't worked. It was Mexican figured it was just like I just like to talk to you because you're my friend. My colleague and I'd like to have a chat because everything's working because it's all automated, so it's consistent. It's repeatable. That's a nice, nice way. It can change the way that people get to interact because it's no longer only phoned me up when something's wrong. I think that absent an interesting dynamic >>on our survey, our customer base in our community before things one of the four things that came up was happier employees. Because if they're getting stuff done and more efficient, they have more time to actually self actualizing their job. That becomes an interesting It's not just a checkbox in some HR manual actually really impact. >>And I kind of think the customers we've heard talk rvs, gentlemen, this morning gave me a lot of the fear initially is, well, I automate myself out of a job, and what we've heard from everybody is that's not absolutely That's not actually true at all. It just allows them to do higher value things that, um or pro >>after that big data, that automation thing. That's ridiculous. >>I didn't use it yesterday. My little Joe Comet with that is when I tried to explain to my father what I do. Andi just said Well, in the 19 seventies, they said that computers you mean we'll do a two day week on? That hasn't come >>true. Trade your beeper and for a phone full of pots. But Richard, Thanks for coming on. Thanks for unpacking the ants. Full automation platforms with features. Congratulations. Great to see the progress. Thank you, Jonah. Everybody will be following you guys to Cuba. Coverage here in Atlanta, First Amendment Stevens for day two of cube coverage after this short break.

>> from Bahrain. It's the Q covering AWS Public sector Bahrain brought to you by Amazon Web service is >> Welcome back. It runs the cube coverage for Amazon whips were summoned by rain and Middle East jump for cloud computing. Our startup panel at two great guests. So, Abdullah, who's with Flat Labs? Flat? Six Labs Incubator Investor. Same dull bet with sink. Sorry. So sorry They got that wrong with little glare on my spring there. Thanks for coming on. Appreciate it. Guys, start up. Scene here is robust. Last year from this year, More energy, more deployed capital because you're deploying capital. You're building a company. Give us the update start. >> Yeah, I would say over the past year, just our Bahrain location alone. We've already hit 23 startups that we've invested in, and we're looking to increase that number by about 68 start ups every six months. Um, as you've seen over the past year and Amina region and the GCC, there's rapid growth in the startup scene. Um and we're slowly starting to see each vertical fill up with the relevant startups and get more saturated. I think for a while we were one of the regions that were a lot less saturated when it came to our local startup because systems and the rest of the world The good thing is, now that we've gotta love the financial infrastructure into place, flat six Labs is one of them. And I think that's where we really um, we're lacking support before on DSO were signed to feel see players come into every stage of the startup growth be ableto help the stops raise their seed funds. Siri's a Series B >> and your role here is outreach building out my rain. Yet where the Economic Development Board trying to figure out that put together an entrepreneur strategy and not figure it out. We know what it is. You get money? Yeah, yeah. Party together, >> Yes. So what we're trying to do is there's two main things. One is that we're trying Thio finally be the first kind of financial investor that can help people going from a full time job in tow. You know, full time entrepreneurship rolls on to give them access to three of the biggest barriers that they usually will face, which is a business development network mentorship on Capitol andan. Everything that we're doing is weed. So personally, What I take care of is that I go to a lot of the international events around the globe, would start up because systems and try and find very early stage founders and educate them on the market. The region see where people would fit, where the gaps are in the market on dumb kind of raise awareness of old advantages that we have here in Bahrain. >> What makes you guys different? What's the differentiation >> as a country or as flat six labs? Both. So I'd say, as a country behinds in a very unique position where we have, ah, cultural mindset that is very easy for a lot of foreigners and expatriates to adapt to, Um, I think we've even been ranked number one in the world as a place for expect us to live several times on DA number. Thing is that we have a very high skilled workforce. Um, overhead costs are lower. So, for example, when it comes to the cost of rent when it comes to hiring a team, you also have subsidies that come into place like Tom Keen, uh, which Sam has also benefited from where if you go and you hire fresh graduate Bahrainis, you'll get >> ah, >> large margin of that subsidized by the government. So you're looking at, ah, mix where you have a high quality of life. But at the same time, it's the best starting point for a lot of start ups. Because you can extend your runway. You have, ah, much lower cash burn, and at the same time you've got one of the biggest market places right next door, which is Saudi Arabia, is the 30 minute drive across the bridge. So we've kind of got the best of all worlds over here, and and because we're a small country, we have a government that's incredibly reactive. So the regulatory authorities are very close with the startup ecosystem, for example, were always involved in the economic development board round table meetings on the ministries, all working closely together to try and make this as friendly and atmosphere is possible for the startup >> and they're authentic. That's interesting and see government authentically aligning. >> Yeah, it's in the interest of entrepreneur, I would say. One thing we really have going on is it's really an nationwide initiative from the founders to the private entities and investors like us and to the governmental agencies where we all are really dedicated towards making this start up >> san talk about your company. What do you guys do and what's your situation? >> Right? So my company's name is sinking. We're software as a service company that helps businesses manage the really hard aspects of managing their employees like things like timesheets scheduling. Job safety is a big one for us and job costing, and our target market is actually us small businesses and way were early stage company, and we met Salar and Flat six Labs, and they convinced us to come sit up here in Bahrain and never looked back. But the access to talent here is just amazing the cost of very low and were able to do a lot of a very small amount of money. And so far we've got to a total of four and 1/2 1000 U. S. Businesses using the platform. And we've done that all here from Bahrain, >> so very low, low cost leverage, a model, and that's because of the substance of just talent >> as a >> mixer, so it's a little bit cheaper to hire. People have more access to tell him it's a number of things. It's both of those things. >> Yeah, the university programs were interesting there. Got a degree in cloud Computing. They announced that we heard that news today. I mean, that's compelling. I mean, have you want to make the market just teach it? Yeah, exact. This is good, I think. >> I think the good thing is that everyone's come to an understanding that all parties have to get actively involved to make it the right atmosphere. So the universities are also working very closely with us hand in hand. And I've seen Percy a gigantic improvement over the past year where their senior projects of the universities are turning out where they got legitimate startup. It's Dex on Dhe. Some of them are even ready to go straight into acceleration, which was not the case a few years ago. So everybody's really on board. >> That's one of things we met last year in the economic Dillman for that round table. Lotte. I won't say complaints but concerns, and they're very listening to the whiteboards out their charts. How are they doing? Checking the boxes? They are checking the items off, moving these blockers and what's remaining in your mind in terms of things to make it frictionless. >> Yeah, I'd say like there's so far. We've done a great start andan the space of a year. We've accomplished a lot. But of course there's still shifting the whole mentality to understand the startup scene and also, you know, to get people to be less as, ah, cultural mentality, risk averse and start letting people feel that failure is an okay thing. It's okay to go straight out of university and give it a shot and try and start your own startup, Um, and also educating people of all the tools that are available to them. So although we do a lot of outreach and roadshows, still, there's, ah, a lot of people that need to be educated on how exploration works, how the VC side of it works. And I'd say another thing. We need Thio See coming is bridge funds. So we've got people that are ready to come in at Siri's a that precede that seed. But then there's usually these gaps where we need to kind of help Fila's well to keep people on target towards seriously >> like a bullpen. Capital kind of model. Like Paul Martinez Company? Yeah, sass coming that are in between being Air B or B and C just need that little bridge. Yeah, exactly. That. Just >> that extra runway so that they can hit the targets that the later stage investors want us. >> Guys, give it plug for your reference. What you working on? Now? What do you quit your to do? Item? What's, uh what's the plan? Give a pitch for the company. >> Looks way No. The first company to attack time tendons. And we won't be the last. But where we think that we can win his job costing and job tracking, which is something that the customers that we talked to it really screaming out, too. So we've been building a really complex but simple to use system for managing jobs the last 3 to 6 months, and we're about to deploy that to our users in a few weeks. We're very excited about that. And that's really our secret source. We just a lot of guys doing the time in attendance. We're doing it very well, but we want to be the best of jobs. And we also want to stay laser focused throughout our particular users, which is actually employers with 1 to 20 employees in the states. And that's actually that actually makes up 89% of all employers in the States. And it's very hard to historically to find these guys. But we'll be having a smart phone in their pocket. It's actually becoming easier and easier for us, and we find it. >> And those coming need the most help, too, because they're the ones that could grow to 50 employees next. Exactly. So what's the U. R L? Our website and app Tick and download. What's the head of someone contact U S. >> So they will go to sync dot business and they can use the Web version there. But we also have to mobile app so we could be found in the APP store and on the place. >> Awesome. Congratulations and updates for you guys. What's next for you here by rain in general? >> Well, in Bahrain and Demeanor Region, we're continuing to expand their several locations that we're gonna launch again as accelerator programs on dhe. Locally, over here, we're always accepting applications from international startups. We're actually having our demo day tomorrow So you should drop by if you're gonna be here. Yes. Did I would be great if you come down and a CZ that happens. We're accepting applications to the next cycle on dhe. They can just log onto flat six labs, bahrain dot com All the information's over there. And if they want to get in touch with me, they can just put my name into Lincoln. So >> I beat him up into a system, and when they're ready to accelerate, they go. Good to go. Congratulates. Good job, guys. Thanks for the update. Startup scene is robust here by rain. The Cube coverage for our second year covering Amazon Web service is summit. I'm Jumper Stevens for more coverage after this short break.

>> From the SiliconANGLE Media Office in Boston, Massachusetts, it's theCube. (techno music) Now here's your host, Dave Vellante. >> Hi everybody, this is Dave Vellante and welcome to this special Cube Insights, powered by ETR. We've been running these Breaking Analysis Segments and today we're going to talk about some spending data that shows that there's continued interest in hyperconverged infrastructure. So we've been running these segments over the last several weeks with our partner ETR. They've got a database of about 4,500 IT Practitioners and CIOs. They go out quarterly and ask spending intentions. So we've been sharing that, along with our opinions. These are completely independent segments. I want to disclose that a number of the companies that we're talking about today: Nutanix, VMware, Dell EMC, Cisco, HPE. They sponsor theCube, but they have absolutely no input into editorial. They don't affect our opinion in any way, shape or form. So let's get into it. I'm here with Stu Miniman. Stu is an expert in this field. He's covered the space. Stu, let's look at some of the fundamentals. What do people need to know... Alex, if ya put up the slide, Stu, maybe you could talk to it. >> Yeah. Dave, thanks. I've been watching you have some fun with this. I enjoyed swimming in some of the data here and as you know, Dave, we've been watching since before hyperconverged infrastructure, or HCI, was a term that everybody talked about. We've been looking at how these hyperscale trends are going to impact the Enterprise. We put out our server SAN research years and years ago, so we know all these companies really well. And despite the latest AI and cloud and everything, the data shows, HCI, the simplification of the data center, building out what we would call True Private Cloud is important today. So right, we wanted to know when you look at the data, first of all, how are the vendors doing? Who are the leaders in this space here? There were a whole number of startups that came in this space. When we first analyzed the market it was companies like Microsoft and VMware that owned the operating system we thought would be hugely important. If you look in the big names this environment: Dell partnered with everyone, of course they bought Dell, bought EMC, which included a stake in VMware. What's that relationship with Nutanix? How is that shaping the market? As well as how is cloud impacting things? Both from a spending standpoint, has cloud sucked away revenue from HCI as that specter has overhung everybody in the IT space? And also, how does HCI fit into multicloud and how does that fit? >> Okay, great. So thanks for that setup, Stu, now let's get into some of the data. Alex, if you bring up the slide, the next slide. This is spending intentions for Nutanix, VMware and some other vendors. I'll go through that. But it's basically showing Nutanix and VMware are fighting it out. You know they're in this internecine battle and in social, and (chuckles) there's a war goin' on, because there's big money to be made here. So for those of you who are familiar with these segments, this is data from Enterprise Technology Research, from their July 2019 Spending Intentions Survey. So they're asking about spending intentions for the second half of 2019. The end of the survey, out of the 4,500 people in the panel, 1,068 responded to this survey. So on the left hand side you see the vendors: Nutanix, VMware with vSAN, Dell EMC with VxRail, specifically. Then SimpliVity, and then Springpath, or Cisco. So what the chart shows is what we call, Net Score. And net score is calculated by taking the red, on the bar, which is, we're going to leave the platform, that's the dark red. The lighter red, which is, we're going to spend less in the second half. The gray, which their spending's going to be flat. The dark green, or the evergreen, which says, we're going to increase spending. And the lime green, which I'm going to add to the platform. You take the green, minus the red, you get net score. Higher the net score, the better. You can see, Nutanix and VMware with vSAN are leading the pack. And then we'll go through that. But then you see, Shared Accounts. That's the number of indications for spending that they received out of those 1068. So Stu, what is this data telling you? >> So first of all, Dave, it confirmed kind of the general market share numbers that we hear out there. The vendors that track that on quarterly. VMware has the most customers, has the largest revenue, and their largest partner for that, of course, is Dell. VMware and Dell go to market, joint product development, joint engineering, joint go to market and it's the biggest piece of vSAN, so that's where we specifically wanted to look at the VxRail. And vSAN and VxRail, doing very well. They're adding new customers; was interesting to me that you saw VxRail kind of ramping up a little more on the, attracting new companies, but also looked to be losing some on the tail end of the dark red. As opposed to vSAN in general, is a little bit more stable. We know how many thousands of customers they have out there, and Vmware's a software story as opposed to VxRail is that full appliance. Nutanix is the second horse in this two-horse race that we're really talking about here, from HCI. There's some discussion in the marketplace after two quarters being down, is Nutanix showing weakness? What's happening there? The most recent quarter announcement was that Nutanix is doing well, seems to... They had a little bit of change as they're going through their move to a software model and sorting things out with sales and marketing in their channel. The data here shows that the second half of the year looks good for Nutanix. So to some of the questions I asked in the first slide, Dave, Nutanix and VMware, of course the clear leaders in this space. SimpliVity, which was of course bought be HP, Springpath which is the hyperflex from Cisco, are far behind those two out there. And it seems that even though Dell and VMware are fighting, very much with Nutanix, that is not heavily dampening Nutanix's from the respondents in this survey. >> Okay, and just a word on the data, so you see 184 shared accounts for Nutanix, 174 for VMware and down the line. Only 42 for SimpliVity and only 18 for Springpath, and Cisco. It's an indication of the size of the install base, obviously the more shared accounts, the more mentions, the larger the install base. Again, they're statistically significant; ETR does a very good job of that. Let's look Stu, at... Oh, actually I want to make another point here. So how are these net scores? Well let's put 'em in context. The hottest net scores we've seen recently are: Snowflake, and UiPath, with 80% plus, net score. Okay, so that's really, they're off the charts, they're growing like crazy. We saw Salesforce with 55%, so, and Workday sort of in there as well. Companies that are growing share. So SAP in the 30% range, and so you see the Dell EMC, VxRail, that's kind of holding serve. It's not like, dramatically gaining share, but they're growing a little bit and then-- >> And I think it's a lot, Dave, it shows to the maturity of this market. HCI is not new, both Nutanix and VMware have thousands of customers, specifically with V's then we're talking VMware. So it was more, when I saw some of your charts, Microsoft has a similar net score. >> Right >> Well liked, good install based, still growing and the like. And brings in the discussion of when we did some cross section of the analysis looking at cloud companies and how does this impact their public cloud spend; is this detracting if this customer's also doing public cloud? And the long and the short of it is VMware and Nutanix are pretty much the same if not actually a little bit better when you talk about a customer that's looking at their overall cloud spend. So to me that really signals that both VMware and Nutanix are doing a good job into how their solution fits into the customer's overall hybrid cloud strategy. >> All right, let's take a look at the next slide, which talks to time series. So this is hyperconverged infrastructure spending intentions again, for the second half of 2019, over time. So the July '19 Survey you can see is the most recent one. We go all the way back to January '17 and you can see Nutanix on the top, VMware or vSAN on the bottom. We just selected those two. We're just repeating the net score and the shared accounts. And you can see these things tend to bounce around a little bit. You can see Nutanix maintains a lead, but the market's startin' to converge. These two companies are coming together. We hear a lot about vSAN doing very well, it's kind of held on. You can see a slight downward pressure in July, in the July survey. It's unclear what that means. That could be an indication of just some uncertainty in the marketplace. Some economic macro concerns. Tariffs, potential headwinds there, so there could be some uncertainty there. But what do you takeaway from this slide, Stu? >> Yeah, first of all right. As you show, Dave, VMware is a bit more steady, Nutanix gone up for bit and come down. Both of them stayed relatively stable. Somewhere between kind of the 45 and 55 lately. A little bit, if you look at the overall trend, Nutanix is down. VMware could surpass them from the net score in the future, if this trend holds. But both of them doing quite well. When you looked at all the other vendors in there, of course the scale is just showing 40-70%, if you put all the others, which are down much lower, you can see once again, that kind of the clear leadership. These two companies, just strong lead. Does not look like there any challengers in this space that are ready to be a clear number three yet, in the market. >> But Nutanix at one point had no competition. >> Yeah. >> Okay, now vSAN comes in and of course-- >> Oh no, absolutely. So no, SimpliVity and Scale Computing, and there were a whole host of startups. There's all the brand new startups in the space. Everything from little companies like Diamante, Pivot3, who was around doing this before it came. So there's always been a lot there, but Nutanix is the one that separated from the pack. The only one in this space that's gone IPO. But VMware's there, Microsoft won that, they rebranded their Azure Stack HCI for what they put in the data center last year. So expect Microsoft partnering with all of the big server manufacturers to push farther into HCI, but really has not directly impacted this market too much, just yet. >> But there's definitely been some pressure on Nutanix from an earning standpoint, the stock's been hit. You've had some executive departures. There's some rumors about acquisition with Google. Your thoughts on-- >> Yeah, definitely. So John Furrier just had Dheeraj Pandey, the CEO of Nutanix, in our Palo Alto studio, leading up to the Copenhagen show for Nutanix that I will be at. Sure. Sunil Potti who was basically the number two at Nutanix, is now working for Thomas Kurian, TK, over at Google Cloud. My indication from what I hear, he is not over there to help broker a deal. Sunil had a great run at Nutanix, there was a clean break there, but there is a mostly new executive team at Nutanix. Now a couple of years past the IPO and the team at Nutanix, they have their platform. The have a bunch of SaaS offerings that they're doing there. Do they have a relationship with Google? Absolutely! They had Diane Greene at one of their events a couple of years ago. They did joint engineering. But I actually saw that engineering effort cool off a little bit in the last year or so since the new regime came on in Google Cloud. So does Nutanix have a lot of Enterprise accounts and know how to work with the Enterprise and could that be a boon to Google? Absolutely! But the personnel of a Nutanix executive over at Google, and Brian Stevens who's the CTO of Google Cloud being on the Board of Nutanix? I do not think that that is telegraphing that an acquisition is going to happen. It could. We see lots of big acquisitions. Nine or 10 billion dollars from Nutanix could be interesting for Nutanix and help them get in a lot of places and help Google. But Dave, I goin' on record say, I don't think it's going to happen. I don't think Cisco is going to buy Nutanix. Infrastructure's not the real push for Chuck Robbins and that team. And at the Google Cloud event, Dave, that we were at, we saw Sanjay Poonen from VMware up on stage touting how deeply VMware was going to partner. So both VMware and Nutanix are partnering with all of the clouds. VMware of course has a very deep relationship with VMware. They're going deeper with Google, they are even partnering with the old enemy of Microsoft, so I would give VMware definitely has a deeper and more public relationship with all the public cloud providers but Nutanix is also partnering and expanding their portfolio to give themselves good growth beyond just the core HCI market. >> HP's another one. So Nutanix and HPE are workin' together. Kind of the enemy of my enemy is my friend. Nutanix was not at VMworld this year; they're kind of booted out. So they belly up to HP. >> Yeah, HP loves having, they have their, "As a service offerings," and Nutanix is one of those as well as Nutanix can sell the HP. So as the, right, the Dell relationship is likely going to die down over time, as Michael Dell on the team, want to sell more Dell hardware with VMware software. HPE is another... And they also partner with Lenovo on the Nutanix side. >> All right, Stu, bring it home. What are the key takeaways on this cube Insights. >> Okay, so HCI, who is a two-horse race right now. There are interesting companies to look at beyond the two, but if you want to understand who the leaders are in the space it is: VMware, especially with their VxRail and Nutanix, are the two leaders in that space. Really looking and understanding how they're expanding into multicloud and hybrid cloud solutions. VMware very much with their VCF offering, which packages vSAN to go into the VMware cloud offerings. And Nutanix with an interesting strategy, both with how they really spread some of their services like what they're doing with Xi Cloud, as well as some SaaS offerings, which some of them really have a disconnect. Not in a bad way, but just are not tied directly to the hardware. What the infrastructure companies have tried to do for years. Both of them, VMware's done tons of acquisitions. Nutanix has done quite a few acquisitions too. >> So your second point here, what's the impact of Dell VMware versus the Nutanix battle? You say not a significant impact on spending intentions yet. I mean there's clearly some evidence that those two markets are comin' together, that VMware's pressuring Nutanix. But why do you say, yet? What do you expect? I mean is it the OEM deal with Dell? >> It's the OAM relationship. There is huge pipeline of Dell hardware with Nutanix software and they're at loggerheads. So absolutely, the Dell family: Dell, EMC and VMware are doing all they can to dial that down. So they put pressure on the channel. And even some of the most loyal Nutanix channel partners that work with Dell, have had pressure to do more and more VxRail. So I expect it to have impact, but just as, Dave, I'll dial back the clock. You probably remember when EMC had a relationship with HP and HP killed the OEM of EMC storage. EMC stormed back and got a lot of those accounts. Same thing happened when EMC and Dell broke up a couple of years before the acquisition. So Nutanix is storming to go with HPE as one of their server partners, and (mumbles). So can Nutanix keep their growth and momentum going as Dell is no longer their biggest partner? >> Well, they're fighting a two-front war. They've got one with Dell VMware and they're also fighting the war with the public cloud guys, even though they're partnering with the public cloud guys. All right, they're sort of taking that cloud model but of course it's on prim. So you say how this public cloud affects HCI spending; not a significant impact on spending intentions yet. Can I infer from that that you do expect there to be pressure on that second front? >> Yeah, so as I've talked about before Dave, when we look at VMware and VMware gives the VMware cloud in AWS. Some say, "Great, that gives me a nice path to be able to use public cloud. But maybe I don't need some of this VMware licensing and software in there." The question for Nutanix is very similar. What services do they have? How do they become more sticky in customer environments? And absolutely, they're driving a roadmap for that in working with their customers. >> Well the thing about Nutanix is that customer's really happy. The customer's really like Nutanix. They like the simplicity. I've talked to a number of Nutanix customers that are very happy in that regard. And they have a leading product in that regard. But they're aiming at the multicloud space and can they play there? >> And Dave, you make a really good point. The killer use case, what did HCI deliver? It delivered simplicity. Today, if you talk about public cloud in general or even hybrid or multicloud, (chuckles) simplicity is not how you would describe this. So can the customers, the companies that did HCI, so, VMware, Nutanix, HPE and Cisco, they're all fighting for that hybrid and multicloud environment. And if they can help deliver simplicity of management, simplicity of leveraging my data, they can be successful in that space. >> Okay, so you're sort of positive on the multicloud, their position in multicloud. Even though they're not one of the big five. >> Yeah, and the good news for a Nutanix is that they're growing off of a much smaller base then say VMware, when you say they have five or 600,000 customers. Hey, how big of an impact will public cloud have on them? >> All right, so we don't pick stocks. We're not making recommendations. (laughs) But, do you feel like it's overdone, that it's undervalued? Independent of the macro. Do you feel like the pressure on Nutanix is warranted, or do you feel like it's got legs? >> So I feel Wall Street tends to over adjust when they go through things. When I talk to my friends on the Wall Street stuff. Definitely Nutanix took more of a beating probably then they should have. But they had two quarters that weren't great. And some of that was the management changes, they blamed that they couldn't hire sales and marketing fast enough. Something we'd asked, if you're a company in the Valley and you've gone from a few hundred people to a few thousand people. How do you keep adding good quality people? That's challenging. So yes, I think we've actually seen Dave, in the last week, or so Nutanix has been one of the fastest growing stocks in the tech market. So they're adjusting some. So I still think Nutanix has plenty of room for growth. The question is, what's their path to say, two billion dollars? Or is it an exit for 9-10 billion dollars down the road? >> All right, Stu, some great stuff. Thank you for that analysis. And thank you for watching this episode of theCube Insights, powered by ETR. This is Dave Vellante, for Stu Miniman, we'll see ya next time. (techno music)

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019. Brought to you by IBM Wear and its ecosystem partners. >> Welcome back to the cubes Live coverage Of'em World 2019 in San Francisco, California We're here at Mosconi North Lobby. Two sets. Jumper of my Coast. David wanted Dave 10 years. Our 10th season of the cue coming up on our 10 year anniversary May of 2020. But this corner are 10 years of the Cube. Our next guest is Sanjay Putting Chief Operating Officer Of'em where who took the time out of his busy schedule to help us do a commemorative look back. Thanks for coming to our studio. Hello, John. That was great. Fans of yours was really regulations on the 10 year mark with the, um well, we really appreciate your partnership. We really appreciate one. Things we love doing is covering as we call that thing. David, I coined the term tech athletes, you know, kind of the whole joke of ESPN effect that we've been called and they're really tech athlete is just someone who's a strong in tech always fighting for that extra inch. Always putting in the hard work discipline, smart, competitive. You get all that above. Plus, you interviewed athletes today on state real athletes. Real athletes, Tech show. So I guess they would qualify as Tech athlete Steve Young. That's pretty funny. It was a >> great time. We've been trying to, you know, Veum World is now the first time was 2004. So it's 1/16 season here, and traditionally many of these tech conference is a really boring because it's just PowerPoint dead by power point lots of Tec Tec Tec Tec breakout sessions. And we're like, You know, last year we thought, Why don't we mix it up and have something that's inspirational education We had Malala was a huge hit. People are crying at the end of the session. Well, let's try something different this year, and we thought the combination of Steve Young and Lyndsey one would be great. Uh, you know, Listen, just like you guys prepped for these interviews, I did a lot of prep. I mean, I'm not I'm a skier, but I'm nowhere close to an avid skier that watch in the Olympics huge fan of Steve Young so that part was easy, but preparing for Lindsay was tough. There were many dynamics of that interview that I had to really think through. You want to get both of them to converse, you know, he's She's 34 he's 55. You want to get them to really feel like it's a good and I think it kind of played out well. >> You were watching videos. A great prep. Congratulations >> trying t o show. It's the culture of bringing the humanization aspect of your team about tech for good. Also, you believe in culture, too, and I don't get your thoughts on that. You recently promoted one of your person that she has a chief communications Johnstone Johnstone about stars you promote from within. This >> is the >> culture you believe it. Talk about the ethos. Jones is a rock star. We love her. She's just >> hardworking, credible, well respected. Inside VM where and when we had a opening in that area a few months ago, I remember going to the her team meeting and announcing, and the team erupted in cheers. I mean that to me tells me that somebody was well liked from within, respected within and pure level and you know the organization's support for a promotion of that kind of battlefield promotion. It's great big fan of hers, and this is obviously her first show at Vienna. Well, along with Robin, Matt, look. So we kind of both of them as the chief marketing officer, Robin and Jones >> and Robinson story. Low Crawl made her interim first, but they then she became Steve Made it Permanent way. >> Want them to both do well. They have different disciplines. Susan, uh, national does our alliances, you know, if you include my chief of staff for the six of my direct reports are women, and I'm a big believer in more women. And take why? Because I want my Sophia, who's 13 year old do not feel like the tech industry is something that is not welcome to women in tech. So, you know, we really want to see more of them. And I hope that the folks who are reporting to me in senior positions senior vice president is an example can be a role model to other women who are aspiring, say, one day I wanna be like a Jones Stone or Robin. Madam Local Susan Nash, >> John and I both have daughters, so we're passionate about this. Tech is everywhere, so virtually whatever industry they go into. But I've asked this question Sanjay of women before on the Cube. I've never asked him in. And because you have a track record of hiring women, how do you succeed in hiring women? Sometimes way have challenges because way go into our little network. Convenient. What? What's your approach? Gotta >> blow off that network and basically say First off, if that network is only male or sometimes unfortunately white male or just Indian male, which is sometimes the nature of tech I mean, if you're looking for a new position, tell the recruiters to find you something that's different. Find me, Ah woman. Find me on underrepresented minority like an African American Latino and those people exist. You just have a goal. Either build a network yourself. So you've got those people on your radar. We'll go look, and that's more work on us, says leaders. But we should be doing that work. We should be cultivating those people because the more you promote capable. First off, you have to be capable. This is not, you know, some kind of affirmative action away. We want capable people. Someone shouldn't get the job just because they're a woman just because the minority, that's not the way we work. We want capable people to do it. But if we have to go a little further to find them, we'll go do it. That's okay. They exist. So part of my desires to cultivate relationships with women and underrepresented minorities in the world that can actually in the world of tech and maintain those relationships because you never know you're not gonna hire them immediately. But at some point in time, you might need to have them on your radar. >> Sanjay, I wanna ask you a big picture question. I didn't get a chance to ask path this morning. I was at the bar last night just having a little dinner, and I was checking out Twitter. And he said that the time has never been. It's never been a greater time arm or important time to be a technologist. Now I saw that I went interesting. What does that mean? Economic impact, social impact? And I know we often say that, and I don't say this to disparage the comment. It's just to provide historical context and get a get it open discussion about what is actually achievable with tech in this era and what we actually believe. So I started to do some research and I started right down. First of all, I presume you believe that right on your >> trusty napkin at the >> bar. So there has never been a more important time to be a technologist. You know, it's your company at your league. You know, Pat, I presume you agree with it. Yeah, absolutely. I slipped it back to the 1900. Electricity, autos, airplanes, telephones. So you we, as an industry are up against some pretty major innovations. With that historical context, Do you feel as though we can have a similar greater economic and social impact? >> Let's start with economic first and social. Next time. Maybe we should do the opposite, but economic? Absolutely. All those inventions that you >> have are all being reinvented. The technology the airplanes all been joined by software telephones are all driving through, you know, five g, which is all software in the future. So tech is really reinventing every industry, including the mundane non tech industries like agriculture. If you look at what's happening. Agriculture, I ot devices are monitoring the amount of water that should go to particular plant in Brazil, or the way in which you're able to use big data to kind of figure out what's the right way to think about health care, which is becoming very much tech oriented financial service. Every industry is becoming a tech industry. People are putting tech executives on their boards because they need an advice on what is the digital transformations impact on them cybersecurity. Everyone started by this. Part of the reason we made these big moves and security, including the acquisition of carbon black, is because that's a fundamental topic. Now social, we have to really use this as a platform for good. So just the same way that you know a matchstick could help. You know, Warm house and could also tear down the house. Is fire good or bad? That's been the perennial debate since people first discovered fire technology. Is this the same way it can be used? Reboot. It could be bad in our job is leaders is to channel the good and use examples aware tech is making a bit force for good. And then listen. Some parts of it may not be tech, but just our influence in society. One thing that pains me about San Francisco's homelessness and all of the executives that a partner to help rid this wonderful city of homeless men. They have nothing to attack. It might be a lot of our philanthropy that helps solve that and those of us who have much. I mean, I grew up in a poor, uh, bringing from Bangla, India, but now I have much more than I have. Then I grew up my obligations to give back, and that may have nothing to do with Tech would have to do all with my philanthropy. Those are just principles by which I think when you live with your a happier man, happier woman, you build a happier >> society and I want to get your thoughts on common. And I asked a random set of college students, thanks to my son that the network is you said your daughter to look at the key to Pat's King Pat's commentary in The Cube here this morning that was talking about tech for good. And here's some of the comments, but I liked the part about tech for good and humanity. Tech with no purpose is meaningless tech back by purposes. More impactful is what path said then the final comments and Pat's point quality engineering backing quality purpose was great. So again, this is like this is Gen Z, not Millennials. But again, this is the purpose where it's not just window dressing on on industry. It's, you know, neutral fire. I like that argument. Fire. That's a good way Facebook weaponizing Facebook could be good or bad, right? Same thing. But the younger generation. You're new demographics that are coming into cloud. Native. Yeah, what do you think? >> No. And I think that's absolutely right. We have to build a purpose driven company that's purposes much more than just being the world's best softer infrastructure company or being the most profit. We have to obviously deliver results to our shareholders. But I think if you look at the Milton Friedman quote, you know, paper that was written that said, the sole purpose of a company is just making profits, and every business school student is made to read that I >> think even he >> would probably agree that listen today While that's important, the modern company has to also have a appropriate good that they are focused on, you know, with social good or not. And I don't think it's a trade off being able to have a purpose driven culture that makes an impact on society and being profitable. >> And a pointed out yesterday on our intro analysis, the old term was You guys go Oh, yeah, Michael Dell and PAD shareholder value. They point out that stakeholder value, because now the stakeholder Employees and society. So congratulations could keep keep keep it going on the millennial generation. >> Just like your son and our kids want a purpose driven company. They want to know that the company that working for is having an impact. Um, not just making an impression. You do that. It shows like, but having an impact. >> And fire is the most popular icon on instagram. Is that right? Yeah, I know that fire is good. Like your fire. Your hot I don't know. I guess. Whatever. Um fire. Come comment. There was good Sanjay now on business front. Okay, again, A lot of inflection points happen over 10 years. We look back at some of this era, the Abel's relationship would you know about. But they've also brought up a nuance which we talked about on the intro air Watch. You were part of that acquisition again. Pig part of it. So what Nasiriyah did for the networking STD see movement that shaped VM. Whereas it is today your acquisition that you were involved and also shaping the end user computing was also kind of come together with the cloud Natives. >> How is >> this coming to market? I mean, you could get with >> my comparison with carbon black there watch was out of the building. Carbon black is not considered. >> Let's talk about it openly. And we talked about it some of the earnings because we got that question. Listen, I was very fortunate. Bless to work on the revitalization of end user computing that was Turbo charged to the acquisition of a watch. At that time was the biggest acquisition we did on both Nice era and air watch put us into court new markets, networking and enterprise mobility of what we call not additional work space. And they've been so successful thanks to know not just me. It was a team of village that made those successful. There's a lot of parallels what we're doing. Carbon, black and security. As we looked at the security industry, we feel it's broken. I alluded to this, but if I could replay just 30 seconds of what I said on some very important for your viewers to know this if I went to my doctor, my mom's a doctor and I asked her how Doe I get well, and she proposed 5000 tablets to me. Okay, it would take me at 30 seconds of pop to eat a tablet a couple of weeks to eat 5000 tablets. That's not how you stay healthy. And the analogy is 5000 metres and security all saying that they're important fact. They use similar words to the health care industry viruses. I mean, you know, you and what do you do instead, to stay healthy, you have a good diet. You eat your vegetables or fruit. Your proteins drink water. So part of a diet is making security intrinsic to the platform. So the more that we could make security intrinsic to the platform, we avoid the bloatware of agents, the number of different consuls, all of this pleasure of tools that led to this morass. And what happens at the end of that is you about these point vendors, Okay, Who get gobbled up by hardware companies that's happening spattered my hardware companies and sold to private equity companies. What happens? The talent they all leave, we look at the landscape is that's ripe for disruption, much the same way we saw things with their watch. And, you know, we had only companies focusing VD I and we revitalize and innovative that space. So what we're gonna do in securities make it intrinsic and take a modern cloud security company carbon black, and make that part of our endpoint Security and Security Analytics strategy? Yes, they're one of two companies that focus in the space. And when we did air watch, they were number three. Good was number one. Mobile line was number two and that which was number three and the embers hands. We got number one. The perception in this space is common. Lacks number two and crowdstrike number one. That's okay, you know, that might be placed with multiple vendors, but that's the state of it today, and we're not going point against Crowdstrike. Our competition's not just an endpoint security point to a were reshaping the entire security industry, and we believe with the integration that we have planned, like that product is really good. I would say just a cz good upper hand in some areas ahead of common black, not even counting the things we're gonna integrate with it. It's just that they didn't have the gold market muscle. I mean, the sales and marketing of that company was not as further ahead that >> we >> change Of'em where we've got an incredible distribution will bundle that also with the Dell distribution, and that can change. And it doesn't take long for that to take a lot of customers here. One copy black. So that's the way in which we were old. >> A lot of growth there. >> Yeah, plenty of >> opportunity to follow up on that because you've obviously looked at a lot of companies and crowdstrike. I mean, huge valuation compared to what you guys paid for carbon black. I mean, >> I'm a buyer. I mean, if I'm a buyer, I liked what we paid. >> Well, I had some color to it. Just when you line up the Was it really go to market. I mean some functions. Maybe not that there >> was a >> few product gaps, but it's not very nominal. But when you add what we announced in a road map app, defensive alderman management, the integration of works based one this category is gonna be reshaped very quickly. Nobody, I mean, the place. We're probably gonna compete more semantic and McAfee because most of those companies that kind of decaying assets, you know, they've gotten acquired by the companies and they're not innovating. So I'd say the bulk of the market will be eating up the leftover fossils of those sort of companies as as companies decided they want to invest in legacy. Technology is a more modern, but I think the differentiation from Crowdstrike very clear is we integrate these, these technology and the V's fear. Let me give an example. With that defense, we can make that that workload security agent list. Nobody can do that. Nobody, And that's apt defense with carbon black huge innovation. I described on stage workspace one plus carbon black is like peanut butter and jelly management. Security should go together. Nobody could do that as good as us. Okay, what we do inside NSX. So those four areas that I outlined in our plans with carbon black pending the close of the transaction into V sphere Agent Lis with workspace one unified with NSX integrated and into secure state, You know, in the cloud security area we take that and then send it through the V m. Where the devil and other ecosystem channels like you No idea. Security operative CDW You know, I think Dimension data, all the security savvy partners here. I think the distribution and the innovation of any of'em were takes over long term across strike may have a very legitimate place, but our strategy is very different. We're not going point tool against 0.0.2 wish reshaping the security industry. Yeah, What platform? >> You're not done building that platform. My obvious question is the other other assets inside of Arcee and secureworks that you'd like to get your hands on. >> I mean, listen, at this point in time, we are good. I mean, it's the same thing like asking me when we acquired air watching. Nice Here. Are you gonna do more networking and mobility? Yeah, but we're right now. We got enough to Digest in due course you. For five years later, we did acquire Arkin for network Analytics. We acquired fellow Cloud for SD when we're cloud recently, Avi. So the approach we take a hammer to innovations first. You know, if you're gonna have an anchor acquisition, make sure it's got critical mass. I mean, buying a small start up with only 35 people 10 people doesn't really work for us. So we got 1100 people would come back, we're gonna build on it. But let's build, build, build, build, partner and then acquire. So we will partner a lot with a lot of players. That compliment competition will build a lot around this. >> And years from now, we need >> add another tuck in acquisition. But we feel we get a lot in this acquisition from both endpoint security and Security Analytics. Okay, it's too early to say how much more we will need and when we will need that. But, you know, our goal would be Let's go plot away. I have a billion dollar business and then take it from there. >> One more security question, if I may say so. I'm not trying to pit you against your friends and AWS. But there are some cleared areas where your counter poise >> Stevens just runs on eight of us comin back. >> That part about a cloud that helps your class ass business. I like the acquisition. But Steven Schmidt, it reinforced the cloud security conference, said, You know, this narrative in the industry that security is broken is not the right one. Now, by the way, agree with this. Security's a do over pat kill singer. And we talked about that for five years ago. Um, but then in eight of you says the shared security model, when you talk to the practitioners like, yeah, they they cover, that's three and compute. But we have the the real work to d'oh! So help me square that circle. >> Yeah, I think if aws bills Security Service is that our intrinsic to their platform and they open up a prize, we should leverage it. But I don't think aws is gonna build workload security for azure compute or for Gogol compute. That's against the embers or into the sphere. Like after finishing third accordion. And they're like, That's not a goal. You go do it via more So from my perspective. Come back to hydrogen. 80. If there's a workload security problem that's going to require security at the kernel of the hyper visor E C to azure compute containers. Google Compute. >> Who's gonna do >> that? Jammer? Hopefully, hopefully better than because we understand the so workloads. Okay, now go to the client site. There's Windows endpoints. There's Mac. There's Lennox. Who should do it? We've been doing that for a while on the client side and added with workspace one. So I think if you believe there is a Switzerland case for security, just like there was a Switzerland case for management endpoint management I described in Point management in Point Security going together like peanut butter and jelly, Whatever your favorite analogy is, if we do that well, we will prove to the market just like we did with their watch An endpoint management. There is a new way of doing endpoint security. Dan has been done ever before. Okay, none >> of these >> guys let me give an example. I've worked at Semantic 15 years ago. I know a lot about the space. None of these guys built a really strategic partnership with the laptop vendors. Okay, Del was not partnering strategically on their laptops with semantic micro. Why? Because if this wasn't a priority, then they were, you know, and a key part of what we're doing here is gonna be able to do end point management. And in point security and partner Adult, they announced unified workspace integrated into the silicon of Dell laptops. Okay, we can add endpoint security that capability next. Why not? I mean, if you could do management security. So, you know, we think that workspace one, we'll get standing toe work space security with the combination of workspace one and security moving and carbon black. >> Sanjay, we talked about this on our little preview and delivery. Done us. We don't need to go into it. The Amazon relationship cleared the way for the strategy in stock price since October 2016 up. But >> one of the >> things I remember from that announcement that I heard from the field sales folks that that were salespeople for VM wear as well as customers, was finally clarity around. What the hell? We're doing the cloud. So I bring up the go to market In the business side, the business results are still strong. Doing great. You guys doing a great job? >> How do you >> keep your field troops motivated? I know Michael Dell says these are all in a strategy line. So when we do these acquisitions, you >> had a lot >> of new stuff coming in. I mean, what's how do you keep him trained? Motivated constantly simplifying whenever >> you get complex because you add into your portfolio, you go back and simplify, simplify, simplify, make it Sesame Street simple. So we go back to that any cloud, any app, any device diagram, if you would, which had security on the side. And we say Now, let's tell you looking this diagram how the new moves that we've made, whether it's pivotal and what we're announcing with tanz ou in the container layer that's in that any Apple air carbon black on the security there. But the core strategy of the emer stays the same. So the any cloud strategy now with the relevance now what, what eight of us, Who's our first and preferred partner? But if you watched on stage, Freddie Mac was incredible. Story. Off moving 600 absent of the N word cloud made of us Fred and Tim Snyder talked about that very eloquently. The deputy CTO. They're ratty Murthy. CTO off Gap basically goes out and says, Listen, I got 800 APS. I'm gonna invest a lot on premise, and when I go to the cloud, I'm actually going to Azure. >> Thanks for joining you. Keep winning. Keep motivated through winning >> and you articulate a strategy that constantly tells people Listen. It's their choice of how they run in the data center in the cloud. It's their choice, and we basically on top of all of those in the any cloud AP world. That's how we play on the same with the device and the >> security. A lot of great things having Sanjay. Thanks >> for you know what a cricket fan I am. Congratulations. India won by 318 goals. Is that >> what they call girls run against the West Indies? I think you >> should stay on and be a 40 niner fan for when you get Tom baseball get Tom Brady's a keynote will know will be in good Wasn't Steve Young and today love so inspirational and we just love them? Thank you for coming on the Cube. 10 years. Congratulations. Any cute moments you can point out >> all of them. I mean, I think when I first came to, I was Who's the d? I said ASAP, like these guys, John and Dave, and I was like, Man, they're authentic people. What I like about you is your authentic real good questions. When I came first year, you groomed me a lot of their watch like, Hey, this could be a big hat. No cattle. What you gonna do? And you made me accountable. You grilled me on eight of us. You're grilling me right now on cloud native and modern, absent security, which is good. You keep us accountable. Hopefully, every you're that we come to you, we want to show as a team that we're making progress and then were credible back with you. That's the way we roll. >> Sanjay. Thanks for coming. Appreciate. Okay, we're live here. Stay with us for more of this short break from San Francisco v emerald 2019

>> live from San Francisco. It's the Cube covering Google Club Next nineteen Rodeo by Google Cloud and its ecosystem partners. >> Hey, welcome back. Everyone's the live Cube covers here in San Francisco for Google Cloud. Next nineteen. I'm Javert Day Volante here on the ground floor, day two of three days of wall to wall coverage to great guests. We got Kartik lost. Meena Ryan, product management director of Cloud Identity for Google and Kim parent chief security officer for Doctor on Demand. Guys, welcome to the Cube. Appreciated Coming on. >> Great to be here. >> Thank you so honestly Way covering Google Cloud and Google for many, many years. And one of the things that jumps out at me, besides allows the transformation for the enterprise is Google's always had great technology, and last year I did an interview, and we learned a lot about what's going on the chip level with the devices you got. Chrome browser. Always extension. All these security features built into a lot of the edge devices that Google has, so there's definitely a security DNA in there and Google the world. But now, when you start getting into cloud access and permissions yesterday and the Kino, Thomas Kurian and Jennifer Lin said, Hey, let's focus on agility. Not all his access stuff. This is kind of really were identity matters. Kartik talk about what's going on with cloud identity. Where are we? What's the big news? >> Yeah, thank you. So clouded. Entities are solution to manage identity devices and the whole axis management for the clouds. And you must have heard of beyond Corp and the whole zero trust model and access. One thing we know about the cloud if you don't make the access simple and easy and at the same time you don't provide security. You can get it right. So you need security and you need that consumer level simplicity. >> Think it meant explain beyond core. This is important. Just take a minute to refresh for the folks that might not know some of the innovations. They're just start >> awesome. Yeah. So traditional on premises world, the security model was your corporate network. Your trust smaller. Lose The corporate network invested a lot to get to keep the bad people out. You get the right people on and that made ten T applications on premises. Your data was on premises now the Internet being a new network, you work from anywhere. Work is no longer a thing. You work from anywhere. What gets done right? So what is the new access? More look like? That's what people have been struggling with. What Google came up with in two thousand eleven is this model called Beyond Core versus Security Access Model will rely on three things. Who you are is a user authentication the device identity and security question and last but not least, the context off. What are you trying to access in very trying to access from So these things together from how you security and access model And this is all about identity. And this is Bianca. >> And anyone who has a mobile device knows what two factor authentication is. That's when you get a text messages. That's just two factor M. F. A multi factor. Authentication really is where the action is, and you mentioned three of them. There's also other dimensions. This is where you guys are really taking to the next level. Yeah, where are we with FAA and some of the advances around multi factor >> s O. So I think keeping you on the highlight is wear always about customer choice. We meet customers where they are. So customers today have invested in things like one time use passwords and things like that. So we support all of that here in cloud identity. But a technology that we are super excited about the security, Keith. And it's built on the fighter standard. And it's inserted this into your USB slot of that make sense. And we just announced here at next you can now use your android phone as a security key. So this basically means you don't have to enter any codes because all those codes you enter can be fished on way. Have this thing at Google and we talked about it last time. Since we roll our security keys. No Google account, it's >> harder for the hackers. Really Good job, Kim. Let's get the reality. You run a business. You've been involved in a lot of start ups. You've been cloud nated with your company. Now talk about your environment does at the end of the year, the chief security officer, the buck stops with you. You've got to figure this out. How are you dealing with all this? These threats at the same time trying to be innovative with your company. >> So for clarity. So I've been there six years since the very beginning of the company. And we started the company with zero hardware, all cloud and before there was beaten beyond Corp. Where there was it was called de-perimeterization. And that's effectively the posture we took from the very beginning so our users could go anywhere. And our I always say, our corporate network is like your local coffee shop. You know, WiFi like that's the way we view it. We wanted to be just a secure there at the coffee shop, you know, we don't care. Like we always have people assessing us and they're looking at a corporate network saying, You know, where your switches that you're, you know, like where your hardware like, we want to come in and look at all like we don't have anything like, >> there's no force. The scan >> is like way. Just all go to the Starbucks will be the same thing. So that's part of it. And now you know, when we started like way wanted to wrap a lot of our services in the Google, but we had the problem with hip a compliance. So in the early days, Google didn't have six years ago. In our early days, Google didn't have a lot of hip, a compliant services. Now they do. Now we're moving. We're trying to move everything we do almost in the Google. That's not because we just love everything about Google. It's for me. I have assessed Google security are team has assessed their security. We have contracts with them and in health care. It's very hard to take on new vendors and say Hey, is there security? Okay, are their contracts okay? It's like a months long process and then even at the end of the day, you still have another vendor out there that sharing your day, that you're sharing your data with them and it's precarious for me. It just it doubles my threat landscape. When I go from Google toe one more, it's like if I put my data there, >> so you're saying multi vendor the old way. This is actually a problematic situation for you. Both technically and what operate timewise or both are super >> problematic for me in terms of like where we spread our data to like It just means that company every hack against that company is brutal for us, like And you know, the other side of the equation is Google has really good pricing. Comparatively, yes, Today we're talking about Big Query, for example, and they wanted to compare Big Query to some other systems and be crazy. G, c p. And And we looked at the other systems and we couldn't find the pricing online. And, like Google's pricing was right there was completely transparent. Easy to understand. The >> security's been vetted. The security's >> exactly Kim. Can you explain when you said the multi vendor of creates problems for you? Why is this? Is it not so much that one vendor is better? The other assistant? It's different. It's different processes or their discernible differences in the quality of the security. >> There are definitely discernible differences in quality, for sure. Yeah, >> and then add to that different processes. Skill sets. Is that writer? Yes, Double click on that E >> everybody away. There's always some I mean almost every vendor. You know, there's always something that you're not perfectly okay with. On the part of the security is something you don't totally like about it. And the more vendors you add, you have. Okay. This person, they're not too good on their physical security at their data center or they're not too good on their policies. They're not too good on their disaster recovery. Like there's you always give a little bit somewhere. I hate to say it, but it's true. It's like nobody's super >> perfect like it's It's so it's a multiplication effects on the trade offs that you have to make. Yeah, it's necessarily bad, but it's just not the way you want to do it. All right? Okay. >> All the time. So you got to get in an S L A u have meetings. You gotta do something vetting. It's learning curves like on the airport taking your shoes off. Yeah. Yeah. And then there's the >> other part. Beyond the security is also downtime. Like if they suffer downtime. How much is that going to impact our company? >> Karthik, you talked about this This new access mall, this three layer who authentication that is the device trusted in the context. I don't understand how you balance the ratio between sort of false positives versus blocking. I think for authentication and devices pretty clear I can authenticate. You are. I don't trust this device. You're not getting in, but the context is interesting. Is that like a tap on the shoulder with with looking at mail? Hey, be careful. Or how are you balancing that? The context realm? >> Yeah, I think it's all about customer choice. Again, customers have, but they look at their application footprint there, making clear decisions on Hey, this is a parole application is a super sensitive as an example, maybe about based meeting application. Brotherly, not a sensitive. So when they're making decisions about hey, you have a manage device. I will need a manage device in order for you to access the payroll application. But if you have you bring your own device. I'm off perfectly fine if you launch a meeting from that. So those are the levels that people are making decisions on today, and it's super easy to segment and classify your application. >> Talk about the the people that are out there watching might say, You know what? I've been really struggling with identity. I've had, you know, l'd app servers at all this stuff out there, you name it. They've all kinds of access medals over the years, the perimeters now gone. So I got a deal to coffee shop, kind of working experience and multiple devices. All these things are reality. I gotta put a plan together. So the folks that are trying to figure this out, what's that? You guys have both weigh in on on approach to take or certain framework. What's what's? How does someone get the first few steps off to go out towards good cloud identity? >> Sure, I only go first, so I think many ways. That's what we try to simplify it. One solution that we call cloud identity because what people want is I want that model. Seems like a huge mountain in front of me, like how do I figure these things out? I'm getting a lot of these terminologies, so I think the key is to just get started on. We've given them lots of ways. You can take the whole of cloud identity solution back to Kim's point. It can be one license from us, that's it and you're done. It's one unified. You I thinks like that. You can also, if you just want to run state three applications on DCP we have something called identity ofher Proxy. It's very fast. Just load yaps random on disability and experience this beyond >> work Classic enterprise Khun >> Yeah, you run all the applications and dcpd and you can And now they're announcing some things that help you connect back with John Thomas application. That's a great way to get started. >> Karthik painted this picture of Okay, it's no perimeter. You can't just dig a moat. The queen wants to leave the castle. All the security, you know, metaphors that we use. I'm interested in how you're approaching response to these days because you have to make trade us because there are discernible differences with different vendors. Make the assumption that people are going to get in so response becomes increasingly important. What have you changed to respond more quickly? What is Google doing to help? >> Well, yeah, So in a model where we are using, a lot of different vendors were having to like they're not necessarily giving us response and detection. Google. Every service we'd wrap into them automatically gets effectively gets wrapped into our security dashboard. There's a couple of different passwords we can use and weaken. Do reporting. We do it. A tremendous amount of compliance content, compliance controls on our DLP, out of e mail out of Dr and there's detection. There's like it's like we don't have to buy an extra tool for detection for every different type of service we have, it's just built into the Google platform, which is it's It's phenomenal from >> detection baked in, It's just >> baked in. We're not to pay extra for it. In fact, I mean way by the enterprise license because it's completely worth it for us. Um, you know, assumes that came out, the enterprise part of it and all the extra tools. We were just immediately on that because the vault is a big thing for us as well. It's like not only response, but how you dig through your assets toe. Look for evidence of things like, if you have some sort of legal case, you need vault, Tio, you know, make the proper ah, data store for that stuff >> is prioritization to Is it not like, figure it out? Okay, which, which threats to actually go after and step out? And I guess other automation. I mean, I don't know if you're automating your run book and things of that nature. But automation is our friends. Ah, big friend of starting >> on the product measures I What's the roadmap looks like and you share any insight into what your priorities are to go the next level. Aussie Enterprise Focus. For Google Cloud is clear Customs on stage. You guys have got a lot of integration points from Chromebooks G Sweep all the way down through Big Query with Auto ML All the stuff's happening. What's on your plate for road map? What things are you innovating around? >> I mean, it's beyond car vision that we're continuing to roll out. We've just ruled out this bit of a sweet access, for example, but all these conditions come in. Do you want to take that to G et? You're gonna look. We're looking at extending that context framework with all the third party applications that we have even answers Thing called beyond our devices FBI and beyond Corp Alliance, because we know it's not just Google security posture. Customers are made investments and other security companies and you want to make sure all of that interoperate really nicely. So you see a lot more of that coming out >> immigration with other security platform. Certainly, enterprises require that I buy everything on the planet these days to protect themselves >> Like there's another company. Let's say that you're using for securing your devices. That sends a signal thing. I trust this device. It security, passing my checks. You want to make sure that that comes through and >> now we're gonna go. But what's your boss's title? Kim Theo, you report to the CEO. Yeah, Awesome guys. >> Creation. Thank you >> way. We've seen a lot of shifts in where security is usually now pretty much right. Strategic is core for the operations with their own practices. So, guys, thanks for coming on. Thanks for the thing you think of the show so far. What's the What's The takeaway came I'll go to you first. What's your What's the vibe of the >> show? It's a little tough for me because I have one of my senior security engineers here, and he's been going to a lot of the events and he comes to me and just >> look at all >> this stuff that they have like, way were just going over before this. I was like, Oh my God, we want to go back to our r R R office and take it all in right today. You know, if we could So yeah, it's a little tough because >> in the candy store way >> love it because again, it's like it's already paying for it. It's like they're just adding on services that we wanted, that we're gonna pay for it now. It's >> and carted quickly. Just get the last word I know was commenting on our opening this morning around how Google's got all five been falling Google since really the beginning of the company and I know for a fact is a tana big day that secures all spread for the company matter. Just kind of getting it. Yeah, share some inside quickly about what's inside Google. From a security asset standpoint, I p software. >> Absolutely. I mean, security's built from the ground up. We've been seeing that and going back to the candy store analogy. It feels like you've always had this amazing candy, but now there's like a stampede to get it, and it's just built in from the ground up. I love the solution. Focus that you found the keynotes and all the sessions that's happening. >> That's handsome connective tissue like Antos. Maybe the kind of people together. >> Yeah. I don't like >> guys. Thanks for coming on. We appreciate Kartik, Kim. Thanks for coming on. It's accused. Live coverage here on the ground floor were on the floor here. Day two of Google Cloud next here in San Francisco on Jeffrey David Lantz Stevens for more coverage after this short break.