>> 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.

>>Hey everyone, welcome to this event, HPE Compute Security. I'm your host, Lisa Martin. Kevin Dee joins me next Senior director, future Surfer Architecture at hpe. Kevin, it's great to have you back on the program. >>Thanks, Lisa. I'm glad to be here. >>One of the topics that we're gonna unpack in this segment is, is all about cybersecurity. And if we think of how dramatically the landscape has changed in the last couple of years, I was looking at some numbers that H P V E had provided. Cybercrime will reach 10.5 trillion by 2025. It's a couple years away. The average total cost of a data breach is now over 4 million, 15% year over year crime growth predicted over the next five years. It's no longer if we get hit, it's when it's how often. What's the severity? Talk to me about the current situation with the cybersecurity landscape that you're seeing. >>Yeah, I mean the, the numbers you're talking about are just staggering and then that's exactly what we're seeing and that's exactly what we're hearing from our customers is just absolutely key. Customers have too much to lose. The, the dollar cost is just, like I said, staggering. And, and here at HP we know we have a huge part to play, but we also know that we need partnerships across the industry to solve these problems. So we have partnered with, with our, our various partners to deliver these Gen 11 products. Whether we're talking about partners like a M D or partners like our Nick vendors, storage card vendors. We know we can't solve the problem alone. And we know this, the issue is huge. And like you said, the numbers are staggering. So we're really, we're really partnering with, with all the right players to ensure we have a secure solution so we can stay ahead of the bad guys to try to limit the, the attacks on our customers. >>Right. Limit the damage. What are some of the things that you've seen particularly change in the last 18 months or so? Anything that you can share with us that's eye-opening, more eye-opening than some of the stats we already shared? >>Well, there, there's been a massive number of attacks just in the last 12 months, but I wouldn't really say it's so much changed because the amount of attacks has been increasing dramatically over the years for many, many, many years. It's just a very lucrative area for the bad guys, whether it's ransomware or stealing personal data, whatever it is, it's there. There's unfortunately a lot of money to be made into it, made from it, and a lot of money to be lost by the good guys, the good guys being our customers. So it's not so much that it's changed, it's just that it's even accelerating faster. So the real change is, it's accelerating even faster because it's becoming even more lucrative. So we have to stay ahead of these bad guys. One of the statistics of Microsoft operating environments, the number of tax in the last year, up 50% year over year, that's a huge acceleration and we've gotta stay ahead of that. We have to make sure our customers don't get impacted to the level that these, these staggering number of attacks are. The, the bad guys are out there. We've gotta protect, protect our customers from the bad guys. >>Absolutely. The acceleration that you talked about is, it's, it's kind of frightening. It's very eye-opening. We do know that security, you know, we've talked about it for so long as a, as a a C-suite priority, a board level priority. We know that as some of the data that HPE e also sent over organizations are risking are, are listing cyber risks as a top five concern in their organization. IT budgets spend is going up where security is concerned. And so security security's on everyone's mind. In fact, the cube did, I guess in the middle part of last, I did a series on this really focusing on cybersecurity as a board issue and they went into how companies are structuring security teams changing their assumptions about the right security model, offense versus defense. But security's gone beyond the board, it's top of mind and it's on, it's in an integral part of every conversation. So my question for you is, when you're talking to customers, what are some of the key challenges that they're saying, Kevin, these are some of the things the landscape is accelerating, we know it's a matter of time. What are some of those challenges and that they're key pain points that they're coming to you to help solve? >>Yeah, at the highest level it's simply that security is incredibly important to them. We talked about the numbers. There's so much money to be lost that what they come to us and say, is security's important for us? What can you do to protect us? What can you do to prevent us from being one of those statistics? So at a high level, that's kind of what we're seeing at a, with a little more detail. We know that there's customers doing digital transformations. We know that there's customers going hybrid cloud, they've got a lot of initiatives on their own. They've gotta spend a lot of time and a lot of bandwidth tackling things that are important to their business. They just don't have the bandwidth to worry about yet. Another thing which is security. So we are doing everything we can and partnering with everyone we can to help solve those problems for customers. >>Cuz we're hearing, hey, this is huge, this is too big of a risk. How do you protect us? And by the way, we only have limited bandwidth, so what can we do? What we can do is make them assured that that platform is secure, that we're, we are creating a foundation for a very secure platform and that we've worked with our partners to secure all the pieces. So yes, they still have to worry about security, but there's pieces that we've taken care of that they don't have to worry about and there's capabilities that we've provided that they can use and we've made that easy so they can build su secure solutions on top of it. >>What are some of the things when you're in customer conversations, Kevin, that you talk about with customers in terms of what makes HPE E'S approach to security really unique? >>Well, I think a big thing is security is part of our, our dna. It's part of everything we do. Whether we're designing our own asics for our bmc, the ilo ASIC ILO six used on Gen 11, or whether it's our firmware stack, the ILO firmware, our our system, UFI firmware, all those pieces in everything we do. We're thinking about security. When we're building products in our factory, we're thinking about security. When we're think designing our supply chain, we're thinking about security. When we make requirements on our suppliers, we're driving security to be a key part of those components. So security is in our D N a security's top of mind. Security is something we think about in everything we do. We have to think like the bad guys, what could the bad guy take advantage of? What could the bad guy exploit? So we try to think like them so that we can protect our customers. >>And so security is something that that really is pervasive across all of our development organizations, our supply chain organizations, our factories, and our partners. So that's what we think is unique about HPE is because security is so important and there's a whole lot of pieces of our reliance servers that we do ourselves that many others don't do themselves. And since we do it ourselves, we can make sure that security's in the design from the start, that those pieces work together in a secure manner. So we think that gives us a, an advantage from a security standpoint. >>Security is very much intention based at HPE e I was reading in some notes, and you just did a great job of talking about this, that fundamental security approach, security is fundamental to defend against threats that are increasingly complex through what you also call an uncompromising focus to state-of-the-art security and in in innovations built into your D N A. And then organizations can protect their infrastructure, their workloads, their data from the bad guys. Talk to us briefly in our final few minutes here, Kevin, about fundamental uncompromising protected the value in it for me as an HPE customer. >>Yeah, when we talk about fundamental, we're talking about the those fundamental technologies that are part of our platform. Things like we've integrated TPMS and sorted them down in our platforms. We now have platform certificates as a standard part of the platform. We have I dev id and probably most importantly, our platforms continue to support what we really believe was a groundbreaking technology, Silicon Root of trust and what that's able to do. We have millions of lines of firmware code in our platforms and with Silicon Root of trust, we can authenticate all of those lines of firmware. Whether we're talking about the the ILO six firmware, our U E I firmware, our C P L D in the system, there's other pieces of firmware. We authenticate all those to make sure that not a single line of code, not a single bit has been changed by a bad guy, even if the bad guy has physical access to the platform. >>So that silicon route of trust technology is making sure that when that system boots off and that hands off to the operating system and then eventually the customer's application stack that it's starting with a solid foundation, that it's starting with a system that hasn't been compromised. And then we build other things into that silicon root of trust, such as the ability to do the scans and the authentications at runtime, the ability to automatically recover if we detect something has been compromised, we can automatically update that compromised piece of firmware to a good piece before we've run it because we never want to run firmware that's been compromised. So that's all part of that Silicon Root of Trust solution and that's a fundamental piece of the platform. And then when we talk about uncompromising, what we're really talking about there is how we don't compromise security. >>And one of the ways we do that is through an extension of our Silicon Root of trust with a capability called S Spdm. And this is a technology that we saw the need for, we saw the need to authenticate our option cards and the firmware in those option cards. Silicon Root Prota, Silicon Root Trust protects against many attacks, but one piece it didn't do is verify the actual option card firmware and the option cards. So we knew to solve that problem we would have to partner with others in the industry, our nick vendors, our storage controller vendors, our G vendors. So we worked with industry standards bodies and those other partners to design a capability that allows us to authenticate all of those devices. And we worked with those vendors to get the support both in their side and in our platform side so that now Silicon Rivers and trust has been extended to where we protect and we trust those option cards as well. >>So that's when, when what we're talking about with Uncompromising and with with Protect, what we're talking about there is our capabilities around protecting against, for example, supply chain attacks. We have our, our trusted supply chain solution, which allows us to guarantee that our server, when it leaves our factory, what the server is, when it leaves our factory, will be what it is when it arrives at the customer. And if a bad guy does anything in that transition, the transit from our factory to the customer, they'll be able to detect that. So we enable certain capabilities by default capability called server configuration lock, which can ensure that nothing in the server exchange, whether it's firmware, hardware, configurations, swapping out processors, whatever it is, we'll detect if a bad guy did any of that and the customer will know it before they deploy the system. That gets enabled by default. >>We have an intrusion detection technology option when you use by the, the trusted supply chain that is included by default. That lets you know, did anybody open that system up, even if the system's not plugged in, did somebody take the hood off and potentially do something malicious to it? We also enable a capability called U EFI secure Boot, which can go authenticate some of the drivers that are located on the option card itself. Those kind of capabilities. Also ilo high security mode gets enabled by default. So all these things are enabled in the platform to ensure that if it's attacked going from our factory to the customer, it will be detected and the customer won't deploy a system that's been maliciously attacked. So that's got >>It, >>How we protect the customer through those capabilities. >>Outstanding. You mentioned partners, my last question for you, we've got about a minute left, Kevin is bring AMD into the conversation, where do they fit in this >>AMD's an absolutely crucial partner. No one company even HP can do it all themselves. There's a lot of partnerships, there's a lot of synergies working with amd. We've been working with AMD for almost 20 years since we delivered our first AM MD base ProLiant back in 2004 H HP ProLiant, DL 5 85. So we've been working with them a long time. We work with them years ahead of when a processor is announced, we benefit each other. We look at their designs and help them make their designs better. They let us know about their technology so we can take advantage of it in our designs. So they have a lot of security capabilities, like their memory encryption technologies, their a MD secure processor, their secure encrypted virtualization, which is an absolutely unique and breakthrough technology to protect virtual machines and hypervisor environments and protect them from malicious hypervisors. So they have some really great capabilities that they've built into their processor, and we also take advantage of the capabilities they have and ensure those are used in our solutions and in securing the platform. So a really such >>A great, great partnership. Great synergies there. Kevin, thank you so much for joining me on the program, talking about compute security, what HPE is doing to ensure that security is fundamental, that it is unpromised and that your customers are protected end to end. We appreciate your insights, we appreciate your time. >>Thank you very much, Lisa. >>We've just had a great conversation with Kevin Depu. Now I get to talk with David Chang, data center solutions marketing lead at a md. David, welcome to the program. >>Thank, thank you. And thank you for having me. >>So one of the hot topics of conversation that we can't avoid is security. Talk to me about some of the things that AMD is seeing from the customer's perspective, why security is so important for businesses across industries. >>Yeah, sure. Yeah. Security is, is top of mind for, for almost every, every customer I'm talking to right now. You know, there's several key market drivers and, and trends, you know, in, out there today that's really needing a better and innovative solution for, for security, right? So, you know, the high cost of data breaches, for example, will cost enterprises in downtime of, of the data center. And that time is time that you're not making money, right? And potentially even leading to your, to the loss of customer confidence in your, in your cust in your company's offerings. So there's real costs that you, you know, our customers are facing every day not being prepared and not having proper security measures set up in the data center. In fact, according to to one report, over 400 high-tech threats are being introduced every minute. So every day, numerous new threats are popping up and they're just, you know, the, you know, the bad guys are just getting more and more sophisticated. So you have to take, you know, measures today and you have to protect yourself, you know, end to end with solutions like what a AM MD and HPE has to offer. >>Yeah, you talked about some of the costs there. They're exorbitant. I've seen recent figures about the average, you know, cost of data breacher ransomware is, is close to, is over $4 million, the cost of, of brand reputation you brought up. That's a great point because nobody wants to be the next headline and security, I'm sure in your experiences. It's a board level conversation. It's, it's absolutely table stakes for every organization. Let's talk a little bit about some of the specific things now that A M D and HPE E are doing. I know that you have a really solid focus on building security features into the EPIC processors. Talk to me a little bit about that focus and some of the great things that you're doing there. >>Yeah, so, you know, we partner with H P E for a long time now. I think it's almost 20 years that we've been in business together. And, and you know, we, we help, you know, we, we work together design in security features even before the silicons even, you know, even born. So, you know, we have a great relationship with, with, with all our partners, including hpe and you know, HPE has, you know, an end really great end to end security story and AMD fits really well into that. You know, if you kind of think about how security all started, you know, in, in the data center, you, you've had strategies around encryption of the, you know, the data in, in flight, the network security, you know, you know, VPNs and, and, and security on the NS. And, and even on the, on the hard drives, you know, data that's at rest. >>You know, encryption has, you know, security has been sort of part of that strategy for a a long time and really for, you know, for ages, nobody really thought about the, the actual data in use, which is, you know, the, the information that's being passed from the C P U to the, the, the memory and, and even in virtualized environments to the, the, the virtual machines that, that everybody uses now. So, you know, for a long time nobody really thought about that app, you know, that third leg of, of encryption. And so a d comes in and says, Hey, you know, this is things that as, as the bad guys are getting more sophisticated, you, you have to start worrying about that, right? And, you know, for example, you know, you know, think, think people think about memory, you know, being sort of, you know, non-persistent and you know, when after, you know, after a certain time, the, the, you know, the, the data in the memory kind of goes away, right? >>But that's not true anymore because even in in memory data now, you know, there's a lot of memory modules that still can retain data up to 90 minutes even after p power loss. And with something as simple as compressed, compressed air or, or liquid nitrogen, you can actually freeze memory dams now long enough to extract the data from that memory module for up, you know, up, up to two or three hours, right? So lo more than enough time to read valuable data and, and, and even encryption keys off of that memory module. So our, our world's getting more complex and you know, more, the more data out there, the more insatiable need for compute and storage. You know, data management is becoming all, all the more important, you know, to keep all of that going and secure, you know, and, and creating security for those threats. It becomes more and more important. And, and again, especially in virtualized environments where, you know, like hyperconverged infrastructure or vir virtual desktop memories, it's really hard to keep up with all those different attacks, all those different attack surfaces. >>It sounds like what you were just talking about is what AMD has been able to do is identify yet another vulnerability Yes. Another attack surface in memory to be able to, to plug that hole for organizations that didn't, weren't able to do that before. >>Yeah. And, you know, and, and we kind of started out with that belief that security needed to be scalable and, and able to adapt to, to changing environments. So, you know, we, we came up with, you know, the, you know, the, the philosophy or the design philosophy that we're gonna continue to build on those security features generational generations and stay ahead of those evolving attacks. You know, great example is in, in the third gen, you know, epic C P U, that family that we had, we actually created this feature called S E V S N P, which stands for SECURENESS Paging. And it's really all around this, this new attack where, you know, your, the, the, you know, it's basically hypervisor based attacks where people are, you know, the bad actors are writing in to the memory and writing in basically bad data to corrupt the mem, you know, to corrupt the data in the memory. So s e V S and P is, was put in place to help, you know, secure that, you know, before that became a problem. And, you know, you heard in the news just recently that that becoming a more and more, more of a bigger issue. And the great news is that we had that feature built in, you know, before that became a big problem. >>And now you're on the fourth gen, those epic crosses talk of those epic processes. Talk to me a little bit about some of the innovations that are now in fourth gen. >>Yeah, so in fourth gen we actually added, you know, on top of that. So we've, we've got, you know, the sec the, the base of our, our, what we call infinity guard is, is all around the secure boot. The, you know, the, the, the, the secure root of trust that, you know, that we, we work with HPE on the, the strong memory encryption and the S E V, which is the secure encrypted virtualization. And so remember those s s and p, you know, incap capabilities that I talked about earlier. We've actually, in the fourth gen added two x the number of sev v s and P guests for even higher number of confidential VMs to support even more customers than before. Right? We've also added more guest protection from simultaneous multi threading or S M T side channel attacks. And, you know, while it's not officially part of Infinity Guard, we've actually added more APEC acceleration, which greatly benefits the security of those confidential VMs with the larger number of VCPUs, which basically means that you can build larger VMs and still be secured. And then lastly, we actually added even stronger a e s encryption. So we went from 128 bit to 256 bit, which is now military grade encryption on top of that. And, you know, and, and that's really, you know, the de facto crypto cryptography that is used for most of the applications for, you know, customers like the US federal government and, and all, you know, the, is really an essential element for memory security and the H B C applications. And I always say if it's good enough for the US government, it's good enough for you. >>Exactly. Well, it's got to be, talk a little bit about how AMD is doing this together with HPE a little bit about the partnership as we round out our conversation. >>Sure, absolutely. So security is only as strong as the layer below it, right? So, you know, that's why modern security must be built in rather than, than, you know, bolted on or, or, or, you know, added after the fact, right? So HPE and a MD actually developed this layered approach for protecting critical data together, right? Through our leadership and, and security features and innovations, we really deliver a set of hardware based features that, that help decrease potential attack surfaces. With, with that holistic approach that, you know, that safeguards the critical information across system, you know, the, the entire system lifecycle. And we provide the confidence of built-in silicon authentication on the world's most secure industry standard servers. And with a 360 degree approach that brings high availability to critical workloads while helping to defend, you know, against internal and external threats. So things like h hp, root of silicon root of trust with the trusted supply chain, which, you know, obviously AMD's part of that supply chain combined with AMD's Infinity guard technology really helps provide that end-to-end data protection in today's business. >>And that is so critical for businesses in every industry. As you mentioned, the attackers are getting more and more sophisticated, the vulnerabilities are increasing. The ability to have a pa, a partnership like H P E and a MD to deliver that end-to-end data protection is table stakes for businesses. David, thank you so much for joining me on the program, really walking us through what am MD is doing, the the fourth gen epic processors and how you're working together with HPE to really enable security to be successfully accomplished by businesses across industries. We appreciate your insights. >>Well, thank you again for having me, and we appreciate the partnership with hpe. >>Well, you wanna thank you for watching our special program HPE Compute Security. I do have a call to action for you. Go ahead and visit hpe com slash security slash compute. Thanks for watching.

>>Good afternoon everyone, and thank you so much for joining us. My name is Savannah Peterson, joined by my co-host Paul for the afternoon. Very excited. Oh, Savannah. Hello. I'm, I'm pumped for this. This is our first bit together. Exactly. >>It's gonna be fun. Yes. We have a great guest to kick off with. >>We absolutely do. We're at Supercomputing 2022 today, and very excited to talk to our next guest. We're gonna be talking about data at scale and data that really matters to us joining us. Kelly Gayer, thank you so much for being here and you are with tech. Tell everyone what TAC is. >>Tech is the Texas Advanced Computing Center at the University of Texas at Austin. And thank you so much for having me here. >>It is wonderful to have you. Your smile's contagious. And one of the themes that's come up a lot with all of our guests, and we just talked about it, is how good it is to be back in person, how good it is to be around our hardware, community tech. You did some very interesting research during the pandemic. Can you tell us about that? >>I can. I did. So when we realized sort of mid-March, we realized that, that this was really not normal times and the pandemic was statement. Yes. That pandemic was really gonna touch everyone. I think a lot of us at the center and me personally, we dropped everything to plug in and that's what we do. So UT's tagline is what starts here changes the world and tax tagline is powering discoveries that change the world. So we're all about impact, but I plugged in with the research group there at UT Austin, Dr. Lauren Myers, who's an epidemiologist, and just we figured out how to plug in and compute so that we could predict the spread of, of Covid 19. >>And you did that through the use of mobility data, cell phone signals. Tell us more about what exactly you were choreographing. >>Yeah, so that was really interesting. Safe graph during the pandemic made their mobility data. Typically it was used for marketing purposes to know who was going into Walmart. The offenses >>For advertising. >>Absolutely, yeah. They made all of their mobility data available for free to people who were doing research and plugging in trying to understand Covid. 19, I picked that data up and we used it as a proxy for human behavior. So we knew we had some idea, we got weekly mobility updates, but it was really mobility all day long, you know, anonymized. I didn't know who they were by cell phones across the US by census block group or zip code if we wanted to look at it that way. And we could see how people were moving around. We knew what their neighbor, their home neighborhoods were. We knew how they were traveling or not traveling. We knew where people were congregating, and we could get some idea of, of how people were behaving. Were they really, were they really locking down or were they moving in their neighborhoods or were they going outside of their neighborhoods? >>What a, what a fascinating window into our pandemic lives. So now that you were able to do this for this pandemic, as we look forward, what have you learned? How quickly could we forecast? What's the prognosis? >>Yeah, so we, we learned a tremendous amount. I think during the pandemic we were reacting, we were really trying. It was a, it was an interesting time as a scientist, we were reacting to things almost as if the earth was moving underneath us every single day. So it was something new every day. And I've told people since I've, I haven't, I haven't worked that hard since I was a graduate student. So it was really daylight to dark 24 7 for a long period of time because it was so important. And we knew, we, we knew we were, we were being a part of history and affecting something that was gonna make a difference for a really long time. And, and I think what we've learned is that indeed there is a lot of data being collected that we can use for good. We can really understand if we get organized and we get set up, we can use this data as a means of perhaps predicting our next pandemic or our next outbreak of whatever. It is almost like using it as a canary in the coal mine. There's a lot in human behavior we can use, given >>All the politicization of, of this last pandemic, knowing what we know now, making us better prepared in theory for the next one. How confident are you that at least in the US we will respond proactively and, and effectively when the next one comes around? >>Yeah, I mean, that's a, that's a great question and, and I certainly understand why you ask. I think in my experience as a scientist, certainly at tech, the more transparent you are with what you do and the more you explain things. Again, during the pandemic, things were shifting so rapidly we were reacting and doing the best that we could. And I think one thing we did right was we admitted where we felt uncertain. And that's important. You have to really be transparent to the general public. I, I don't know how well people are gonna react. I think if we have time to prepare, to communicate and always be really transparent about it. I think those are three factors that go into really increasing people's trust. >>I think you nailed it. And, and especially during times of chaos and disaster, you don't know who to trust or what to believe. And it sounds like, you know, providing a transparent source of truth is, is so critical. How do you protect the sensitive data that you're working with? I know it's a top priority for you and the team. >>It is, it is. And we, we've adopted the medical mantra, do no harm. So we have, we feel a great responsibility there. There's, you know, two things that you have to really keep in mind when you've got sensitive data. One is the physical protection of it. And so that's, that's governed by rule, federal rules, hipaa, ferpa, whatever, whatever kind of data that you have. So we certainly focus on the physical protection of it, but there's also sort of the ethical protection of it. What, what is the quote? There's lies, damn lies and statistics. >>Yes. Twain. >>Yeah. So you, you really have to be responsible with what you're doing with the data, how you're portraying the results. And again, I think it comes back to transparency is is basically if people are gonna reproduce what I did, I have to be really transparent with what I did. >>I, yeah, I think that's super important. And one of the themes with, with HPC that we've been talking about a lot too is, you know, do people trust ai? Do they trust all the data that's going into these systems? And I love that you just talked about the storytelling aspect of that, because there is a duty, it's not, you can cut data kind of however you want. I mean, I come from marketing background and we can massage it to, to do whatever we want. So in addition to being the deputy director at Tech, you are also the DEI officer. And diversity I know is important to you probably both as an individual, but also in the work that you're doing. Talk to us about that. >>Yeah, I mean, I, I very passionate about diversity, equity and inclusion in a sense of belongingness. I think that's one of the key aspects of it. Core >>Of community too. >>I got a computer science degree back in the eighties. I was akin to a unicorn in a, in an engineering computer science department. And, but I was really lucky in a couple of respects. I had a, I had a father that was into science that told me I could do anything I, I wanted to set my mind to do. So that was my whole life, was really having that support system. >>He was cheers to dad. >>Yeah. Oh yeah. And my mom as well, actually, you know, they were educators. I grew up, you know, in that respect, very, very privileged, but it was still really hard to make it. And I couldn't have told you back in that time why I made it and, and others didn't, why they dropped out. But I made it a mission probably back, gosh, maybe 10, 15 years ago, that I was really gonna do all that I could to change the needle. And it turns out that there are a number of things that you can do grassroots. There are certainly best practices. There are rules and there are things that you really, you know, best practices to follow to make people feel more included in an organization, to feel like they belong it, shared mission. But there are also clever things that you can do with programming to really engage students, to meet people and students where they are interested and where they are engaged. And I think that's what, that's what we've done over, you know, the course of our programming over the course of about maybe since 2016. We have built a lot of programming ATAC that really focuses on that as well, because I'm determined the needle is gonna change before it's all said and done. It just really has to. >>So what, what progress have you made and what goals have you set in this area? >>Yeah, that, that's a great question. So, you know, at first I was a little bit reluctant to set concrete goals because I really didn't know what we could accomplish. I really wasn't sure what grassroots efforts was gonna be able to, you're >>So honest, you can tell how transparent you are with the data as well. That's >>Great. Yeah, I mean, if I really, most of the successful work that I've done is both a scientist and in the education and outreach space is really trust relationships. If I break that trust, I'm done. I'm no longer effective. So yeah, I am really transparent about it. But, but what we did was, you know, the first thing we did was we counted, you know, to the extent that we could, what does the current picture look like? Let's be honest about it. Start where we are. Yep. It was not a pretty picture. I mean, we knew that anecdotally it was not gonna be a great picture, but we put it out there and we leaned into it. We said, this is what it is. We, you know, I hesitated to say we're gonna look 10% better next year because I'm, I'm gonna be honest, I don't always know we're gonna do our best. >>The things that I think we did really well was that we stopped to take time to talk and find out what people were interested in. It's almost like being present and listening. My grandmother had a saying, you have two errors in one mouth for a reason, just respect the ratio. Oh, I love that. Yeah. And I think it's just been building relationships, building trust, really focusing on making a difference, making it a priority. And I think now what we're doing is we've been successful in pockets of people in the center and we are, we are getting everybody on board. There's, there's something everyone can do, >>But the problem you're addressing doesn't begin in college. It begins much, much, that's right. And there's been a lot of talk about STEM education, particularly for girls, how they're pushed out of the system early on. Also for, for people of color. Do you see meaningful progress being made there now after years of, of lip service? >>I do. I do. But it is, again, grassroots. We do have a, a, a researcher who was a former teacher at the center, Carol Fletcher, who is doing research and for CS for all we know that the workforce, so if you work from the current workforce, her projected workforce backwards, we know that digital skills of some kind are gonna be needed. We also know we have a, a, a shortage. There's debate on how large that shortage is, but about roughly about 1 million unmet jobs was projected in 2020. It hasn't gotten a lot better. We can work that problem backwards. So what we do there is a little, like a scatter shot approach. We know that people come in all forms, all shapes, all sizes. They get interested for all different kinds of reasons. We expanded our set of pathways so that we can get them where they can get on to the path all the way back K through 12, that's Carol's work. Rosie Gomez at the center is doing sort of the undergraduate space. We've got Don Hunter that does it, middle school, high school space. So we are working all parts of the problem. I am pretty passionate about what we consider opportunity youth people who never had the opportunity to go to college. Is there a way that we can skill them and get, get them engaged in some aspect and perhaps get them into this workforce. >>I love that you're starting off so young. So give us an example of one of those programs. What are you talking to kindergartners about when it comes to CS education? >>You know, I mean, gaming. Yes. Right. It's what everybody can wrap their head around. So most kids have had some sort of gaming device. You talk in the context, in the context of something they understand. I'm not gonna talk to them about high performance computing. It, it would go right over their heads. And I think, yeah, you know, I, I'll go back to something that you said Paul, about, you know, girls were pushed out. I don't know that girls are being pushed out. I think girls aren't interested and things that are being presented and I think they, I >>Think you're generous. >>Yeah. I mean, I was a young girl and I don't know why I stayed. Well, I do know why I stayed with it because I had a father that saw something in me and I had people at critical points in my life that saw something in me that I didn't see. But I think if we ch, if we change the way we teach it, maybe in your words they don't get pushed out or they, or they won't lose interest. There's, there's some sort of computing in everything we do. Well, >>Absolutely. There's also the bro culture, which begins at a very early >>Age. Yeah, that's a different problem. Yeah. That's just having boys in the classroom. Absolutely. You got >>It. That's a whole nother case. >>That's a whole other thing. >>Last question for you, when we are sitting here, well actually I've got, it's two parter, let's put it that way. Is there a tool or something you wish you could flick a magic wand that would make your job easier? Where you, you know, is there, can you identify the, the linchpin in the DEI challenge? Or is it all still prototyping and iterating to figure out the best fit? >>Yeah, that is a, that's a wonderful question. I can tell you what I get frustrated with is that, that >>Counts >>Is that I, I feel like a lot of people don't fully understand the level of effort and engagement it takes to do something meaningful. The >>Commitment to a program, >>The commitment to a program. Totally agree. It's, there is no one and done. No. And in fact, if I do that, I will lose them forever. They'll be, they will, they will be lost in the space forever. Rather. The engagement is really sort of time intensive. It's relationship intensive, but there's a lot of follow up too. And the, the amount of funding that goes into this space really is not, it, it, it's not equal to the amount of time and effort that it really takes. And I think, you know, I think what you work in this space, you realize that what you gain is, is really more of, it's, it really feels good to make a difference in somebody's life, but it's really hard to do on a shoer budget. So if I could kind of wave a magic wand, yes, I would increase understanding. I would get people to understand that it's all of our responsibility. Yes, everybody is needed to make the difference and I would increase the funding that goes to the programs. >>I think that's awesome, Kelly, thank you for that. You all heard that. More funding for diversity, equity, and inclusion. Please Paul, thank you for a fantastic interview, Kelly. Hopefully everyone is now inspired to check out tac perhaps become a, a Longhorn, hook 'em and, and come deal with some of the most important data that we have going through our systems and predicting the future of our pandemics. Ladies and gentlemen, thank you for joining us online. We are here in Dallas, Texas at Supercomputing. My name is Savannah Peterson and I look forward to seeing you for our next segment.

>>We're back in Boston, the Cube's coverage of AWS reinforced 2022. My name is Dave ante. Steve Malanney is here as the CEO of Aviatrix longtime cube alum sort of collaborator on super cloud. Yeah. Uh, which we have an event, uh, August 9th, which you guys are participating in. So, um, thank you for that. And, yep. Welcome to the cube. >>Yeah. Thank you so great to be here as >>Always back in Boston. Yeah. I'd say good show. Not, not like blow me away. We were AWS, um, summit in New York city three weeks ago. I >>Took, heard it took three hours to get in >>Out control. I heard, well, there were some people two I, maybe three <laugh>, but there was, they expected like maybe nine, 10,000, 19,000 showed up. Now it's a free event. Yeah. 19,000 people. >>Oh, I didn't know it >>Was that many. It was unbelievable. I mean, it was packed. Yeah. You know, so it's a little light here and I think it's cuz you know, everybody's down the Cape, >>There are down the Cape, Rhode Island that's after the fourth. The thing is that we were talking about this. The quality of people are pretty good though. Yeah. Right. This is there's no looky lose it's everybody. That's doing stuff in cloud. They're moving in. This is no longer, Hey, what's this thing called cloud. Right. I remember three, four years ago at AWS. You'd get a lot of that, that kind of stuff. Some the summit meetings and things like that. Now it's, we're a full on deployment mode even >>Here in 2019, the conversation was like, so there's this shared responsibility model and we may have to make sure you understand. I mean, nobody's questioning that today. Yeah. It's more really hardcore best practices and you know how to apply tools. Yeah. You know, dos and don't and so it's a much more sophisticated narrative, I think. Yeah. >>Well, I mean, that's one of the things that Aviatrix does is our whole thing is architecturally. I would say, where does network security belong in the network? It shouldn't be a bolt on it. Shouldn't be something that you add on. It should be something that actually gets integrated into the fabric of the network. So you shouldn't be able to point to network security. It's like, can you point to the network? It's everywhere. Point to air it's everywhere. Network security should be integrated in the fabric and that wasn't done. On-prem that way you steered traffic to this thing called a firewall. But in the cloud, that's not the right architectural way. It it's a choke point. Uh, operationally adds tremendous amount of complexity, which is the whole reason we're going to cloud in the first place is for that agility and the ability to operationally swipe the card and get our developers running to put in these choke points is completely the wrong architecture. So conversations we're having with customers is integrate that security into the fabric of the network. And you get rid of all those, all those operational >>Issues. So explain that how you're not a, a checkpoint, but if you funnel everything into one sort of place >>In the, so we are a networking company, uh, it is uh, cloud networking company. So we, we were born in the cloud cloud native. We, we are not some on-prem networking solution that was jammed in the cloud, uh, wrapped >>In stack wrapped >>In, you know, or like that. No, no, no. And looking for wires, right? That's VM series from Palo. It doesn't even know it's in the cloud. Right. It's looking for wires. Um, and of course multicloud, cuz you know, Larry E said now, could you believe that on stage with sat, Nadela talking about multi-cloud now you really know we've crossed over to this is a, this is a thing, whoever would've thought you'd see that. But anyway, so we're networking. We're cloud networking, of course it's multi-cloud networking and we're gonna integrate these intelligent services into the fabric. And one of those is, is networking. So what happens is you should do security everywhere. So the place to do it is at every single point in the network that you can make a decision and you embed it and actually embed it into the network. So it's that when you're making a decision of does that traffic need to go somewhere or not, you're doing a little bit of security everywhere. And so what, it looks like a giant firewall effectively, but it's actually distributed in software through every single point in a network. >>Can I call it a mesh? >>It's kind of a mesh you can think of. Yeah, it's a fabric. >>Okay. It's >>A, it's a fabric that these advanced services, including security are integrated into that fabric. >>So you've been in networking much of >>Your career career, >>37 years. All your career. Right? So yay. Cisco Palo Alto. Nicera probably missing one or two, but so what do you do with all blue coat? Blue coat? What do you do with all that stuff? That's out there that >>Symantics. >>Yes. <laugh> keep going. >>Yeah, I think that's it. That's >>All I got. Okay. So what do you do with all that stuff? That's that's out there, you rip and replace it. You, >>So in the cloud you mean yeah. >>All this infrastructure that's out there. What is that? Well, you >>Don't have it in the right. And so right now what's happening is people, look, you can't change too many things. If you're a human, you know, they always tell you don't change a job, get married and have a kid or something all in the same year. Like they just, just do one of 'em cuz you it's too much. When people move to the cloud, what they do is they tend to take what they do on Preem and they say, look, I'm gonna change one thing. We're gonna go to the cloud, everything else. I'm gonna keep the same. Cuz I don't wanna change three things. So they kind of lift and shift their same mentality. They take their firewalls, their next gen fire. I want them, they take all the things that they currently do. And they say, I'm gonna try to do that in the cloud. >>It's not really the right way to do it. But sometimes for people that are on-prem people, that's the way to get started and I'll screw it up and not screw it up and, and not change too many things. And look, I'm just used to that. And, and then I'll, then I'll go to change things, to be more cloud native, then I'll realize I can get rid of this and get rid of that and do that. But, but that's where people are. The first thing is bring these things over. We help them do that, right? From a networking perspective, I'll make it easier to bring your old security stuff in. But in parallel to that, we start adding things into the fabric and what's gonna happen is eventually we start adding all these things and things that you can't do separately. We start doing anomaly detection. We start doing behavioral analysis. Why? Because the entire network, we are the data plan. We see everything. And so we can start doing things that a standalone device can't do because not all the traffic steered to them. It can only control what's steered to you. And then eventually what's happening is people look at that device. And then they look at us and then they look at the device and they look at us and they go, why do I have both of this? And we go, I don't know. >>You don't need it. >>Well, can I get rid of that other thing? That's a tool. >>Sure. And there's not a trade off. There's not a trade off. You >>Don't have to. No. Now people rid belts and suspenders. Yeah. Cause it's just, who has, who has enough? Who has too much security buddy? They're gonna, they're gonna do belt suspenders. You know anything they can do. But eventually what will happened is they'll look at what we do and they'll go, that's good enough. That happened to me. When I was at Palo Alto networks, we inserted as a firewall. They kept their existing firewall. They had all these other devices and eventually all those went away and you just had a NextGen >>Firewall just through attrition, >>Through Atian. You're like, you're looking, you go, well, that platform is doing all these functions. Same. Thing's gonna happen to us. The platform of networking's gonna do all your network security devices. So any tool or agent or external, you know, device that you have to steer traffic to ISS gonna go away. You're not gonna need it. >>And, and you talking multi-cloud obviously, >>And then don't wanna do the same thing. Whether man Azure, you know the same. >>Yeah. >>Same, same experie architecture, same experience, same set of services. True. Multi-cloud native. Like you, that's what you want. And oh, by the way, skill, gap, skill shortage is a real thing. And it's getting worse. Cause now with the recession, you think you're gonna be able to add more people. Nope. You're gonna have less people. How do I do this? Any multicloud world with security and all this kind of stuff. You have to put the intelligence in the software, not on your people. Right? >>So speaking of recession. Yep. As a CEO of a well funded company, that's got some momentum. How are you approaching it? Do you have like, did you bring in the war time? Conig I mean, you've been through, you know, downturns before. This is you are you >>I'm on war time already. >>Okay. So yeah. Tell me more about how you you're kind of approaching this >>So recession down. So didn't change what we were doing one bit, because I run it that way from the very beginning. So I've been around 30 years, that's >>Told me he he's like me. You know what he said? >>Yeah. Or maybe >>I'm like, I want be D cuz he said, you know, people talk about, you know, only do things that are absolutely necessary during times like this. I always do things that are only, >>That's all I >>Do necessary. Why would you ever do things that aren't necessary? >><laugh> you'd be surprised. Most companies don't. Yeah. Uh, recession's very good for people like snowflake and for us because we run that way anyway. Mm-hmm <affirmative> um, I, I constantly make decisions that we have to go and dip there's people that aren't right for the business. I move 'em out. Like I don't wait for some like Sequoia stupid rest in peace. The world's ending fire all your people that has no impact on me because I already operated that way. So we, we kind of operate that way and we are, we are like sat Nadel even came out and kind of said, I don't wanna say cloud is recession proof, but it kind of is, is we are so look, our top customer spends 5 million a year. Nothing. We haven't even started yet. David that's minuscule. We're not macro. We're micro 5 million a year for these big enterprises is nothing right. SA Nadel is now starting to count people who do billion dollar agreements with him billion over a period of number of years. Like that's the, the scale we have not even >>Gun billion dollar >>Agreements. We haven't even under begun to understand the scope of what's happening in the cloud. Right. And so yeah, the recession's happening. I don't know. I guess it's impacting somebody. It's not impacting me. It's actually accelerating things because it's a flight to quality and customers go and say, I can't get gear on on-prem anyway, cuz of the, uh, shortage, you know, the, uh, uh, get chips. Um, and that's not the right thing. So guess what the recession says, I'm gonna stop spending more money there and I'm gonna put it into the cloud. >>All right. So you opened up Pandora's box, man. I wanna ask you about your sort of management philosophy. When you come into a company to take, to go lead a company like that. Yeah. How, what, what's your approach to assess the team? Who do you, who do you decide? How do you decide who to keep on the bus? Who to throw off the bus put in the right seats. So how long does that take you? >>Doesn't take long. When I join, we were 30, 30, 8 people. We're now 525. Um, and my view on everything and I I've never met Frank Lubin, but I guarantee you, he has the same philosophy. You have a one year contract me included next year, the board might come to me and say, you were the right CEO for this year. You're not next year. Ben Horowitz taught me that it's a one year contract. There's no multi-year contract. So everybody in the company, including the CEO has a one year >>Contract. So you would say that to the board. Hey, if you can find somebody better, >>If, and, and you know what, I'll be the first one to pull myself, fire myself and say, we're, we're replacing me with somebody better right now. There isn't anybody better. So it's me. So, okay, next year maybe there's somebody better. Or we hit a certain point where I'm not the right guy. I'll I'll, I'll pull myself out as the CEO, but also internally the same thing just because you're the right guy this year. And we hire people for the, what you need to do this year. We're not gonna, we don't hire, oh, like this is the mistake. A lot of companies make, well, we wanna be a billion dollars in sales. So we're gonna go hire some loser from HPE. Who's worked at a company for a billion dollars. And by the way has no idea how they became a billion dollars, right. In revenue or billions of dollars. >>But we're gonna go hire 'em because they must know more than we do. And what every single time you bring them in what you realize, they're idiots. They have no idea how we got to that. And so you, you don't pre-hire for where you want to be. You hire for where you are that year. And then if it's not right, and then if it's not right, you'd be really nice to them. Have great severance packages, be, be respectful for people and be honest with them. I guarantee you Frank, Salman's not, if you're not just have this conversation with a sales guy before I came into here, very straight conversation, Northeast hockey player mentality. We're straight. If you're not working out or I don't think you're doing things right. You're gonna know. And so it's a one year, it's a one year contract. That's what you do. So you don't have time. You don't the luxury of >>Time. So, so that's probably the hardest part of, of any leadership job is, and people don't like confrontation. They like to put it off, but you don't run away from it. It's >>All in a confrontation, right? That's what relationships have built. Why do war buddies hang out with each other? Cuz they've gone through hell, right? It's in the confrontation. And it's, it's actually with customers too, right? If there's an issue, you don't run from it. You actually bring it up in a very straightforward manner and say, Hey, we got a problem, right? They respect you. You respect them, blah, blah, blah. And then you come out of it and go, you know, you have to fight like, look with your wife. You have to fight. If you don't fight, it's not a relationship you've gotta see in that, in that tension is where the relationship's >>Built. See, I should go home and have a fight tonight. You gotta have a fight with your wife. <laugh> you know, you mentioned Satia and Nadella and Larry Ellison. Interesting point. I wanna come back to that. What Oracle did is actually pretty interesting, do we? For their use case? Yeah. You know, it's not your thing. It's like low latency database across clouds. Yeah. Who would ever thought that? But >>We love it. We love it because it drives multi-cloud it drives. Um, and, and, and I actually think we're gonna have multi-cloud applications that are gonna start happening. Um, right now you don't, you have developers that, that, that kind of will use one cloud. But as we start developing and you call it the super cloud, right. When that starts really happening, the infrastructure's gonna allow that networking and network security is that bottom layer that Aviatrix helps once that gets all handled. The app, people are gonna say, so there's no friction. So maybe I can use autonomous database here. I can use this service from GCP. I can use that service and, and put it all into one app. So where's the app run. It's a multicloud app. Doesn't exist today. >>No, that doesn't happen today. >>It's it's happen. It's gonna happen. >>But that's kind of what the vision was. No, seven, eight years ago of what >>It's >>Gonna, that would be, you know, the original premise of hybrid. Right? Right. Um, I think Chuck Hollis, the guy was at EMC at the time he wrote this piece on, he called it private cloud, but he was really describing hybrid cloud application and running in both places that never happened. But it's starting to, I mean, the infrastructure is getting put in place to enable that, I guess is what you're saying. >>Yep. >>Yeah. >>Cool. And multicloud is, is becoming not just four plus one is a lot of enterprises it's becoming plus one, meaning you're gonna have more and more. And then there won't be infrastructure clouds like AWS and so forth, but it's gonna be industry clouds. Right? You've you've talked about that again, back to super clouds. You're gonna have Goldman Sachs creating clouds and you're gonna have AI companies creating clouds. You're gonna have clouds at the edge, you know, for edge computing and all these things all need to be networked with network security integrated. And you mentioned fact >>Aviatrix you mentioned Ben Horowitz, that's mark Andreesen. All, all companies are software companies. All companies are becoming cloud companies. Yeah. Or, or they're missing missing opportunities or they might get disrupted. >>Yeah. Every single company I talk to now, you know, whether you're Heineken, they don't think of themselves as a beer company anymore. We are the most technologically, you know, advanced brewer in the world. Like they all think they're a technology company. Now, whether you're making trucks, whether you're making sneakers, whether you're making beer, you're now a technology company, every single company in >>The world, we are too, we're we're building a media cloud. You're you know, John's, it's a technology company laying that out and yeah. That's we got developers doing that. That's our, that's our future. Yep. You know? Cool. Hey, thanks for coming on, man. Thank you. Great to see you. Thank you for watching. Keep it right there. We'll be back right after this short break. It keeps coverage. AWS reinforced 20, 22 from Boston. Keep it right there. >>You tired? How many interviewed.

(bright upbeat music) >> Hello, everyone, and welcome to this #CUBEConversation here in Palo Alto, California. I'm John Furrier, host of "theCUBE." We have the co-founder and CTO of Armis here, Nadir Izrael. Thanks for coming on. Appreciate it. Armis is hot company, RSA, we just happened. Last week, a lot of action going on. Thanks for coming on. >> Thank you for having me. Sure. >> I love CTOs and co-founders. One, you have the entrepreneurial DNA, also technical in a space with cyber security, that is the hottest most important area. It's always been important, but now more than ever, as the service areas are everywhere, tons of attacks, global threats. You got national security at every level, and you got personal liberties for privacy, and other things going on for average citizens. So, important topic. Talk about Armis? Why did you guys start this company? What was the motivation? Give a quick commercial what you guys do, and then we'll get into some of the questions around, who you guys are targeting. >> Sure, so yeah, I couldn't agree more about the importance of cybersecurity, especially I think in these days. And given some of the geopolitical changes happening right now, more than ever, I would say that if we go back 6.5 years or so, when Armis was founded, we at the time talked to dozens of different CIOs, CSOs, it managers. And every single one of them told us the same thing. And this was at least to me surprising at the time. We have no idea what we have. We have no idea what the assets that are connected to our network, or our environment are. At the time, when we started Armis, we thought this was simply, let's call it the other devices. IOT, OT, all kinds of different buzzwords that were kind of flying around at the time, and really that's, what we should focus on. But with time, what we understood, it's actually a problem of scale. Organizations are growing massively. The diversity of different assets they have to deal with is incredible. And if 6.5 or 7 years ago, it was all about just growth of actual physical devices, these days it's virtual, it's containerized, it's cloud-based. It's actually quite insane. And organizations find themselves really quickly dealing with billions of assets within their environment, but no real way to see, account for them, and be able to manage them. That's what Armis is here to solve. It's here to bring back visibility and order into the mix. It's here to bring a complete map of everything within the organization, and the ability to manage different security processes on top of that. And it couldn't have come, I think at a better time for organizations, because the ability to manage these days, the attack surface of an organization, understand where are different weak spots, what way to invest in? They start and end with a complete asset map, and that's really what we're here to solve. >> As I look at your story and understand what you guys are doing, certainly, a lot of great momentum at RSA. But also digging under the hood, you guys really crack the code with on the scale side as well. And also it's lockstep with the environment. If you look at the trends that we've been covering on "theCUBE," system on chip, you're seeing a lot of Silicon action going on, on all the hyperscalers. You're starting to see, again, you mentioned IOT devices and OT, IP enabled processors. I mean, that's basically you can run multi-threaded applications on a light bulb, basically. So, you have these new things going on that are just popping in into the environment. Just people are hanging them on the network. So, anything on the network is risk and that's happening massively, so I see that. But also you guys have this contextualization capability, scope the problem statement for us? How hard is it to do this? Because you got tons of challenges. What's the scale of the problem that you guys have been solving? 'Cause it's not easy. I mean, it's not network management, not just doing auto discovery, there's a lot of secret sauce there, scope the problem? >> Okay, so first of all, just to get a measure of how difficult this is, organizations have been trying to solve this for the better part of the last two decades. I think even when the problem was way smaller, they've still been struggling with being able to do this. It's an age old problem, that for the most part, I got to say that when I describe the problem the way that I did, usually, what the reaction from clients are, "Yes, I'd love for you to solve that." "I just heard this pitch from like five other vendors and I've yet to solve this problem. So, how do you do it?" So, as I kind of scope this, it's also a measure of just basically, how do you go about solving a complex situation where, to kind of list out some of the bold claims here in what I said. Number one, it's the ability to just fingerprint and be able to understand what your assets are. Secondly, being able to do it with very dirty data, if you will. I would say, in many cases, solutions that exist today, basically tell clients, or tell the users, were as good as the data that you provide us. And because the data isn't very good, the results aren't very good. Armis aspires to do something more than that. It aspires to create a logically perfect map of your assets despite being hindered by incomplete and basically wrong data, many times. And third, the ability to infer things about the environment where no source data even exists. So, to all of that, really Armis' approach is pretty straightforward, and it relies on something that we call our collective intelligence. We basically use the power and scale of these masses to our advantage, and not just as a shortcoming. What I mean by that, is Armis today tracks overall, over 2 billion assets worldwide. That's an astounding number. And it thanks to the size of some of the organization that we work with. Armis proudly serves today, for instance, over 35 of Fortune 100. Some of those environments, let me tell you, are huge. So, what Armis basically does, is really simple. It uses thousands, tens of thousands, hundreds of thousands sometimes, of instances of the same device and same assets to basically figure out what it is. Figure out how to fingerprint it best. Figure out how to marry conflicting data sources about it and figure out what's the right host name? What's the right IP address? What are all the different details that you should know about it? And be able to basically find the most minimalist fingerprints for different attributes of an asset in a changing environment. It's something that works really, really well. It's something that we honestly, may have applied to this problem, but it's not something that we fully invented. It's been used effectively to solve other problems as well. For instance, if you think about any kind of mapping software. And I use that analogy a lot. But if you think about mapping software, I happened to work for Google in the past, and specifically on Google Map. So, I know quite a bit about how to solve similar problems. But I can tell you that you think about something like a mapping software, it takes very dirty, incomplete data from lots of different sources, and creates not a pixel perfect map, but a logically perfect map for the use cases you need it to be. And that's exactly what Armis strives to do. Build the Google Maps, if you will, of your organization, or the kind of real time map of everything, and be able to supply that or project that for different business processes. >> Yeah, I love the approach, and I love that search analogy. Discover is a big part of mapping as you know, and reasoning in there with the metadata you have and the dirty data is critical. And by the way, we love bold statements on "theCUBE," because as long as you can back 'em up, then we'll dig into that. But let's back up some of those bold claims. Okay, you have a lot of devices, you've got the collective intelligence. How do you manage the real time nature of devices changing in real time? 'Cause if you do fingerprint on it, and you got some characteristics of the assets in the map, what happens in real time? How fast are you guys managing that? What's the process for that? >> So, very quickly, I think another quick analogy I like to use, because I think it orients people around kind of how Armis operates, is imagine that Armis is kind of like a Shazam for assets. We take different attributes coming from your environment, and we match it up, that collective intelligence to figure out what that asset is. So, we recognize an asset based off of its behavioral fingerprint, or based off of different attributes, figure out what it is. Now, if you take something that recognizes tunes on the radio or anything like that, it's built pretty similarly. Once you have access to different sources. Once we see real environments that introduce new devices or new assets, Armis is immediately learning. It's immediately taking those different queues, those different attributes and learning from them. And to your point, even if something changes its behavioral fingerprint. For instance, it gets updated, a new patch rolls out, something that changes a meaningful aspect of how that asset operates, Armis sees so many environments, and so much these days that it reacts in almost real time to the introduction of these new things. A patch rolls out, it starts changing multiple devices and multiple different environments around the world, Armis is already learning and adapting this model for the new type of asset and device out there. It works very quickly, and it's part of the effectiveness of being able to operate at the scale that we do. >> Well, Nadir, you guys got a great opportunity there at Armis. And as co-founder, you must be pretty pumped, actually working hard, stay up to date, and got a great, great opportunity there. How was RSA this year? And what's your take on the landscape? Because you're kind of in this, I call the new category of lockstep with an environment. Obviously, there's no perimeter, everyone knows that. Service area is the whole internet, basically, distributed computing paradigms and understanding things like discovery and mapping data that you guys are doing. And it's a data problem as well. It's a lot of problems that you guys are solving. But the industry's got some old beggars, as I still hear endpoint protection, zero trust. I hear trust, if you're talking about supply chain, software supply chain, S bombs, you mentioned in a previous interview. You got software supply chain issues with open source, 'cause everything's open source now on infrastructure, so that's happening. How do you manage all that? I mean, is it zero trust or is it trust? 'Cause as you hear, I hear you talking about Armis, it's like, you got to have trusted components in there and you got to trust the data. So, that's not zero trust, that's trust. So, where zero trust and trust solve? What's your take on that? How do you resolve? What's your reaction to that? >> Usually, I wait for someone else to bring up the zero trust buzzword before I touch on that. So, because to your point, it's such an overused buzzword. But let me try and tackle that for a second. First of all, I think that Armis treats assets in a way as, let's call it the vessels of everything. And what I mean by that, is that at a very atomic aspect, assets are the atoms of the environment. They're the vessels of everything. They're the vessels of vulnerabilities. There's the vessels of actual attacks. Like something, some asset needs to exist for something to happen. And every aspect of trust or zero trust, or anything like that applies to basically assets. Now, to your point, Armis, ironically, or like a lot of security tools, I think it assists greatly or even manages a zero trust policy within the environment. It provides the asset intelligence into the mix of how to manage an effective zero trust policy. But in essence, you need to trust Armis, right? I mean, Armis is a critical function now within your environment. And there has to be a degree of trust, but I would say, trust but verified. And that's something that I think the security industry as a whole is evolving into quite a bit, especially post events like solar, winds, or other things that happened in recent years. Armis is a SaaS platform. And in being a SaaS platform, there is an inherent aspect of trust and risk that you take on as a security organization. I think anyone who says differently, is either lying or mistaken. I mean, there are no foolproof, a 100% systems out there. But to mitigate some of that risk, we adhere to a very strict risk in security policy on our end. What that means, is we're incredibly transparent about every aspect of our own environment. We publish to our clients our latest penetration test reports. We publish our security controls and policies. We're very transparent about the different aspects we're involve in our own environment. We give our clients access to our own internal security organization, our own CSO, to be able to provide them with all the security controls they need. And we take a very least privileged approach in how we deploy Armis within an environment. No need for extra permissions. Everything read-only unless there is an explicit reason to do else... I think differently within the environment. And something that we take very seriously, is also anything that we deploy within the environment, should be walled off, except for whatever lease privilege that we need. On top of that, I'd add one more thing that adds, I think a lot of peace of mind to our clients. We are FeRAMP ready, and soon to be certified, We work with DOD clients within the U.S kind of DOD apparatus. And I think that this gives a lot of peace of mind to our clients, even commercial clients, because they know that we need to adhere to hundreds of different security controls that are monitored and government by U.S federal agencies. And that I think gives a lot of extra security measures, a lot of knowledge that this risk is being mitigated and controlled, and governed by different agencies. >> Good stuff there. Also at RSA, you kind of saw people come back together face-to-face, which is great. A lot of kind of similar, everyone kind of knows each other in the security business, but it's getting bigger. What was the big takeaways from you for the folks watching here that didn't get to go to RSA this year? What was the most important stories that came out of RSA this year? Just generally across the industry, from your perspective that people should pay attention to? >> First of all, I think that people were just really happy to get back together. I think it was a really fun RSA. I think that people had a lot of energy and excitement, and they love just walking around. I am obviously, somewhat biased here, but I will say, I've heard from other people too, that our event there, and the formal party that was there was by far the kind of the the talk of the show. And we were fortunate to do that with Sentinel One. with Torque who are both great partners of ours, and, of course, Insight partners. I think a lot of the themes that have come up during RSA, are really around some of the things that we already talked about, visibility as a driver for business processes. The understanding of where do assets and tax surfaces, and things like that play in. But also, I think that everything was, in light of macroeconomics and geopolitics that are kind of happening in the background, that no one can really avoid that. On the one hand, if we look at macroeconomics, obviously, markets are going through quite a shake up right now. And especially, when you talk about tech, the one thing that was really, really evident though, is it's cybersecurity is, I think market-wise just faring way better than others because the demand is absolutely there. I think that no one has slowed down one bit on buying and arming themselves, I'd say, with defensive solutions for cybersecurity. And the reason, is that the threats are there. I mean, we're all very, very much aware of that. And even in situations where companies are spending less on other things, they're definitely spending on cybersecurity, because the toll on the industry is going up significantly year by year, which really ties into also the geopolitics. One of the themes that I've heard significantly, is all the buzz around different initiatives coming from both U.S federal agencies, as well as different governing bodies around anything, from things like shields up in critical infrastructure, all the way to different governance aspects of the TSA. Or even the SCC on different companies with regards to what are they doing on cyber? If some of the initiatives coming from the SCC on public companies come out the way that they are right now, cyber security companies will elevate... Well, sorry, companies in general, would actually elevate cyber security to board level discussions on a regular basis. And everyone wants to be ready to answer effectively, different questions there. And then on top of all of that, I think we're all very aware of, I think, and not to be too doom and gloom here, but the geopolitical aspect of things. It's very clear that we could be facing a very significant and very different cyber warfare aspect than anything that we've seen before in the coming months and years. I think that one of the things you could hear a lot of companies and clients talk about, is the fact that it used to be that you could say, "Look, if a nation state is out to get me, then a nation state is out to get me, and they're going to get me. And I am out to protect myself from common criminals, or cybersecurity criminals, or things like that." But it's no longer the case. I mean, you very well might be attacked by a nation state, and it's no longer something that you can afford to just say, "Yeah, we'll just deal with that if that happens." I think some of the attacks on critical infrastructure in particular have proven to us all, that this is a very, very important topic to deal with. And companies are paying a lot of attention to what can give them visibility and control over their extended attack surface, and anything in between. >> Well, we've been certainly ringing the bell for years. I've been a hawk on this for many, many years, saying we're at cyber war, well below everyone else. So, we've been pounding our fist on the table saying, it's not just a national security issue. Finally, they're waking up and kind of figuring out countermeasures. But private companies don't have their own, they should have their own militia basically. So, what's the role of government and all this? So, all this is about competency and actually understanding what's going on. So, the whole red line, lowering that red line, the adversaries have been operating onside our infrastructure for years. So, the industrial IOT side has been aware of this for years, now it's being streamed, right? So, what do we do? Is the government going to come in and help, and bring some cyber militia to companies to protect their business? I mean, if troops dropped on our shores, I'm sure the government would react, right? So, where is that red line, Nadir? Where do you see the gap being filled? Certainly, people will defend their companies, they have assets obviously. And then, you critical infrastructure on the industrial side is super important, that's the national security issue. What do we do? What's the action here? >> That is such a difficult question. Such a good question I think to tackle, I think, there are similarities and there are differences, right? On the one hand, we do and should expect the government to do more. I think it should do more in policy making. I mean, really, really work to streamline and work much faster on that. And it would do good to all of us because I think that ultimately, policy can mean that the third party vendors that we use are more secure, and in turn, our own organizations are more secure in how they operate. But also, they hold our organizations accountable. And in doing so, consumers who use different services feel safer as well because basically, companies are mandated to protect data, to protect themselves, and do everything else. On the other hand, I'd say that government's support on this is difficult. I think the better way to look at this, is imagine for a second, no troops landing on our kind of shores, if you will. But imagine instead, a situation where Americans are spread all over the world and expect the government to protect them in any country, or in any situation they're at. I think that depicts maybe a little better, how infrastructure looks like today. If you look at multinational companies, they have offices everywhere. They have assets spread out everywhere. They have people working from everywhere around the world. It's become an attack surface, that I think you said this earlier, or in a different interview as well. There's no more perimeter to speak of. There are no more borders to this virtual country, if you will. And so, on the one hand, we do expect our government to do a lot. But on the other hand, we also need to take responsibility as companies, and as vendors, and as suppliers of services, we need to take accountability and take responsibility for the assets that we deploy and put in place. And we should have a very security conscious mind in doing this. >> Yeah. >> So, I think tricky government policy aspect to tackle. I think the government should be doing more, but on the other hand, we should absolutely be pointing internally at where can we do better as companies? >> And the asset understanding the context of what's critical asset too, can impact how you protect it, defend it, and ensure it, or manage it. I mean, this is what people want. It's a data problem in flight, at rest, and in action. So, Armis, you guys are doing a great job there. Congratulations, Nadir on the venture, on your success. I love the product, love the approach. I think it scales nicely with the industry where it's going. So, especially with the intelligent edge booming, and it's just so much happening, you guys are in the middle of it. Thanks for coming on "theCUBE." Appreciate it. >> Thank you so much. As I like to say, it takes a village, and there's so many people in the company who make this happen. I'm just the one who gets to take credit for it. So, I appreciate the time today and the conversation. And thank you for having me. >> Well, we'll check in with you. You guys are right there with us, and we'll be in covering you guys pretty deeply. Thanks for coming on. Appreciate it. Okay, it's #CUBEConversation here in Palo Alto. I'm John Furrier. Thanks for watching. Clear. (bright upbeat music)

>>Thanks. Hey, everyone, welcome back to the cubes. Continuing coverage of snowflakes. Summit 22 live from Caesars Forum in Las Vegas. Lisa Martin here. I have three guests here with me. We're gonna be talking about Snowflake Ventures and the snowflakes start up Challenge. That's in its second year. I've got Harry Glaser with me. Co founder and CEO of Model Bit Start Up Challenge finalist Damon Bryan joins us as well. The CTO and co founder of Hyper Affinity. Also a startup Challenge Finalists. And Stephane Williams to my left here, VP of Corporate development and snowflake Ventures. Guys, great to have you all on this little mini panel this morning. >>Thank you. >>Thank you. >>Let's go ahead, Harry, and we'll start with you. Talk to the audience about model. But what do you guys do? And then we'll kind of unpack the snowflake. The Snowflakes challenge >>Model bit is the easiest way for data scientists to deploy machine learning models directly into Snowflake. We make use of the latest snowflake functionality called Snow Park for python that allows those models to run adjacent to the data so that machine learning models can be much more efficient and much more powerful than they were before. >>Awesome. Damon. Give us an overview of hyper affinity. >>Yes, so hyper affinity were Decision Intelligence platform. So we helped. Specifically retailers and brands make intelligent decisions through the use of their own customer, data their product data and put data science in a I into the heart of the decision makers across their business. >>Nice Step seven. Tell us about the startup challenge. We talked a little bit about it yesterday with CMO Denise Pearson, but I know it's in its second year. Give us the idea of the impetus for it, what it's all about and what these companies embody. >>Yeah, so we This is the second year that we've done it. Um, we it was really out of, um Well, it starts with snowflake Ventures when we started to invest in companies, and we quickly realised that there's there's a massive opportunity for companies to be building on top of the Lego blocks, uh, of snowflake. And so, um, open up the competition. Last year it was the inaugural competition overlay analytics one, Um, and since then, you've seen a number of different functionalities and features as part of snowflakes snow part. Being one of them native applications is a really exciting one going forward. Um, the companies can really use to accelerate their ability to kind of deliver best in class applications using best in class technology to deliver real customer outcomes and value. Um, so we've we've seen tremendous traction across the globe, 250 applicants across 50. I think 70 countries was mentioned today, so truly global in nature. And it's really exciting to see how some of the start ups are taking snowflake to to to new and interesting use cases and new personas and new industries. >>So you had 200 over 250 software companies applied for this. How did you did you narrow it down to three? >>We did. Yeah, >>you do that. >>So, behind the scenes, we had a sub judging panel, the ones you didn't see up on stage, which I was luckily part of. We had kind of very distinct evaluation criteria that we were evaluating every company across. Um and we kind of took in tranches, right? We we took the first big garden, and we kind of try to get that down to a top 50 and top 50. Then we really went into the details and we kind of across, um, myself in ventures with some of my venture partners. Um, some of the market teams, some of the product and engineering team, all kind of came together and evaluated all of these different companies to get to the top 10, which was our semifinalists and then the semi finalists, or had a chance to present in front of the group. So we get. We got to meet over Zoom along the way where they did a pitch, a five minute pitch followed by a Q and A in a similar former, I guess, to what we just went through the startup challenge live, um, to get to the top three. And then here we are today, just coming out of the competition with with With folks here on the table. >>Wow, Harry talked to us about How did you just still down what model bit is doing into five minutes over Zoom and then five minutes this morning in person? >>I think it was really fun to have that pressure test where, you know, we've only been doing this for a short time. In fact model. It's only been a company for four or five months now, and to have this process where we pitch and pitch again and pitch again and pitch again really helped us nail the one sentence value proposition, which we hadn't done previously. So in that way, very grateful to step on in the team for giving us that opportunity. >>That helps tremendously. I can imagine being a 4 to 5 months young start up and really trying to figure out I've worked with those young start ups before. Messaging is challenging the narrative. Who are we? What do we do? How are we changing or chasing the market? What are our customers saying we are? That's challenging. So this was a good opportunity for you, Damon. Would you say the same as well for hyper affinity? >>Yeah, definitely conquer. It's really helped us to shape our our value proposition early and how we speak about that. It's quite complicated stuff, data science when you're trying to get across what you do, especially in retail, that we work in. So part of what our platform does is to help them make sense of data science and Ai and implement that into commercial decisions. So you have to be really kind of snappy with how you position things. And it's really helped us to do that. We're a little bit further down the line than than these guys we've been going for three years. So we've had the benefit of working with a lot of retailers to this point to actually identify what their problems are and shape our product and our proposition towards. >>Are you primarily working with the retail industry? >>Yes, Retail and CPG? Our primary use case. We have seen any kind of consumer related industries. >>Got it. Massive changes right in retail and CPG the last couple of years, the rise of consumer expectations. It's not going to go back down, right? We're impatient. We want brands to know who we are. I want you to deliver relevant content to me that if I if I bought a tent, go back on your website, don't show me more tense. Show me things that go with that. We have this expectation. You >>just explain the whole business. But >>it's so challenging because the brothers brands have to respond to that. How do you what is the value for retailers working with hyper affinity and snowflake together. What's that powerhouse? >>Yeah, exactly. So you're exactly right. The retail landscape is changing massively. There's inflation everywhere. The pandemic really impacted what consumers really value out of shopping with retailers. And those decisions are even harder for retailers to make. So that's kind of what our platform does. It helps them to make those decisions quickly, get the power of data science or democratise it into the hands of those decision makers. Um, so our platform helps to do that. And Snowflake really underpins that. You know, the scalability of snowflake means that we can scale the data and the capability that platform in tangent with that and snowflake have been innovating a lot of things like Snow Park and then the new announcements, announcements, uni store and a native APP framework really helping us to make developments to our product as quick as snowflakes are doing it. So it's really beneficial. >>You get kind of that tailwind from snowflakes acceleration. It sounds like >>exactly that. Yeah. So as soon as we hear about new things were like, Can we use it? You know, and Snow Park in particular was music to our ears, and we actually part of private preview for that. So we've been using that while and again some of the new developments will be. I'm on the phone to my guys saying, Can we use this? Get it, get it implemented pretty quickly. So yeah, >>fantastic. Sounds like a great aligned partnership there, Harry. Talk to us a little bit about model bit and how it's enabling customers. Maybe you've got a favourite customer example at model bit plus snowflake, the power that delivers to the end user customer? >>Absolutely. I mean, as I said, it allows you to deploy the M L model directly into snowflake. But sometimes you need to use the exact same machine learning model in multiple endpoints simultaneously. For example, one of our customers uses model bit to train and deploy a lead scoring model. So you know when somebody comes into your website and they fill out the form like they want to talk to a sales person, is this gonna be a really good customer? Do we think or maybe not so great? Maybe they won't pay quite as much, and that lead scoring model actually runs on the website using model bit so that you can deploy display a custom experience to that customer we know right away. If this is an A, B, C or D lead, and therefore do we show them a salesperson contact form? Do we just put them in the marketing funnel? Based on that lead score simultaneously, the business needs to know in the back office the score of the lead so that they can do things like routed to the appropriate salesperson or update their sales forecasts for the end of the quarter. That same model also runs in the in the snowflake warehouse so that those back office systems can be powered directly off of snowflake. The fact that they're able to train and deploy one model into two production environment simultaneously and manage all that is something they can only do with bottled it. >>Lead scoring has been traditionally challenging for businesses in every industry, but it's so incredibly important, especially as consumers get pickier and pickier with. I don't want I don't want to be measured. I want to opt out. What sounds like what model but is enabling is especially alignment between sales and marketing within companies, which is That's also a big challenge at many companies face for >>us. It starts with the data scientist, right? The fact that sales and marketing may not be aligned might be an issue with the source of truth. And do we have a source of truth at this company? And so the idea that we can empower these data scientists who are creating this value in the company by giving them best in class tools and resources That's our dream. That's our mission. >>Talk to me a little bit, Harry. You said you're only 4 to 5 months old. What were the gaps in the market that you and your co founders saw and said, Guys, we've got to solve this. And Snowflake is the right partner to help us do it. >>Absolutely. We This is actually our second start up, and we started previously a data Analytics company that was somewhat successful, and it got caught up in this big wave of migration of cloud tools. So all of data tools moved and are moving from on premise tools to cloud based tools. This is really a migration. That snowflake catalyst Snowflake, of course, is the ultimate in cloud based data platforms, moving customers from on premise data warehouses to modern cloud based data clouds that dragged and pulled the rest of the industry along with it. Data Science is one of the last pieces of the data industry that really hasn't moved to the cloud yet. We were almost surprised when we got done with our last start up. We were thinking about what to do next. The data scientists were still using Jupiter notebooks locally on their laptops, and we thought, This is a big market opportunity and we're We're almost surprised it hasn't been captured yet, and we're going to get in there. >>The other thing. I think it's really interesting on your business that we haven't talked about is just the the flow of data, right? So that the data scientist is usually taking data out of a of a of a day like something like Smoke like a data platform and the security kind of breaks down because then it's one. It's two, it's three, it's five, it's 20. Its, you know, big companies just gets really big. And so I think the really interesting thing with what you guys are doing is enabling the data to stay where it's at, not copping out keeping that security, that that highly governed environment that big companies want but allowing the data science community to really unlock that value from the data, which is really, really >>cool. Wonderful for small startups like Model Bit. Because you talk to a big company, you want them to become a customer. You want them to use your data science technology. They want to see your fed ramp certification. They want to talk to your C. So we're two guys in Silicon Valley with a dream. But if we can tell them the data is staying in snowflake and you have that conversation with Snowflake all the time and you trust them were just built on top. That is an easy and very smooth way to have that conversation with the customer. >>Would you both say that there's credibility like you got street cred, especially being so so early in this stage? Harry, with the partnership with With Snowflake Damon, we'll start with you. >>Yeah, absolutely. We've been using Snowflake from day one. We leave from when we started our company, and it was a little bit of an unknown, I guess maybe 23 years ago, especially in retail. A lot of retailers using all the legacy kind of enterprise software, are really starting to adopt the cloud now with what they're doing and obviously snowflake really innovating in that area. So what we're finding is we use Snowflake to host our platform and our infrastructure. We're finding a lot of retailers doing that as well, which makes it great for when they wanted to use products like ours because of the whole data share thing. It just becomes really easy. And it really simplifies it'll and data transformation and data sharing. >>Stephane, talk about the startup challenge, the innovation that you guys have seen, and only the second year I can. I can just hear it from the two of you. And I know that the winner is back in India, but tremendous amount of of potential, like to me the last 2.5 days, the flywheel that is snowflake is getting faster and faster and more and more powerful. What are some of the things that excite you about working on the start up challenge and some of the vision going forward that it's driving. >>I think the incredible thing about Snowflake is that we really focus as a company on the data infrastructure and and we're hyper focused on enabling and incubating and encouraging partners to kind of stand on top of a best of breed platform, um, unlocked value across the different, either personas within I T organisations or industries like hypothermia is doing. And so it's it's it's really incredible to see kind of domain knowledge and subject matter expertise, able to kind of plug into best of breed underlying data infrastructure and really divide, drive, drive real meaningful outcomes for for for our customers in the community. Um, it's just been incredible to see. I mean, we just saw three today. Um, there was 250 incredible applications that past the initial. Like, do they check all the boxes and then actually, wow, they just take you to these completely different areas. You never thought that the technology would go and solve. And yet here we are talking about, you know, really interesting use cases that have partners are taking us to two >>150. Did that surprise you? And what was it last year. >>I think it was actually close to close to 2 to 40 to 50 as well, and I think it was above to 50 this year. I think that's the number that is in my head from last year, but I think it's actually above that. But the momentum is, Yeah, it's there and and again, we're gonna be back next year with the full competition, too. So >>awesome. Harry, what is what are some of the things that are next for model bed as it progresses through its early stages? >>You know, one thing I've learned and I think probably everyone at this table has internalised this lesson. Product market fit really is everything for a start up. And so for us, it's We're fortunate to have a set of early design partners who will become our customers, who we work with every day to build features, get their feedback, make sure they love the product, and the most exciting thing that happened to me here this week was one of our early design partner. Customers wanted us to completely rethink how we integrate with gets so that they can use their CI CD workflows their continuous integration that they have in their own get platform, which is advanced. They've built it over many years, and so can they back, all of model, but with their get. And it was it was one of those conversations. I know this is getting a little bit in the weeds, but it was one of those conversations that, as a founder, makes your head explode. If we can have a critical mass of those conversations and get to that product market fit, then the flywheel starts. Then the investment money comes. Then you're hiring a big team and you're off to the races. >>Awesome. Sounds like there's a lot of potential and momentum there. Damon. Last question for you is what's next for hyper affinity. Obviously you've got we talked about the street cred. >>Yeah, what's >>next for the business? >>Well, so yeah, we we've got a lot of exciting times coming up, so we're about to really fully launch our products. So we've been trading for three years with consultancy in retail analytics and data science and actually using our product before it was fully ready to launch. So we have the kind of main launch of our product and we actually starting to onboard some clients now as we speak. Um, I think the climate with regards to trying to find data, science, resources, you know, a problem across the globe. So it really helps companies like ours that allow, you know, allow retailers or whoever is to democratise the use of data science. And perhaps, you know, really help them in this current climate where they're struggling to get world class resource to enable them to do that >>right so critical stuff and take us home with your overall summary of snowflake summit. Fourth annual, nearly 10,000 people here. Huge increase from the last time we were all in person. What's your bumper sticker takeaway from Summit 22 the Startup Challenge? >>Uh, that's a big closing statement for me. It's been just the energy. It's been incredible energy, incredible excitement. I feel the the products that have been unveiled just unlock a tonne, more value and a tonne, more interesting things for companies like the model bit I profanity and all the other startups here. And to go and think about so there's there's just this incredible energy, incredible excitement, both internally, our product and engineering teams, the partners that we have spoke. I've spoken here with the event, the portfolio companies that we've invested in. And so there's there's there's just this. Yeah, incredible momentum and excitement around what we're able to do with data in today's world, powered by underlying platform, like snowflakes. >>Right? And we've heard that energy, I think, through l 30 plus guests we've had on the show since Tuesday and certainly from the two of you as well. Congratulations on being finalist. We wish you the best of luck. You have to come back next year and talk about some of the great things. More great >>things hopefully will be exhibited next year. >>Yeah, that's a good thing to look for. Guys really appreciate your time and your insights. Congratulations on another successful start up challenge. >>Thank you so much >>for Harry, Damon and Stefan. I'm Lisa Martin. You're watching the cubes. Continuing coverage of snowflakes. Summit 22 live from Vegas. Stick around. We'll be right back with a volonte and our final guest of the day. Mhm, mhm

>>Hey everyone. Welcome back to the Cube's live coverage of snowflake summit 22. We are live at Caesar's forum in Vegas, Lisa Martin, with Dave ante, excited to welcome a VIP fresh from the keynote stage, the SAP, a product at snowflake Christian C Claman Christian. Thank you so much for joining us on the queue today. >>Thank you for having me very exciting. >>And thanks for bringing your energy, loved your keynote. I thought, wow. He is really excited about all of the announcements jam packed. We, and we didn't even get to see the entire keynote talk to us about, and, and for the audience, some of the things going on the product revenue in Q1 fiscal 23, 390 4 million, 85% growth, lot of momentum at snowflake. No doubt. >>So I think that the, the punch line is our innovation is if anything, gaining speed. Uh, we were over the moon excited to share many of these projects with customers and partners, cuz some of these efforts have been going on for multiple years. So, um, lots of interesting announcements across the board from making the existing workloads faster, but also we announced some new workloads getting into cyber security, getting into more transactional workloads with uni store. Um, so we're very excited. >>Well first time being back, this is the fourth summit, but the first time being back since 2019 a tremendous amount has changed for snowflake in that time, the IPO, the massive growth in customers, the massive growth in growth in customers with over 1 million in ARR, you talked about one of the things that clearly did not slow down during the last two years is innovation at snowflake. >>Yeah, that, that, that for, for sure, like, um, even though we, we had a, um, highly in the office culture, we did not miss a beat the moment that we said, Hey, let's all start doing zoom based calls. We, we did. So, uh, I dunno if you saw the, the first five minute minutes of my section in the keynote. Yeah. We, we originally talked about summarizing it and no we're gonna spend 40 minutes here. So we did a one minute clip and whatever gets flashed there. So no, the, the pace of innovation, I think it's second to none and maybe I'll highlight the something that we're very proud of. Snowflake is a single product, a single engine. So if we're making a query performance enhancement, it will help the cyber security workload and the low high concurrency, low latency workload. And eventually we're starting to see some of those enhancements all the way to uni store. So, so we get a lot of leverage out of our investments. What's >>Your favorite announcement? >>That's like picking children. Of course. Um, I think the native applications is the one that looks like, eh, I don't know about it on the surface, but it has the biggest potential to change everything like create an entire ecosystem of solutions for within a company or across companies that I don't know that we know what's possible. >>Well, I I've been saying for a while now that you have this application development stack over here, the database is kind of here and then you have the analytics and data pipeline stack. Those are those separate worlds. We, we talk about bringing data and AI and machine intelligence into applications. The only way that that is actually gonna move forward is if you bring those worlds together is a good example of that happening, um, within a proprietary framework, uh, it's probably gonna happen open source organically and you can sort of roll your own. Is that by design or is it just sort of happening? Well, >>The, the, they bring it all into a single platform obviously by design, cuz there is so much friction today on making all the pieces work together, which database do I use for transactions and how do I move data to my analytics system? And how do I keep system, uh, reference data in sync between the two? So, so it's complicated and our mission was remove all of this friction from, from, from the equation. Uh, the open source versus not the way we think about it is opensourcing open formats or even open APIs it's does it help us deliver the solution that we want for our customer? Does it help us solve their problems? In certain instances, it has done in the past and we've opened source frameworks in, in others. We mentioned at the keynote today, the, the integration of iceberg tables, that's an strong embrace of open technologies, but that does not mean that we want to continue to innovate in our formats. A lot of what you see in the open formats is because snowflake proprietary, uh, innovation. So, uh, we have a very clear philosophy around this. Well >>Like any cloud player, you have to bring open source tools in and make them available for your application developers. But take us through an example of, of uni store and specifically how you're embracing transaction data. What's a customer gonna actually do take us paint a picture >>For us. I I'm gonna give you a very simple use case, but I love it because it, it shows the power of the scenario today. When people are ingesting data into snowflake, you wanna do some book capping associating with those loads. So imagine I have, I dunno, a million files. How many of those files have I loaded? Imagine that one of those loads fail, how do you keep in sync? Whether the data made or not with your bookkeeping today, if you had to do it with a separate transactional database for the bookkeeping and the loading in, in snowflake, it is a lot of complexity for you to know what's where with uni store, you can just say, I'm gonna do the bookkeeping with these new table. It's called hybrid tables. The lows are transactional and all of this is a single transaction. So for, for anyone that has dealt with inconsistencies in database world, this is like a godsend. >>Okay. So my interpretation of that's all about what happens when something goes wrong >><laugh> which is a lot of the, everything about transactions. Yeah. It's what happens when goes wrong and goes wrong. Doesn't mean failures like goes wrong is when you're debiting money from your bank account, not having enough balance that counts as go wrong and the transactions should be aborted. So yes, transactions are all about conflict management and we're simplifying that in a broader set of use cases >>And, and in recovery. So you're, you're in fast recovery. So you're, you're the, the business impact of what you're doing is to sort of simplify that process. Is that the easy way to >>Boil down? Pretty much everything we do is about simplification. Like we, we we've seen organizations are large focusing on wrestling infrastructure as opposed to what are the business problems for a Frank who reference something that, that, that I believe very much in like, which is mission alignment. We are working on helping our customers achieve what they're set out to achieve, not giving them more technology for them to their goal to become, to wrestle the infrastructure. So it's all about ease of use all about simplification removal, friction, >>Just so if I may, so mission alignment, you know, you always hear about technology companies that, you know, provide infrastructure or a service, and then the customer takes that and, and, you know, monetizes it pretty much on their own. What the big change that I'm discerning from these announcements is you're talking about directly monetizing and participating in that monetization as a technology partner, but also the marketplace as well. >>Correct. And I would say in some ways this is not new. This has been happening for the last couple of years with data. Like if you just saw our industry data cloud launches, the financial services cloud, it comes with data providers that help you achieve specific outcomes on a specific industry. Mm-hmm <affirmative> what we're doing now is saying, it's not just data. Maybe it's some business logic, maybe it's some machine learning, maybe it's some user interface. So I think we're just turning the knob on collaboration and it's a continuation of what we've been doing. >>Talk a little bit more about mission alignment. When I heard Frank, Sweetman talk about that this morning. I always love that when I hear cultural alignment with organizations, but as you just said, it's really about enabling our customers to deliver outcomes to their customers as the SVP product. Can you, uh, talk a little bit about how the customers are influencing the product roadmap, the innovations and the speed with which things are coming out at snowflake? >>Yeah, so great question. We have several organizations at snowflake that are organized by vertical by industry. So the, the major sales organization is part of ed that the marketplace business development team is organized like that. We have a separate team that provides top leadership by industry vertical, um, globally. And then even within our solution engineering, there is verticals. So we have a longitudinal view of all the different functions and what do we need to do to achieve a set of use cases in a vertical? And all of those functions are in con constant communication with us on this is where the product is, um, seeing an opportunity or could do better for that vertical. So yeah, I can tell you, and obviously we love when, when there's alignment between those, but that's not always the case. You heard us talk about clean rooms now for some time, clean rooms are applicable to almost any industry, but it's red hot for media and advertising, third party, cookie deprecation, and all of that. So we, we get to, to see that lens, that our innovation is informed by industries. >>So we, we're seeing, obviously the evolution of snowflake we talked about in the keynotes today, you guys talked about 2019 and, you know, pre 2019, even it was to me anyway, your first phase was, Hey, we got a simpler EDW. You know, we're gonna pick that off and put it in the cloud and make it elastic and separate compute from storage, all that kind of cool stuff. And then during the pandemic, it was really IPO, but also the data cloud concept, you sort of laid that vision out. And now you're talking about application development, monetization, what I call the super cloud that layer. Right. Okay. So I, are >>You determin it best? >>Yes. You talk about this, uh, these announcements, how they fit into that larger vision where you're >>Going. Great question. The, the, the notion of the data cloud has not changed one bit. The data cloud thesis is that we want to provide amazing technology for our customers, but also facilitate collaboration and content exchange VR platform. And all that we did today is expand what that content can be. It's not just data or little helper function, it's entire applications, entire experiences. That is the, the summing up the, the, the impact of our announcements today. That, that that's the end of it. So it's still about the data cloud. >>So what is impressive to me is that you guys wouldn't couldn't have a company without the hyperscalers, right? It would be a lot different, right? So you built on top of that and, and now you have your customers building their own super clouds. I call it, I get a lot of grief for that term it's but the, the, the big area of criticism I get is, ah, that's just SAS. And I'm like, no, it's not, no, uh, I, I is everybody public who's announcing stuff. I, I better be careful, but you have customers that are actually building services, taking their data, their tooling, their proprietary information, and putting it on the snowflake data cloud and building their own clouds. Yeah. That's different. Then that's not multi-cloud, which is I can run on a different cloud and it's not, is it sass? If it feels like it's something new from a, from your perspective, is, is it different? >>I, I, I love that you called out that running on all clouds is not what we do right. This days, everyone is multi-cloud, you, you run on a VM or a container, and I multi-cloud check, no, we have a single platform that does multi-region multi-cloud but also cross region cross cloud globally, that that is the essence of what we're doing. So it, it is enabling new capabilities. >>I've I've also said, you know, in many respects, the super cloud hides, the underlying complexity, you think about things like exploiting graviton and a developer. Doesn't need to worry about that. You're gonna worry about that. Uh, but at the same time, they, the, as you get into the develop, the world of application development, some of your developers may want access to some of those cloud primitives. Are you providing both? What's the strategy there? >>Generally not in some areas, we, we, we, I would say bleed through some details that are material, but think of the reality of someone that wants to build a solution, it's really difficult to build an awesome solution in one cloud, Hey, you need to do this. What's the latest instance, and is gravity tank gonna help you or not all of that. Now do it for another one and then do it for another one. And I can tell you it's really difficult because we go through that exercise. Snowflake pouring to a new cloud is somewhere between one and two years of effort and not, not a small number of people because you're looking at security models and storage models. So that's the value that we give to anyone know, wants to build a solution and target customers in all three clouds. I >>Mean, people are still gonna do it themselves, but they're gonna spend a lot more and they're gonna lose their focus on what their real business is. And there'll still be that. I think that D DIY market is enormous for you guys, huge >>Opportunity. And there's also the question on what is the cost of that analysis and that effort. And can we amortize it on behalf of all of our customers? Like we talk about graviton, we have not talked about the many things that we evaluated that were not better price performance for our customers. That evaluation happened. That value was delivered by not moving there. >>And when you do it yourself, the curve looks like, okay, Hey, we can do it ourselves. We can make it pretty Inex. And then, and then the costs are gonna decline, but what really happens, like developing a mobile app, you gotta maintain it. And then if you don't have the scale and you don't have the engineering resources, you're just, the, the costs are gonna continue to go through the roof. I, >>I, I love that you compare it to mobile apps. Like, yeah. I still don't understand why every company that wants to build an app has to build two <laugh>. They got it. Yeah. There is no super cloud for the phone. >>Right. >>That's sort of our, our, our broad vision. Not yet. Not, not the phone, but the super cloud. Yeah, >>Yeah, absolutely. >>You >>Get it. This is, and you look out the ecosystem here. I mean, what a difference that you've been pointing this out, Lisa from, from, from 2019, a lot of buzz, it's all about innovation. You see this at, at thing at the reinvent is like the super bowl obviously. And you see that and it used to be, oh, how is, how is AWS gonna compete with snowflake and separate compute with stores? That's I, I feel like in a large way, that's all gone. It's like, okay, how do we like rise the whole, the whole industry? And that's really where the innovation is. >>We have an amazing partnership with AWS and they benefit from what we do. Yes. There's some competitive elements, but we're changing so many things creating so much opportunity that we're more aligned than not. Yeah. >>Last question for you is continuing on the part AWS partnership front, how does a partner like AWS and other partners, how do they fit into the data cloud narrative that you're talking about to customers? >>I would say that other than the one or two teams that are directly competitive, the rest of their teams are part of in data cloud. Like, uh, our relationship with SageMaker as an example is amazing. And a lot of what we wanna deliver to our customers is choice around machine learning, frameworks and tools. And they're part of the data cloud. We're working with them on how do you push down computation to avoid getting data out, to reinforce governance? So I, I would say that and, and go look at it that they have a hundred and something teams. So if two teams out of hundreds, uh, are, are the competitive element, we are largely aligned. And they're part of data cloud. >>Yeah. I mean, you, your customers consume a lot of compute and storage for, >>For a lot. Yes. >>AWS and, and also, you know, increasingly Azure and, and Google. I mean, it's, um, pretty amazing times, uh, Christian, I want to ask you about, um, couple of terms. Uh, one term that came up a couple of times today in Frank's keynote, he said, I'm not gonna call it a data mesh out kind of out of respect for the purists, which is cool, I thought, but then you had a customer stand up Geico and said, we're building a data. Mesh JPMC is, is speaking at this event, building a data mesh. And I look at things through that prism and say, okay, data mesh is about, you know, decentralization. Some, I I'd be curious as to whether or not you tick that box, but it's about building data products. It's about, uh, uh, self-service infrastructure. And it's about automated computational governance. You are actually tipping a lot of the ticking, a lot of those boxes and, and Mike, I guess the big one is, are, are you building a bigger walled garden? But I, I think you would say, no, it's a, it's a giant distributed network, but, but what, what, what do you say to that? We, >>The latter, the latter, yeah, giant distributed, open cloud and open in the sense that we want anyone to plug in and, and someone can say, well, but I cannot read your file formats. Sure. You can with what we announced today, but it's not about that. Our APIs are open. We have rest APIs. We have JDC ODC, probably most popular interfaces ever. Um, and we want everyone to be part of it. If anything, there's lots of areas that we would not want to go into ourselves cause we want partners and customers to go in there. So, no, we we're looking at a very broad ecosystem. We win based on the value created on top of the platform. Yeah. >>And I makes total sense to me. I mean, I think the imaculate conception of data mesh might be a purely open source version of snowflake. I just don't see that happening anytime soon. And so I, I think you're gonna, you are, I wrote about this creating a defacto standard and >>Exactly, and, and I don't like to get into the terminology that, oh, is the data measure? Not, no go look at the concepts like people used to say, but snowflake is not a data lake. Okay. What is the data lake? It's just a pattern. And if you follow the pattern and you can do it, that's fine. Then there's the, uh, emotional quasi-religious overlay open versus not, I think that's a choice. Not necessarily the concept, >>It's a moving target. I mean, I Unix used to be open. You know, that was the, I agree. Now, the reason why I do think the data mesh conversation is important is because Shaak Dani, when she defined data mesh, she pointed out in my view. Anyway, the problems of getting value outta data is that you go through these hyper specialized teams and they're they're blockers in the organization. And I think you in many respects are attacking that. And it's an organizational issue. >>The, the insights in the pattern are a hundred percent value and aligned with what we do, which is they, you want some amount of centralization, some amount of decentralization living in harmony. Uh, yeah. I have no problem with, with terminology. >>And the governance piece is, is, is massive. Especially it's the, the picture's becoming much more clear. Um, whatever's in the data cloud is a first class citizen, right? And you give all these wonderful benefits. I mean, the interesting thing, what you're doing with Dell and, and pure, I, I asked you that on the analyst call, it's a start. You know, I, I, I mean, >>And I said it briefly in, in, in the keynote this morning, we're publishing a set of standard conformance tests. So any storage system can plug into data cloud. >>Yeah. >>And by the way, it's based on S three APIs, another defect of standard. Like it's not a standard, but everyone is emulating that. And we're plugging >>Into that. Yeah. Nobody's complaining against, against S3 API >>About it is a, oh, it's not a Apache project. We shouldn't, who cares. Everyone has standard horizon net. That's it? >>Well, we've seen the mistakes of the past with this. I mean, look at, look at Hadoop, right? There was this huge battle between, you know, Cloudera and Horton works and map, oh, map bar is proprietary. Oh, Horton works is purely open. Cloudera is open. They're, they're all gone now. I mean, not gone, but they're just, they didn't have it. Right. You know, they, they got unfocused. I go back to Frank's book. They were trying to do too much to, to too many of those, the, the, the zoo animals and you can't fund it all >>To be effective for us. It's very important. I can give you, I don't know, 20 announcements or 50 announcements from the conference, but they're all going a singular goal. And it's, this do not trade off governance of data with the ability to get value out of data. That's everything we do. >>And that's critical for every company in every industry these days that has to be a data company to be, to survive, to be competitive, to be able to extract value from data. If data's currency, how do I leverage a tool like snowflake to be able to extract insights from it that I can act on and create value for my organization, Geico was on stage this morning. Everyone knows Geico and their beloved, um, gecko. Yeah. Is there another customer that you had that you think really articulates the value of the data cloud and to Dave's point how snowflake is becoming that defacto standard data platform? >>Well, we had Goldman Goldman Sachs on stage as well today. And he, he, he, he mentioned it that people think of Goldman as investment banking and all of that, but no, at the heart of what they do, there's a lot of data. And how do they make better decisions? So I think we could run through 20 different examples cuz your premise is the most important. Everything is a data problem. If it is not a data problem, you're not collecting the right data and getting the sense that you could be getting. >>These guys are public, right. >>Adobe. >>Yeah. Right. Adobe's doing it. Yeah. I dunno if the other one is, I don't wanna say, I'll have to ask you off camera, but the other financial firm building a super cloud, right. <laugh> yeah. I call it super cloud. So let be taking advantage of uni store. Yeah. To bring different data types in and monetize it. That's to me, that's the future of data. That's that's been the holy grail, right. >>We, we tried to emphasize that this is, is not a, Hey six, six months ago. We decided to do this. No, this is years in the making mm-hmm <affirmative>, which is why we were so excited to finally share it. Cuz you don't wanna say three years from now, we're gonna have something. No, it was the, now we have it. We have it in preview and it's working at it is as close to the holy grail as it gets. >>Yeah. I mean, look, pressure's on Kristin. Let's face it. Enterprise data warehouse failed to live up to the promises. Uh, certainly the data lakes fail to deliver master data management, all that's a Hadoop, all that stuff. There was a lot of hype around that. And a lot of us got really excited. Me included and then customers spent and they were underwhelmed. Yeah. So you know, you, you, you gotta deliver, you say it, you gotta do it. >>And correct. And then the, the other thing is I would say all of those waves of technology, there was no real better choice. >>Right. They added value. I wouldn't >>Debate that. You have to give it a shot. Like when you've bought 20 different appliances and you have all these silos and someone sells you, Hey, Hadoop will unify it. It sounds good. Just didn't do it. >>Yeah. And no debate that it brought some value for those that were agree. Sophisticated enough to deploy it. And I agree. Yeah. But, but this is a whole different ball game. >>Oh, everything we want to do is democratize and simplify mm-hmm <affirmative> yeah. We could go build something that I don't know. 10 companies in the world could use. That's not the sweet spot. Like how do we advance like the, the state of value generation in the world? That's the scale that we're talking about is go make it easy, accessible for everyone. >>Governed >>Governance and imperative this these days it's law. Yes. So >>Yeah, you have to, but it's not, it's, that's a, that's a ch really difficult challenge to create what I'll call automated or computational governance in a federated manner. That's not trivial. >>And that's our thesis. Everything we're doing is snow park, big announcement today. Python. I I've had people tell me well, but Python should be easy to host the Python run time. Like you can do it. Like I think in a week it took us years. Why? Oh, secure. Oh, details a lot. And <inaudible> mentioned it like securing. That is no easy, uh, feed >>Christian. Thank you so much for joining Dave and me bringing your energy from the keynote stage to the cube, set, breaking down some of the major announcements that have come out today. There's no doubt that the flywheel of innovation at snowflake is alive well and moving quickly, >>Innovation is, uh, at an all time hat snowflake. Thank you for having me. All >>Right. Our pleasure Christian from our guest, Dave ante, Lisa Martin here live in Las Vegas at Caesar's forum covering snowflake summit 22. We right back with our next guest.

(upbeat music) >> Welcome back to the Javits in the big apple, New York City. This is theCUBE's coverage of MongoDB World 2022. We're here for a full day of coverage. We're talking to customers, partners, executives and analysts as well. Peder Ulander is here. He's the Chief Marketing Officer of MongoDB and he's joined by Radhika Krishnan, who's the Chief Product Officer at Hitachi Ventara. Folks, welcome back to theCUBE. Great to see you both again. >> Good to see you. >> Thank you David, it's good to be back again. >> Peder, first time since 2019, we've been doing a lot of these conferences and many of them, it's the first time people have been out in a physical event in three years. Amazing. >> I mean, after three years to come back here in our hometown of New York and get together with a few thousand of our favorite customers, partners, analysts, and such, to have real good discussions around where we're taking the world with regards to our developer data platform. It's been great. >> I think a big part of that story of course, is ecosystem and partnerships and Radhika, I remember I was at an event when Hitachi announced its strategy and it's name change, and really tried to understand why and the what's behind that. And of course, Hitachi's a company that looks out over the long term, and of course it has to perform tactically, but it thinks about the future. So give us the update on what's new at Hitachi Ventara, especially as it relates to data. >> Sure thing, Dave. As many, many folks might be aware, there's a very strong heritage that Hitachi has had in the data space, right. By virtue of our products and our presence in the data storage market, which dates back to many decades, right? And then on the industrial side, the parent company Hitachi has been heavily focused on the OT sector. And as you know, there is a pretty significant digital transformation underway in the OT arena, which is all being led by data. So if you look at our mission statement, for instance, it's actually engineering the data driven because we do believe that data is the fundamental platform that's going to drive that digital transformation, irrespective of what industry you're in. >> So one of the themes that you guys both talk about is modernization. I mean, you can take a cloud, I remember Alan Nance, who was at the time, he was a CIO at Philips, he said, look, you could take a cloud workload, or on-prem workload, stick it into the cloud and lift it and shift it. And in your case, you could just put it on, run it on an RDBMS, but you're not going to affect the operational models. >> Peder Ulander: It's just your mess for less, man. >> If you do that. >> It's your mess, for less. >> And so, he goes, you'll get a few, you know, you'll get a couple of zeros out of that. But if you want to have, in his case, billion dollar impact to the business, you have to modernize. So what does modernize mean to each of you? >> Maybe Peder, you can start. >> Yeah, no, I'm happy to start. I think it comes down to what's going on in the industry. I mean, we are truly moving from a world of data centers to centers of data, and these centers of data are happening further and further out along the network, all the way down to the edges. And if you look at the transformation of infrastructure or software that has enabled us to get there, we've seen apps go from monoliths to microservices. We've seen compute go from physical to serverless. We've seen networking go from old wireline copper to high powered 5G networks. They've all transformed. What's the one layer that hasn't completely transformed yet, data, right? So if we do see this world where things are getting further and further out, you've got to rethink your data architecture and how you basically support this move to modernization. And we feel that MongoDB with our partners, especially with Hitachi, we're best suited to really kind of help with this transition for our customers as they move from data centers to centers of data. >> So architecture. And at the failure, I will say this and you tell me if you agree or not. A lot of the failures of sort of the big data architectures of today are there's, everything's in this monolithic database, you've got to go through a series of hyper-specialized professionals to get to the data. If you're a business individual, you're so frustrated because the market's changing faster than you can get answers. So you guys, I know, use this concept of data fabric, people talk about data mesh. So how do you think, Radhika, about modernization in the future of data, which by its very nature is distributed? >> Yeah. So Dave, everybody talks about the hybrid cloud, right? And so the reality is, every one of our customers is having to deal with data that's straddled across on-prem as well as the public cloud and many other places as well. And so it becomes incredibly important that you have a fairly seamless framework, that's relatively low friction, that allows you to go from the capture of the data, which could be happening at the edge, could be happening at the core, any number of places, all the way to publish, right. Which is ultimately what you want to do with data because data exists to deliver insights, right? And therefore you dramatically want to minimize the friction in the process. And that is exactly what we're attempting to do with our data fabric construct, right. We're essentially saying, customers don't have to worry about, like you mentioned, they may have federated data structures, architectures, data lakes, fitting in multiple locations. How do you ensure that you're not having to double up custom code in order to drive the pipelines, in order to drive the data movement from one location to the other and so forth. And so essentially what we're providing is a mechanism whereby they can be confident about the quality of the data at the end of the day. And this is so paramount. Every customer that I talk to is most worried about ensuring that they have data that is trustworthy. >> So this is a really important point because I've always felt like, from a data quality standpoint, you know you get the data engineers who might not have any business context, trying to figure out the quality problem. If you can put the data responsibility in the hands of the business owner, who, he or she, has context, that maybe starts to solve this problem. There's some buts though. So infrastructure becomes an operational detail. Let's hide that. Don't worry about it. Figure it out, okay, so the business can run, but you need self-service infrastructure and you have to figure out how to have federated governance so that the right people can have access. So how do you guys think about that problem in the future? 'Cause it's almost like this vision creates those two challenges. Oh, by the way, you got to get your organization behind it. Right, 'cause there's an organizational construct as well. But those are, to me, wonderful opportunities but they create technology challenges. So how are you guys thinking about that and how are you working on it? >> Yeah, no, that's exactly right, Dave. As we talk to data practitioners, the recurring theme that we keep hearing is, there is just a lot of use cases that require you to have deep understanding of data and require you to have that background in data sciences and so on, such as data governance and vary for their use cases. But ultimately, the reason that data exists is to be able to drive those insights for the end customer, for the domain expert, for the end user. And therefore it becomes incredibly important that we be able to bridge that chasm that exists today between the data universe and the end customer. And that is what we essentially are focused on by virtue of leaning into capabilities like publishing, right? Like self, ad hoc reporting and things that allow citizen data scientists to be able to take advantage of the plethora of data that exists. >> Peder, I'm interested in this notion of IT and OT. Of course, Hitachi is a partner, established in both. Talk about Mongo's position in thinking. 'Cause you've got on-prem customers, you're running now across all clouds. I call it super cloud connecting all these things. But part of that is the edge. Is Mongo running there? Can Mongo run there, sort of a lightweight version? How do you see that evolve? Give us some details there. >> So I think first and foremost, we were born on-prem, obviously with the origins of MongoDB, a little over five years ago, we introduced Atlas and today we run across a hundred different availability zones around the globe, so we're pretty well covered there. The third bit that I think people miss is we also picked up a product called Realm. Realm is an embedded database for mobile devices. So if you think about car companies, Toyota, for example, building connected cars, they'll have Realm in the car for the telemetry, connects back into an Atlas system for the bigger operational side of things. So there's this seamless kind of, or consistency that runs between data center to cloud to edge to device, that MongoDB plays across all the way through. And then taking that to the next level. We talked about this before we sat down, we're also building in the security elements of that because obviously you not only have that data in rest and data in motion, but what happens when you have that data in use? And announced, I think today? We purchased a little company, Aroki, experts in encryption, some of the smartest security minds on the planet. And today we introduce query-able encryption, which basically enables developers, without any security background, to be able to build searchable capabilities into their applications to access data and do it in a way where the security rules and the privacy all remain constant, regardless of whether that developer or the end user actually knows how that works. >> This is a great example of people talk about shift left, designing security in, for the developer, right from the start, not as a bolt-on. It's a great example. >> And I'm actually going to ground that with a real life customer example, if that's okay, Dave. We actually have a utility company in North Carolina that's responsible for energy and water. And so you can imagine, I mean, you alluded to the IO to use case, the industrial use case and this particular customer has to contend with millions of sensors that are constantly streaming data back, right. And now think about the challenge that they were encountering. They had all this data streaming in and in large quantities and they were actually resident on numerous databases, right. And so they had this very real challenge of getting to that quality data that I, data quality that I talked about earlier, as well, they had this challenge of being able to consolidate all of it and make sense of it. And so that's where our partnership with MongoDB really paid off where we were able to leverage Pentaho to integrate all of the data, have that be resident on MongoDB. And now they're leveraging some of the data capabilities, the data fabric capabilities that we bring to the table to actually deliver meaningful insights to their customers. Now their customers are actually able to save on their electricity and water bills. So great success story right there. >> So I love the business impact there, and also you mentioned Pentaho, I remember that acquisition was transformative for Hitachi because it was the beginning of sort of your new vector, which became Hitachi Ventara. What is Lumada? That's, I presume the evolution of Pentaho? You brought in organic, and added capabilities on top of that, bringing in your knowledge of IOT and OT? Explain what Lumada is. >> Yeah, no, that's a great question, Dave. And I'll say this, I mentioned this early on, we fundamentally believe that data is the backbone for all digital transformation. And so to that end, Hitachi has actually been making a series of acquisitions as well as investing organically to build up these data capabilities. And so Pentaho, as you know, gives us some of that front-end capability in terms of integrations and so forth. And the Lumada platform, the umbrella brand name is really connoting everything that we do in the data space that allow customers to go through that, to derive those meaningful insights. Lumada literally stands for illuminating data. And so that's exactly what we do. Irrespective of what vertical, what use case we're talking about. As you know very well, Hitachi is very prominent in just about every vertical. We're in like 90% of the Fortune 500 customers across banking and financial, retail, telecom. And as you know very well, very, very strong in the industrial space as well. >> You know, it's interesting, Peder, you and Radhika were both talking about this sort of edge model. And so if I understand it correctly, and maybe you could bring in sort of the IOT requirements as well. You think about AI, most of the AI that's done today is modeling in the cloud. But in the future and as we're seeing this, it's real-time inferencing at the edge and it's massive amounts of data. But you're probably not, you're going to persist some, I'm hearing, probably not going to persist all of it, some of it's going to be throwaway. And then you're going to send some back to the cloud. I think of EVs or, a deer runs in front of the vehicle and they capture that, okay, send that back. The amounts of data is just massive. Is that the right way to think about this new model? Is that going to require new architectures and hearing that Mongo fits in. >> Yeah. >> Beautifully with that. >> So this is a little bit what we talked about earlier, where historically there have been three silos of data. Whether it's classic system of record, system of engagement or system of intelligence and they've each operated independently. But as applications are pushing in further and further to the edge and real time becomes more and more important, you need to be able to take all three types of workloads or models, data models and actually incorporate it into a single platform. That's the vision we have behind our developer data platform. And it enables us to handle those transactional, operational and analytical workloads in real time, right. One of the things that we announced here this week was our columnar indexing, which enables some of that step into the analytics so that we can actually do in-app analytics for those things that are not going back into the data warehouse or not going back into the cloud, real time happening with the application itself. >> As you add, this is interesting, as basically Mongo's becoming this all-in-one database, as you add those capabilities, are you able to preserve, it sounds like you've still focused on simplicity, developer product productivity. Are there trade off, as you add, does it detract from those things or are you able to architecturally preserve those? >> I think it comes down to how we're thinking through the use case and what's going to be important for the developers. So if you look at the model today, the legacy model was, let's put it all in one big monolith. We recognize that that doesn't work for everyone but the counter to that was this explosion of niche databases, right? You go to certain cloud providers, you get to choose between 15 different databases for whatever workload you want. Time series here, graph here, in-memory here. It becomes a big mess that is pushed back on the company to glue back together and figure out how to work within those systems. We're focused on really kind of embracing the document model. We obviously believe that's a great general purpose model for all types of workloads. And then focusing in on not taking a full search platform that's doing everything from log management all the way through in-app, we're optimizing for in-app experiences. We're optimizing analytics for in-app experiences. We're optimizing all of the different things we're doing for what the developer is trying to go accomplish. That helps us maintain consistency on the architectural design. It helps us maintain consistency in the model by which we're engaging with our customers. And I think it helps us innovate as quickly as we've been been able to innovate. >> Great, thank you. Radhika, we'll give you the last word. We're seeing this convergence of function in the data based, data models, but at the same time, we're seeing the distribution of data. We're not, you're clearly not fighting that, you're embracing that. What does the future look like from Hitachi Ventara's standpoint over the next half decade or even further out? >> So, we're trying to lean into what customers are trying to solve for, Dave. And so that fundamentally comes down to use cases and the approaches just may look dramatically different with every customer and every use case, right? And that's perfectly fine. We're leaning into those models, whether that is data refining on the edge or the core or the cloud. We're leaning into it. And our intent really is to ensure that we're providing that frictionless experience from end to end, right. And I'll give a couple of examples. We had this very large bank, one of the top 10 banks here in the US, that essentially had multiple data catalogs that they were using to essentially sort through their metadata and make sense of all of this data that was coming into their systems. And we were able to essentially, dramatically simplify it. Cut down on the amount of time that it takes to deliver insights to them, right. And it was like, the metric shared was 600% improvement. And so this is the kind of thing that we're manically focused on is, how do we deliver that quantifiable end-customer improvement, right? Whether it's in terms of shortening the amount to drive the insights, whether it's in terms of the number of data practitioners that they have to throw at a problem, the level of manual intervention that is required, so we're automating everything. We're trying to build in a lot of security as Peder talked about, that is a common goal for both sides. We're trying to address it through a combination of security solutions at varying ends of the spectrum. And then finally, as well, delivering that resiliency and scale that is required. Because again, the one thing we know for sure that we can take for granted is data is exploding, right? And so you need that scale, you need that resiliency. You need for customers to feel like there is high quality, it's not dirty, it's not dark and it's something that they can rely upon. >> Yeah, if it's not trusted, they're not going to use it. The interesting thing about the partnership, especially with Hitachi, is you're in so many different examples and use cases. You've got IT. You've got OT. You've got industrial and so many different examples. And if Mongo can truly fit into all those, it's just, the rocket ship's going to continue. Peder, Radhika, thank you so much for coming back in theCUBE, it's great to see you both. >> Thank you, appreciate it. >> Thank you, my pleasure. >> All right. Keep it right there. This is Dave Vellante from the Javits Center in New York City at MongoDB World 2022. We'll be right back. (upbeat music)

>>Welcome back to Boston. We're down in the Seaport. This is the Cube's coverage of red hat summit, 2022. I'm Dave ante with my co-host Paul Gillon, Sebastian Moes. Here he is a senior enterprise architect at bit mark Sebastian. Thanks for coming to the queue. Welcome to the United States. Good to have you in Boston. >>Thank you. Thank you for the invitation. It's uh, good to be on a live summit again after, uh, those, uh, testing two years >>Strange, isn't it? I mean, people kind of don't know what to do. Shake, bump this bump, >>And >>It's like, but where everybody wants to get out of the, the home, the lockdown and, you know, there's a real pent up demand. Tell us about bit mark. >>Um, bit mark is a managed service, uh, provider for, um, German statutory health insurance companies. Um, we manage about our software that we develop, um, is for about 85% of the, uh, German health insurance companies. Um, we have, uh, not only do we build the software, we also have data centers where we run software for, for our customers. Um, and it's everything that a health insurance company is, uh, mandatory to have to run their business, so to >>Speak what, what's the life of an enterprise architect like these days and how, how has it evolved? How has it changed? Uh, I mean, independent of the pandemic, will we get to that, but, but, you know, technology changes, organizational objectives of, of changed the public policy changes. How, how was your, the life of an enterprise architect changed? >>Um, well we, we have this, uh, big monolith JG E application that is, uh, run on JBO. Um, and now we want to, we want to change that into a more modern environment and using, uh, OpenShift to do that. Um, and yeah, there's, uh, there's a lot of reg regulatory things that come up that need to be, um, need to be figured in. Uh, there is new demands that our customers have that we need to figure out how to get to market, uh, and to be able to deliver software more faster and, you know, make the turnaround, uh, or have the turnaround be less. >>So kind of following the technology trends of going from big monolith to microservices and containerization and distributed data, the, the, >>The whole, the scalability, uh, you know, and quick turnaround, that is, that is the main focus. >>So the application that you're here talking about this pace to face in application, kind of a new market for you, a new direction, is this part of that overall shift to a more modular microservices based, uh, >>Structure? Um, well, we, we, we had applications like this before, but this is a new branch of it because, um, there's a strong drive in Germany too, for more digital digitalization. Um, and to have a new interacting model with the customer from basic things to more advanced features like medication services, vaccination status, um, managing your allergies, and that's an edit value that we want to give, uh, for our customers. So they can, their customers can benefit. >>I dunno what it's like in Germany, but in the United States used to call up the doctor and say, Hey, can I just, can we do this over the phone? No, you gotta come into the office. Mm-hmm <affirmative> and then of course, with the pandemic, it was like, you can't come into the office. It was just total flipping, cuz you could get 80% of what you needed done, and this is what your app enabled essentially. Right? >>Yeah. And, and some that and some added value as well, uh, to, to give, um, yeah, a benefit for using this, uh, online interaction for, um, the insured people, the, the patients, >>Essentially a digital gateway, including your data. Well, that's the other thing you can't get right. As a patient, you can't ever get through your data, it's like right. You >>Get it, but nobody else can >>Get it. <laugh> sometimes it's hard for you to get it cuz of again, in the United States, HIPAA and the, and the, and the requirements for privacy restrict often access to, to data, you have to go through hoops to get it. So, uh, so, so that experience is what you codified in your application. Yes. >>Um, yes, we have this, uh, unique data set of all health related information that people have to, uh, interact with in, in when they're sick or when they deal with their healthcare company. Um, and yeah, we wanna provide that data to the customers. So they're able to look at it. Um, there's also the, uh, electronic patient folder. You can say, um, where there's data like medical exams and stuff in there that they have access to. We provide that as well for, for our customers. Um, but, uh, yeah, it is about the interaction and that I can see when I put something in to my insurance company via email or the doctor put something in that I have the interaction on my phone and see when it was delivered, um, to them when it's active, when I get the money, stuff like that. >>Now this application is built on OpenShift, it's cloud native, uh, has all the constructs. How different was that for your development team from building something like you mentioned, the monolithic Jbos application that you already have, how different was building the cloud native, uh, >>Constructs. Um, it is quite different. I mean, it's building software, there's a lot of the same things involved. We've been, we've done agile and scrum, uh, before and so on, but we now have a, um, we're trying to be, or no, we're actually achieved to be faster in bringing this to market, um, deploying it in different data centers, doing it all automatically doing automatic tested, uh, right as part of the pipeline. Um, there's, there's a lot of huge steps that we can, we're able to take because of the technology. And that's why we did go there in the first place. That's why we said, okay, this is, it needs to be, uh, cloud native. >>You found that red, red hat had the full suite of tools that you needed. >>Um, yeah, I mean, we, there's some open source stuff that we've also integrated into the pipeline and everything, but there's a lot of, for example, we are using the, uh, three scale, uh, the API management from, from red hat, um, just to be able to, um, use the functionality that we build, that the customers can use the functionality in other products that they use that serve partner people, uh, uh, certain partner companies can, are able to use the services as well. >>Okay. So the, the, the dumb question is, but I'll ask it anyway is you could get this stuff for free Kubernetes, open source, you know, you get E Ks for free. Why didn't you just use the freebie? >>Why? Um, well, we're, we're on a scale with so many, um, uh, customers and data centers that we have to take that we do need support in, in a way. Um, and I usually say, so if we take software from whoever, whatever company it is, we're gonna break it. Yeah. <laugh> um, the, the, the transaction load that we have is, is quite, uh, intense and the performance that we need, uh, especially in the, in the business to business, um, market is, is so big that we do need the interaction with, with a vendor and that they're able to help us, uh, with certain escalations >>German Germans play rough. So <laugh>, um, you know, when a, when a vendor announces an innovation lab, I always go, okay, that's an EBC, like an executive briefing center. It's all gonna be used for sales. But my understanding is you actually leveraged the innovation labs. It was actually helpful in building this application. Is that true? >>I, I, I actually, uh, to part in the open innovation that we did with RA hat, and we knew we knew what we wanted to do. We, we knew the technology, we knew what we wanted to have done, um, but they helped us to, to get there step by step with the, with the tools they have, the, um, uh, you know, the ways of working and how this is, this is built. It really lends itself to, to build that step by step and worry about some stuff later and just do it. Um, yeah, piecemeal, >>This is Al is also a new market for you. It's your first real business to consumer facing application. That's that implies a very different approach to experience design, uh, to how you >>And performance yeah, >>Yeah. Perform exactly. Uh, how did your development team adapt to that? >>Um, well, there's, there's, you know, certain things that you build into the process, like integration, testing, automated integration testing, where the application just gets checked right after you check in your software. Um, we built in low testing to, you know, we have an idea of how many transactions per second, there will be. And so the low testing takes care of that as well. Um, and that is easier if you have a small piece of software instead of the whole monolith that we usually have. And so you, we are able to, to build it quicker and get it out quick in, in hours. >>How, how have you, um, accessed customer feedback, you do your, you know, net promoter score surveys, what, what's the been the customer reaction, your, your consumer >>Reaction? Um, they, they, I mean, I'm kind of the wrong guy to talk to, to, uh, about <laugh> to >>Talk about, come on the architected, the thing. >>Yeah, I, I did. And, and then the feedback has been, it's been very good so far, uh, and we are pretty happy with it. Uh, it's it's running, uh, very well. Um, I don't quite know how they got there. Our customer does, uh, you know, uh, questionnaires and, and stuff like that. Yeah. We have a, a different depart, uh, department to, to solicit feedback on that. But from what I hear, uh, it's, it's received very well. >>One of the cloud native features, I understand you used extensively with APIs, uh, for integrations. How are you making this application accessible to partners? What, I mean, what are you exposing? How will you use those APIs to enhance the value through, through an ecosystem of >>Partners? Um, well, we document them, um, and so they're out there to use. And as long as there's a, um, a security process within, um, em that we have in front of it, um, they're open source, um, APIs. So, uh, as I said, they have other programs that they wrote themselves or that they bought that are able to use those APIs, um, from an open API document. Uh, and, and just interact with that as long as the user is, uh, authenticated, they're able to, to get this data and show it in a different context and use it in a different context. >>Did you play golf? >>Um, I used to time ago, not anymore. >>Now, do you know what a Mulligan is? Yes, I did. Okay. If you had a Mulligan, you'd do this all over again. What, what would you do differently? >>Um, an interesting question. I, don't not sure. Um, you, you say you're smarter after, after you've done that. Yeah. And, and of course there's, uh, there's, there, there there's certainly were things that I didn't expect that would happen. Um, like how, how really you need to go modular and on, on everything and need your own resource and infrastructure. Um, because we came from a very centralized, um, uh, scope. We had a database that is a big DB database, um, and now we're going into smaller database and not decentralized a lot. Um, and that was something that the extent of it, I didn't expect, I, I wanted to use more smaller things. And, and that was something that we very quickly learned that no, we need really need to separate stuff out. >>Was that an organizational sort of mindset shift? Um, are you, are you rethinking or rearchitecting your data, um, your data architecture as part of that, or is that more, or is this more just sort of tactical for this app? >>Um, no, we're definitely need to need to do this because, uh, it really gets, um, or it really is a, um, something to handle a, a big pool of data is, is really a challenge or can be a challenge at times >>To scale, >>To, to, to scale that up. Right. Yeah. Um, and so, yeah, we are going to, to separate that out and double some data. That's, that's gonna be a thing it's gonna be more data at the end, but since it's scaled out and, and decentralized, that will >>Help a lot of organizations would say, well, we wanna keep it centralized monolithic, which is kind of a negative term, but I think it's true, uh, because it's more cost effective. We're not gonna duplicate things as much. We're gonna have roles that are dedicated, but it sounds like you're seeing a business advantage of distributing those functions, decentralizing those functions to a >>Extent, right, right. Because if you, if you have a centralized Mon monolith, then it, I, yeah, it might be negative, but it really is. It's a good working software. Um, but to have that, it's, um, it's really hard to release new features and new, new, you know, even buck fixes it, it just takes time. It, it is, uh, uh, a time consuming process. And if you have it decentralized and in smaller packages, you can just do, Afix run it through the pipeline, have the testing done and just put that out within hours. >>How important was it to bit more to build this application on an open source platform? >>Um, the open source didn't come so much in our perspective of things, or we didn't consider it that much. It was just, this is there. This works. We have a good support behind that. Um, we are, our, our coach is not open sourced, then we're not going to anytime soon tell about it. Um, we're actually thinking about having parts that might be, uh, a kind of open source dish, uh, just in the healthcare community kind of thing. Um, but, uh, yeah, no, that didn't F factor in as much. Um, it was just something that we had >>Experienced another architecture question. So you've got the application stack, right. If I can use that term, although application development tools that you build use to build the application, and then you've got the data that the application needs, how are those architected, are they sort of separate entities? Are they coming together? >>Um, we used to have, we used to have, uh, uh, data, um, net a, a, um, an MDA approach, a J hue. Um, so they're very strong connected. That is, there's just in the database. There are models and entities that we use in the, in the JBO. Um, and well, we're still gonna use hibernate to, to, uh, to do the G GPA, but it's, uh, yeah, it's something that needs to be restructured because it just takes a lot of resources to manage data from different parts of the application, bringing them together, um, that will, will need to change. >>And what about new data sources? If I came to and say, Sebastian, I need to inject new data into the, the app. I need to get this to how, how, how difficult or, or fast easy is that, >>Uh, now in the, in the world now, or actually we wanna >>Compare, can you compare before and now, I mean, it wouldn' have to happen before would be >>Like, in the time in the timeframe it's, it's, it's not, it's hard to say. I mean, but if you have a project right now, we're talking, uh, months, um, like a year to, to get it done, get it tested, and then it even takes, um, up to a month to before it's out to every customer. Yeah. The rollout process takes some time. Yeah. Um, and we're planning on, or we, we developed the new, uh, the new software we developed in a couple of months. Uh, and then it is deployed and then it's in production and it's in production for all the customers that wanted to use it for now. I mean, it's not deployed to all customers yet, uh, because they need to adapt it and in their way. Um, but they have it, you know, it's, it's right there. It's deployed. Yeah. When we fix it, it's in a, you know, hours, couple days it's out and it's out in production, in different data centers for different customers. >>And we've come full circle the life of a, of an architect. It's, uh, it sounds like it's much better today. Sebastian, thanks so much for coming to the cube. Appreciate your time and your insights. And thank you for watching. Keep it right there that you watching the Cube's coverage of red hat summit, 2022 from Boston, Dave Valante for Paul Gillon, we'll be right back.

(upbeat rhythmic music) >> Everyone, I'm John Furrier with theCUBE here at Palo Alto on a remote interview for a special video interview. The EC2 15th birthday party celebration event. Raj Pai, who's the Vice President of EC2 Product Management AWS is here with me. Congratulations on Amazon Web Services, EC2 with the compute. What a journey. 15 years old. Soon we got the keys to the car at a couple more years. So Raj, great to see you. You guys have been doing great work. Congratulations. >> Thank you. It's great being here. It's super exciting for me too. I can't believe it's 15 years and you know that big, we're still at the very beginning as you know, that we often say. >> The building blocks that have been there from the beginning really set the table, and it's just been fun to watch the innovation on behalf of customers that you guys have done at AWS and more, and for entrepreneurs and for developers, it just continues to be great and the edge is right on the corner. Wavelength, all the great stuff. But let's talk about the specific topic here that I really want to drill into is that as you look at the 15th year and birthday for EC2, okay? You're looking at the future as well. You're looking at the past, present and future. And one of the things that's most compelling about recent re-invent was the Graviton performance numbers are amazing. You guys have been building custom silicon for a while. You also worked with Intel and AMD. What is it about? What's the huge investment for you guys? Where do you started to see some returns? Are you seeing returns? And then why did AWS decide to build its own processors? >> Yeah, now, that's a really good question. And I mean, like with everything else we do in AWS, it's all about innovating on behalf of our customers. And one of the things our customers are telling us, that they continue to tell us is they want to see better performance at lower prices. And we've been able to deliver that with our hardware partners for the last 15 years. But as we've understood the workloads that run on EC2 and AWS, we saw an opportunity. Like, what if we were going to go and design our own processor that was really optimized for the sort of workload that customers run on the Cloud? And make design decisions when designing the CPU and the system and the chip around the CPU that does things like bring a lot more core local cache and speed up the parts of the operations that really benefit real-world workload. So, this isn't about benchmarks. It's about how do real world workloads perform and how do we build systems that optimize that performance? And with Graviton, we were able to hit the nail on the head. We were also very pleasantly surprised when we got our first chips off the line. And we're seeing that a customer, like about 40% performance improvement at significantly lower cost. And that's super exciting. And that's one of the reasons we're getting so much interest from our customers. >> I got to say as a geek and a tech nerd, I love the silicon development. And there's benefits there, also the performance is there. The thing that also is pretty obvious that's happening is and the world seeing it is the shift towards ARM-based computing. What kinds of customers and use cases are you seeing adopt to Graviton? And what kind of workloads were they running on? What are the things that surprise you guys, that didn't surprise me. Did you guys always talk about the upcheck and how everyone's leveraging it? What are some of the examples? Take us through some of the customers, use cases, workloads. What's surprising you and what's going on with Graviton? >> Yes, so I think that the biggest surprise for us is how broadly applicable it's been. So one of the things we did, we launched with reinvent is like we have different form factors of compute. We have memory-optimized instances that are good for databases and in memory caches. We have compute optimized for HPC and workloads that really take advantage of the performance of the chip and then we have general purpose workloads. And we we introduced Graviton variants of all those instance families And we're actually seeing the same sort of performance benefits across workload. So, and it's one of the reasons why companies like Metrol, and Snap and SmugMug, they move one workload over, they see the performance benefit and before you know it, they're starting to move workloads and mass over across kind of that spectrum. So, I think that's one of the biggest surprises is that Graviton seems to do well across a wide range and we're going to keep on introducing it more and more of instance families, because we've seen this uptick well. >> You're seeing a lot of people move to the Graviton. You mentioned a few of those early adopters who were pushing the envelope, and they're always kind of trotted out there as examples at reinvent, which is always fun to see what they're working on next. And now is that people see the Graviton2 instances, okay, the architecture's different, higher performance. How much effort do our customers typically need to move to Graviton2 instances? And what are some of the benefits they're seeing on performance and price performance? Can you talk about that transition? Because that's natural evolution for them. >> Yeah. It's actually a lot less than they originally think. So, some of the hardest effort is just getting them over the line to try it. So, one of the things that we tell our customers who are considering Graviton is it just takes one or two developers take one workload and go off for a couple of weeks and just try reporting it to Graviton. And more often than not, they come back to us in four or five days. They're like, it works. And we just had to do some testing and verification, but we were able to afford it because, you know, the operating system support was there, the ISP support was there and the tools that they use, and they're using most cases, modern programming languages like Python or Go or Java or PhD where, you know, interpret the language and it just run. And so there's very little lift in comparison to what people think it's going to be. And that's one of the reasons that, you know, one of the big announcements we made in the last few weeks is what we're calling the Graviton challenge, right? So it's a set of blueprints for customers to essentially have best practices on how to in four days take, you know, a piece of code and piece of that workload and execute it and run it and migrate it to the Graviton. And we're seeing a lot of interest in that as people in the community realize how easy that actually is. >> What are some of the cool price performance things that are emerging? Obviously it makes sense if you don't really need it, don't pay for it, but you have that option. A lot of people are going there. Is there a wave you see coming that Graviton2 is going to be really set up for that you kind of see some early signals coming in, Raj? Because, I can see the 64 bit. I can see where Graviton fits today. Obviously, performance is key. Is it certain things that are emerging? What's the main problems that it solves? >> Well, I think anything that's a multi-threaded architecture is going to do really well in Graviton because of the, we have really densely packed 64 course. And so you're going to see things like microservices and containers and workloads that are more, that are able to take advantage of that parallel execution do really, really well. And so, we say 40% performance improvement, but like, when our customers have gone and tried this, they've seen upwards of 50% depending on the workload. So yeah, it's going to be more your multi-threaded application. There's some applications that may not be a fit, like it can give a legacy, you know, for example, like, there's some software that hasn't yet been moved over and we're going to continue to invest super heavily in our whole ecosystem of hardware, for the longterm. So I think that because there's a great option and we just encourage them to try it. And then they'll learn from their experience what works and what doesn't. >> Wow. 15th birthday. Still growing up and it's starting to get more mature. You're the VP of Product Management. You have the keys to the kingdom. So, you have wide-ranging responsibilities. Share with us if you can. I know that you really can't say much, but try to give a little bit of teaser. You got Wavelength. I can see the dots connecting at the edge. You got Outposts, so we see all that emerging. I can almost imagine that this is going to get stronger. What should people think about? Where's the headroom for EC2 with Graviton and Graviton2? >> Yeah, I know. I think like, a new architect (mumbles) yourself. But like, our goal is to have AWS kind of everywhere our customers are. And that means the full power of AWS. So, I think you're going to see more and more of us having EC2 in compute capacity, wherever customers need it. That could be in an Outpost. That could be on their 5G network. That could be in a city right next to them, right? And you're going to see us continue to offer the variety, the selection of instances and platforms in all those locations as well. So, I think the key for us is to be ubiquitous and have compute power everywhere our customers need it, in the form factors they need it. >> That's awesome. Congratulations. I love the power. You can't go wrong with sending computers where the data is, where the customers are. AWS, Amazon Web Services. Building their own custom silicon with Graviton2 processors. This is EC2 continuing to grow up. Raj Pai, Vice President of EC2 Product Management. Thank you for coming on and sharing the update and congratulations on the 15th birthday to EC2. >> Yeah, thanks for having me. It's been great. Have a great Friday. >> All right. Great. I'm Jeffrey with theCUBE. You're watching theCUBE coverage of EC2's 15th birthday event. Thanks for watching. (soft rhythmic music)

(digital music) >> Welcome to this CUBE Conversation. I'm Lisa Martin. Bren Briggs, joins me next, the Director of DevOps and Cybersecurity at Hypergiant. Bren, welcome to theCUBE. >> Hey there, I'm glad to be here. >> You have a very cool background, which I wish we had time to get into your mandolin playing, but we don't. Tell me a little bit about Hypergiant this is a company that's new to me? >> So we are an AI and Machine Learning Company, and we had the slogan we talked about a lot, it's almost tongue in cheek, "Tomorrowing Today" where we want to build and focus on technology that advances the state-of-the-art and we want to, where this deep history and background in services, where we build custom solutions for companies that have data problems and that have AI and machine learning problems. And they come to us and we help them make sense of their data and we build a custom software solution from top to bottom. And we help them with their data problems and their really difficult problems that they have there in a very specialized way. And yeah, that's what we do. It's really fun. >> "Tomorrowing Today", I like that build T-shirts with that on that. (Bren chuckles) So talk to me about the work that you guys are doing with SUSE Rancher Government Labs. You're doing some very cool work with the air force, help me understand that. >> Sure, so about a year and some change ago, we had a government contract, an air force contract, to develop some new or just to basically write an experiment with some new sensing technology onboard a satellite. So we built this satellite, we were talking about how we're going to employ DevOps' best practices on the satellite and if that's even a thing that can be done. How we get these rights of space and really thinking through the entire process. And as we did this, we were getting more and more deeply involved with a very very new group. Actually, we kind of started at the same time. A new group within the air force called, Platform One. Platform One's mission is to bring DevSecOps to the DoD Enterprise. And so as we're kind of starting off together and getting to know each other, Rob Slaughter who started and ran Platform One for the first bit of his existence, he said, "hey, we're going to incorporate some Platform One stuff into this. Let's talk about just building an actual Platform One satellite and see what that looks like." And so that was kind of the start of this whole idea was what do we do and how do we do DevSecOps in low Earth orbit? Can we put Kubernetes on satellite and will it work? >> And tell me some of the results? So, I used to work for NASA, so I would geek out on anything that has to do with the space program. But talk to me about some of the things that you uncovered bringing Kubernetes, AI, machine learning to this, outer Edge of Earth? >> I think the first thing that we learned that I think, it's an understatement to say that space is hard. (Both laughing) But it really is. And that was the part that we learned about was it was hard in all of the ways that we did not expect. And a lot of it had to do with just government and logistics. We learned that it is difficult a lot of times to just to find a way to get into space and then once you're there, how you operate in the conditions that you're in and how you could even communicate with your satellite is it's just a logistical adventure on top of all of the other engineering problems that you have while you're on low Earth orbit? The other thing that we figured out was awkward things are difficult. While you're on orbit, they can be slow or fragmented and so it pays to get it right the first time but that's not the nature of modern software development is you'd never get it right and you're continually updating. So that was a problem that really nagged us for awhile was after we did the wider experiment, like how would we continuously update this and what would we do? And those ideas and questions fed into the experiment that became Sat One and then the follow one much bigger experiment that became the Edge One and Edge working group. >> Tell me a little bit about the wider experiment, give me some context of how that relates to Platform One, Sat One? >> I can't (laughing) I can't really go into details about what wider did or anything like that. It was not a classified mission, it's just not something that I can disclose. >> Okay, got it. >> Sorry. >> So talk to me about some of the work that you guys are doing together Hypergiant with SUSE in terms of pushing forward the next generation of Kubernetes to low Earth orbit and beyond. >> Sure, so SUSE RGS, specifically, Chris Nuber, like, one of the things that I have to do is I have to be a cheerleader for all of the amazing people that were on this project. And two people in particular, Chris Tacke and Chris Nuber, were instrumental in making this work. I was like almost tangentially involved where I was doing some input and architecture and helping debug but it was really Chris Tacke and Chris Nuber that made this thing, that built this thing and made it work. And Chris Nuber, was our assigned resource from SUSE RGS. And he said, "Obviously SUSE is going to prefer, or SUSE is going to prefer SUSE products." That it makes sense. But there's a reason because the products that he implemented and the patterns that he implemented and the architecture and expertise that he brought were second to none, I don't think that we could have done better with any other distribution of Kubernetes. He recommended a K3s is a very lightweight Kubernetes distribution that had really good opinions. It's a single binary. It was very easy to deploy and manage and update and it just, it really didn't break. That was the best thing that we were looking for (chuckles) it was one solid piece with no moving parts, relatively speaking. And so Chris Nuber was very essential in providing the Kubernetes architecture while Chris Tacky was the one who helped us write some of the demo applications and build the fail over and out of band interaction that we were going to have from the hardware on the satellite to the Kubernetes control plane. >> Very cool. It sounds like you had a great collaborative team there, which is essential in any environment. >> We deed. >> And I liked how you described space as a logistical adventure that reminds me very much of my days at NASA. (Bren laughing) It definitely is a logistical adventure to put it mildly. Talk to me a little bit about the work that you're doing to define the Edge for the Department of Defense? That sounds very intriguing. >> Yeah, so this was almost a direct result of what happened with the sat one experiment where Rob Slaughter and a few of the other folks who saw what we did with sat one, you know, were again, logistical adventure. We built this entire thing and we worked so hard and we're moving through fright flight readiness checks and as things happen, funding kind of went. And so you've got all this experience and this like, prototype that this really confident that it's space ready and everything and they said, "hey, listen, you know, we have the same problem on our flight with terrestrial environments, they're nearly identical the only difference is, you know, you don't have to worry about radiation nearly as much." (laughing) So then, you know, we joked about that and we started this new idea, this Edge One idea as part of the AVMs program, where they're figuring out this new, like battlefield communications pattern of the future. And one of the things that they're really concerned about is secure processing and how do you do applications at like where people are stationed, which could be anywhere in very remote locations. Then that's what turned into Edge One is, you know, we imagined initially Edge One as satellite one without wings and earth bound and that grew into, well, what about submarines? What about carriers? What about command and control squadrons that are stationed in cities? What about special operators that are far forward? What about first responders who are moving into, you know, hazardous environmental conditions? Can you wear a Kubernetes cluster with like super low power arm chips? And so we started thinking of all these different applications of what Edge could be anywhere from a five volt board all the way up to a data center in a box. And that caused us to realize that we're going to break Edge into really three categories based on the amount of material or resources needed to power it and how hard it is to get to. So we have the Near Edge, which is, you know, you have data center like capabilities, and it's easy to get to it, but you, because you have people stationed with it, but you may have reached back once every month or so. So think, you know, a shift that's underway or an air gap system or something like that. And then you have a Tiny Edge, which is exactly like kind of the more traditional idea that you think of when you think of Edge, which is really, really tiny compute, maybe it's on a windmill or something I don't really know, pick your thing to put Kubernetes on that should never have Kubernetes, that's the kind of thing. And then you've got Far Edge, which is, you know, if the control plane crashes, good luck, you'll never getting to it. And so that would be a satellite. And so the far it... so really a lot of these, it depends on the failure mode. Like what happens when it fails and that for the most part defines kind of what category you're going to be in. >> Tiny Edge, Near Edge and Far Edge. I think Sir. Richard Branson and his team went to the Far Edge (chuckles) low Earth orbit >> He did (laughing). >> This last weekend, I guess, yeah. That low Earth orbit does seem like it would be the Far Edge. Talk to me a little bit about, I mean, you talk about these applications then from a defense perspective that very dramatically, what are some of the important lessons that you've learned besides if it breaks in the Far Edge, you're not getting to it. >> Some of the important lessons that we learned. So I actually did this exact job in the air force. I was a combat communicator, which meant that we took, by pure coincidence I'm back in this, like, I did not intend for this to happen its pure coincidence, (Lisa laughing) but, you know, we communicate, we went out to the Edge, right. We went out to the Near Edge and we did all of this stuff. And the biggest lesson, I think learning from doing this or doing that and then going into this is that the world doesn't have to revolve around SharePoint anymore (Lisa laughing) because we can shape our own habitation (Both laughing) >> That is good to know. >> If it can be done on SharePoint, the air force and the army will do it in SharePoint, I promise you. They've done some actually terrifying things with it. All joking aside though, I think that one of the things that we learned was the difference between like something being complex and complicated when it came to systems engineering and management, like this is a very complex system it's actually orders of magnitude more complex than the current deployments that are out there which is effectively VMware and you're migrating virtual machines across multiple physical nodes in these remote data centers. But it's also complicated, it's really difficult to manage these deployments and the hardware. And I remember like when I was in combat comm, we had this 72 hour goal to get all of our systems up. And it was kind of like a 50-50, if we would make it, it felt like most of the time where you had priorities for getting things up and running. And obviously, you know, that certain applications weren't as important as others. So they were the ones that had to fall on the wayside if you're going to make your 72 hour mark. But I'm just thinking about like how difficult it was to deploy and manage all of this stuff and now with Kubernetes, yes, the complexity is far higher, but we can make it so it's not as complicated. We can offload a lot of that brain sweat, the people in the rear echelon, where they can connect in remotely after you come up and you get reached back, they push your config and your mission profile is there. And now you're focused on the mission you're not focused on debugging pods, and you're focused on the mission and not focused on, you know, why my virtual machine didn't migrate or something like that. And we can get applications that are built in-house and updated continuously, and we can verify and validate the sources of where these things are coming from. And all of these are important problems to everybody, not just the military, but the military tends to have the money and the ability to think about these things first, 'cause that's where these problems tend to get solved first. >> So interesting. You've sort of had this circular experience being in the air force, now coming back and working on projects like this, what are some of the things that Hypergiant has learned? And some of the things that are next next for Hypergiant as a company? >> I think that we are getting really good at being a small contractor in the Federal space where we actually were just awarded an IDIQ with a cap of $950 million in a small group of, I think, 23 other companies. And so that shows right there the investment that the Federal Government has in us and the potential that they see for us to build and deliver these highly tailored and specialized solutions. The other thing that we've learned is how to form like coalitions to collaborate with a lot of these other smaller companies. I think that the days of seeing the Defense Industrial Base dominated by the same four people or five people are over. And it's not that these people, I mean, they've been, they've basically been propping up most of the defense industry for a very long time and I think a lot of people would argue that, you know, this is a problem, right, you have this near monopoly of a very few people, but the other thing is that they're not as nimble, they grow by acquisition and we have this ability to be highly tailored and specialized and we don't need to do everything in the world to survive. We can go and form coalitions with other groups to go solve a particular problem. Like we're great at AI and ML, and we're great at DevSecOps, then maybe we're not so great at, you know, hardware or you know, things like that. Like we can go partner up with these people and solve problems together and we don't have to be a Boeing to do it and you don't have to go hire a Boeing to do this. And I think that's really, really great, no slight to Boeing, but I think it's really great that it's a lot easier for smaller companies to do this and we are navigating this new world and we're bringing Agile into the government and that's, yeah, in some cases we have to drag them, kicking and screaming into this decade, but, you know, that's what we're doing and I'm very excited to see that because when I was in Agile and DevOps, those were words you didn't say, you weren't allowed to do that. >> No. >> Now they've done a complete 180, it's really cool. >> That's cool. I have a minimum that brings in thought diversity, having more companies to work with, but to your point, the agility that you bring in as a smaller company helping them to actually embrace Agile, that's huge because to your point, that's kind of historically not what government organizations are used to. So it sounds like a little bit they've learned a tremendous amount from working with small companies like Pepperdine. >> I like the thing so. Platform One is a fantastic example. So it was really started as a what we're calling software factories within the air force and within the DOD and other DOD branches have now started to replicate the pattern. So we have several software factories within the air force and Platform One is like the DevSecOps Software factory, and we have the ski camp and space camping, Kobayashi Maru and you're noticing a theme here (laughing) and so they're very nerdy names, but so we have these software factories and there's all these projects are being worked. But one of the amazing things I noticed when I showed up to work on the first day was that I had no idea who was uniformed and who was civilian. It was a completely badge off rank, off situation. Very few people showed up in uniform and the ones that did typically had their blouse off so you had no idea what their rank was. Everybody went by first name and we behaved like a start-up. And these civilians were coming from other startups like Hypergiant or a Timo or other very small, very specialized groups and SUSE RGS, of course they were there too and they're embedded in several different teams. And so you have this, like this quasi company that got this startup really that got formed and the culture is very, you know, very varies, you know, bay area startup type in some ways, for both better and worse. There's, I mean, we're, definitely full tilt on (laughs) on the Agile train there, but it's just, it's like nothing I've ever seen inside the DOD. And they're not just learning from these small companies and from Agile companies, but they're behaving like them. And it's spreading, they're seeing what work is getting done and what can be accomplished and how you can continuously deliver value instead of working for, you know, six or eight months and then showing the customer something and them hating it and you sending it back and, you know, it's more of a continuous improvement type thing. And I think that they're embracing that and I'm very excited to see it. >> That's important 'cause changing a culture is incredibly hard but seeing and hearing that they're embracing that is exciting. And I'm sure there's going to be many more things you could talk about generally, but I got to ask you if somebody like SUSE gave you $250,000, and you could buy one of the tickets on Branson's next flight, would you do it? >> I mean, yeah, why would I not? Like, how can I pass up a trip, (Lisa laughing) you know, go to the Edge of space. >> The Far Edge. >> Like yeah, the Far Edge, maybe I'll just, you know, hurdle the satellite out the window, as you know, we're up there, you know, peak and probably could throw it quite that fast, but we'll see. (Lisa laughing) But yeah, no, I think I would take the trip, yeah, that'd be fun. >> You're brave. Brave than I'm, I don't know. Well, Bren it's been delightful talking to you. Thank you for sharing what you guys at Hypergiant and SUSE have been doing together, the Department of Defense, the exciting things going on there and for the new definitions and my lexicon of the Edge, it's been great talking to you. >> Thank you, have a great day. >> You too. For Bren Briggs, I'm Lisa Martin. You're watching a CUBE Conversation. (digital music)

>>Welcome back to HPD discovered 2021. The virtual edition. My name is Dave Volonte and you're watching the cubes continuous coverage of H. P. S. Big customer event. Patrick Moorehead is here of moor insights and strategy is the number one analyst in the research analyst. Business. Patrick. Always a pleasure. Great to see you, >>David. Great to see you too. And I know you're you're up there fighting for that number one spot to. It's great to see you and it's great to see you in the meetings that were in. But it's even more fun to be here on the cube. I love to be on the cube and every once in a while you'll even call me a friend of the cube, >>unquestionably my friend and so and I can't wait second half. I mean you're traveling right now. We're headed to Barcelona to mobile World Congress later on this month. So so we're gonna we're gonna see each other face to face this year. 100%. So looking forward to that. So, you know, let's get into it. Um you know, before we get into H. P. E. Let's talk a little bit about what you're seeing in the market. We've got, you know, we we we finally, it feels like the on prem guys are finally getting their cloud act together. Um, it's maybe taken a while, but we're seeing as a service models emerge. I think it's resonating with customers. The clearly not everything is moving to the cloud. There's this hybrid model emerging. Multi cloud is real despite what, you know, >>some some >>cloud players want to say. And then there's this edges like jump ball, what are you seeing in the marketplace? >>Yeah. Davis, as exciting as ever in. Just to put in perspective, I mean, the public cloud has been around for about 10 years and still only 20%. Around 20% of the data in 20% of the applications are there now, albeit very important ones. And I'm certainly not a public cloud denier, I never have been, but there are some missing pieces that need to come together. And you know, even five years ago we were debating dave the hybrid cloud and I feel like when Amazon brought out outposts, the conversation was over right now, what you have is cloud native folks building out hybrid and on prem capabilities, you have the classic on prem folks building out hybrid and as a service capabilities. And I really think it boils down 22 things. I mean it's wanting to have more flexibility and you know, I hate to use it because it sounds like a marketing word, but agility, the ability to spin up things and spin down things in a very quick way. And uh, you know what they've learned. The veterans also know, hey, let's do this in a way that doesn't lock us in too much into a certain vendor. And I've been around for a long time. David and I'm a realist too. Well, you have to lock yourself into something. It just depends on what do you want to lock yourself into, but super exciting. And what H. P. E. When they threw the acts in the sea with Green Lake, I think it was four years ago, I think really started to stir the pot. >>You know, you mentioned the term cloud denial, but you know, and I feel like the narrative from, I like to determine is I think you should use the term veteran. You know, it's very, they're ours is the only industry patrick where legacy is a pejorative, but but but so but the point I want to make is I feel like there's been a lot of sort of fear from the veteran players, but I look at it differently. I wonder what you're taking. I think, I think, I think I calculated that the Capex spending by the big four public clouds including Alibaba last year was $100 billion. That's like a gift to the world. Here, we're going to spend $100 billion like the internet here you go build. And and so I, and I feel like companies like HP are finally saying, yeah, we're gonna build, we're gonna build a layer and we're gonna hide the complexity and we're gonna add value on top. What do you think about that? >>Yeah. So I think it's now, I wish, I wish the on prem folks like HP, you would have done it 10 years ago, but I don't think anybody expected the cloud to be as big as it's become over the last 10 years. I think we saw companies like salesforce with sas taking off, but I think it is the right direction because there are advantages to having workloads on prem and if you add an as a service capability on top of the top of that, and let's say even do a Coehlo or a managed service, it's pretty close to being similar to the public cloud with the exception, that you can't necessarily swipe a credit card for a bespoke workload if you're a developer and it is a little harder to scale out. But that is the next step in the equation day, which is having, having these folks make capital expenditures, make them in a polo facility and then put a layer to swipe a credit card and you literally have the public cloud. >>Yeah. So that's, that's a great point and that's where it's headed, isn't it? Um, so let's, let's talk about the horses on the track. Hp. As you mentioned, I didn't realize it was four years ago. I thought it was, wow, That's amazing. So everybody's followed suit. You see, Dallas announced, Cisco has announced, uh, Lenovo was announced, I think IBM as well. So we, so everybody started following suit there. The reality is, is it's taken some time to get this stuff standardized. What are you seeing from, from HP? They've made some additional announcements, discover what's your take on all this. >>Yeah. So HPD was definitely the rabbit here and they were first in the market. It was good to see, first off some of their, Um, announcements on, on how it's going. And they talked about 4, $28 billion 1200 customers over 900 partners and 95% retention. And I think that's important anybody who's in the lead and remember what Aws used to do with the slide with the amount of customers would just get bigger and bigger and bigger and that's a good way to show momentum. I like the retention part two which is 95%. And I think that that says a lot uh probably the more important announcements that they made is they talked about the G. A. Of some of their solutions on Green Lake and whether it was S. A. P. Hana Ml apps HPC with Francis V. I was Citrus in video but they also brought more of what I would call a vertical layer and I'm sure you've seen the vertical ization of all of these cloud and as a service workloads. But what they're doing with Epic with EMR and looseness, with financial payments and Splunk and intel with data and risk analysis and finally, a full stack for telco five G. One of the biggest secrets and I covered this about five years ago is HPV actually has a full stack that western european carriers use and they're now extending that to five G. And um, so more horizontal uh and and more vertical. That was the one of the big swipes uh that I saw that there was a second though, but maybe we can talk about these. >>Yeah. Okay, Okay. So, so the other piece of that of course is standardization right there there because there was a, there was, there was a lot of customization leading up to this and everybody sort of, everybody always had some kind of financial game they can play and say, hey, there's an adversary as a service model, but this is definitely more of a standardized scalable move that H P E. Is making with what they call Lighthouse, Right? >>Yeah, that's exactly right. And I've talked to some Green Lake customers and they obviously gave it kudos or they wouldn't have HP wouldn't have served them up and they wouldn't have been buying it. But they did say, um, it took, it took a while, took some paperwork to get it going. It's not 100% of push button, but that's partially because hp allows you to customize the hardware. You want a one off network adapter. Hp says yes, right. You want to integrate a different type of storage? They said yes. But with Green Lake Lighthouse, it's more of a, what you see is what you get, which by the way is very much like the public cloud or you go to a public cloud product sheet or order sheet. You're picking from a list and you really don't know everything that's underneath the covers, aside from, let's say the speed of the network, the type of the storage and the amount of the storage you get. You do get to pick between, let's say, an intel processor, Graviton two or an M. D processor. You get to pick your own GPU. But that's pretty much it. And HP Lighthouse, sorry, Green Lake Lighthouse uh, is bringing, I think a simplification to Green Lake that it needs to truly scale beyond, let's say, the white house customers at HP. Yeah, >>Well done. So, you know, and I hear your point about 10 years in, you know, plus and to me this is like a mandate. I mean, this is okay. Good, good job guys about time. But if I had a, you know, sort of look at the big players, like, can we have an oligopoly here in this, in this business? It's HP, Cisco, you got Dell Lenovo, you've got, you know, IBM, they're all doing this and they all have a different little difference, you know, waste of skin of catch. And your point about simplicity, it seems like HP HP is all in Antonio's like, okay, here's what we're going to announce that, you know, while ago, so, and they seem to have done a good job with Wall Street and they get a simple model, you know, Dell's obviously bigger portfolio, much more complicated. IBM is even more complicated than that. I don't know so much about Lenovo and in Cisco of course, has acquired a ton of SAAS companies and sort of they've got a lot of bespoke products that they're trying to put together, so they've got, but they do have SAS models. So each of them is coming at it from a different perspective. How do you think? And so and the other point we got lighthouse, which is sort of Phase one, get product market fit. Phase two now is scale codify standardized and then phase three is the moat build your unique advantage that protects your business. What do you see as HP? Es sort of unique value proposition and moat that they can build longer term. >>That's a great, great question. And let me rattle off kind of what I'm seeing that some of these these players here. So Cisco, ironically, has sells the most software of any of those players that you mentioned, uh with the exception of IBM. Um, and yeah, C >>ICSDB two. Yeah, >>yeah, they're the they're the number two security player, uh, Microsoft, number one. So and I think the evaluation on the street uh indicate that shows that I feel like uh Deltek is a is a very broad play because not only do they have servers, storage, networking and security, but they also have Pcs and devices, so it's a it's a scale and end play with a focus on VM ware solutions, not exclusively, of course. Uh And um then you've got Lenovo who is just getting into the as a service game and are gosh, they're doing great in hyper scale, they've got scale there vertically integrated. I don't know if if too many people talk about that, but Lenovo does a lot of their own manufacturing and they actually manufacture Netapp storage solutions as well. So yeah, each of these folks brings a different game to the table, I think with h P E, what your bring to the table is nimble. When HP and HP split, the number one thing that I said was that uh huh H P E is going to have to be so much faster than it offsets the scale that Dell technology has and the HBs credit, although there, I don't think we're getting credit for this in the stock market yet. Um, and I know you and I are both industry folks, not financial folks, but I think their biggest thing is speed and the ability to move faster and that is what I've seen as it relates to the moat, which is a unique uh, competitive advantage. Quite frankly, I'm still looking for that day in, in, in what that is and I think in this industry it's nearly impossible and I would posit that that any, even the cloud folks, if you say, is there something that AWS can do that Azure can't, if it put it put its mind to it or G C P. I don't think so. I think it's more of a kind of land and expand and I think for H P E, when it comes to high performance computing and I'm not just talking about government installations, I'm talking about product development, drug development, I think that is a landing place where H P E already does pretty well can come in and expand its footprint, >>you know, that's really interesting um, observations. So, and I would agree with you, it's kind of like, this is a copycat industry, it's like the west coast offense, like the NFL >>and >>so, so the moat comes from, you know, brand execution and your other point about when HP and HP split, that was a game changer, because all of a sudden you saw companies like them, you always had a long term relationship with H P E but or HP, but then they came out of the woodworks and started to explode. And so it really opened up opportunities. So it really >>is an execution, >>isn't it? But go ahead, please >>Dave if I had to pick something that I think HP HPV needs to always be ahead and as a service and listen, you know, I both know announcements don't mean delivery, but there is correlation between if you start four years ahead of somebody that other company is going to have to put just, I mean they're gonna have to turn that ship and many of its competitors really big ships to be able to get there. So I think what Antonio needs to do is run like hell, right, Because it, it, I think it is in the lead and as a service holistically doesn't mean they're going to be there forever, but they have to stay ahead. They have to add more horizontal solutions. They have to add more vertical solutions. And I believe that at some point it does need to invest in some Capex at somebody like ANna Quinn x play credit card swiper on top of that. And Dave, you have the public, you have the public cloud, you don't have all the availability zones, but you have a public cloud. >>Yeah, that's going to happen. I think you're right on. So we see this notion of cloud expanding. It's no longer just remote set of services. Somewhere out in the cloud. It's as you said, outpost was the sort of signal. Okay, We're coming on prem clearly the on prem, uh, guys are connecting to the cloud. Multi cloud exists, we know this and then there's the edge but but but that brings me to that sort of vision and everybody's laying out of this this this seamless integration hiding the complexity log into my cloud and then life will be good. But the edge is different. Right? It's not just, you know, retail store or a race track. I mean there's the far edge, there's the Tesla car, there's gonna be compute everywhere. And that sort of ties into the data. The data flows, you know the real time influencing at the edge ai new semiconductor models. You you came out of the semiconductor industry, you know it inside and out arm is exploding is dominating in the edge with with with apple and amazon Alexa and things like that. That's really where the action is. So this is a really interesting cocktail and soup that we have going on. How do you >>say? Well, you know, Dave if the data most data, I think one thing most everybody agrees on is that most of the data will be created on the edge. Whether that's a moving edge a car, a smartphone or what I call an edge data center without tile flooring. Like that server that's bolted to the wall of Mcdonald's. When you drive through, you can see it versus the walmart. Every walmart has a raised tile floor. It's the edge to economically and performance wise, it doesn't make any sense to send all that data to the mother ships. Okay. And whether that's unproven data center or the giant public cloud, more efficient way is to do the compute at the closest way possible. But what it does, it does bring up challenges. The first challenge is security. If I wanted to, I could walk in and I could take that server off the Mcdonald's or the Shell gas station wall. So I can't do that in a big data center. Okay, so security, Physical security is a challenge. The second is you don't have the people to go in there and fix stuff that are qualified. If you have a networking problem that goes wrong and Mcdonald's, there's nobody there that can help uh, they can they can help you fix that. So this notion of autonomy and management and not keeping hyper critical data sitting out there and it becomes it becomes a security issue becomes a management issue. Let me talk about the benefits though. The benefits are lower latency. You want you want answers more quickly when that car is driving down the road and it has a five G V two X communication cameras can't see around corners, but that car communicating ahead, that ran into the stop sign, can I through vi to X. Talk to the car behind it and say, hey, something is going on there, you can't go to, you can't go to the big data center in the sky to make that happen, that is to be in near real time and that computer has to happen on the edge. So I think this is a tremendous opportunity and ironically the classic on prem guys, they own this, they own this space aside from smartphones of course, but if you look at compute on a light pole, companies like Intel have built Complete architectures to do that, putting compute into 5G base stations. Heck, I just, there was an announcement this week of google cloud in its gaming solution putting compute in a carrier edge to give lower latency to deliver a better experience. >>Yeah, so there, of course there is no one edge, it's highly fragmented, but I'm interested in your thoughts on kind of who's stack actually can play at the edge. And I've been sort of poking uh H P E about this. And the one thing that comes back consistently is Aruba, we we can take a room but not only to the, to the near edge, but to the far edge. And and that, do you see that as a competitive advantage? >>Oh gosh, yes. I mean, I would say the best acquisition That hp has made in 10 years has been aruba it's fantastic. And they also managed it in the right way. I mean, it was part of HB but it was it was managed a lot more loosely then, you know, a company that might get sucked into the board. And I think that paid off tremendously. They're giving Cisco on the edge a absolute run for their money, their first with new technologies. But it's about the solution. What I love about what a ruble looks at is it's looking at entertainment solutions inside of a stadium, um a information solution inside of an airport as opposed to just pushing the technology forward. And then when you integrate compute with with with Aruba, I think that's where the real magic happens. Most of the data on a permanent basis is actually video data. And a lot of it's for security uh for surveillance. And quite frankly, people taking videos off, they're off their smartphones and downloading video. I I just interviewed the chief network officer of T mobile and their number one bit of data is video, video uploaded, video download. But that's where the magic happens when you put that connectivity and the compute together and you can manage it in a, in an orderly and secure fashion >>while I have you, we have a ton of time here, but I I don't pick your brain about intel, the future of intel. I know you've been following it quite closely, you always have Intel's fighting a forefront war. You got there, battling A. M. D. There, battling your arm slash and video. They're they're taking on TSMC now and in foundry and, and I'll add china for the looming threat there. So what's your prognosis for for intel? >>Yeah, I liked bob the previous Ceo and I think he was doing a lot of of the right things, but I really think that customers and investors and even their ecosystem wanted somebody leading the company with a high degree of technical aptitude and Pat coming, I mean, Pat had a great job at VM or, I mean, he had a great run there and I think it is a very positive move. I've never seen the energy At Intel probably in the last 10 years that I've seen today. I actually got a chance to talk with pat. I visited pat uhh last month and and talk to him about pretty much everything and where he wanted to take the company the way you looked at technology, what was important, what's not important. But I think first off in the world of semiconductors, there are no quick fixes. Okay. Intel has a another two years Before we see what the results are. And I think 2023 for them is gonna be a huge year. But even with all this competition though, Dave they still have close to 85% market share in servers and revenue share for client computing around 90%. Okay. So and they've built out there networking business, they build out a storage business um with with obtain they have the leading Aid as provider with Mobileye. And and listen I was I was one of Intel's biggest, I was into one of Intel's biggest, I was Intel's biggest customer when I was a compact. I was their biggest competitor at AMG. So um I'm not obviously not overly pushing or there's just got to wait and see. They're doing the right things. They have the right strategy. They need to execute. One of the most important things That Intel did is extend their alliance with TSMC. So in 2023 we're going to see Intel compute units these tiles, they integrate into the larger chips called S. O. C S B. Manufactured by TSMC. Not exclusively, but we could see that. So literally we could have AMG three nanometer on TSMC CPU blocks, competing with intel chips with TSMC three nanometer CPU blocks and it's on with regard to video. I mean in video is one of these companies that just keeps going charging, charging hard and I'm actually meeting with Jensen wang this week and Arms Ceo Simon Segers to talk about this opportunity and that's a company that keeps on moving interestingly enough in video. If the arm deal does go through will be the largest chip license, see CPU licensee and have the largest CPU footprint on the planet. So here we have AMG who's CPU and Gpu and buying an F. P. G. A company called Xilinx, you have Intel, Cpus, Gpus machine learning accelerators and F. P. G. S. And then you've got arms slashing video bit with everything as well. We have three massive ecosystems. They're gonna be colliding here and I think it's gonna be great for competition. Date. Competition is great. You know, when there's not competition in CPUs and Gpus, we know what happens right. Uh, the beach just does not go on and we start to stagnate. And I did, I do feel like the industry on CPU started to stagnate when intel had no competition. So bring it on. This is gonna be great for for enterprises then customers to and then, oh, by the way, you have the custom Chip providers. WS has created no less than 15 custom semiconductors started with networking and nitro and building out an edge that surrounded the general computer. And then it moved to Inferential for inference trainee um, is about to come out for training Graviton and Gravitas to for general purpose CPU and then you've got apple. So innovation is huge and I love to always make fun of the software is eating the world. I always say yeah but has to run on something. And so I think the combination of semiconductors software and cloud is just really a magical combination. >>Real quick handicap the video arm acquisition. What what are the odds that that they will be successful? They say it's on track. You got a 2 to 13 to 1 10 to 1. >>I say 75%. Yes 25%. No China is always the has been the odd odd man out for the last three years. They scuttled the Qualcomm NXp deal. You just don't know what china is going to do. I think the EU with some conditions is going to let this fly. I think the U. S. Is absolutely going to let this fly. And even though the I. P. Will still stay over in the UK, I think the U. S. Wants to see wants to see this happen, Japan and Korea I think we'll allow this china is the odd man out. >>In a word, the future of h p. E is blank >>as a service >>patrick Moorehead. Always a pleasure. My friend. Great to see you. Thanks so much for coming back in the cube. >>Yeah, Thanks for having me on. I appreciate that. >>Everybody stay tuned for more great coverage from HP discover 21 this is day Volonte for the cube. The leader and enterprise tech coverage. We'll be right back.

>>Individuals create developers, translate ideas to code, to create great applications and great applications. Touch everyone. A Docker. We know that collaboration is key to your innovation sharing ideas, working together. Launching the most secure applications. Docker is with you wherever your team innovates, whether it be robots or autonomous cars, we're doing research to save lives during a pandemic, revolutionizing, how to buy and sell goods online, or even going into the unknown frontiers of space. Docker is launching innovation everywhere. Join us on the journey to build, share, run the future. >>Hello and welcome to Docker con 2021. We're incredibly excited to have more than 80,000 of you join us today from all over the world. As it was last year, this year at DockerCon is 100% virtual and 100% free. So as to enable as many community members as possible to join us now, 100%. Virtual is also an acknowledgement of the continuing global pandemic in particular, the ongoing tragedies in India and Brazil, the Docker community is a global one. And on behalf of all Dr. Khan attendees, we are donating $10,000 to UNICEF support efforts to fight the virus in those countries. Now, even in those regions of the world where the pandemic is being brought under control, virtual first is the new normal. It's been a challenging transition. This includes our team here at Docker. And we know from talking with many of you that you and your developer teams are challenged by this as well. So to help application development teams better collaborate and ship faster, we've been working on some powerful new features and we thought it would be fun to start off with a demo of those. How about it? Want to have a look? All right. Then no further delay. I'd like to introduce Youi Cal and Ben, gosh, over to you and Ben >>Morning, Ben, thanks for jumping on real quick. >>Have you seen the email from Scott? The one about updates and the docs landing page Smith, the doc combat and more prominence. >>Yeah. I've got something working on my local machine. I haven't committed anything yet. I was thinking we could try, um, that new Docker dev environments feature. >>Yeah, that's cool. So if you hit the share button, what I should do is it will take all of your code and the dependencies and the image you're basing it on and wrap that up as one image for me. And I can then just monitor all my machines that have been one click, like, and then have it side by side, along with the changes I've been looking at as well, because I was also having a bit of a look and then I can really see how it differs to what I'm doing. Maybe I can combine it to do the best of both worlds. >>Sounds good. Uh, let me get that over to you, >>Wilson. Yeah. If you pay with the image name, I'll get that started up. >>All right. Sen send it over >>Cheesy. Okay, great. Let's have a quick look at what you he was doing then. So I've been messing around similar to do with the batter. I've got movie at the top here and I think it looks pretty cool. Let's just grab that image from you. Pick out that started on a dev environment. What this is doing. It's just going to grab the image down, which you can take all of the code, the dependencies only get brunches working on and I'll get that opened up in my idea. Ready to use. It's a here close. We can see our environment as my Molly image, just coming down there and I've got my new idea. >>We'll load this up and it'll just connect to my dev environment. There we go. It's connected to the container. So we're working all in the container here and now give it a moment. What we'll do is we'll see what changes you've been making as well on the code. So it's like she's been working on a landing page as well, and it looks like she's been changing the banner as well. So let's get this running. Let's see what she's actually doing and how it looks. We'll set up our checklist and then we'll see how that works. >>Great. So that's now rolling. So let's just have a look at what you use doing what changes she had made. Compare those to mine just jumped back into my dev container UI, see that I've got both of those running side by side with my changes and news changes. Okay. So she's put Molly up there rather than mobi or somebody had the same idea. So I think in a way I can make us both happy. So if we just jumped back into what we'll do, just add Molly and Moby and here I'll save that. And what we can see is, cause I'm just working within the container rather than having to do sort of rebuild of everything or serve, or just reload my content. No, that's straight the page. So what I can then do is I can come up with my browser here. Once that's all refreshed, refresh the page once hopefully, maybe twice, we should then be able to see your refresh it or should be able to see that we get Malia mobi come up. So there we go, got Molly mobi. So what we'll do now is we'll describe that state. It sends us our image and then we'll just create one of those to share with URI or share. And we'll get a link for that. I guess we'll send that back over to you. >>So I've had a look at what you were doing and I'm actually going to change. I think that might work for both of us. I wondered if you could take a look at it. If I send it over. >>Sounds good. Let me grab the link. >>Yeah, it's a dev environment link again. So if you just open that back in the doc dashboard, it should be able to open up the code that I've changed and then just run it in the same way you normally do. And that shouldn't interrupt what you're already working on because there'll be able to run side by side with your other brunch. You already got, >>Got it. Got it. Loading here. Well, that's great. It's Molly and movie together. I love it. I think we should ship it. >>Awesome. I guess it's chip it and get on with the rest of.com. Wasn't that cool. Thank you Joey. Thanks Ben. Everyone we'll have more of this later in the keynote. So stay tuned. Let's say earlier, we've all been challenged by this past year, whether the COVID pandemic, the complete evaporation of customer demand in many industries, unemployment or business bankruptcies, we all been touched in some way. And yet, even to miss these tragedies last year, we saw multiple sources of hope and inspiration. For example, in response to COVID we saw global communities, including the tech community rapidly innovate solutions for analyzing the spread of the virus, sequencing its genes and visualizing infection rates. In fact, if all in teams collaborating on solutions for COVID have created more than 1,400 publicly shareable images on Docker hub. As another example, we all witnessed the historic landing and exploration of Mars by the perseverance Rover and its ingenuity drone. >>Now what's common in these examples, these innovative and ambitious accomplishments were made possible not by any single individual, but by teams of individuals collaborating together. The power of teams is why we've made development teams central to Docker's mission to build tools and content development teams love to help them get their ideas from code to cloud as quickly as possible. One of the frictions we've seen that can slow down to them in teams is that the path from code to cloud can be a confusing one, riddle with multiple point products, tools, and images that need to be integrated and maintained an automated pipeline in order for teams to be productive. That's why a year and a half ago we refocused Docker on helping development teams make sense of all this specifically, our goal is to provide development teams with the trusted content, the sharing capabilities and the pipeline integrations with best of breed third-party tools to help teams ship faster in short, to provide a collaborative application development platform. >>Everything a team needs to build. Sharon run create applications. Now, as I noted earlier, it's been a challenging year for everyone on our planet and has been similar for us here at Docker. Our team had to adapt to working from home local lockdowns caused by the pandemic and other challenges. And despite all this together with our community and ecosystem partners, we accomplished many exciting milestones. For example, in open source together with the community and our partners, we open sourced or made major contributions to many projects, including OCI distribution and the composed plugins building on these open source projects. We had powerful new capabilities to the Docker product, both free and subscription. For example, support for WSL two and apple, Silicon and Docker, desktop and vulnerability scanning audit logs and image management and Docker hub. >>And finally delivering an easy to use well-integrated development experience with best of breed tools and content is only possible through close collaboration with our ecosystem partners. For example, this last year we had over 100 commercialized fees, join our Docker verified publisher program and over 200 open source projects, join our Docker sponsored open source program. As a result of these efforts, we've seen some exciting growth in the Docker community in the 12 months since last year's Docker con for example, the number of registered developers grew 80% to over 8 million. These developers created many new images increasing the total by 56% to almost 11 million. And the images in all these repositories were pulled by more than 13 million monthly active IP addresses totaling 13 billion pulls a month. Now while the growth is exciting by Docker, we're even more excited about the stories we hear from you and your development teams about how you're using Docker and its impact on your businesses. For example, cancer researchers and their bioinformatics development team at the Washington university school of medicine needed a way to quickly analyze their clinical trial results and then share the models, the data and the analysis with other researchers they use Docker because it gives them the ease of use choice of pipeline tools and speed of sharing so critical to their research. And most importantly to the lives of their patients stay tuned for another powerful customer story later in the keynote from Matt fall, VP of engineering at Oracle insights. >>So with this last year behind us, what's next for Docker, but challenge you this last year of force changes in how development teams work, but we felt for years to come. And what we've learned in our discussions with you will have long lasting impact on our product roadmap. One of the biggest takeaways from those discussions that you and your development team want to be quicker to adapt, to changes in your environment so you can ship faster. So what is DACA doing to help with this first trusted content to own the teams that can focus their energies on what is unique to their businesses and spend as little time as possible on undifferentiated work are able to adapt more quickly and ship faster in order to do so. They need to be able to trust other components that make up their app together with our partners. >>Docker is doubling down and providing development teams with trusted content and the tools they need to use it in their applications. Second, remote collaboration on a development team, asking a coworker to take a look at your code used to be as easy as swiveling their chair around, but given what's happened in the last year, that's no longer the case. So as you even been hinted in the demo at the beginning, you'll see us deliver more capabilities for remote collaboration within a development team. And we're enabling development team to quickly adapt to any team configuration all on prem hybrid, all work from home, helping them remain productive and focused on shipping third ecosystem integrations, those development teams that can quickly take advantage of innovations throughout the ecosystem. Instead of getting locked into a single monolithic pipeline, there'll be the ones able to deliver amps, which impact their businesses faster. >>So together with our ecosystem partners, we are investing in more integrations with best of breed tools, right? Integrated automated app pipelines. Furthermore, we'll be writing more public API APIs and SDKs to enable ecosystem partners and development teams to roll their own integrations. We'll be sharing more details about remote collaboration and ecosystem integrations. Later in the keynote, I'd like to take a moment to share with Docker and our partners are doing for trusted content, providing development teams, access to content. They can trust, allows them to focus their coding efforts on what's unique and differentiated to that end Docker and our partners are bringing more and more trusted content to Docker hub Docker official images are 160 images of popular upstream open source projects that serve as foundational building blocks for any application. These include operating systems, programming, languages, databases, and more. Furthermore, these are updated patch scan and certified frequently. So I said, no image is older than 30 days. >>Docker verified publisher images are published by more than 100 commercialized feeds. The image Rebos are explicitly designated verify. So the developers searching for components for their app know that the ISV is actively maintaining the image. Docker sponsored open source projects announced late last year features images for more than 200 open source communities. Docker sponsors these communities through providing free storage and networking resources and offering their community members unrestricted access repos for businesses allow businesses to update and share their apps privately within their organizations using role-based access control and user authentication. No, and finally, public repos for communities enable community projects to be freely shared with anonymous and authenticated users alike. >>And for all these different types of content, we provide services for both development teams and ISP, for example, vulnerability scanning and digital signing for enhanced security search and filtering for discoverability packaging and updating services and analytics about how these products are being used. All this trusted content, we make available to develop teams for them directly to discover poll and integrate into their applications. Our goal is to meet development teams where they live. So for those organizations that prefer to manage their internal distribution of trusted content, we've collaborated with leading container registry partners. We announced our partnership with J frog late last year. And today we're very pleased to announce our partnerships with Amazon and Miranda's for providing an integrated seamless experience for joint for our joint customers. Lastly, the container images themselves and this end to end flow are built on open industry standards, which provided all the teams with flexibility and choice trusted content enables development teams to rapidly build. >>As I let them focus on their unique differentiated features and use trusted building blocks for the rest. We'll be talking more about trusted content as well as remote collaboration and ecosystem integrations later in the keynote. Now ecosystem partners are not only integral to the Docker experience for development teams. They're also integral to a great DockerCon experience, but please join me in thanking our Dr. Kent on sponsors and checking out their talks throughout the day. I also want to thank some others first up Docker team. Like all of you this last year has been extremely challenging for us, but the Docker team rose to the challenge and worked together to continue shipping great product, the Docker community of captains, community leaders, and contributors with your welcoming newcomers, enthusiasm for Docker and open exchanges of best practices and ideas talker, wouldn't be Docker without you. And finally, our development team customers. >>You trust us to help you build apps. Your businesses rely on. We don't take that trust for granted. Thank you. In closing, we often hear about the tenant's developer capable of great individual feeds that can transform project. But I wonder if we, as an industry have perhaps gotten this wrong by putting so much emphasis on weight, on the individual as discussed at the beginning, great accomplishments like innovative responses to COVID-19 like landing on Mars are more often the results of individuals collaborating together as a team, which is why our mission here at Docker is delivered tools and content developers love to help their team succeed and become 10 X teams. Thanks again for joining us, we look forward to having a great DockerCon with you today, as well as a great year ahead of us. Thanks and be well. >>Hi, I'm Dana Lawson, VP of engineering here at get hub. And my job is to enable this rich interconnected community of builders and makers to build even more and hopefully have a great time doing it in order to enable the best platform for developers, which I know is something we are all passionate about. We need to partner across the ecosystem to ensure that developers can have a great experience across get hub and all the tools that they want to use. No matter what they are. My team works to build the tools and relationships to make that possible. I am so excited to join Scott on this virtual stage to talk about increasing developer velocity. So let's dive in now, I know this may be hard for some of you to believe, but as a former CIS admin, some 21 years ago, working on sense spark workstations, we've come such a long way for random scripts and desperate systems that we've stitched together to this whole inclusive developer workflow experience being a CIS admin. >>Then you were just one piece of the siloed experience, but I didn't want to just push code to production. So I created scripts that did it for me. I taught myself how to code. I was the model lazy CIS admin that got dangerous and having pushed a little too far. I realized that working in production and building features is really a team sport that we had the opportunity, all of us to be customer obsessed today. As developers, we can go beyond the traditional dev ops mindset. We can really focus on adding value to the customer experience by ensuring that we have work that contributes to increasing uptime via and SLS all while being agile and productive. We get there. When we move from a pass the Baton system to now having an interconnected developer workflow that increases velocity in every part of the cycle, we get to work better and smarter. >>And honestly, in a way that is so much more enjoyable because we automate away all the mundane and manual and boring tasks. So we get to focus on what really matters shipping, the things that humans get to use and love. Docker has been a big part of enabling this transformation. 10, 20 years ago, we had Tomcat containers, which are not Docker containers. And for y'all hearing this the first time go Google it. But that was the way we built our applications. We had to segment them on the server and give them resources. Today. We have Docker containers, these little mini Oasys and Docker images. You can do it multiple times in an orchestrated manner with the power of actions enabled and Docker. It's just so incredible what you can do. And by the way, I'm showing you actions in Docker, which I hope you use because both are great and free for open source. >>But the key takeaway is really the workflow and the automation, which you certainly can do with other tools. Okay, I'm going to show you just how easy this is, because believe me, if this is something I can learn and do anybody out there can, and in this demo, I'll show you about the basic components needed to create and use a package, Docker container actions. And like I said, you won't believe how awesome the combination of Docker and actions is because you can enable your workflow to do no matter what you're trying to do in this super baby example. We're so small. You could take like 10 seconds. Like I am here creating an action due to a simple task, like pushing a message to your logs. And the cool thing is you can use it on any the bit on this one. Like I said, we're going to use push. >>You can do, uh, even to order a pizza every time you roll into production, if you wanted, but at get hub, that'd be a lot of pizzas. And the funny thing is somebody out there is actually tried this and written that action. If you haven't used Docker and actions together, check out the docs on either get hub or Docker to get you started. And a huge shout out to all those doc writers out there. I built this demo today using those instructions. And if I can do it, I know you can too, but enough yapping let's get started to save some time. And since a lot of us are Docker and get hub nerds, I've already created a repo with a Docker file. So we're going to skip that step. Next. I'm going to create an action's Yammel file. And if you don't Yammer, you know, actions, the metadata defines my important log stuff to capture and the input and my time out per parameter to pass and puts to the Docker container, get up a build image from your Docker file and run the commands in a new container. >>Using the Sigma image. The cool thing is, is you can use any Docker image in any language for your actions. It doesn't matter if it's go or whatever in today's I'm going to use a shell script and an input variable to print my important log stuff to file. And like I said, you know me, I love me some. So let's see this action in a workflow. When an action is in a private repo, like the one I demonstrating today, the action can only be used in workflows in the same repository, but public actions can be used by workflows in any repository. So unfortunately you won't get access to the super awesome action, but don't worry in the Guild marketplace, there are over 8,000 actions available, especially the most important one, that pizza action. So go try it out. Now you can do this in a couple of ways, whether you're doing it in your preferred ID or for today's demo, I'm just going to use the gooey. I'm going to navigate to my actions tab as I've done here. And I'm going to in my workflow, select new work, hello, probably load some workflows to Claire to get you started, but I'm using the one I've copied. Like I said, the lazy developer I am in. I'm going to replace it with my action. >>That's it. So now we're going to go and we're going to start our commitment new file. Now, if we go over to our actions tab, we can see the workflow in progress in my repository. I just click the actions tab. And because they wrote the actions on push, we can watch the visualization under jobs and click the job to see the important stuff we're logging in the input stamp in the printed log. And we'll just wait for this to run. Hello, Mona and boom. Just like that. It runs automatically within our action. We told it to go run as soon as the files updated because we're doing it on push merge. That's right. Folks in just a few minutes, I built an action that writes an entry to a log file every time I push. So I don't have to do it manually. In essence, with automation, you can be kind to your future self and save time and effort to focus on what really matters. >>Imagine what I could do with even a little more time, probably order all y'all pieces. That is the power of the interconnected workflow. And it's amazing. And I hope you all go try it out, but why do we care about all of that? Just like in the demo, I took a manual task with both tape, which both takes time and it's easy to forget and automated it. So I don't have to think about it. And it's executed every time consistently. That means less time for me to worry about my human errors and mistakes, and more time to focus on actually building the cool stuff that people want. Obviously, automation, developer productivity, but what is even more important to me is the developer happiness tools like BS, code actions, Docker, Heroku, and many others reduce manual work, which allows us to focus on building things that are awesome. >>And to get into that wonderful state that we call flow. According to research by UC Irvine in Humboldt university in Germany, it takes an average of 23 minutes to enter optimal creative state. What we call the flow or to reenter it after distraction like your dog on your office store. So staying in flow is so critical to developer productivity and as a developer, it just feels good to be cranking away at something with deep focus. I certainly know that I love that feeling intuitive collaboration and automation features we built in to get hub help developer, Sam flow, allowing you and your team to do so much more, to bring the benefits of automation into perspective in our annual October's report by Dr. Nicole, Forsgren. One of my buddies here at get hub, took a look at the developer productivity in the stork year. You know what we found? >>We found that public GitHub repositories that use the Automational pull requests, merge those pull requests. 1.2 times faster. And the number of pooled merged pull requests increased by 1.3 times, that is 34% more poor requests merged. And other words, automation can con can dramatically increase, but the speed and quantity of work completed in any role, just like an open source development, you'll work more efficiently with greater impact when you invest the bulk of your time in the work that adds the most value and eliminate or outsource the rest because you don't need to do it, make the machines by elaborate by leveraging automation in their workflows teams, minimize manual work and reclaim that time for innovation and maintain that state of flow with development and collaboration. More importantly, their work is more enjoyable because they're not wasting the time doing the things that the machines or robots can do for them. >>And I remember what I said at the beginning. Many of us want to be efficient, heck even lazy. So why would I spend my time doing something I can automate? Now you can read more about this research behind the art behind this at October set, get hub.com, which also includes a lot of other cool info about the open source ecosystem and how it's evolving. Speaking of the open source ecosystem we at get hub are so honored to be the home of more than 65 million developers who build software together for everywhere across the globe. Today, we're seeing software development taking shape as the world's largest team sport, where development teams collaborate, build and ship products. It's no longer a solo effort like it was for me. You don't have to take my word for it. Check out this globe. This globe shows real data. Every speck of light you see here represents a contribution to an open source project, somewhere on earth. >>These arts reach across continents, cultures, and other divides. It's distributed collaboration at its finest. 20 years ago, we had no concept of dev ops, SecOps and lots, or the new ops that are going to be happening. But today's development and ops teams are connected like ever before. This is only going to continue to evolve at a rapid pace, especially as we continue to empower the next hundred million developers, automation helps us focus on what's important and to greatly accelerate innovation. Just this past year, we saw some of the most groundbreaking technological advancements and achievements I'll say ever, including critical COVID-19 vaccine trials, as well as the first power flight on Mars. This past month, these breakthroughs were only possible because of the interconnected collaborative open source communities on get hub and the amazing tools and workflows that empower us all to create and innovate. Let's continue building, integrating, and automating. So we collectively can give developers the experience. They deserve all of the automation and beautiful eye UIs that we can muster so they can continue to build the things that truly do change the world. Thank you again for having me today, Dr. Khan, it has been a pleasure to be here with all you nerds. >>Hello. I'm Justin. Komack lovely to see you here. Talking to developers, their world is getting much more complex. Developers are being asked to do everything security ops on goal data analysis, all being put on the rockers. Software's eating the world. Of course, and this all make sense in that view, but they need help. One team. I told you it's shifted all our.net apps to run on Linux from windows, but their developers found the complexity of Docker files based on the Linux shell scripts really difficult has helped make these things easier for your teams. Your ones collaborate more in a virtual world, but you've asked us to make this simpler and more lightweight. You, the developers have asked for a paved road experience. You want things to just work with a simple options to be there, but it's not just the paved road. You also want to be able to go off-road and do interesting and different things. >>Use different components, experiments, innovate as well. We'll always offer you both those choices at different times. Different developers want different things. It may shift for ones the other paved road or off road. Sometimes you want reliability, dependability in the zone for day to day work, but sometimes you have to do something new, incorporate new things in your pipeline, build applications for new places. Then you knew those off-road abilities too. So you can really get under the hood and go and build something weird and wonderful and amazing. That gives you new options. Talk as an independent choice. We don't own the roads. We're not pushing you into any technology choices because we own them. We're really supporting and driving open standards, such as ISEI working opensource with the CNCF. We want to help you get your applications from your laptops, the clouds, and beyond, even into space. >>Let's talk about the key focus areas, that frame, what DACA is doing going forward. These are simplicity, sharing, flexibility, trusted content and care supply chain compared to building where the underlying kernel primitives like namespaces and Seagraves the original Docker CLI was just amazing Docker engine. It's a magical experience for everyone. It really brought those innovations and put them in a world where anyone would use that, but that's not enough. We need to continue to innovate. And it was trying to get more done faster all the time. And there's a lot more we can do. We're here to take complexity away from deeply complicated underlying things and give developers tools that are just amazing and magical. One of the area we haven't done enough and make things magical enough that we're really planning around now is that, you know, Docker images, uh, they're the key parts of your application, but you know, how do I do something with an image? How do I, where do I attach volumes with this image? What's the API. Whereas the SDK for this image, how do I find an example or docs in an API driven world? Every bit of software should have an API and an API description. And our vision is that every container should have this API description and the ability for you to understand how to use it. And it's all a seamless thing from, you know, from your code to the cloud local and remote, you can, you can use containers in this amazing and exciting way. >>One thing I really noticed in the last year is that companies that started off remote fast have constant collaboration. They have zoom calls, apron all day terminals, shattering that always working together. Other teams are really trying to learn how to do this style because they didn't start like that. We used to walk around to other people's desks or share services on the local office network. And it's very difficult to do that anymore. You want sharing to be really simple, lightweight, and informal. Let me try your container or just maybe let's collaborate on this together. Um, you know, fast collaboration on the analysts, fast iteration, fast working together, and he wants to share more. You want to share how to develop environments, not just an image. And we all work by seeing something someone else in our team is doing saying, how can I do that too? I can, I want to make that sharing really, really easy. Ben's going to talk about this more in the interest of one minute. >>We know how you're excited by apple. Silicon and gravis are not excited because there's a new architecture, but excited because it's faster, cooler, cheaper, better, and offers new possibilities. The M one support was the most asked for thing on our public roadmap, EFA, and we listened and share that we see really exciting possibilities, usership arm applications, all the way from desktop to production. We know that you all use different clouds and different bases have deployed to, um, you know, we work with AWS and Azure and Google and more, um, and we want to help you ship on prime as well. And we know that you use huge number of languages and the containers help build applications that use different languages for different parts of the application or for different applications, right? You can choose the best tool. You have JavaScript hat or everywhere go. And re-ask Python for data and ML, perhaps getting excited about WebAssembly after hearing about a cube con, you know, there's all sorts of things. >>So we need to make that as easier. We've been running the whole month of Python on the blog, and we're doing a month of JavaScript because we had one specific support about how do I best put this language into production of that language into production. That detail is important for you. GPS have been difficult to use. We've added GPS suppose in desktop for windows, but we know there's a lot more to do to make the, how multi architecture, multi hardware, multi accelerator world work better and also securely. Um, so there's a lot more work to do to support you in all these things you want to do. >>How do we start building a tenor has applications, but it turns out we're using existing images as components. I couldn't assist survey earlier this year, almost half of container image usage was public images rather than private images. And this is growing rapidly. Almost all software has open source components and maybe 85% of the average application is open source code. And what you're doing is taking whole container images as modules in your application. And this was always the model with Docker compose. And it's a model that you're already et cetera, writing you trust Docker, official images. We know that they might go to 25% of poles on Docker hub and Docker hub provides you the widest choice and the best support that trusted content. We're talking to people about how to make this more helpful. We know, for example, that winter 69 four is just showing us as support, but the image doesn't yet tell you that we're working with canonical to improve messaging from specific images about left lifecycle and support. >>We know that you need more images, regularly updated free of vulnerabilities, easy to use and discover, and Donnie and Marie neuro, going to talk about that more this last year, the solar winds attack has been in the, in the news. A lot, the software you're using and trusting could be compromised and might be all over your organization. We need to reduce the risk of using vital open-source components. We're seeing more software supply chain attacks being targeted as the supply chain, because it's often an easier place to attack and production software. We need to be able to use this external code safely. We need to, everyone needs to start from trusted sources like photography images. They need to scan for known vulnerabilities using Docker scan that we built in partnership with sneak and lost DockerCon last year, we need just keep updating base images and dependencies, and we'll, we're going to help you have the control and understanding about your images that you need to do this. >>And there's more, we're also working on the nursery V2 project in the CNCF to revamp container signings, or you can tell way or software comes from we're working on tooling to make updates easier, and to help you understand and manage all the principals carrier you're using security is a growing concern for all of us. It's really important. And we're going to help you work with security. We can't achieve all our dreams, whether that's space travel or amazing developer products ever see without deep partnerships with our community to cloud is RA and the cloud providers aware most of you ship your occasion production and simple routes that take your work and deploy it easily. Reliably and securely are really important. Just get into production simply and easily and securely. And we've done a bunch of work on that. And, um, but we know there's more to do. >>The CNCF on the open source cloud native community are an amazing ecosystem of creators and lovely people creating an amazing strong community and supporting a huge amount of innovation has its roots in the container ecosystem and his dreams beyond that much of the innovation is focused around operate experience so far, but developer experience is really a growing concern in that community as well. And we're really excited to work on that. We also uses appraiser tool. Then we know you do, and we know that you want it to be easier to use in your environment. We just shifted Docker hub to work on, um, Kubernetes fully. And, um, we're also using many of the other projects are Argo from atheists. We're spending a lot of time working with Microsoft, Amazon right now on getting natural UV to ready to ship in the next few. That's a really detailed piece of collaboration we've been working on for a long term. Long time is really important for our community as the scarcity of the container containers and, um, getting content for you, working together makes us stronger. Our community is made up of all of you have. Um, it's always amazing to be reminded of that as a huge open source community that we already proud to work with. It's an amazing amount of innovation that you're all creating and where perhaps it, what with you and share with you as well. Thank you very much. And thank you for being here. >>Really excited to talk to you today and share more about what Docker is doing to help make you faster, make your team faster and turn your application delivery into something that makes you a 10 X team. What we're hearing from you, the developers using Docker everyday fits across three common themes that we hear consistently over and over. We hear that your time is super important. It's critical, and you want to move faster. You want your tools to get out of your way, and instead to enable you to accelerate and focus on the things you want to be doing. And part of that is that finding great content, great application components that you can incorporate into your apps to move faster is really hard. It's hard to discover. It's hard to find high quality content that you can trust that, you know, passes your test and your configuration needs. >>And it's hard to create good content as well. And you're looking for more safety, more guardrails to help guide you along that way so that you can focus on creating value for your company. Secondly, you're telling us that it's a really far to collaborate effectively with your team and you want to do more, to work more effectively together to help your tools become more and more seamless to help you stay in sync, both with yourself across all of your development environments, as well as with your teammates so that you can more effectively collaborate together. Review each other's work, maintain things and keep them in sync. And finally, you want your applications to run consistently in every single environment, whether that's your local development environment, a cloud-based development environment, your CGI pipeline, or the cloud for production, and you want that micro service to provide that consistent experience everywhere you go so that you have similar tools, similar environments, and you don't need to worry about things getting in your way, but instead things make it easy for you to focus on what you wanna do and what Docker is doing to help solve all of these problems for you and your colleagues is creating a collaborative app dev platform. >>And this collaborative application development platform consists of multiple different pieces. I'm not going to walk through all of them today, but the overall view is that we're providing all the tooling you need from the development environment, to the container images, to the collaboration services, to the pipelines and integrations that enable you to focus on making your applications amazing and changing the world. If we start zooming on a one of those aspects, collaboration we hear from developers regularly is that they're challenged in synchronizing their own setups across environments. They want to be able to duplicate the setup of their teammates. Look, then they can easily get up and running with the same applications, the same tooling, the same version of the same libraries, the same frameworks. And they want to know if their applications are good before they're ready to share them in an official space. >>They want to collaborate on things before they're done, rather than feeling like they have to officially published something before they can effectively share it with others to work on it, to solve this. We're thrilled today to announce Docker, dev environments, Docker, dev environments, transform how your team collaborates. They make creating, sharing standardized development environments. As simple as a Docker poll, they make it easy to review your colleagues work without affecting your own work. And they increase the reproducibility of your own work and decreased production issues in doing so because you've got consistent environments all the way through. Now, I'm going to pass it off to our principal product manager, Ben Gotch to walk you through more detail on Docker dev environments. >>Hi, I'm Ben. I work as a principal program manager at DACA. One of the areas that doc has been looking at to see what's hard today for developers is sharing changes that you make from the inner loop where the inner loop is a better development, where you write code, test it, build it, run it, and ultimately get feedback on those changes before you merge them and try and actually ship them out to production. Most amount of us build this flow and get there still leaves a lot of challenges. People need to jump between branches to look at each other's work. Independence. Dependencies can be different when you're doing that and doing this in this new hybrid wall of work. Isn't any easier either the ability to just save someone, Hey, come and check this out. It's become much harder. People can't come and sit down at your desk or take your laptop away for 10 minutes to just grab and look at what you're doing. >>A lot of the reason that development is hard when you're remote, is that looking at changes and what's going on requires more than just code requires all the dependencies and everything you've got set up and that complete context of your development environment, to understand what you're doing and solving this in a remote first world is hard. We wanted to look at how we could make this better. Let's do that in a way that let you keep working the way you do today. Didn't want you to have to use a browser. We didn't want you to have to use a new idea. And we wanted to do this in a way that was application centric. We wanted to let you work with all the rest of the application already using C for all the services and all those dependencies you need as part of that. And with that, we're excited to talk more about docket developer environments, dev environments are new part of the Docker experience that makes it easier you to get started with your whole inner leap, working inside a container, then able to share and collaborate more than just the code. >>We want it to enable you to share your whole modern development environment, your whole setup from DACA, with your team on any operating system, we'll be launching a limited beta of dev environments in the coming month. And a GA dev environments will be ID agnostic and supporting composts. This means you'll be able to use an extend your existing composed files to create your own development environment in whatever idea, working in dev environments designed to be local. First, they work with Docker desktop and say your existing ID, and let you share that whole inner loop, that whole development context, all of your teammates in just one collect. This means if you want to get feedback on the working progress change or the PR it's as simple as opening another idea instance, and looking at what your team is working on because we're using compose. You can just extend your existing oppose file when you're already working with, to actually create this whole application and have it all working in the context of the rest of the services. >>So it's actually the whole environment you're working with module one service that doesn't really understand what it's doing alone. And with that, let's jump into a quick demo. So you can see here, two dev environments up and running. First one here is the same container dev environment. So if I want to go into that, let's see what's going on in the various code button here. If that one open, I can get straight into my application to start making changes inside that dev container. And I've got all my dependencies in here, so I can just run that straight in that second application I have here is one that's opened up in compose, and I can see that I've also got my backend, my front end and my database. So I've got all my services running here. So if I want, I can open one or more of these in a dev environment, meaning that that container has the context that dev environment has the context of the whole application. >>So I can get back into and connect to all the other services that I need to test this application properly, all of them, one unit. And then when I've made my changes and I'm ready to share, I can hit my share button type in the refund them on to share that too. And then give that image to someone to get going, pick that up and just start working with that code and all my dependencies, simple as putting an image, looking ahead, we're going to be expanding development environments, more of your dependencies for the whole developer worst space. We want to look at backing up and letting you share your volumes to make data science and database setups more repeatable and going. I'm still all of this under a single workspace for your team containing images, your dev environments, your volumes, and more we've really want to allow you to create a fully portable Linux development environment. >>So everyone you're working with on any operating system, as I said, our MVP we're coming next month. And that was for vs code using their dev container primitive and more support for other ideas. We'll follow to find out more about what's happening and what's coming up next in the future of this. And to actually get a bit of a deeper dive in the experience. Can we check out the talk I'm doing with Georgie and girl later on today? Thank you, Ben, amazing story about how Docker is helping to make developer teams more collaborative. Now I'd like to talk more about applications while the dev environment is like the workbench around what you're building. The application itself has all the different components, libraries, and frameworks, and other code that make up the application itself. And we hear developers saying all the time things like, how do they know if their images are good? >>How do they know if they're secure? How do they know if they're minimal? How do they make great images and great Docker files and how do they keep their images secure? And up-to-date on every one of those ties into how do I create more trust? How do I know that I'm building high quality applications to enable you to do this even more effectively than today? We are pleased to announce the DACA verified polisher program. This broadens trusted content by extending beyond Docker official images, to give you more and more trusted building blocks that you can incorporate into your applications. It gives you confidence that you're getting what you expect because Docker verifies every single one of these publishers to make sure they are who they say they are. This improves our secure supply chain story. And finally it simplifies your discovery of the best building blocks by making it easy for you to find things that you know, you can trust so that you can incorporate them into your applications and move on and on the right. You can see some examples of the publishers that are involved in Docker, official images and our Docker verified publisher program. Now I'm pleased to introduce you to marina. Kubicki our senior product manager who will walk you through more about what we're doing to create a better experience for you around trust. >>Thank you, Dani, >>Mario Andretti, who is a famous Italian sports car driver. One said that if everything feels under control, you're just not driving. You're not driving fast enough. Maya Andretti is not a software developer and a software developers. We know that no matter how fast we need to go in order to drive the innovation that we're working on, we can never allow our applications to spin out of control and a Docker. As we continue talking to our, to the developers, what we're realizing is that in order to reach that speed, the developers are the, the, the development community is looking for the building blocks and the tools that will, they will enable them to drive at the speed that they need to go and have the trust in those building blocks. And in those tools that they will be able to maintain control over their applications. So as we think about some of the things that we can do to, to address those concerns, uh, we're realizing that we can pursue them in a number of different venues, including creating reliable content, including creating partnerships that expands the options for the reliable content. >>Um, in order to, in a we're looking at creating integrations, no link security tools, talk about the reliable content. The first thing that comes to mind are the Docker official images, which is a program that we launched several years ago. And this is a set of curated, actively maintained, open source images that, uh, include, uh, operating systems and databases and programming languages. And it would become immensely popular for, for, for creating the base layers of, of the images of, of the different images, images, and applications. And would we realizing that, uh, many developers are, instead of creating something from scratch, basically start with one of the official images for their basis, and then build on top of that. And this program has become so popular that it now makes up a quarter of all of the, uh, Docker poles, which essentially ends up being several billion pulse every single month. >>As we look beyond what we can do for the open source. Uh, we're very ability on the open source, uh, spectrum. We are very excited to announce that we're launching the Docker verified publishers program, which is continuing providing the trust around the content, but now working with, uh, some of the industry leaders, uh, in multiple, in multiple verticals across the entire technology technical spec, it costs entire, uh, high tech in order to provide you with more options of the images that you can use for building your applications. And it still comes back to trust that when you are searching for content in Docker hub, and you see the verified publisher badge, you know, that this is, this is the content that, that is part of the, that comes from one of our partners. And you're not running the risk of pulling the malicious image from an employee master source. >>As we look beyond what we can do for, for providing the reliable content, we're also looking at some of the tools and the infrastructure that we can do, uh, to create a security around the content that you're creating. So last year at the last ad, the last year's DockerCon, we announced partnership with sneak. And later on last year, we launched our DACA, desktop and Docker hub vulnerability scans that allow you the options of writing scans in them along multiple points in your dev cycle. And in addition to providing you with information on the vulnerability on, on the vulnerabilities, in, in your code, uh, it also provides you with a guidance on how to re remediate those vulnerabilities. But as we look beyond the vulnerability scans, we're also looking at some of the other things that we can do, you know, to, to, to, uh, further ensure that the integrity and the security around your images, your images, and with that, uh, later on this year, we're looking to, uh, launch the scope, personal access tokens, and instead of talking about them, I will simply show you what they look like. >>So if you can see here, this is my page in Docker hub, where I've created a four, uh, tokens, uh, read-write delete, read, write, read only in public read in public creeper read only. So, uh, earlier today I went in and I, I logged in, uh, with my read only token. And when you see, when I'm going to pull an image, it's going to allow me to pull an image, not a problem success. And then when I do the next step, I'm going to ask to push an image into the same repo. Uh, would you see is that it's going to give me an error message saying that they access is denied, uh, because there is an additional authentication required. So these are the things that we're looking to add to our roadmap. As we continue thinking about the things that we can do to provide, um, to provide additional building blocks, content, building blocks, uh, and, and, and tools to build the trust so that our DACA developer and skinned code faster than Mario Andretti could ever imagine. Uh, thank you to >>Thank you, marina. It's amazing what you can do to improve the trusted content so that you can accelerate your development more and move more quickly, move more collaboratively and build upon the great work of others. Finally, we hear over and over as that developers are working on their applications that they're looking for, environments that are consistent, that are the same as production, and that they want their applications to really run anywhere, any environment, any architecture, any cloud one great example is the recent announcement of apple Silicon. We heard from developers on uproar that they needed Docker to be available for that architecture before they could add those to it and be successful. And we listened. And based on that, we are pleased to share with you Docker, desktop on apple Silicon. This enables you to run your apps consistently anywhere, whether that's developing on your team's latest dev hardware, deploying an ARM-based cloud environments and having a consistent architecture across your development and production or using multi-year architecture support, which enables your whole team to collaborate on its application, using private repositories on Docker hub, and thrilled to introduce you to Hughie cower, senior director for product management, who will walk you through more of what we're doing to create a great developer experience. >>Senior director of product management at Docker. And I'd like to jump straight into a demo. This is the Mac mini with the apple Silicon processor. And I want to show you how you can now do an end-to-end arm workflow from my M one Mac mini to raspberry PI. As you can see, we have vs code and Docker desktop installed on a, my, the Mac mini. I have a small example here, and I have a raspberry PI three with an led strip, and I want to turn those LEDs into a moving rainbow. This Dockerfile here, builds the application. We build the image with the Docker, build X command to make the image compatible for all raspberry pies with the arm. 64. Part of this build is built with the native power of the M one chip. I also add the push option to easily share the image with my team so they can give it a try to now Dr. >>Creates the local image with the application and uploads it to Docker hub after we've built and pushed the image. We can go to Docker hub and see the new image on Docker hub. You can also explore a variety of images that are compatible with arm processors. Now let's go to the raspberry PI. I have Docker already installed and it's running Ubuntu 64 bit with the Docker run command. I can run the application and let's see what will happen from there. You can see Docker is downloading the image automatically from Docker hub and when it's running, if it's works right, there are some nice colors. And with that, if we have an end-to-end workflow for arm, where continuing to invest into providing you a great developer experience, that's easy to install. Easy to get started with. As you saw in the demo, if you're interested in the new Mac, mini are interested in developing for our platforms in general, we've got you covered with the same experience you've come to expect from Docker with over 95,000 arm images on hub, including many Docker official images. >>We think you'll find what you're looking for. Thank you again to the community that helped us to test the tech previews. We're so delighted to hear when folks say that the new Docker desktop for apple Silicon, it just works for them, but that's not all we've been working on. As Dani mentioned, consistency of developer experience across environments is so important. We're introducing composed V2 that makes compose a first-class citizen in the Docker CLI you no longer need to install a separate composed biter in order to use composed, deploying to production is simpler than ever with the new compose integration that enables you to deploy directly to Amazon ECS or Azure ACI with the same methods you use to run your application locally. If you're interested in running slightly different services, when you're debugging versus testing or, um, just general development, you can manage that all in one place with the new composed service to hear more about what's new and Docker desktop, please join me in the three 15 breakout session this afternoon. >>And now I'd love to tell you a bit more about bill decks and convince you to try it. If you haven't already it's our next gen build command, and it's no longer experimental as shown in the demo with built X, you'll be able to do multi architecture builds, share those builds with your team and the community on Docker hub. With build X, you can speed up your build processes with remote caches or build all the targets in your composed file in parallel with build X bake. And there's so much more if you're using Docker, desktop or Docker, CE you can use build X checkout tonus is talk this afternoon at three 45 to learn more about build X. And with that, I hope everyone has a great Dr. Khan and back over to you, Donnie. >>Thank you UA. It's amazing to hear about what we're doing to create a better developer experience and make sure that Docker works everywhere you need to work. Finally, I'd like to wrap up by showing you everything that we've announced today and everything that we've done recently to make your lives better and give you more and more for the single price of your Docker subscription. We've announced the Docker verified publisher program we've announced scoped personal access tokens to make it easier for you to have a secure CCI pipeline. We've announced Docker dev environments to improve your collaboration with your team. Uh, we shared with you Docker, desktop and apple Silicon, to make sure that, you know, Docker runs everywhere. You need it to run. And we've announced Docker compose version two, finally making it a first-class citizen amongst all the other great Docker tools. And we've done so much more recently as well from audit logs to advanced image management, to compose service profiles, to improve where you can run Docker more easily. >>Finally, as we look forward, where we're headed in the upcoming year is continuing to invest in these themes of helping you build, share, and run modern apps more effectively. We're going to be doing more to help you create a secure supply chain with which only grows more and more important as time goes on. We're going to be optimizing your update experience to make sure that you can easily understand the current state of your application, all its components and keep them all current without worrying about breaking everything as you're doing. So we're going to make it easier for you to synchronize your work. Using cloud sync features. We're going to improve collaboration through dev environments and beyond, and we're going to do make it easy for you to run your microservice in your environments without worrying about things like architecture or differences between those environments. Thank you so much. I'm thrilled about what we're able to do to help make your lives better. And now you're going to be hearing from one of our customers about what they're doing to launch their business with Docker >>I'm Matt Falk, I'm the head of engineering and orbital insight. And today I want to talk to you a little bit about data from space. So who am I like many of you, I'm a software developer and a software developer about seven companies so far, and now I'm a head of engineering. So I spend most of my time doing meetings, but occasionally I'll still spend time doing design discussions, doing code reviews. And in my free time, I still like to dabble on things like project oiler. So who's Oberlin site. What do we do? Portal insight is a large data supplier and analytics provider where we take data geospatial data anywhere on the planet, any overhead sensor, and translate that into insights for the end customer. So specifically we have a suite of high performance, artificial intelligence and machine learning analytics that run on this geospatial data. >>And we build them to specifically determine natural and human service level activity anywhere on the planet. What that really means is we take any type of data associated with a latitude and longitude and we identify patterns so that we can, so we can detect anomalies. And that's everything that we do is all about identifying those patterns to detect anomalies. So more specifically, what type of problems do we solve? So supply chain intelligence, this is one of the use cases that we we'd like to talk about a lot. It's one of our main primary verticals that we go after right now. And as Scott mentioned earlier, this had a huge impact last year when COVID hit. So specifically supply chain intelligence is all about identifying movement patterns to and from operating facilities to identify changes in those supply chains. How do we do this? So for us, we can do things where we track the movement of trucks. >>So identifying trucks, moving from one location to another in aggregate, same thing we can do with foot traffic. We can do the same thing for looking at aggregate groups of people moving from one location to another and analyzing their patterns of life. We can look at two different locations to determine how people are moving from one location to another, or going back and forth. All of this is extremely valuable for detecting how a supply chain operates and then identifying the changes to that supply chain. As I said last year with COVID, everything changed in particular supply chains changed incredibly, and it was hugely important for customers to know where their goods or their products are coming from and where they were going, where there were disruptions in their supply chain and how that's affecting their overall supply and demand. So to use our platform, our suite of tools, you can start to gain a much better picture of where your suppliers or your distributors are going from coming from or going to. >>So what's our team look like? So my team is currently about 50 engineers. Um, we're spread into four different teams and the teams are structured like this. So the first team that we have is infrastructure engineering and this team largely deals with deploying our Dockers using Kubernetes. So this team is all about taking Dockers, built by other teams, sometimes building the Dockers themselves and putting them into our production system, our platform engineering team, they produce these microservices. So they produce microservice, Docker images. They develop and test with them locally. Their entire environments are dockerized. They produce these doctors, hand them over to him for infrastructure engineering to be deployed. Similarly, our product engineering team does the same thing. They develop and test with Dr. Locally. They also produce a suite of Docker images that the infrastructure team can then deploy. And lastly, we have our R and D team, and this team specifically produces machine learning algorithms using Nvidia Docker collectively, we've actually built 381 Docker repositories and 14 million. >>We've had 14 million Docker pools over the lifetime of the company, just a few stats about us. Um, but what I'm really getting to here is you can see actually doctors becoming almost a form of communication between these teams. So one of the paradigms in software engineering that you're probably familiar with encapsulation, it's really helpful for a lot of software engineering problems to break the problem down, isolate the different pieces of it and start building interfaces between the code. This allows you to scale different pieces of the platform or different pieces of your code in different ways that allows you to scale up certain pieces and keep others at a smaller level so that you can meet customer demands. And for us, one of the things that we can largely do now is use Dockers as that interface. So instead of having an entire platform where all teams are talking to each other, and everything's kind of, mishmashed in a monolithic application, we can now say this team is only able to talk to this team by passing over a particular Docker image that defines the interface of what needs to be built before it passes to the team and really allows us to scalp our development and be much more efficient. >>Also, I'd like to say we are hiring. Um, so we have a number of open roles. We have about 30 open roles in our engineering team that we're looking to fill by the end of this year. So if any of this sounds really interesting to you, please reach out after the presentation. >>So what does our platform do? Really? Our platform allows you to answer any geospatial question, and we do this at three different inputs. So first off, where do you want to look? So we did this as what we call an AOI or an area of interest larger. You can think of this as a polygon drawn on the map. So we have a curated data set of almost 4 million AOIs, which you can go and you can search and use for your analysis, but you're also free to build your own. Second question is what you want to look for. We do this with the more interesting part of our platform of our machine learning and AI capabilities. So we have a suite of algorithms that automatically allow you to identify trucks, buildings, hundreds of different types of aircraft, different types of land use, how many people are moving from one location to another different locations that people in a particular area are moving to or coming from all of these different analyses or all these different analytics are available at the click of a button, and then determine what you want to look for. >>Lastly, you determine when you want to find what you're looking for. So that's just, uh, you know, do you want to look for the next three hours? Do you want to look for the last week? Do you want to look every month for the past two, whatever the time cadence is, you decide that you hit go and out pops a time series, and that time series tells you specifically where you want it to look what you want it to look for and how many, or what percentage of the thing you're looking for appears in that area. Again, we do all of this to work towards patterns. So we use all this data to produce a time series from there. We can look at it, determine the patterns, and then specifically identify the anomalies. As I mentioned with supply chain, this is extremely valuable to identify where things change. So we can answer these questions, looking at a particular operating facility, looking at particular, what is happening with the level of activity is at that operating facility where people are coming from, where they're going to, after visiting that particular facility and identify when and where that changes here, you can just see it's a picture of our platform. It's actually showing all the devices in Manhattan, um, over a period of time. And it's more of a heat map view. So you can actually see the hotspots in the area. >>So really the, and this is the heart of the talk, but what happened in 2020? So for men, you know, like many of you, 2020 was a difficult year COVID hit. And that changed a lot of what we're doing, not from an engineering perspective, but also from an entire company perspective for us, the motivation really became to make sure that we were lowering our costs and increasing innovation simultaneously. Now those two things often compete with each other. A lot of times you want to increase innovation, that's going to increase your costs, but the challenge last year was how to do both simultaneously. So here's a few stats for you from our team. In Q1 of last year, we were spending almost $600,000 per month on compute costs prior to COVID happening. That wasn't hugely a concern for us. It was a lot of money, but it wasn't as critical as it was last year when we really needed to be much more efficient. >>Second one is flexibility for us. We were deployed on a single cloud environment while we were cloud thought ready, and that was great. We want it to be more flexible. We want it to be on more cloud environments so that we could reach more customers. And also eventually get onto class side networks, extending the base of our customers as well from a custom analytics perspective. This is where we get into our traction. So last year, over the entire year, we computed 54,000 custom analytics for different users. We wanted to make sure that this number was steadily increasing despite us trying to lower our costs. So we didn't want the lowering cost to come as the sacrifice of our user base. Lastly, of particular percentage here that I'll say definitely needs to be improved is 75% of our projects never fail. So this is where we start to get into a bit of stability of our platform. >>Now I'm not saying that 25% of our projects fail the way we measure this is if you have a particular project or computation that runs every day and any one of those runs sale account, that is a failure because from an end-user perspective, that's an issue. So this is something that we know we needed to improve on and we needed to grow and make our platform more stable. I'm going to something that we really focused on last year. So where are we now? So now coming out of the COVID valley, we are starting to soar again. Um, we had, uh, back in April of last year, we had the entire engineering team. We actually paused all development for about four weeks. You had everyone focused on reducing our compute costs in the cloud. We got it down to 200 K over the period of a few months. >>And for the next 12 months, we hit that number every month. This is huge for us. This is extremely important. Like I said, in the COVID time period where costs and operating efficiency was everything. So for us to do that, that was a huge accomplishment last year and something we'll keep going forward. One thing I would actually like to really highlight here, two is what allowed us to do that. So first off, being in the cloud, being able to migrate things like that, that was one thing. And we were able to use there's different cloud services in a more particular, in a more efficient way. We had a very detailed tracking of how we were spending things. We increased our data retention policies. We optimized our processing. However, one additional piece was switching to new technologies on, in particular, we migrated to get lab CICB. >>Um, and this is something that the costs we use Docker was extremely, extremely easy. We didn't have to go build new new code containers or repositories or change our code in order to do this. We were simply able to migrate the containers over and start using a new CIC so much. In fact, that we were able to do that migration with three engineers in just two weeks from a cloud environment and flexibility standpoint, we're now operating in two different clouds. We were able to last night, I've over the last nine months to operate in the second cloud environment. And again, this is something that Docker helped with incredibly. Um, we didn't have to go and build all new interfaces to all new, different services or all different tools in the next cloud provider. All we had to do was build a base cloud infrastructure that ups agnostic the way, all the different details of the cloud provider. >>And then our doctors just worked. We can move them to another environment up and running, and our platform was ready to go from a traction perspective. We're about a third of the way through the year. At this point, we've already exceeded the amount of customer analytics we produce last year. And this is thanks to a ton more albums, that whole suite of new analytics that we've been able to build over the past 12 months and we'll continue to build going forward. So this is really, really great outcome for us because we were able to show that our costs are staying down, but our analytics and our customer traction, honestly, from a stability perspective, we improved from 75% to 86%, not quite yet 99 or three nines or four nines, but we are getting there. Um, and this is actually thanks to really containerizing and modularizing different pieces of our platform so that we could scale up in different areas. This allowed us to increase that stability. This piece of the code works over here, toxin an interface to the rest of the system. We can scale this piece up separately from the rest of the system, and that allows us much more easily identify issues in the system, fix those and then correct the system overall. So basically this is a summary of where we were last year, where we are now and how much more successful we are now because of the issues that we went through last year and largely brought on by COVID. >>But that this is just a screenshot of the, our, our solution actually working on supply chain. So this is in particular, it is showing traceability of a distribution warehouse in salt lake city. It's right in the center of the screen here. You can see the nice kind of orange red center. That's a distribution warehouse and all the lines outside of that, all the dots outside of that are showing where people are, where trucks are moving from that location. So this is really helpful for supply chain companies because they can start to identify where their suppliers are, are coming from or where their distributors are going to. So with that, I want to say, thanks again for following along and enjoy the rest of DockerCon.

>>from around the >>globe it's the cube with >>coverage of Kublai >>Khan and Cloud Native Con Europe 2020 >>one virtual >>brought to you by red hat. The cloud native computing foundation and ecosystem partners. Welcome back to the cubes coverage of Kublai khan 21 cloud native gone 21 virtual. I'm john for your host of the cube. We're here with a great segment of an entrepreneur also the creator and maintainer of fluent bit Eduardo Silva who's now the founder of Palihapitiya was a startup. Going to commercialize and have an enterprise grade fluent D influence bit Eduardo. Great to have you on. Thanks for coming on the cube >>during the place for having me here. So I'm pretty happy to share the news about the crew and whenever you want, >>exciting trends, exciting trends happening with C N C f koo Kahne cloud native cloud native a lot of data, a lot of management, a lot of logging, a lot of observe ability, a lot of end user um contributions and enterprise adoption. So let's get into it first by give us a quick update on fluent D anything upcoming to highlight. >>Yeah, well fluent is actually turning two years old right now. So it's the more metric project that we have a lot of management and processing in the market. And we're really happy to see that the sides are project that was started 10 years ago, its adoption. You can see continues growing ecosystem from a planning perspective and companies adopting the technology that that is really great. So it's very overwhelming and actually really happy to take this project and continue working with companies, individuals and and right now what is the position where we are now with through And these are part of the Roma is like one of the things that people is facing not because of the tool because people have every time there has more data, more Metro services the system are scaling up is like about performance, right? And performance is critical if you're slowing down data processing actually you're not getting the data at the right time where you need it right. Nobody's people needs real time query is real time analysis. So from a security perspective we're going to focus a lot on everything that is about performance I would say for this year and maybe the other one, I would say that we won't see many new futures around fluently itself as as a project so we'll be mostly about back texting and performance improvements. >>Yeah, I definitely want to dig in with you on the data and logging challenges around kubernetes especially with and to end workflows and there's the different environments that sits in the middle of. But first before we get there, just take a minute to explain for the folks um not that savvy with fluent bit. What is fluent bit real quick, explain what it is. >>Okay, so I will start with a quick story about this, so when we started flowing the, we envision that at some point I'm talking about six years ago, right, all this IOT train or embedded or h will be available and for that you we got back to heavy right? If you have a constraint environment or you want to process data in a more faster way without all the capabilities at that time we say that he might not be suitable for that. So the thing is okay and it was not longer like a single software piece right? We want to say through in this an ecosystem, right? And as part of the ecosystem we have sck where people can connect applications fluid the but also we say we need like a flu Indie but that could be lightweight and faster. Burundi is reading ruby right? And the critical part in C. But since it's written ruby of course there's some process calls on how do you process the data and how much you can scale? Right. So we said if you're going to dig into embedded or small constrained environment, let's write a similar solution. But in C language so we can optimize a memory, can optimize scenario and all this kind of um needs will be will will be effective, right? And we started to spread called fluent bed and through a bit it's like a nowadays like a lightweight version of Wendy, it has started for the Marilyn knows, but after a few years people from the cloud space, I'm talking about containers, kubernetes, they started to ask for more futures for flowing it because they wanted they have influence, but also they wanted to have flowing better than because of it was lively and nowadays we can see that what fluent established the market and true indeed, we're getting around $2 million dollars every single day. So nowadays the attraction of the break is incredible. And it's mostly used to um want to collect logs from the files from system be and for most of coordinated environment disabled, process all this information on a pen, meta data and solve all the problem of how do I collect my data? How do I make sure that the data has the right context meta data and I'm able to deliver this data. So a central place like a job provider or any kind of storage. >>That's great. And I love the fact that's written C, which kind of gives the, I'll say it more performance on the code. Less overhead, get deeper closer um and people No, no, see it's high performance, quick, quick stats. So how old is the project through a bit, What version are you on? >>Uh, a little bit. It's, I'm not sure it is turning six or seven this year, 96. It's been around >>for a while. >>Yeah, yeah. We just released this this week, one at 73 right. We have done more than 100 releases actually really settled two and it's pretty past sometimes we have releases every 23 weeks. So the operation, the club medical system is quite fast. People once and more future more fixes and they don't want to wait for a couple of months for the next release. They wanted to have the continue image right away to test it out and actually sends away as a project. We worked with most of providers like AWS Microsoft actor google cloud platform, the demon for this fixes and improvements are in a weekly basis. >>You guys got a lot of props, I was checking around on the internet, you guys are getting strong um, reviews on logging for kubernetes with the couple releases ago, you had higher performance improvements for google AWS logged in postgres equal and other environments. Um but the question that I'm getting and I'm hearing from folks is, you know, I have end to end workflows and they've been steady. They've been strong. But as more data comes in and more services are connecting to it from network protocols, two Other cloud services, the complexity of what was once a straight straightforward workflow and to end is impacted by this new data. How do you guys address that? How would you speak to that use case? >>Well, for for us data we have taken approaches. Data for us is agnostic on the way that it comes from but that it comes from and the format that comes from for for example, if you talk about the common uses case that we have now is like data come from different formats. Every single developer use the all looking format come from different channels, TCP file system or another services. So it is very, very different. How do we get this data? And that is a big challenge. Right? How do we take data from different sources, different format and you try to unify this internal and then if you're going to talk for example to less exert let's say you Jason you're going to talk to africa, they have their own binary protocol. So we are kind of the backbone that takes all the data transfer data and try to adapt to the destination expected payload from a technical perspective. Yeah, is really challenging. Is really challenging also that Nowadays, so two years ago people was finding processing, I don't know 500,000 messages per second, But nowadays they won 10, 20 40,000. So prime architecture perspective Yeah, there are many challenges and and I think that the teamwork from the maintaining this and with companies has provided a lot of value, a lot of value. And I think that the biggest proof here is that the adoption like adoption and big adoption, you have more banks reported more enhancement requests. All right. So if I get >>this right, you got different sources of data collection issues. If you look on the front end and then you got some secret sauce with bit fluent, I mean uh inside the kubernetes clusters um and then you deliver it to multiple services and databases and cloud services. That that right. Is that the key? The key value is that is that the key value proposition? Did I get that right with fluent bit? >>Mhm. Yeah, I would say most of the technical implementation when the of the value of the technical implementation, I would say that is towards being the vendor neutral. Right? So when you come, when you go to the market and you go to the talk to bank institution hospital form and if the company right, most of them are facing this concept of bender looking right, they use a Bender database but you have to get married. So they're tooling, right? And I'm not going to mention any inventor name. Right? Actually it's very fun. Well for example, the business model, this company that start with S and ends with swung right? For example is you pay as much money so you pay as much money compared to the data that you ingested. But the default tools in just the whole data. But in reality if you go to the enterprise they say yeah. I mean just in all my data into Splunk or X provider right? But from 100 that I'm interesting, which I'm paying for, I'm just using this service to query at least 20 of the data. So why I mean just in this 80 extra I didn't get it right. That's why I want to send and this is real use case there's this language is really good for where is analyzed the data But they said yeah, 80 of my data is just a five data. I will need it maybe in a couple of months just I want to send it to Amazon history or any kind of other a archive service. So users, the value that says is that I want to have a mentor neutral pipeline which me as a user, I went to this side work went to send data, went to send it and also I can come to my bills. Right? And I think that is the biggest value. So you can go to the market. They will find maybe other tools for logging or tools for Matrix because there's a ton of them. But I think that none of them can say we are gender neutral. Not all of them can offer this flexibility to the use, right? So from a technical language performance but from an end user is being the neutrality. >>Okay. So I have to ask you then here in the C n C F projects that are going on and the community around um um fluent bit, you have to have those kinds of enhancements integrations, for instance, for not only performance improvement, but extensive bility. So enterprises there, they want everything right. They make things very >>complicated. They're very >>complicated infrastructure. So if they want some policy they want to have data ingestion policies or take advantage of no vendor lock in, how is the community responding? How did what's your vision for helping companies now? You've got your new venture and you got the open source project, How does this evolve? How do you see this evolving eduardo? Because there is a need for use cases that don't need all the data, but you need all the data to get some of the data. Right. So it's a you have a new new >>paradigm of >>coding and you want to be dynamic and relevant. What's the how do you see this evolving? >>Yeah. Actually going to give you some spoilers. Right. So some years before report. Yeah. So users has this a lot of they have a lot of problems how to collect the data processing data and send the data. We just told them right, Performance is a continuous improvement, Right? Because you have always more data, more formats, that's fine. But one critical thing that people say, hey, you say, hey, I want to put my business logic in the pipeline. So think about this if you have to embed we are the platform for data. Right? But we also provide capabilities to do data processing because you can grab the data or you can do custom modifications over the data. One thing that we did like a year two years ago is we added this kind of stream processing capabilities, can you taste equal for Kaka? But we have our own sequel engine influence them. So when the data is flowing without having any data banks, any index or anything, we can do data aggregation. You can, you can put some business logic on it and says for all the data that matches this pattern, stand it to a different destination, otherwise send it to caracas plan or elastic. So we have, this is what we have now. Extreme processing capabilities. Now what is the spoiler and what we're going next. Right now there are two major areas. One of them is distributed. Extreme processing right? The capabilities to put this intelligence on the age, on the age I'm referring to for example, a cooper needs note right or constrained environment, right? Communities on the age is something that is going on. There are many companies using that approach but they want to put some intelligence and data processing where the data is being generated. Because there is one problem when you have more data and you want to create the data, you have to wait and to centralize all the data in the database for your service. And there's a legend see right, millions sometimes hours because data needs to be in Mexico. But what about it? To have 100 of notes, but each one is already right, influenced it. Why you don't run the queries there. That is one of the features that we have. And well now talking from the challenges from spoil perspectives, people says, okay, I love this pipeline. I noticed Lambert has a political architecture but the language see it's not my thing, right? I don't want to go and see. Nobody likes see that we are honest about that. And there are many mass words about security or not just nothing, which is true, right? It's really easy to mess up things and see. Right? So, and we said, okay, so now our next level, it's like we're going to provide this year the ability to write your own plug ins in Western webassembly. So with the web is simply interface. You can run your own pregnancy goal, rust or any kind of weapon sending support language and translate that implementation to native. Wasn't that fluent that will understand. So C as a language won't be with one being longer uploaded for you as a developer. As a company that wants to put more business logic into the bike. Well that is one of the things that are coming up and really we already have some docs but they're not ready to show. So maybe we can expect something for us at the end of this year. >>Great stuff by the way, from a c standpoint us, old timers like me used to program and see, and not a lot of C courses being taught, but if you do know see it's very valuable. But again, to your point, the developers are are focused on coding the apps, not so much the underlying. So I think that's that's key. I will like to ask you one final question of water before we wrap up, how do you deploy fluid bid? What's the is it is that you're putting it inside the cluster? Is there is that scripts, What's the what's the architecture real quick? Give us a quick overview of the architecture. >>Okay, so that it's not just for a classroom, you can run it on any machine. Windows, Linux, IBM Yeah, and that doesn't need to be a kubernetes. Classic. Right? When we created to invade Copernicus was quite new at the same time. So if you talk about kubernetes deploys as a demon set at the moment is pretty much a part that runs on every note like an agent. Right? Uh, all you can run necessarily on any kind of machine. Oh and one thing before we were, I just need to mention something that from the spoil it. But because it's just getting, we're having many news these days. Is that fluently used to be mostly for logging right? And influence the specifically project. We've got many people from years ago saying, you know what? I'm losing my agent for logging to a bed but I have my agents for metrics and sometimes this is quite heavy to have multiple agents on your age. So now flowing bed is extending the capabilities to deal with native metrics. Right. The first version will be available about this week in cuba come right. We will be able to process host matrix for application metrics and send them to permit use with open matrix format in a native way. So we extended the political system to be a better citizen with open metrics and in the future also with open telemetry, which is a hot thing that is coming up on this month. >>Everyone loves metrics. That's super important. Having the data Is really, really important as day two operations and get all this stuff is happening. I wanna thank you for coming on and sharing the update and congratulations on. The new venture will keep following you and look good for the big launch but fluent bit looking good. Congratulations. Thanks for coming on. >>Thank you so much help governments. >>Okay this is the cubes coverage of Kublai khan 21 cloud Native Con 21 virtual soon we'll be back in real life at the events extracting the signal from the noise. Thanks for watching. Yeah.

(bright upbeat music) >> Hello everyone, and welcome to the special exclusive CUBE Conversation, where we continue our coverage of the trends of the database market. With me is Nipun Agarwal, who's the vice president, MySQL HeatWave in advanced development at Oracle. Nipun, welcome. >> Thank you Dave. >> I love to have technical people on the Cube to educate, debate, inform, and we've extensively covered this market. We were all over the Snowflake IPO and at that time I remember, I challenged organizations bring your best people. Because I want to better understand what's happening at Database. After Oracle kind of won the Database wars 20 years ago, Database kind of got boring. And then it got really exciting with the big data movement, and all the, not only SQL stuff coming out, and Hadoop and blah, blah, blah. And now it's just exploding. You're seeing huge investments from many of your competitors, VCs are trying to get into the action. Meanwhile, as I've said many, many times, your chairman and head of technology, CTO, Larry Ellison, continues to invest to keep Oracle relevant. So it's really been fun to watch and I really appreciate you coming on. >> Sure thing. >> We have written extensively, we talked to a lot of Oracle customers. You get the leading mission critical database in the world. Everybody from Fortune 100, we evaluated what Gardner said about the operational databases. I think there's not a lot of question there. And we've written about that on WikiBound about you're converged databases, and the strategy there, and we're going to get into that. We've covered Autonomous Data Warehouse Exadata Cloud at Customer, and then we just want to really try to get into your area, which has been, kind of caught our attention recently. And I'm talking about the MySQL Database Service with HeatWave. I love the name, I laugh. It was an unveiled, I don't know, a few months ago. So Nipun, let's start the discussion today. Maybe you can update our viewers on what is HeatWave? What's the overall focus with Oracle? And how does it fit into the Cloud Database Service? >> Sure Dave. So HeatWave is a in-memory query accelerator for the MySQL Database Service for speeding up analytic queries as well as long running complex OLTP queries. And this is all done in the context of a single database which is the MySQL Database Service. Also, all existing MySQL applications or MySQL compatible tools and applications continue to work as is. So there is no change. And with this HeatWave, Oracle is delivering the only MySQL service which provides customers with a single unified platform for both analytic as well as transaction processing workloads. >> Okay, so, we've seen open source databases in the cloud growing very rapidly. I mentioned Snowflake, I think Google's BigQuery, get some mention, we'll talk, we'll maybe talk more about Redshift later on, but what I'm wondering, well let's talk about now, how does MySQL HeatWave service, how does that compare to MySQL-based services from other cloud vendors? I can get MySQL from others. In fact, I think we do. I think we run WikiBound on the LAMP stack. I think it's running on Amazon, but so how does your service compare? >> No other vendor, like, no other vendor offers this differentiated solution with an open source database namely, having a single database, which is optimized both for transactional processing and analytics, right? So the example is like MySQL. A lot of other cloud vendors provide MySQL service but MySQL has been optimized for transaction processing so when customs need to run analytics they need to move the data out of MySQL into some other database for any analytics, right? So we are the only vendor which is now offering this unified solution for both transactional processing analytics. That's the first point. Second thing is, most of the vendors out there have taken open source databases and they're basically hosting it in the cloud. Whereas HeatWave, has been designed from the ground up for the cloud, and it is a 100% compatible with MySQL applications. And the fact that we have designed it from the ground up for the cloud, maybe I'll spend 100s of person years of research and engineering means that we have a solution, which is very, very scalable, it's very optimized in terms of performance, and it is very inexpensive in terms of the cost. >> Are you saying, well, wait, are you saying that you essentially rewrote MySQL to create HeatWave but at the same time maintained compatibility with existing applications? >> Right. So we enhanced MySQL significantly and we wrote a whole bunch of new code which is brand new code optimized for the cloud in such a manner that yes, it is 100% compatible with all existing MySQL applications. >> What does it mean? And if I'm to optimize for the cloud, I mean, I hear that and I say, okay, it's taking advantage of cloud-native. I hear kind of the buzzwords, cloud-first, cloud-native. What does it specifically mean from a technical standpoint? >> Right. So first, let's talk about performance. What we have done is that we have looked at two aspects. We have worked with shapes like for instance, like, the compute shapes which provide the best performance for dollar, per dollar. So I'll give you a couple of examples. We have optimized for certain shifts. So, HeatWave is in-memory query accelerator. So the cost of the system is dominated by the cost. So we are working with chips which provide the cheapest cost per terabyte of memory. Secondly, we are using commodity cloud services in such a manner that it's in-optimized for both performance as well as performance per dollar. So, example is, we are not using any locally-attached SSDs. We use ObjectStore because it's very inexpensive. And then I guess at some point I will get into the details of the architecture. The system has been really, really designed for massive scalability. So as you add more compute, as you add more service, the system continues to scale almost perfectly linearly. So this is what I mean in terms of being optimized for the cloud. >> All right, great. >> And furthermore, (indistinct). >> Thank you. No, carry on. >> Over the next few months, you will see a bunch of other announcements where we're adding a whole bunch of machine learning and data driven-based automation which we believe is critical for the cloud. So optimized for performance, optimized for the cloud, and machine learning-based automation which we believe is critical for any good cloud-based service. >> All right, I want to come back and ask you more about the architecture, but you mentioned some of the others taking open source databases and shoving them into the cloud. Let's take the example of AWS. They have a series of specialized data stores and, for different workloads, Aurora is for OLTP I actually think it's based on MySQL Redshift which is based on ParAccel. And so, and I've asked Amazon about this, and their response is, actually kind of made sense to me. Look, we want the right tool for the right job, we want access to the primitives because when the market changes we can change faster as opposed to, if we put, if we start building bigger and bigger databases with more functionality, it's, we're not as agile. So that kind of made sense to me. I know we, again, we use a lot, we use, I think I said MySQL in Amazon we're using DynamoDB, works, that's cool. We're not huge. And I, we fully admit and we've researched this, when you start to get big that starts to get maybe expensive. But what do you think about that approach and why is your approach better? >> Right, we believe that there are multiple drawbacks of having different databases or different services, one, optimized for transactional processing and one for analytics and having to ETL between these different services. First of all, it's expensive because you have to manage different databases. Secondly, it's complex. From an application standpoint, applications need, now need to understand the semantics of two different databases. It's inefficient because you have to transfer data at some PRPC from one database to the other one. It's not secure because there is security aspects involved when your transferring data and also the identity of users in the two different databases is different. So it's, the approach which has been taken by Amazons and such, we believe, is more costly, complex, inefficient and not secure. Whereas with HeatWave, all the data resides in one database which is MySQL and it can run both transaction processing and analytics. So in addition to all the benefits I talked about, customers can also make their decisions in real time because there is no need to move the data. All the data resides in a single database. So as soon as you make any changes, those changes are visible to customers for queries right away, which is not the case when you have different siloed specialized databases. >> Okay, that, a lot of ways to skin a cat and that what you just said makes sense. By the way, we were saying before, companies have taken off the shelf or open source database has shoved them in the cloud. I have to give Amazon some props. They actually have done engineering to Aurora and Redshift. And they've got the engineering capabilities to do that. But you can see, for example, in Redshift the way they handle separating compute from storage it's maybe not as elegant as some of the other players like a Snowflake, for example, but they get there and they, maybe it's a little bit more brute force but so I don't want to just make it sound like they're just hosting off the shelf in the cloud. But is it fair to say that there's like a crossover point? So in other words, if I'm smaller and I'm not, like doing a bunch of big, like us, I mean, it's fine. It's easy, I spin it up. It's cheaper than having to host my own servers. So there's, presumably there's a sweet spot for that approach and a sweet spot for your approach. Is that fair or do you feel like you can cover a wider spectrum? >> We feel we can cover the entire spectrum, not wider, the entire spectrum. And we have benchmarks published which are actually available on GitHub for anyone to try. You will see that this approach you have taken with the MySQL Database Service in HeatWave, we are faster, we are cheaper without having to move the data. And the mileage or the amount of improvement you will get, surely vary. So if you have less data the amount of improvement you will get, maybe like say 100 times, right, or 500 times, but smaller data sizes. If you get to lots of data sizes this improvement amplifies to 1000 times or 10,000 times. And similarly for the cost, if the data size is smaller, the cost advantage you will have is less, maybe MySQL HeatWave is one third the cost. If the data size is larger, the cost advantage amplifies. So to your point, MySQL Database Service in HeatWave is going to be better for all sizes but the amount of mileage or the amount of benefit you will get increases as the size of the data increases. >> Okay, so you're saying you got better performance, better cost, better price performance. Let me just push back a little bit on this because I, having been around for awhile, I often see these performance and price comparisons. And what often happens is a vendor will take the latest and greatest, the one they just announced and they'll compare it to an N-1 or an N-2, running on old hardware. So, is, you're normalizing for that, is that the game you're playing here? I mean, how can you, give us confidence that this is easier kind of legitimate benchmarks in your GitHub repo. >> Absolutely. I'll give you a bunch of like, information. But let me preface this by saying that all of our scripts are available in the open source in the GitHub repo for anyone to try and we would welcome feedback otherwise. So we have taken, yes, the latest version of MySQL Database Service in HeatWave, we have optimized it, and we have run multiple benchmarks. For instance, TBC-H, TPC-DS, right? Because the amount of improvement a query will get depends upon the specific query, depends upon the predicates, it depends on the selectivity so we just wanted to use standard benchmarks. So it's not the case that if you're using certain classes of query, excuse me, benefit, get them more. So, standard benchmarks. Similarly, for the other vendors or other services like Redshift, we have run benchmarks on the latest shapes of Redshift the most optimized configuration which they recommend, running their scripts. So this is not something that, hey, we're just running out of the box. We have optimized Aurora, we have optimized (indistinct) to the best and possible extent we can based on their guidelines, based on their latest release, and that's what you're talking about in terms of the numbers. >> All right. Please continue. >> Now, for some other vendors, if we get to the benchmark section, we'll talk about, we are comparing with other services, let's say Snowflake. Well there, there are issues in terms of you can't legally run Snowflake numbers, right? So there, we have looked at some reports published by Gigaom report. and we are taking the numbers published by the Gigaom report for Snowflake, Google BigQuery and as you'll see maps numbers, right? So those, we have not won ourselves. But for AWS Redshift, as well as AWS Aurora, we have run the numbers and I believe these are the best numbers anyone can get. >> I saw that Gigaom report and I got to say, Gigaom, sometimes I'm like, eh, but I got to say that, I forget the guy's name, he knew what he was talking about. He did a good job, I thought. I was curious as to the workload. I always say, well, what's the workload. And, but I thought that report was pretty detailed. And Snowflake did not look great in that report. Oftentimes, and they've been marketing the heck out of it. I forget who sponsored it. It is, it was sponsored content. But, I did, I remember seeing that and thinking, hmm. So, I think maybe for Snowflake that sweet spot is not, maybe not that performance, maybe it's the simplicity and I think that's where they're making their mark. And most of their databases are small and a lot of read-only stuff. And so they've found a market there. But I want to come back to the architecture and really sort of understand how you've able, you've been able to get this range of both performance and cost you talked about. I thought I heard that you're optimizing the chips, you're using ObjectStore. You're, you've got an architecture that's not using SSD, it's using ObjectStore. So this, is their cashing there? I wonder if you could just give us some details of the architecture and tell us how you got to where you are. >> Right, so let me start off saying like, what are the kind of numbers we are talking about just to kind of be clear, like what the improvements are. So if you take the MySQL Database Service in HeatWave in Oracle Cloud and compare it with MySQL service in any other cloud, and if you look at smaller data sizes, say data sizes which are about half a terabyte or so, HeatWave is 400 times faster, 400 times faster. And as you get to... >> Sorry. Sorry to interrupt. What are you measuring there? Faster in terms of what? >> Latency. So we take TCP-H 22 queries, we run them on HeatWave, and we run the same queries on MySQL service on any other cloud, half a terabyte and the performance in terms of latency is 400 times faster in HeatWave. >> Thank you. Okay. >> If you go to a lot of other data sites, then the other data point of view, we're looking at say something like, 4 TB, there, we did two comparisons. One is with AWS Aurora, which is, as you said, they have taken MySQL. They have done a bunch of innovations over there and we are offering it as a premier service. So on 4 TB TPC-H, MySQL Database Service with HeatWave is 1100 times faster than Aurora. It is three times faster than the fastest shape of Redshift. So Redshift comes in different flavors some talking about dense compute too, right? And again, looking at the most recommended configuration from Redshift. So 1100 times faster that Aurora, three times faster than Redshift and at one third, the cost. So this where I just really want to point out that it is much faster and much cheaper. One third the cost. And then going back to the Gigaom report, there was a comparison done with Snowflake, Google BigQuery, Redshift, Azure Synapse. I wouldn't go into the numbers here but HeatWave was faster on both TPC-H as well as TPC-DS across all these products and cheaper compared to any of these products. So faster, cheaper on both the benchmarks across all these products. Now let's come to, like, what is the technology underneath? >> Great. >> So, basically there are three parts which you're going to see. One is, improve performance, very good scale, and improve a lower cost. So the first thing is that HeatWave has been optimized and, for the cloud. And when I say that, we talked about this a bit earlier. One is we are using the cheapest shapes which are available. We're using the cheapest services which are available without having to compromise the performance and then there is this machine learning-based automation. Now, underneath, in terms of the architecture of HeatWave there are basically, I would say, four key things. First is, HeatWave is an in-memory engine that a presentation which we have in memory is a hybrid columnar representation which is optimized for vector process. That's the first thing. And that's pretty table stakes these days for anyone who wants to do in-memory analytics except that it's hybrid columnar which is optimized for vector processing. So that's the first thing. The second thing which starts getting to be novel is that HeatWave has a massively parallel architecture which is enabled by a massively partitioned architecture. So we take the data, we read the data from MySQL into the memory of the HeatWave and we massively partition this data. So as we're reading the data, we're partitioning the data based on the workload, the sizes of these partitions is such that it fits in the cache of the underlying processor and then we're able to consume these partitions really, really fast. So that's the second bit which is like, massively parallel architecture enabled by massively partitioned architecture. Then the third thing is, that we have developed new state-of-art algorithms for distributed query processing. So for many of the workloads, we find that joints are the long pole in terms of the amount of time it takes. So we at Oracle have developed new algorithms for distributed joint processing and similarly for many other operators. And this is how we're being able to consume this data or process this data, which is in-memory really, really fast. And finally, and what we have, is that we have an eye for scalability and we have designed algorithms such that there's a lot of overlap between compute and communication, which means that as you're sending data across various nodes and there could be like, dozens of of nodes or 100s of nodes that they're able to overlap the computation time with the communication time and this is what gives us massive scalability in the cloud. >> Yeah, so, some hard core database techniques that you've brought to HeatWave, that's impressive. Thank you for that description. Let me ask you, just to go to quicker side. So, MySQL is open source, HeatWave is what? Is it like, open core? Is it open source? >> No, so, HeatWave is something which has been designed and optimized for the cloud. So it can't be open source. So any, it's not open service. >> It is a service. >> It is a service. That's correct. >> So it's a managed service that I pay Oracle to host for me. Okay. Got it. >> That's right. >> Okay, I wonder if you could talk about some of the use cases that you're seeing for HeatWave, any patterns that you're seeing with customers? >> Sure, so we've had the service, we had the HeatWave service in limited availability for almost 15 months and it's been about five months since we have gone G. And there's a very interesting trend of our customers we're seeing. The first one is, we are seeing many migrations from AWS specifically from Aurora. Similarly, we are seeing many migrations from Azure MySQL we're migrations from Google. And the number one reason customers are coming is because of ease of use. Because they have their databases currently siloed. As you were talking about some for optimized for transactional processing, some for analytics. Here, what customers find is that in a single database, they're able to get very good performance, they don't need to move the data around, they don't need to manage multiple databaes. So we are seeing many migrations from these services. And the number one reason is reduce complexity of ease of use. And the second one is, much better performance and reduced costs, right? So that's the first thing. We are very excited and delighted to see the number of migrations we're getting. The second thing which we're seeing is, initially, when we had the service announced, we were like, targeting really towards analytics. But now what are finding is, many of these customers, for instance, who have be running on Aurora, when they are moving from MySQL in HeatWave, they are finding that many of the OLTP queries as well, are seeing significant acceleration with the HeatWave. So now customers are moving their entire applications or, to HeatWave. So that's the second trend we're seeing. The third thing, and I think I kind of missed mentioning this earlier, one of the very key and unique value propositions we provide with the MySQL Database Service in HeatWave, is that we provide a mechanism where if customers have their data stored on premise they can still leverage the HeatWave service by enabling MySQL replication. So they can have their data on premise, they can replicate this data in the Oracle Cloud and then they can run analytics. So this deployment which we are calling the hybrid deployment is turning out to be very, very popular because there are customers, there are some customers who for various reasons, compliance or regulatory reasons cannot move the entire data to the cloud or migrate the data to the cloud completely. So this provides them a very good setup where they can continue to run their existing database and when it comes to getting benefits of HeatWave for query acceleration, they can set up this replication. >> And I can run that on anyone, any available server capacity or is there an appliance to facilitate that? >> No, this is just standard MySQL replication. So if a customer is running MySQL on premise they can just turn off this application. We have obviously enhanced it to support this inbound replication between on-premise and Oracle Cloud with something which can be enabled as long as the source and destination are both MySQL. >> Okay, so I want to come back to this sort of idea of the architecture a little bit. I mean, it's hard for me to go toe to toe with the, I'm not an engineer, but I'm going to try anyway. So you've talked about OLTP queries. I thought, I always thought HeatWave was optimized for analytics. But so, I want to push on this notion because people think of this the converged database, and what you're talking about here with HeatWave is sort of the Swiss army knife which is great 'cause you got a screwdriver and you got Phillips and a flathead and some scissors, maybe they're not as good. They're not as good necessarily as the purpose-built tool. But you're arguing that this is best of breed for OLTP and best of breed for analytics, both in terms of performance and cost. Am I getting that right or is this really a Swiss army knife where that flathead is really not as good as the big, long screwdriver that I have in my bag? >> Yes, so, you're getting it right but I did want to make a clarification. That HeatWave is definitely the accelerator for all your queries, all analytic queries and also for the long running complex transaction processing inquiries. So yes, HeatWave the uber query accelerator engine. However, when it comes to transaction processing in terms of your insert statements, delete statements, those are still all done and served by the MySQL database. So all, the transactions are still sent to the MySQL database and they're persistent there, it's the queries for which HeatWave is the accelerator. So what you said is correct. For all query acceleration, HeatWave is the engine. >> Makes sense. Okay, so if I'm a MySQL customer and I want to use HeatWave, what do I have to do? Do I have to make changes to my existing applications? You applied earlier that, no, it's just sort of plugs right in. But can you clarify that. >> Yes, there are absolutely no changes, which any MySQL or MySQL compatible application needs to make to take advantage of HeatWave. HeatWave is an in-memory accelerator and it's completely transparent to the application. So we have like, dozens and dozens of like, applications which have migrated to HeatWave, and they are seeing the same thing, similarly tools. So if you look at various tools which work for analytics like, Tableau, Looker, Oracle Analytics Cloud, all of them will work just seamlessly. And this is one of the reasons we had to do a lot of heavy lifting in the MySQL database itself. So the MySQL database engineering team was, has been very actively working on this. And one of the reasons is because we did the heavy lifting and we meet enhancements to the MySQL optimizer in the MySQL storage layer to do the integration of HeatWave in such a seamless manner. So there is absolutely no change which an application needs to make in order to leverage or benefit from HeatWave. >> You said earlier, Nipun, that you're seeing migrations from, I think you said Aurora and Google BigQuery, you might've said Redshift as well. Do you, what kind of tooling do you have to facilitate migrations? >> Right, now, there are multiple ways in which customers may want to do this, right? So the first tooling which we have is that customers, as I was talking about the replication or the inbound replication mechanism, customers can set up heat HeatWave in the Oracle Cloud and they can send the data, they can set up replication within their instances in their cloud and HeatWave. Second thing is we have various kinds of tools to like, facilitate the data migration in terms of like, fast ingestion sites. So there are a lot of such customers we are seeing who are kind of migrating and we have a plethora of like, tools and applications, in addition to like, setting up this inbound application, which is the most seamless way of getting customers started with HeatWave. >> So, I think you mentioned before, I have my notes, machine intelligence and machine learning. We've seen that with autonomous database it's a big, big deal obviously. How does HeatWave take advantage of machine intelligence and machine learning? >> Yeah, and I'm probably going to be talking more about this in the future, but what we have already is that HeatWave uses machine learning to intelligently automate many operations. So we know that when there's a service being offered in the cloud, our customers expect automation. And there're a lot of vendors and a lot of services which do a good job in automation. One of the places where we're going to be very unique is that HeatWave uses machine learning to automate many of these operations. And I'll give you one such example which is provisioning. Right now with HeatWave, when a customer wants to determine how many nodes are needed for running their workload, they don't need to make a guess. They invoke a provisioning advisor and this advisor uses machine learning to sample a very small percentage of the data. We're talking about, like, 0.1% sampling and it's able to predict the amount of memory with 95% accuracy, which this data is going to take. And based on that, it's able to make a prediction of how many servers are needed. So just a simple operation, the first step of provisioning, this is something which is done manually across, on any of the service, whereas at HeatWave, we have machine learning-based advisor. So this is an example of what we're doing. And in the future, we'll be offering many such innovations as a part of the MySQL Database and the HeatWave service. >> Well, I've got to say I was skeptic but I really appreciate it, you're, answering my questions. And, a lot of people when you made the acquisition and inherited MySQL, thought you were going to kill it because they thought it would be competitive to Oracle Database. I'm happy to see that you've invested and figured out a way to, hey, we can serve our community and continue to be the steward of MySQL. So Nipun, thanks very much for coming to the CUBE. Appreciate your time. >> Sure. Thank you so much for the time, Dave. I appreciate it. >> And thank you for watching everybody. This is Dave Vellante with another CUBE Conversation. We'll see you next time. (bright upbeat music)