>> 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.

>>Good afternoon and welcome back to the Classiest Show in Technology. This is the Cube we are at AWS Reinvent 2022 in Fabulous Sin City. That's why I've got my sequence on. We love a little Vegas, don't we? I'm joined by John Farer, another, another Vegas >>Fan. I don't have my sequence, I left it in my room. We're >>Gonna have to figure out how to get us 20 as soon as possible. What's been your biggest shock for you at the show so far? >>Well, I think the data story and security is so awesome. I love how that's front and center. If you look at the minutes of the keynote of Adamski, the CEO on day one, it's all bulked into data and security. All worked hand in hand. That's on top of already the innovation of their infrastructure. So I think you're gonna see a lot of interplay going on in this next segment. It's gonna tell a lot of that innovation story that's coming next. It's pretty awesome. >>It is pretty awesome, and I'm super excited. It's not only what we do here on the Cube, it's also in my show notes. We are gonna be geeking out for the next segment. Please welcome Paul and Puja. Wonderful to have you both here. Paul from Amazon, s3, glacier, and Pujan, CEO of kuo. I wanna turn to you Pujan, to start us off, just in case the audience isn't familiar, give us the Kuo pitch. >>Yeah, so basically Kuo is a, a backup as a service offering, right? Built in AWS four aws, right? And effectively going after, you know, any service that a customer uses on top of aws, right? And so a lot of the data sitting on s3, right? So that's been like our, our big use case going and basically building backup and air gap protection for, for s3. But we basically go to every other service, e c two, ebs, dynamo, you know, you name it, right? So basically do the whole thing >>And the relationship with aws. Can you guys share, I mean, you got you here together. You guys are a great partnership. Born in the cloud, operation in the cloud. Absolutely. I think talk about the partnership with aws. >>Absolutely. I think the last five years of building on AWS has been phenomenal, right? And I love the platform. It's, it's a very pure platform for us. You know, the APIs and, and the access you get and access you get to the service teams like Paul sitting here and the other teams you have gotten access to, I think has been phenomenal. But we also have, I would say, pushed the envelope in terms of how innovative we have been and how aggressive we have been in utilizing all the innovation that AWS has built in over the last few years. But it would not have happened without the fantastic partnership with the service teams. >>Paul, talk about the, AM the S3 part of this. What's the story there? >>Well, it's been great working with the CUO team over the course of the last few years. We were just upstairs diving deep into the, to the features that they're taking advantage of. They really push us hard on behalf of customers, and it's been a, it's just been a great relationship over the last years. >>That's awesome. And the ecosystem at such a, we're gonna hear tomorrow, the keynote on the, from Aruba who's gonna tend over the ecosystem. You guys are working together. There's a lot of strategic partnerships, so much collaboration between you guys that makes it very, this is the next gen cloud of cloud environment we're seeing. And you heard the, the economies around the corner. It's still gonna be challenging, but still there's more growth in the cloud. This is not stopping. This is impacts the customers. What are the customers saying to you guys when you work backwards from their needs? They want it faster, easier, cheaper. They want it more integrated. What are some of the things, all those you guys hearing from customers? >>So for us, you know, if you think about it, like, you know, as people are moving to the cloud, especially like take a use case like s3, right? So much of critical data sitting on top of S3 today. And so what folks have realized that as they're, you know, putting all of those, you know, what, over two 50 trillion objects, you know, sitting on s3, a lot of them need backup and data protection because there could be accidental deletions, there could be software bugs, there could be a ransomware type event due to which you need a second copy of the data that is outside of your security domain, right? But again, that needs to get be done at the, at the right price point, right? And that's where like a technology like Columbia comes in because since we've been built on the cloud, we've optimized it correctly. So especially for folks who are very cost conscious, given the macroeconomic conditions, we are heading into a technology that's built correctly so that, you know, you get the right architecture and the right solution at the right price point and the scale, right? Talking about trillions of objects, billions of objects within a single customer, within a single bucket sometimes. And that's where Columbia comes in. Cause we basically do that at scale without, again, impacting the, the customer's wallet more than it needs to. >>The porridge has to be the right temperature and the right size bowl. With the right spoon. You've got a lot of complexity when it comes to solving those customer challenges. You have a couple customer story examples you're allowed to share with us. Correct? Paul, do you want to kick one off? Go ahead. Oh, puja. All right. >>No, absolutely. I think there's a ton of them. I, I'll talk about, you know, want to begin with like Cox Automotive, right? A phenomenal customer that we, all of us have worked together with them. And again, looking for a solution to backup S3 to essentially go air gap protection outside of their account, right? They looked at doing it themselves, right? They thought they'll go and basically do it themselves. And then they fortunately bumped into Columbia, they looked at our architecture, looked at what it would really go and take to build it. And guess what, sitting in 2022, getting 23 right now, nobody wants to go and build this themselves. They actually want a turnkey solution that just does it, right? And so, again, we are a phenomenal joint customer of ours doing this at a pretty massive scale, right? And there are many more like that. There's Warner Brothers that are essentially going into the cloud from on premises, right? And they're going really fast accelerating the usage on aws again, looking at, you know, backup and data protection and using clum because of our extreme simplicity that we provide. >>Yeah, I think it's, you've got a, a lot of different people solving different problems that you're working with all the time. Millions of customers. Well, how do you prioritize? >>Well, for us, it really all comes down to fundamentals, right? So Amazon, s3 s unique distributed architecture delivers industry leading durability, availability, performance and security at virtually unlimited scale, right? And it's really been delivering on the fundamentals that has earned the trust of so many customers of all sizes and industries over the course of over 16 years. Now, in terms of how we prioritize on behalf of those customers, we always say that 90% of our roadmap comes directly from what customers are telling us is important. And a large number of our customers now are using S3 through lumino, which is why the relationship is so important. We're here talking about customer use cases here at the show, and we do that regularly throughout the year as well. And that's, that's how we land on a road. >>And what are the, what are the top stories from customers? What, what are they telling you? What's the number one top three things you're hearing? >>I tell you, like, again, it just comes down to the fundamentals, right? Of security, availability, durability and performance at virtually unlimited scale. Like that is the first customer first discussions that we have with customers talking about durable storage, for >>Sure. What I find interesting in, you mentioned scale, right? That comes up a lot scale with data. Yeah. That we heard data. The big theme here, security, what's in my S3 bucket? Can you find out what's in there? Is it backed up properly? How do I get it back? Where's the ransomware? Why not just target the ransomware? So how do you navigate the, the security challenges, the, the need to store all that scale data? What's the secret sauce? >>Yeah, so I think the, the big thing is we'll start with the, you know, how we have architected the product, right? If you think about it, this, you're dealing with a lot of scale, right? You get to a hundred million, a billion and billions very fast on S3 few, especially on a cloud native application. So it starts with the visibility, right? It's basically about, like we have things where you do, where you create a subset of your buckets called protection groups that you can essentially, you know, do it based on prefixes. So now you can essentially figure out what prefix you want to back up and what you don't want to back up. Maybe there's log data that you don't care about, so you don't back that up, right? And it all starts with that visibility that you give. And the prefix level data protection then comes the scale, which is where I was telling you, right? We have basically built an orchestration engine, right? It's like we call the ES for Lambdas, right? So we have a internal orchestration engine and essentially what what we have done is we have our own language internally that spawns off these lambdas, right? And they go after these S3 partitions do the right things and then you basically reel them back. So things like that that we do that are not possible if you're not built on the >>Clock. Well also, I mean, just mind blowing and go back 10 years. Yeah. I mean you got Lambda. What you're talking about here is the gift of the cloud innovation. Yeah. So the benefit of S3 is now accelerated. This is the story this year. Yeah. I mean they're highlighting it at scale, not just in the data, but like what we knew when Lambda came out and what S3 could do. But now mainstream solutions are coming in. Does that change your backup plans? Because we're gonna see a lot more end to end, lot more solutions. We heard that on the keynote. Some are saying it's more complexity. Of course it might, but you can abstract another way with the cloud that's the best part of the cloud. So these abstraction leads. So what's your view on that? But I wanna get your thoughts because you guys are perfectly positioned for this scale, but there's more coming. Yes. Yes. Exactly. What, how are you looking at that? >>So again, I think the, you know, obviously the, the S3 teams and every team in AWS is basically pushing the envelope in terms of innovation. But the key for a partner like us is to go and take that innovation. A lot of complex architectures behind the scene. But what you deliver to the customer is simple. I'll give you one more example. One of the things we launched that, you know, Paul and others are very excited about, is this ability to do instant access on the backup, right? So you could have billions of objects that you backed up. Maybe you need just 10,000 of them for a DR test. And we can basically create like an instant virtual bucket on top of that backup that you can instantly restore >>Spinning up a sandbox of temporary data to go check it >>Out. Exactly. Offer an inte application. >>Think we're geeking out right now. >>Yeah, I know. Brought that part of the segment, John. Don't worry, we're safely there. But, >>But that's the thing, right? That all that is possible because of all the, the scale and innovation and all the APIs and everything that, you know, Paul and the team gives us that we go and build on top of >>Paul, geek out on with us on this. We >>Are super excited for instant restore >>For store. I mean, automation programmability. >>It is, I mean it's the logical next step for backup in the cloud. Exactly. Yeah. But it's a super hard engineering problem to go solve for customers. I mean, the RTO benefits alone are super compelling, but then there's a cost element as well of not having to bring back all that stuff for a test restore, for example. And so it's, it's been really great to, to work with the team on that. We have some ideas on how we may help solve it from our side, and we're looking forward to collaborating on it. >>This is a great illustration of what I was writing about this week around the classic cloud, which is great. And as Adam said, and used like to use the word and, and you got this new functionality we're seeing emerge from the growth. Yes. From the companies that are built on Amazon web services that are growing. You're a partner, they have a lot of other partners and people are taking over restaurant here off action. I mean, there's real growth and new functionality on top of aws. You guys are no different. What's, are you prepared for that? Are you ready to go? >>Yeah, no, absolutely. And I think if you think about, if you think about it, right, I think it's also about doing this without impacting the primary application. Like if the customer is running a primary application at scale on s3, a backup application like ours can't come in and really mess with that. So I think being able to do things where, and this is where you solve really hard computer science problems, right? Where you're bottling yourself. If you are essentially seeing any kind of, you know, interfering with the primary, you're going to cut yourself down. You're gonna go after a different partition. So there are a lot of things you need to do behind the scenes, which is again, all the complexity, all of that, but deliver the, to the customer a very, very simple thing. >>You know, Paul, I wanna get your thoughts and I want you to chime in. Yeah. In 2014, I interviewed Steven Schmidt, my first interview with the, he was the CISO then, and now he's a CSO and, and former ciso, he's back at that time, the word was the cloud's not secure. Now we're talking about security. Just in the complexity of how you're partitioning and managing your sub portions, how you explained it, it's harder for the attackers. The cloud in its in its architecture has become a more secure environment. Yeah. Well, and getting more secure as you have laying out this, this is a new dynamic. This is good. Can you explain the, >>I mean, I, I can just tell you that at AWS security is job zero and that it will always be our number one priority, right? We have a, an infrastructure with under AWS that is vetted and approved to run even top secret workloads, which benefits all customers in all regions. >>And your, your security posture is embedded on top of that. And you got your own stuff. >>Yeah. And if you think of it as a shared responsibility model, so security of the cloud is the responsibility of the cloud provider, but then security of the data on top of it. Like you, you go and delete stuff, your software goes and does something that resiliency, the integrity of the data is your responsibility as a customer. And that's where, you know, we come in. Who >>Shared responsibility has been such a hot topic all week. Yeah. >>I gotta ask him one more question. Cause this is fascinating. And we are talking about on the cube all day today after we saw the announcement and Adam's comment on the cube, Adams LE's comment on the keynote. I mean, he said, if you're gonna tighten your belt, meaning economic cost recovery, re right sizing. If you want to tighten your belt, come to the cloud. So I have to ask you guys, Puja, if you can comment, that'd be great. There's a lot of other competitors out there that aren't born on aws. What is the customer gonna do when they tighten the build? What does that mean? They're gonna go to, to the individual contracts. They're gonna work in the marketplace. I mean this, there's a new dynamic in town. It's called AWS 2022. They weren't really around much in the recession of 2008. They were just starting to grow. Now they're an economic force. People like yourselves have embedded in there. There's a lot of competition. What's gonna happen? >>I think people are gonna just go to a place like, you know, AWS marketplace. You're going to essentially look for solutions and essentially like, and, and the right solutions built in are going to be self-service like aws. It's a very self-service thing. A hundred percent. So you go and do self-service, you figure out what's working, what's not working. Also, the model has to be consumption oriented. No longer can you expect the customer to go and pay a bunch of money for shelfware, right? It's like, like how we charge how AWS charges, which is you pay for what you consume. That and all has to be front and center, >>Right? I think that's a really, I think that's a really important >>Point. It's time >>And I think it's time. So we have a new challenge on the cube. We give you 30 seconds roughly to give us your extraordinarily hot take your shining thought leadership moment and, and highlight what you think is the most important takeaway from the show. The biggest soundbite, the juiciest announcement. Paul, I'll >>Start with an Instagram. Real basically. Yeah. Okay. >>Yeah. Hi. Go. I would just say from an S3 perspective, over the course of the last several years, we've really seen workloads shift from just backup and recovery and static images on websites to data lake analytics applications. And you continue to see that here. And I can tell you that some of these scaled applications are running at enormous mind blowing scale, right? And so, so every year we come here, we talk to customers, and it's just every year it sort of blows me away. And I've been in the storage industry for a long time and it's just is, it blows me away. Just the scale at customers are running in >>And >>Blowing scale. And when it comes to backup, let me just say that it's easy to back up and recover a single object, but doing an easy thing, a billion or 10 billion times over, that's actually quite hard. >>And just to, just to bold that a little bit, just pull out my highlighter. S3 now has over 280 trillion objects. That's a lot. >>That's a lot of objects. >>Yeah. You are not, you are not kidding. When you talk about scale, I mean, this is the most scalable. >>That's not solution's not there. Yeah. That, that's right. And we wake up every, we have a culture of durability and we wake up every single day to raise the bar on the fundamentals and make sure that every single one of those objects is protected and safe. >>Okay. You, I, >>I can't imagine worrying about two, two 80 trillion different things. >>Let's go. You're Instagram real >>For me again, you know, between S3 and us, we are two players out there that are really, you know, processing the data at the end of the day, right? And so I'm very excited about, you know, what we are going to do more and more with the instant restore capability where we can integrate third party services on top of it that can do more things with the data that is not, not passively sitting, but now becomes active data that you can analyze and do things with. So that's something where we take this to the next level is something that I'm super excited about. >>There's a lot to be excited about and, and we're excited to have you. We're excited to hear what happens next. Excited to see more collaboration like this. Paul Pon, thank you so much for joining us here on the show. Thank all of you from for tuning into our continuous wall to wall super thrilling live coverage of AWS reinvent here in fabulous Las Vegas, Nevada, with John Furrier. I'm Savannah Peterson. We're the cube, the leading source for high tech coverage.

>>Hello everyone. Welcome back to the Cube Live, AWS Reinvent 2022. This is our first day of three and a half days of wall to wall coverage on the cube. Lisa Martin here with Dave Valante. Dave, it's getting louder and louder behind us. People are back. They're excited. >>You know what somebody told me today? Hm? They said that less than 15% of the audience is developers. I'm like, no way. I don't believe it. But now maybe there's a redefinition of developers because it's all about the data and it's all about the developers in my mind. And that'll never change. >>It is. And one of the things we're gonna be talking about is app modernization. As customers really navigate the journey to do that so that they can be competitive and, and meet the demands of customers. We've got an alumni back with us to talk about that. AJ Patel joins us, the SVP and GM Modern Apps and Management business group at VMware. Aj, welcome back. Thank >>You. It's always great to be here, so thank you David. Good to see >>You. Isn't great. It's great to be back in person. So the VMware Tansu team here back at Reinvent on the Flow Shore Flow show floor. There we go. Talk about some of the things that you guys are doing together, innovating with aws. >>Yeah, so it's, it's great to be back after in person after multiple years and the energy level continues to amaze me. The partnership with AWS started on the infrastructure side with VMware cloud on aws. And when with tanza, we're extending it to the application space. And the work here is really about how do you make developers productive To your earlier point, it's all about developers. It's all about getting applications in production securely, safely, continuously. And tanza is all about making that bridge between great applications being built, getting them deployed and running, running and operating at scale. And EKS is a dominant Kubernetes platform. And so the better together story of tanu and EKS is a great one for us, and we're excited to announce some sort of innovations in that area. >>Well, Tanu was so front and center at VMware Explorer. I wasn't at in, in VMware Explorer, Europe. Right. But I'm sure it was a similar kind of focus. When are customers choosing Tanu? Why are they choosing Tanu? What's, what's, what's the update since last August when >>We, you know, the market settled into three main use cases. One is all about developer productivity. You know, consistently we're all dealing with skill set gap issues. How do we make every developer productive, modern developer? And so 10 is all about enabling that develop productivity. And we can talk quite a bit about it. Second one is security's front and center and security's being shifted left right into how you build great software. How do you secure that through the entire supply chain process? And how do you run and operationalize secure at runtime? So we're hearing consistently about making secure software supply chain heart of what our solution is. And third one is, how do I run and operate the modern application at scale across any Kubernetes, across any cloud? These are the three teams that are continuing to get resonance and empowering. All of this is exciting. David is this formation of platform teams. I just finished a study with Bain Consulting doing some research for me. 40% of our organization now have some form of a central team that's responsive for, for we call platform engineering and building platforms to make developers productive. That is a big change since about two years ago even. So this is becoming mainstream and customers are really focusing on delivering in value to making developers productive. >>Now. And, and, and the other nuance that I see, and you kinda see it here in the ecosystem, but when you talk about your customers with platform engineering, they're actually building their, they're pointing their business. They gonna page outta aws, pointing their businesses to their customers, right? Becoming software companies, becoming cloud companies and really generating new forms of revenue. >>You know, the interesting thing is, some of my customers I would never have thought as leading edge are retailers. Yeah. And not your typical Starbucks that you get a great example. I have an auto parts company that's completely modernizing how they deliver point of sale all the way to the supply chain. All built on ES at scale. You're typically think of that a financial services or a telco leading the pack. But I'm seeing innovation in India. I'm seeing the innovation in AMEA coming out of there, across the board. Every industry is becoming a product company. A digital twin as we would call it. Yeah. And means they become software houses. Yeah. They behave more like you and I in this event versus a, a traditional enterprise. >>And they're building their own ecosystems and that ecosystem's generating data that's generating more value. And it's just this cycle. It's, >>It's a amazing, it's a flywheel. So innovation continues to grow. Talk about really unlocking the developer experience and delivering to them what they need to modernize apps to move as fast and quickly as they want to. >>So, you know, I think AWS coin this word undifferentiated heavy lifting. If you think of a typical developer today, how much effort does he have to put in before he can get a single line of code out in production? If you can take away all the complexity, typically security compliance is a big headache for them, right? Developer doesn't wanna worry about that. Infrastructure provisioning, getting all the configurations right, is a headache for them. Being able to understand what size of infrastructure or resource to use cost effectively. How do you run it operationally? Cuz the application team is responsible for the operational cost of the product or service. So these are the un you know, heavy lifting that developers want to get away from. So they wanna write great code, build great experiences. And we've always talked about frameworks a way to abstract with the complexity. And so for us, there's a massive opportunity to say, how do I simplify and take away all the heavy lifting to get an idea into production seamlessly, continuously, securely. >>Is that part of your partnership? Because you think about a aws, they're really not about frameworks, they're about primitives. I mean, Warner Vos even talks about that in his, in his speech, you know, but, but that makes it more challenging for developers. >>No, actually, if you look at some of their initial investments around proton and et cetera work, they're starting to do, they're recognized, you know, PS is a bad, bad word, but the outcomes a platform as a service offers is what everybody wants. Just talking to the AWS leaders, responsible area, he actually has a separate build team. He didn't know what to call the third team. He has a Kubernetes team, he has a serverless team and has a build team. And that build team is everything above Kubernetes to make the developer productive. Right. And the ecosystem to bring together to make that happen. So I think AWS is recognizing that primitives are great for the elite developers, but if they want to get the mass scale and adoption in the business, it, if you will, they're gonna have to provide richer set of building blocks and reduce the complex and partnership like ours. Make that a reality. And what I'm excited about is there's a clear gap here, and t's the best platform to kind of fill that gap. Well, >>And I, I think that, you know, they're gonna double down triple, I just wrote about this double down, triple down on the primitives. Yes. They have to have the best, you know, servers and storage and database. And I think the way they, they, I call it taping the seams is with the ecosystem. Correct. You know, and they, nobody has a, a better ecosystem. I mean, you guys are, you know, the, the postage child for the ecosystem and now this even exceeds that. But partnering up, that's how they >>Continue to, and they're looking for someone who's open, right? Yeah. Yeah. And so one of the first question is, you know, are you proprie or open? Because one of the things they're fighting against is the lock in. So they can find a friendly partner who is open source, led, you know, upstream committing to the code, delivering that innovation, and bring the ecosystem into orchestrated choreography. It's like singing a music, right? They're running a, running an application delivery team is like running a, a musical orchestra. There's so many moving parts here, right? How do you make them sing together? And so if Tan Zoo and our platform can help them sing and drive more of their services, it's only more valuable for them. And >>I think the partners would generally say, you know, AWS always talking about customer obsession. It's like becomes this bromine, you go, yeah, yeah. But I actually think in the field, the the sellers would say, yeah, we're gonna do what the customer, if that means we're gonna partner up. Yeah. And I think AWS's comp structure makes it sort >>Of, I learned today how, how incentives with marketplaces work. Yeah. And it is powerful. It's very powerful. Yeah. Right. So you line up the sales incentive, you line up the customer and the benefits, you line up bringing the ecosystem to drive business results and everybody, and so everybody wins. And which is what you're seeing here, the excitement and the crowd is really the whole, all boats are rising. Yeah. Yeah. Right, right. And it's driven by the fact that customers are getting true value out of it. >>Oh, absolutely. Tremendous value. Speaking of customers, give us an example of a customer story that you think really articulates the value of what Tanzi was delivering, especially making that developer experience far simpler. What are some of those big business outcomes that that delivers? >>You know, at Explorer we had the CIO of cvs and with their acquisition of Aetna and CVS Health, they're transforming the, the health industry. And they talked about the whole covid and then how they had to deliver the number of, you know, vaccines to u i and how quickly they had to deliver on that. It talked about Tanu and how they leverage, leverage a Tanza platform to get those new applications out and start to build that. And Ro was basically talking about his number one prior is how does he get his developers more productive? Number to priority? How does he make sure the apps are secure? Number three, priority, how does he do it cost effectively in the world? Particularly where we're heading towards where, you know, the budgets are gonna get tighter. So how do I move more dollars to innovation while I continue to drive more efficiency in my platform? And so cloud is the future. How does he make the best use of the cloud both for his developers and his operations team? Right? >>What's happening in serverless, I, in 2017, Andy Chassy was in the cube. He said if AWS or if Amazon had to build all over again, they would build in, in was using serverless. And that was a big quote. We've mined that for years. And as you were talking about developer productivity, I started writing down all the things developers have to do. Yep. With it, they gotta, they gotta build a container image. They said they gotta deploy an EC two instance. They gotta allocate memory, they gotta fence off the apps in a virtual machine. They gotta run the, you know, compute against the app goes, they gotta pay for all that. So, okay, what's your story on, what's the market asking for in terms of serverless? Because there's still some people who want control over the run time. Help us sift through that. >>And it really comes back to the application pattern or the type you're running. If it's a stateless application that you need to spin up and spin down. Serverless is awesome. Why would I wanna worry about scaling it up in, I wanna set up some SLAs, SLIs service level objectives or, or, or indicators and then let the systems bring the resources I need as I need them. That's a perfect example for serverless, right? On the other hand, if you have a, a more of a workflow type application, there's a sequence, there's state, try building an application using serverless where you had to maintain state between two, two steps in the process. Not so much fun, right? So I don't think serverless is the answer for everything, but many use cases, the scale to zero is a tremendous benefit. Events happen. You wanna process something, work is done, you quietly go away. I don't wanna shut down the server started up, I want that to happen magically. So I think there's a role of serverless. So I believe Kubernetes and servers are the new runtime platform. It's not one or the other. It's about marrying that around the application patterns. I DevOps shouldn't care about it. That's an infrastructure concern. Let me just run application, let the infrastructure manage the operations of it, whether it's serverless, whether it's Kubernetes clusters, whether it's orchestration, that's details right. I I I shouldn't worry about it. Right. >>So we shouldn't think of those as separate architectures. We should think of it as an architecture, >>The continuum in some ways Yeah. Of different application workload types. And, and that's a toolkit that the operator has at his disposal to configure and saying, where does, should that application run? Should I want control? You can run it on a, a conveyance cluster. Can I just run it on a serverless infrastructure and and leave it to the cloud provider? Do it all for me. Sure. What, what was PAs? PAs was exactly that. Yeah. Yeah. Write the code once you do the rest. Yeah. Okay. Those are just elements of that. >>And then K native is kinda in the middle, >>Right? K native is just a technology that's starting to build that capability out in a standards way to make serverless available consistently across all clouds. So I'm not building to a, a lambda or a particular, you know, technology type. I'm building it in a standard way, in a standard programming model. And infrastructure just >>Works for me on any cloud. >>The whole idea portability. Consistency. >>Right. Powerful. Yep. >>What are some of the things that, that folks can expect to learn from VMware Tan to AWS this week at the >>Show? Yeah, so there's some really great announcements. First of all, we're excited to extend our, our partnership with AWS in the area of eks. What I mean by that is we traditionally, we would manage an EKS cluster, you visibility of what's running in there, but we weren't able to manage the lifecycle With this announcement. We can give you a full management of lifecycle of S workloads. Our customers have 400 plus EKS clusters, multiple teams sharing those in a multi-tenanted way with common policy. And they wanna manage a full life cycle, including all the upstream open source component that make up Kubernetes people. That ES is the one thing, it's a collection of a lot of open, open source packages. We're making it simple to manage it consistently from a single place on the security front. We're now making tons of service mesh available in the marketplace. >>And if you look at what service MeSHs, it's an overlay. It's an abstraction. I can create an idea of a global name space that cuts across multiple VPCs. I'm, I'm hearing at Amazon's gonna make some announcements around VPC and how they stitch VPCs together. It's all moving towards this idea of abstractions. I can set policy at logical level. I don't have to worry about data security and the communication between services. These are the things we're now enabling, which are really an, and to make EKS even more productive, making enterprise grade enterprise ready. And so a lot of excitement from the EKS development teams as well to partner closely with us to make this an end to end solution for our >>Customers. Yeah. So I mean it's under chasy, it was really driving those primitives and helping developers under continuing that path, but also recognizing the need for solutions. And that's where the ecosystem comes in, >>Right? And the question is, what is that box? As you said last time, right? For the super cloud, there is a cloud infrastructure, which is becoming the new palette, but how do you make sense of the 300 plus primitives? How do you bring them together? What are the best practices, patterns? How do I manage that when something goes wrong? These are real problems that we're looking to solve. >>And if you're gonna have deeper business integration with the cloud and technology in general, you have to have that >>Abstraction. You know, one of the simple question I ask is, how do you know you're getting value from your cloud investment? That's a very hard question. What's your trade off between performance and cost? Do you know where your security, when a lock 4G happens, do you know all the open source packages you need to patch? These are very simple questions, but imagine today having to do that when everybody's doing in a bespoke manner using the set of primitives. You need a platform. The industry is shown at scale. You have to start standardizing and building a consistent way of delivering and abstracting stuff. And that's where the next stage of the cloud journey >>And, and with the economic environment, I think people are also saying, okay, how do we get more? Exactly. We're in the cloud now. How do we get more? How do we >>Value out of the cloud? >>Exactly. Totally. >>How do we transform the business? Last question, AJ for you, is, if you had a bumper sticker and you're gonna put it on your fancy car, what would it say about VMware tan zone aws? >>I would say tan accelerates apps. >>Love >>It. Thank you so much. >>Thank you. Thank you so much for joining us. >>Appreciate it. Always great to be here. >>Pleasure. Likewise. For our guest, I'm Dave Ante. I'm Lisa Martin. You're watching The Cube, the leader in emerging and enterprise tech coverage.

>>Good afternoon and welcome back to Detroit, Lisa Martin here with John Furrier. We are live day two of our coverage of Coan Cloud Native Con North America. John, we've had great conversations. Yeah. All day yesterday. Half a day today. So far we're talking all things, Well, not all things Kubernetes so much more than that. We also have to talk about storage and data management solutions for Kubernetes projects, cuz that's obviously critical. >>Yeah, I mean the big trend here is Kubernetes going mainstream has been for a while. The adopt is crossing over, it's crossing the CADs and with that you're seeing security concerns. You're seeing things being gaps being filled. But enterprise grade is really the, the, the story. It's going enterprise, that's managed services, that's professional service, that's basically making things work at scale. This next segment hits that part and we are gonna talk about it in grade length >>With one of our alumni. Moral morale to Molly is back DP and GM of Port Work's Peer Storage. Great to have you back really? >>Yeah, absolutely. Delightful >>To be here. So I was looking on the website, number one in Kubernetes storage. Three years in a row. Yep. Awesome. What's Coworks doing here at KU Con? >>Well, I'll tell you, we, our engineering crew has been so productive and hard at work that I almost can't decide what to kind of tell you. But I thought what, what, what I thought I would do is kind of tell you that we are in forefront of two major trends in the world of Kubernetes. Right? And the, the two trends that I see are one is as a service, so is trend number one. So it's not software eating the world anymore. That's, that's old, old, old news. It's as a service unifying the world. The world wants easy, We all are, you know, subscribers to things like Netflix. We've been using Salesforce or other HR functions. Everything is as a service. And in the world of Kubernetes, it's a sign of that maturity that John was talking about as a platform that now as a service is the big trend. >>And so headline number one, if you will, is that Port Works is leading in the data management world for Kubernetes by providing, we're going all in on easy on as a service. So everything we do, we are satisfying it, right? So if you think, if you think about, if you think about this, that, that there are really, most of the people who are consuming Kubernetes are people who are building platforms for their dev users. And dev users want self service. That's one of the advantages of, of, of Kubernetes. And the more it is service size and made as a service, the more ready to consume it is. And so we are announcing at the show that we have, you know, the basic Kubernetes data management as a service, ha d r as a service. We have backup as a service and we have database as a service. So these are the three major components of data. And all of those are being made available as a service. And in fact, we're offering and announcing at the show our backup as a service freemium version where you can get free forever a terabyte of, of, you know, stuff to do for Kubernetes for forever. >>Congratulations on the announcement. Totally. In line with what the market wants. Developers want Selfer, they wanna also want simplicity by the way they'll leave if they don't like the service. Correct. So that you, you know that before we get into some more specifics, I want Yeah. Ask you on the industry and some of the point solutions you have, what, it's been two years since the acquisition with Pure Storage. Can you just give an update on how it's gone? Obviously as a service, you guys are hitting all your Marks, developers love it. Storage are big part of the game right now as well as these environments. Yeah. What's the update post acquisition two years. You had a great offering Stay right In >>Point Works. Yeah. So look, John, you're, you're, you're a veteran of the industry and have seen lots of acquisitions, right? And I've been acquired twice before myself. So, you know, there's, there's always best practices and poor practices in terms of acquisitions and I'm, you know, really delighted to say I think this, this acquisition has had some of the best practices. Let me just name a couple of them, right? One of them is just cultural fit, right? Cultural fit is great. Entrepreneurs, anybody, it's not just entrepreneurs. Everybody loves to work in a place they enjoy working with, with people that they, you know, thrive when they, when they interact with. And so the cultural fit with, with Pure is fantastic. The other one is the strategic intent that Pure had when they acquired us is still true. And so that goes a long way, you know, in terms of an investment profile, in terms of the ability to kind of leverage assets within the company. So Pure had kind of disrupted the world of storage using Flash and they wanted to disrupt higher up the stack using Kubernetes. And that's kind of been our role inside their strategy. And it's, it's still true. >>So culture, strategic intent. Yeah. Product market fit as well. You were, you weren't just an asset for customers or acquisition and then let the founders go through their next thing. You are part of their growth play. >>Absolutely. Right. The, the beauty of, of the kind of product market fit is, let's talk about the market is we have been always focused on the global two k and that is at the heart of, you know, purest 10,000 strong customer base, right? They have very strong presence in the, in the global two k. And we, we allow them to kind of go to those same folks with, with the offering. >>So satisfying everything that you do. What's for me as a business, whether I'm a financial services organization, I'm a hospital, I'm a retailer, what's in it for me >>As a customer? Yeah. So the, the what's in it for, for me is two things. It's speed and ease of use, which in a way are related. But, but, but you know, one is when something is provided as a service, it's much more consumable. It's instantly ready. It's like instant oatmeal, right? You just get it just ad hot water and it's there. Yep. So the world of of it has moved from owning large data centers, right? That used to be like 25 years ago and running those data centers better than everybody else to move to let me just consume a data center in the form of a cloud, right? So satisfying the cloud part of the data center. Now people are saying, well I expect that for software and services and I don't want it just from the public cloud, I want it from my own IT department. >>This is old news. And so the, the, the big news here is how fast Kubernetes has kind of moved everything. You know, you take a lot of these changes, Kubernetes is a poster child for things happening faster than the last wave. And in the last couple of years I would say that as a service model has really kind of thrived in the world of Kubernetes. And developers want to be able to get it fast. And the second thing is they want to be able to operate it fast. Self-service is the other benefit. Yeah. So speed and self-service are both benefits of, of >>This. Yeah. And, and the thing that's come up clearly in the cube, this is gonna be part of the headlines we'll probably end up getting a lot of highlights from telling my team to make a note of this, is that developers are gonna be be the, the business if you, if you take digital transformation to its conclusion, they're not a department that serves the business, they are the business that means Exactly. They have to be more productive. So developer productivity has been the top story. Yes. Security as a serves all these things. These are, these are examples to make developers more productive. But one of the things that came up and I wanna get your reaction to is, is that when you have disruption and, and the storage vision, you know what disruption it means. Cuz there's been a whole discussion around disruptive operations. When storage goes down, you have back m dr and failover. If there's a disruption that changes the nature of invisible infrastructure, developers want invisible infrastructure. That's the future steady state. So if there's a disruption in storage >>Yeah. It >>Can't affect the productivity and the tool chains and the workflows of developers. Yep. Right? So how do you guys look at that? Cuz you're a critical component. Storage is a service is a huge thing. Yeah. Storage has to, has to work seamlessly. And let's keep the developers out of the weeds. >>John. I think what, what what you put your finger on is another huge trend in the world of Kubernetes where at Cube Con, after all, which is really where, where all the leading practitioners both come and the leading vendors are. So here's the second trend that we are leading and, and actually I think it's happening not just with us, but with other, for folks in the industry. And that is, you know, the world of DevOps. Like DevOps has been such a catchphrase for all, all of us in the industry last five years. And it's been both a combination of cultural change as well as technology change. Here's what the latest is on the, in the world of DevOps. DevOps is now crystallized. It's not some kind of mysterious art form that you read about how people are practicing. DevOps is, it's broken into two, two things now. >>There is the platform part. So DevOps is now a bunch of platforms. And the other part of DevOps is a bunch of practices. So a little bit on both these, the platforms in the world of es there's only three platforms, right? There's the orchestration platforms, the, you know, eks, the open ships of the world and so on. There are the data management platforms, pro people like Port Works. And the third is security platforms, right? You know, Palo Alto Networks, others Aqua or all in this. So these are the three platforms and there are platform engineering teams now that many of our largest customers, some of the largest banks, the largest service providers, they're all operating as a ES platform engineering team. And then now developers, to your point, developers are in the practice of being able to use these platforms to launch new services. So the, the actual IT ops, the ops are run by developers now and they can do it on these platforms. And the platform engineering team provide that as an ease of use and they're there to troubleshoot when problems happen. So the idea of DevOps as a ops practice and a platform is the newest thing. E and, and ports and pure storage leading in the world of data management platforms >>There. Talk about a customer example that you think really articulates the value that Port Works and Pure Storage delivers from a data management perspective. >>Yeah, so there's so many examples. One of the, one of the longest running examples we have is a very, very large service provider that, you know, you all know and probably use, and they have been using us in the cable kinda set box or cable box business. They get streams of data from, from cable boxes all over the world. They collected all in a centralized large kind of thing and run elastic search and analytics on it. Now what they have done is they couldn't keep up with this at the scale and the depth, right? The speed of, of activity and the distributed nature of the activity. The only way to solve this was to use something like Kubernetes manage with Spark coming, bringing all the data in to deep, deep, deep silos of storage, which are all running not even on a sand, but on kind of, you know, very deep terabytes and terabytes of, of storage. So all of this is orchestrated with the Heco coworks and there's a platform engineering team. We are building that platform for them with some of these other components that allows them to kind of do analytics and, and make some changes in real time. Huge kind of setup for, for >>That. Yeah. Well, you guys have the right architecture. I love the vision. I love what you guys are doing. I think this is right in line with Pures. They've always been disruptors. I remember when we first interviewed the CEO when they started Yep. They, they stayed on path. They didn't waiver. EMC was the big player. They ended up taking their lunch and dinner as well and they beat 'em in the marketplace. But now you got this traction here. So I have to ask you, how's the business, what's the results look like? Either GM cloud native business unit of a storage company that's transformed and transforming? >>Yeah, you know, it's interesting, we just hit the two year anniversary, right John? And so what we did was just kind of like step back and hey, you know, we're running so hard, you just take a step back. And we've tripled the business in the two years since the acquisition, the two years before and, and we were growing through proven. So, you know, that that's quite a fe and we've tripled the number of people, the amount of engineering investments we have, the number of go to market investments have, have been, have been awesome. So business is going really well though, I will say. But I think, you know, we have, we can't be, we we're watching the market closely. You know, as a former ceo, I, you have to kind of learn to read the tea leaves when you invest. And I think, you know, what I would say is we're proceeding with caution in the next two quarters. I view business transformation as not a cancelable activity. So that's the, that's the good news, right? Our customers are large, it's, >>It's >>Right. All they're gonna do is say, Hey, they're gonna put their hand, their hand was always going right on the dial. Now they're kind of putting their hand on the dial going, hey, where, what is happening? But my, my own sense of this is that people will continue to invest through it. The question is at what level? And I also think that this is a six month kind of watch, the watch where, where we put the dial. So Q4 and q1 I think are kind of, you know, we have our, our watch kind of watch the market sign. But I have the highest confidence. What >>Does your gut tell you? You're an entrepreneur, >>Which my, my gut says that we'll go through a little bit of a cautious investment period in the next six months. And after that I think we're gonna be back in, back full, full in the crazy growth that we've always been. We're gonna grow by the way, in the next think >>It's core style. I think I'm, I'm more bullish. I think there's gonna be some, you know, weeding out of some overinvestment pre C or pre bubble. But I think tech's gonna continue to grow. I don't see >>It's stopping. Yeah. And, and the investment is gonna be on these core platforms. See, back to the platform story, it's gonna be in these core platforms and on unifying everything, let's consume it better rather than let's go kind of experiment with a whole bunch of things all over the map, right? So you'll see less experimentation and more kind of, let's harvest some of the investments we've made in the last couple >>Of years and actually be able to, to enable companies in any industry to truly be data companies. Because absolutely. We talked about as a service, we all have these expectations that any service we want, we can get it. Yes. There's no delay because patients has gone Yeah. From the pandemic. >>So it is kind of, you know, tightening up the screws on what they've built. They, you know, adding some polish to it, adding some more capability, like I said, a a a, a combination of harvesting and new investing. It's a combination I think is what we're gonna see. >>Yeah. What are some of the things that you're looking forward to? You talked about some of the, the growth things in the investment, but as we round out Q4 and head into a new year, what are you excited about? >>Yeah, so you know, I mentioned our, as a service kind of platform, the global two K for us has been a set of customers who we co-create stuff with. And so one of the other set of things that we are very excited about and announcing is because we're deployed at scale, we're, we're, we have upgraded our backend. So we have now the ability to go to million IOPS and more and, and for, for the right backends. And so Kubernetes is a add-on which will not slow down your, your core base infrastructure. Second thing that that we, we have is added a bunch of capability in the disaster recovery business continuity front, you know, we always had like metro kind of distance dr. We had long distance dr. We've added a near sync Dr. So now we can provide disaster recovery and business continuity for metro distances across continents and across the planet. Right? That's kind of a major change that we've done. The third thing is we've added the capability for file block and Object. So now by adding object, we're really a complete solution. So it is really that maturity of the business Yeah. That you start seeing as enterprises move to embracing a platform approach, deploying it much more widely. You talked about the early majority. Yeah. Right. And so what they require is more enterprise class capability and those are all the things that we've been adding and we're really looking forward >>To it. Well it sounds like tremendous evolution and maturation of Port Works in the two years since it's been with Pure Storage. You talked about the cultural alignment, great stuff that you're achieving. Congratulations on that. Yeah. Great stuff >>Ahead and having fun. Let's not forget that, that's too life's too short to do. It is right. >>You're right. Thank you. We will definitely, as always on the cube, keep our eyes on this space. Mur. Meley, it's been great to have you back on the program. Thank you for joining, John. >>Thank you so much. It's pleasure. Our, >>For our guests and John Furrier, Lisa Martin here live in Detroit with the cube about Coan Cloud Native Con at 22. We'll be back after a short break.

>>Hello everyone and welcome back to Motor City, Michigan. We're live from the Cube and my name is Savannah Peterson. Joined this afternoon with my co-host John Ferer. John, how you doing? Doing >>Great. This next segment's gonna be awesome about application modernization, scaling pluses. This is what's gonna, how are the next generation software revolution? It's gonna be >>Fun. You know, it's kind of been a theme of our day today is scale. And when we think about the complex orchestration platform that is Kubernetes, everyone wants to scale faster, quicker, more efficiently, and our guests are here to tell us all about that. Please welcome to Char and Andy, thank you so much for being here with us. You were on the Red Hat OpenShift team. Yeah. I suspect most of our audience is familiar, but just in case, let's give 'em a quick one-liner pitch so everyone's on the same page. Tell us about OpenShift. >>I, I'll take that one. OpenShift is our ES platform is our ES distribution. You can consume it as a self-managed platform or you can consume it as a managed service on on public clouds. And so we just call it all OpenShift. So it's basically Kubernetes, but you know, with a CNCF ecosystem around it to make things more easier. So maybe there's two >>Lights. So what does being at coupon mean for you? How does it feel to be here? What's your initial takes? >>Exciting. I'm having a fantastic time. I haven't been to coupon since San Diego, so it's great to be back in person and see old friends, make new friends, have hallway conversations. It's, it's great as an engineer trying to work in this ecosystem, just being able to, to be in the same place with these folks. >>And you gotta ask, before we came on camera, you're like, this is like my sixth co con. We were like, we're seven, you know, But that's a lot of co coupons. It >>Is, yes. I mean, so what, >>Yes. >>Take us status >>For sure. Where we are now. Compare and contrast co. Your first co con, just scope it out. What's the magnitude of change? If you had to put a pin on that, because there's a lot of new people coming in, they might not have seen where it's come from and how we got here is maybe not how we're gonna get to the next >>Level. I've seen it grow tremendously since the first one I went to, which I think was Austin several years ago. And what's great is seeing lots of new people interested in contributing and also seeing end users who are trying to figure out the best way to take advantage of this great ecosystem that we have. >>Awesome. And the project management side, you get the keys to the Kingdom with Red Hat OpenShift, which has been successful. Congratulations by the way. Thank you. We watched that grow and really position right on the wave. It's going great. What's the update on on the product? Kind of, you're in a good, good position right now. Yeah, >>No, we we're feeling good about it. It's all about our customers. Obviously the fact that, you know, we have thousands of customers using OpenShift as the cloud native platform, the container platform. We're very excited. The great thing about them is that, I mean you can go to like OpenShift Commons is kind of a user group that we run on the first day, like on Tuesday we ran. I mean you should see the number of just case studies that our customers went through there, you know? And it is fantastic to see that. I mean it's across so many different industries, across so many different use cases, which is very exciting. >>One of the things we've been reporting here in the Qla scene before, but here more important is just that if you take digital transformation to the, to its conclusion, the IT department and developers, they're not a department to serve the business. They are the business. Yes. That means that the developers are deciding things. Yeah. And running the business. Prove their code. Yeah. Okay. If that's, if that takes place, you gonna have scale. And we also said on many cubes, certainly at Red Hat Summit and other ones, the clouds are distributed computer, it's distributed computing. So you guys are focusing on this project, Andy, that you're working on kcp. >>Yes. >>Which is, I won't platform Kubernetes platform for >>Control >>Planes. Control planes. Yes. Take us through, what's the focus on why is that important and why is that relate to the mission of developers being in charge and large scale? >>Sure. So a lot of times when people are interested in developing on Kubernetes and running workloads, they need a cluster of course. And those are not cheap. It takes time, it takes money, it takes resources to get them. And so we're trying to make that faster and easier for, for end users and everybody involved. So with kcp, we've been able to take what looks like one normal Kubernetes and partition it. And so everybody gets a slice of it. You're an administrator in your little slice and you don't have to ask for permission to install new APIs and they don't conflict with anybody else's APIs. So we're really just trying to make it super fast and make it super flexible. So everybody is their own admin. >>So the developer basically looks at it as a resource blob. They can do whatever they want, but it's shared and provisioned. >>Yes. One option. It's like, it's like they have their own cluster, but you don't have to go through the process of actually provisioning a full >>Cluster. And what's the alternative? What's the what's, what's the, what's the benefit and what was the alternative to >>This? So the alternative, you spin up a full cluster, which you know, maybe that's three control plane nodes, you've got multiple workers, you've got a bunch of virtual machines or bare metal, or maybe you take, >>How much time does that take? Just ballpark. >>Anywhere from five minutes to an hour you can use cloud services. Yeah. Gke, E Ks and so on. >>Keep banging away. You're configuring. Yeah. >>Those are faster. Yeah. But it's still like, you still have to wait for that to happen and it costs money to do all of that too. >>Absolutely. And it's complex. Why do something that's been done, if there's a tool that can get you a couple steps down the path, which makes a ton of sense. Something that we think a lot when we're talking about scale. You mentioned earlier, Tohar, when we were chatting before the cams were alive, scale means a lot of different things. Can you dig in there a little bit? >>Yeah, I >>Mean, so when, when >>We talk about scale, >>We are talking about from a user perspective, we are talking about, you know, there are more users, there are more applications, there are more workloads, there are more services being run on Kubernetes now, right? So, and OpenShift. So, so that's one dimension of this scale. The other dimension of the scale is how do you manage all the underlying infrastructure, the clusters, the name spaces, and all the observability data, et cetera. So that's at least two levels of scale. And then obviously there's a third level of scale, which is, you know, there is scale across not just different clouds, but also from cloud to the edge. So there is that dimension of scale. So there are several dimensions of this scale. And the one that again, we are focused on here really is about, you know, this, the first one that I talk about is a user. And when I say user, it could be a developer, it could be an application architect, or it could be an application owner who wants to develop Kubernetes applications for Kubernetes and wants to publish those APIs, if you will, and make it discoverable and then somebody consumes it. So that's the scale we are talking about >>Here. What are some of the enterprise, you guys have a lot of customers, we've talked to you guys before many, many times and other subjects, Red Hat, I mean you guys have all the customers. Yeah. Enterprise, they've been there, done that. And you know, they're, they're savvy. Yeah. But the cloud is a whole nother ballgame. What are they thinking about? What's the psychology of the customer right now? Because now they have a lot of choices. Okay, we get it, we're gonna re-platform refactor apps, we'll keep some legacy on premises for whatever reasons. But cloud pretty much is gonna be the game. What's the mindset right now of the customer base? Where are they in their, in their psych? Not the executive, but more of the the operators or the developers? >>Yeah, so I mean, first of all, different customers are at different levels of maturity, I would say in this. They're all on a journey how I like to describe it. And in this journey, I mean, I see a customers who are really tip of the sphere. You know, they have containerized everything. They're cloud native, you know, they use best of tools, I mean automation, you know, complete automation, you know, quick deployment of applications and all, and life cycle of applications, et cetera. So that, that's kind of one end of this spectrum >>Advanced. Then >>The advances, you know, and, and I, you know, I don't, I don't have any specific numbers here, but I'd say there are quite a few of them. And we see that. And then there is kind of the middle who are, I would say, who are familiar with containers. They know what app modernization, what a cloud application means. They might have tried a few. So they are in the journey. They are kind of, they want to get there. They have some other kind of other issues, organizational or talent and so, so on and so forth. Kinds of issues to get there. And then there are definitely the quota, what I would call the lag arts still. And there's lots of them. But I think, you know, Covid has certainly accelerated a lot of that. I hear that. And there is definitely, you know, more, the psychology is definitely more towards what I would say public cloud. But I think where we are early also in the other trend that I see is kind of okay, public cloud great, right? So people are going there, but then there is the so-called edge also. Yeah. That is for various regions. You, you gotta have a kind of a regional presence, a edge presence. And that's kind of the next kind of thing taking off here. And we can talk more >>About it. Yeah, let's talk about that a little bit because I, as you know, as we know, we're very excited about Edge here at the Cube. Yeah. What types of trends are you seeing? Is that space emerges a little bit more firmly? >>Yeah, so I mean it's, I mean, so we, when we talk about Edge, you're talking about, you could talk about Edge as a, as a retail, I mean locations, right? >>Could be so many things edges everywhere. Everywhere, right? It's all around us. Quite literally. Even on the >>Scale. Exactly. In space too. You could, I mean, in fact you mentioned space. I was, I was going to >>Kinda, it's this world, >>My space actually Kubernetes and OpenShift running in space, believe it or not, you know, So, so that's the edge, right? So we have Industrial Edge, we have Telco Edge, we have a 5g, then we have, you know, automotive edge now and, and, and retail edge and, and more, right? So, and space, you know, So it's very exciting there. So the reason I tag back to that question that you asked earlier is that that's where customers are. So cloud is one thing, but now they gotta also think about how do I, whatever I do in the cloud, how do I bring it to the edge? Because that's where my end users are, my customers are, and my data is, right? So that's the, >>And I think Kubernetes has brought that attention to the laggards. We had the Laed Martin on yesterday, which is an incredible real example of Kubernetes at the edge. It's just incredible story. We covered it also wrote a story about it. So compelling. Cuz it makes it real. Yes. And Kubernetes is real. So then the question is developer productivity, okay, Things are starting to settle in. We've got KCP scaling clusters, things are happening. What about the tool chains? And how do I develop now I got scale of development, more code coming in. I mean, we are speculating that in the future there's so much code in open source that no one has to write code anymore. Yeah. At some point it's like this gluing things together. So the developers need to be productive. How are we gonna scale the developer equation and eliminate the, the complexity of tool chains and environments. Web assembly is super hyped up at this show. I don't know why, but sounds good. No one, no one can tell me why, but I can kind of connect the dots. But this is a big thing. >>Yeah. And it's fitting that you ask about like no code. So we've been working with our friends at Cross Plain and have integrated with kcp the ability to no code, take a whole bunch of configuration and say, I want a database. I want to be a, a provider of databases. I'm in an IT department, there's a bunch of developers, they don't wanna have to write code to create databases. So I can just take, take my configuration and make it available to them. And through some super cool new easy to use tools that we have as a developer, you can just say, please give me a database and you don't have to write any code. I don't have to write any code to maintain that database. I'm actually using community tooling out there to get that spun up. So there's a lot of opportunities out there. So >>That's ease of use check. What about a large enterprise that's got multiple tool chains and you start having security issues. Does that disrupt the tool chain capability? Like there's all those now weird examples emerging, not weird, but like real plumbing challenges. How do you guys see that evolving with Red >>Hat and Yeah, I mean, I mean, talking about that, right? The software, secure software supply chain is a huge concern for everyone after, especially some of the things that have happened in the past few >>Years. Massive team here at the show. Yeah. And just within the community, we're all a little more aware, I think, even than we were before. >>Before. Yeah. Yeah. And, and I think the, so to step back, I mean from, so, so it's not just even about, you know, run time vulnerability scanning, Oh, that's important, but that's not enough, right? So we are talking about, okay, how did that container, or how did that workload get there? What is that workload? What's the prominence of this workload? How did it get created? What is in it? You know, and what, what are, how do I make, make sure that there are no unsafe attack s there. And so that's the software supply chain. And where Red Hat is very heavily invested. And as you know, with re we kind of have roots in secure operating system. And rel one of the reasons why Rel, which is the foundation of everything we do at Red Hat, is because of security. So an OpenShift has always been secure out of the box with things like scc, rollbacks access control, we, which we added very early in the product. >>And now if you kind of bring that forward, you know, now we are talking about the complete software supply chain security. And this is really about right how from the moment the, the, the developer rights code and checks it into a gateway repository from there on, how do you build it? How do you secure it at each step of the process, how do you sign it? And we are investing and contributing to the community with things like cosign and six store, which is six store project. And so that secures the supply chain. And then you can use things like algo cd and then finally we can do it, deploy it onto the cluster itself. And then we have things like acs, which can do vulnerability scanning, which is a container security platform. >>I wanna thank you guys for coming on. I know Savannah's probably got a last question, but my last question is, could you guys each take a minute to answer why has Kubernetes been so successful today? What, what was the magic of Kubernetes that made it successful? Was it because no one forced it? Yes. Was it lightweight? Was it good timing, right place at the right time community? What's the main reason that Kubernetes is enabling all this, all this shift and goodness that's coming together, kind of defacto unifies people, the stacks, almost middleware markets coming around. Again, not to use that term middleware, but it feels like it's just about to explode. Yeah. Why is this so successful? I, >>I think, I mean, the shortest answer that I can give there really is, you know, as you heard the term, I think Satya Nala from Microsoft has used it. I don't know if he was the original person who pointed, but every company wants to be a software company or is a software company now. And that means that they want to develop stuff fast. They want to develop stuff at scale and develop at, in a cloud native way, right? You know, with the cloud. So that's, and, and Kubernetes came at the right time to address the cloud problem, especially across not just one public cloud or two public clouds, but across a whole bunch of public clouds and infrastructure as, and what we call the hybrid clouds. I think the ES is really exploded because of hybrid cloud, the need for hybrid cloud. >>And what's your take on the, the magic Kubernetes? What made it, what's making it so successful? >>I would agree also that it came about at the right time, but I would add that it has great extensibility and as developers we take it advantage of that every single day. And I think that the, the patterns that we use for developing are very consistent. And I think that consistency that came with Kubernetes, just, you have so many people who are familiar with it and so they can follow the same patterns, implement things similarly, and it's just a good fit for the way that we want to get our software out there and have, and have things operate. >>Keep it simple, stupid almost is that acronym, but the consistency and the de facto alignment Yes. Behind it just created a community. So, so then the question is, are the developers now setting the standards? That seems like that's the new way, right? I mean, >>I'd like to think so. >>So I mean hybrid, you, you're touching everything at scale and you also have mini shift as well, right? Which is taking a super macro micro shift. You ma micro shift. Oh yeah, yeah, exactly. It is a micro shift. That is, that is fantastic. There isn't a base you don't cover. You've spoken a lot about community and both of you have, and serving the community as well as your engagement with them from a, I mean, it's given that you're both leaders stepping back, how, how Community First is Red Hat and OpenShift as an organization when it comes to building the next products and, and developing. >>I'll take and, and I'm sure Andy is actually the community, so I'm sure he'll want to a lot of it. But I mean, right from the start, we have roots in open source. I'll keep it, you know, and, and, and certainly with es we were one of the original contributors to Kubernetes other than Google. So in some ways we think about as co-creators of es, they love that. And then, yeah, then we have added a lot of things in conjunction with the, I I talk about like SCC for Secure, which has become part security right now, which the community, we added things like our back and other what we thought were enterprise features needed because we actually wanted to build a product out of it and sell it to customers where our customers are enterprises. So we have worked with the community. Sometimes we have been ahead of the community and we have convinced the community. Sometimes the community has been ahead of us for other reasons. So it's been a great collaboration, which is I think the right thing to do. But Andy, as I said, >>Is the community well set too? Are well said. >>Yes, I agree with all of that. I spend most of my days thinking about how to interact with the community and engage with them. So the work that we're doing on kcp, we want it to be a community project and we want to involve as many people as we can. So it is a heavy focus for me and my team. And yeah, we we do >>It all the time. How's it going? How's the project going? You feel good >>About it? I do. It is, it started as an experiment or set of prototypes and has grown leaps and bounds from it's roots and it's, it's fantastic. Yeah. >>Controlled planes are hot data planes control planes. >>I >>Know, I love it. Making things work together horizontally scalable. Yeah. Sounds like cloud cloud native. >>Yeah. I mean, just to add to it, there are a couple of talks that on KCP at Con that our colleagues s Stephan Schemanski has, and I, I, I would urge people who have listening, if they have, just Google it, if you will, and you'll get them. And those are really awesome talks to get more about >>It. Oh yeah, no, and you can tell on GitHub that KCP really is a community project and how many people are participating. It's always fun to watch the action live to. Sure. Andy, thank you so much for being here with us, John. Wonderful questions this afternoon. And thank all of you for tuning in and listening to us here on the Cube Live from Detroit. I'm Savannah Peterson. Look forward to seeing you again very soon.

>>Good afternoon and welcome back to Detroit, Lisa Martin here with John Furrier. We are live day two of our coverage of Coan Cloud Native, Con North America. John, we've had great conversations. Yeah. All day yesterday. Half a day today. So far we're talking all things, Well, not all things Kubernetes so much more than that. We also have to talk about storage and data management solutions for Kubernetes projects, cuz that's obviously critical. >>Yeah, I mean the big trend here is Kubernetes going mainstream has been for a while. The adopt is crossing over, it's crossing the CADs and with that you're seeing security concerns. You're seeing things being gaps being filled. But enterprise grade is really the, the, the story. It's going enterprise, that's managed services, that's professional service, that's basically making things work at scale. This next segment hits that, that part, and we're gonna talk about it in grade length >>With one of our alumni morale to Molly is back VP and GM of Port Work's peer Storage. Great to have you back really? >>Yeah, absolutely. Delightful to >>Be here. So I was looking on the website, number one in Kubernetes storage. Three years in a row. Yep. Awesome. What's Coworks doing here at KU Con? >>Well, I'll tell you, we, our engineering crew has been so productive and hard at work that I almost can't decide what to kind of tell you. But I thought what, what, what I thought I would do is kind of tell you that we are in forefront of two major trends in the world of es. Right? And the, the two trends that I see are one is as a service, so is trend number one. So it's not software eating the world anymore. That's, that's old, old, old news. It's as a service, unifying the world. The world wants easy, We all are, you know, subscribers to things like Netflix. We've been using Salesforce or other HR functions. Everything is as a service. And in the world of Kubernetes, it's a sign of that maturity that John was talking about as a platform that now as a service is the big trend. >>And so headline number one, if you will, is that Port Works is leading in the data management world for the Kubernetes by providing, we're going all in on easy on as a service. So everything we do, we are satisfying it, right? So if you think, if you think about, if you think about this, that, that there are really, most of the people who are consuming Kubernetes are people who are building platforms for their dev users and their users want self service. That's one of the advantages of, of, of Kubernetes. And the more it is service size and made as a service, the more ready to consume it is. And so we are announcing at the show that we have, you know, the basic Kubernetes data management as a service, ha d r as a service. We have backup as a service and we have database as a service. So these are the three major components of data. And all of those are being made available as a service. And in fact, we're offering and announcing at the show our backup as a service freemium version where you can get free forever a terabyte of, of, you know, stuff to do for Kubernetes for forever. >>Congratulations on the announcement. Totally. In line with what the market wants. Developers want self serve, they wanna also want simplicity by the way they'll leave if they don't like the service. Correct. So that you, you know, that before we get into some more specifics, I want to Yeah. Ask you on the industry and some of the point solutions you have, what, it's been two years since the acquisition with Pure Storage. Can you just give an update on how it's gone? Obviously as a service, you guys are hitting all your Marks, developers love it. Storage a big part of the game right now as well as these environments. Yeah. What's the update post acquisition two years, You had a great offering Stay >>Right In Point Works. Yeah. So look, John, you're, you're, you're a veteran of the industry and have seen lots of acquisitions, right? And I've been acquired twice before myself. So, you know, there's, there's always best practices and poor practices in terms of acquisitions and I'm, you know, really delighted to say I think this, this acquisition has had some of the best practices. Let me just name a couple of them, right? One of them is just cultural fit, right? Cultural fit is great. Entrepreneurs, anybody, it's not just entrepreneurs. Everybody loves to work in a place they enjoy working with, with people that they, you know, thrive when they, when they interact with. And so the cultural fit with, with Pure is fantastic. The other one is the strategic intent that Pure had when they acquired us is still true. And so that goes a long way, you know, in terms of an investment profile, in terms of the ability to kind of leverage assets within the company. So Pure had kind of disrupted the world of storage using Flash and they wanted to disrupt higher up the stack using Kubernetes. And that's kind of been our role inside their strategy. And it's, it's still true. >>So culture, strategic intent. Yeah. Product market fit as well. You were, you weren't just an asset for customers or acquisition and then let the founders go through their next thing. You are part of their growth play. >>Absolutely. Right. The, the beauty of, of the kind of product market fit is, let's talk about the market is we have been always focused on the global two k and that is at the heart of, you know, purest 10,000 strong customer base, right? They have very strong presence in the, in the global two k. And we, we allow them to kind of go to those same folks with, with the offering. >>So satisfying everything that you do. What's for me as a business, whether I'm a financial services organization, I'm a hospital, I'm a retailer, what's in it for me >>As a customer? Yeah. So the, the what's in it for, for me is two things. It's speed and ease of use, which in a way are related. But, but, but you know, one is when something is provided as a service, it's much more consumable. It's instantly ready. It's like instant oatmeal, right? You just get it just adho water and it's there. Yep. So the world of of IT has moved from owning large data centers, right? That used to be like 25 years ago and running those data centers better than everybody else to move to let me just consume a data center in the form of a cloud, right? So satisfying the cloud part of the data center. Now people are saying, well I expect that for software and services and I don't want it just from the public cloud, I want it from my own IT department. >>This is old news. And so the, the, the big news here is how fast Kubernetes has kind of moved everything. You know, you take a lot of these changes, Kubernetes is a poster child for things happening faster than the last wave. And in the last couple of years I would say that as a service model has really kind of thrived in the world of Kubernetes. And developers want to be able to get it fast. And the second thing is they wanna be able to operate it fast. Self-service is the other benefit. Yeah. So speed and self-service are both benefits of, of >>This. Yeah. And, and the thing that's come up clearly in the cube, and this is gonna be part of the headlines, we'll probably end up getting a lot of highlights from telling my team to make a note of this, is that developers are gonna be be the business if you, if you take digital transformation to its conclusion, they're not a department that serves the business, they are the business that means Exactly. They have to be more productive. So developer productivity has been the top story. Yes. Security as a services, all these things. These are, these are examples to make developers more productive. But one of the things that came up and I wanna get your reaction to Yeah. Is, is that when you have disruption and, and the storage vision, you know what disruption it means. Cuz there's been a whole discussion around disruptive operations. When storage goes down, you have back DR. And failover. If there's a disruption that changes the nature of invisible infrastructure, developers want invisible infrastructure. That's the future steady state. So if there's a disruption in storage >>Yeah. It >>Can't affect the productivity and the tool chains and the workflows of developers. Yep. Right? So how do you guys look at that? Cause you're a critical component. Storage is a service, it's a huge thing. Yeah. Storage has to, has to work seamlessly. And let's keep the developers out of the weeds. >>John. I think what, what what you put your finger on is another huge trend in the world of Kubernetes where Atan after all, which is really where, where all the leading practitioners both come and the leading vendors are. So here's the second trend that we are leading and, and actually I think it's happening not just with us, but with other, for folks in the industry. And that is, you know, the world of DevOps. Like DevOps has been such a catchphrase for all of of us in the industry last five years. And it's been both a combination of cultural change as well as technology change. Here's what the latest is on the, in the world of DevOps. DevOps is now crystallized. It's not some kind of mysterious art form that you read about. Okay. How people are practicing. DevOps is, it's broken into two, two things now. >>There is the platform part. So DevOps is now a bunch of platforms. And the other part of DevOps is a bunch of practices. So a little bit on both these, the platforms in the world of es there's only three platforms, right? There's the orchestration platforms, the, you know, eks, the open ships of the world and so on. There are the data management platforms, pro people like Port Works. And the third is security platforms, right? You know, Palo Alto Networks, others Aqua are all in this. So these are the three platforms and there are platform engineering teams now that many of our largest customers, some of the largest banks, the largest service providers, they're all operating as a ES platform engineering team. And then now developers, to your point, developers are in the practice of being able to use these platforms to launch new services. So the, the actual IT ops, the ops are run by developers now and they can do it on these platforms. And the platform engineering team provide that as an ease of use and they're there to troubleshoot when problems happen. So the idea of DevOps as a ops practice and a platform is the newest thing. And, and ports and pure storage leading in the world of data management >>Platforms there. Talk about a customer example that you think really articulates the value that Port Works and Pure Storage delivers from a data management >>Perspective. Yeah, so there's so many examples. One of the, one of the longest running examples we have is a very, very large service provider that, you know, you all know and probably use, and they have been using us in the cable kind of set box or cable box business. They get streams of data from, from cable boxes all over the world. They collected all in a centralized large kind of thing and run elastic search and analytics on it. Now what they have done is they couldn't keep up with this at the scale and the depth, right? The speed of, of activity and the distributed nature of the activity. The only way to solve this was to use something like Kubernetes manage with Spark coming, bringing all the data in into deep, deep, deep silos of storage, which are all running not even on a sand, but on kind of, you know, very deep terabytes and terabytes of, of storage. So all of this is orchestrated with the he of Coworks and there's a platform engineering team. We are building that platform for them, them with some of these other components that allows them to kind of do analytics and, and make some changes in real time. Huge kind of setup for, for >>That. Yeah. Well, you guys have the right architecture. I love the vision. I love what you guys are doing. I think this is right in line with Pures. They've always been disruptors. I remember when we first interviewed the CEO and they started Yep. They, they stayed on path. They didn't waver. EMC was the big player. They ended up taking their lunch and dinner as well and they beat 'em in the marketplace. But now you got this traction here. So I have to ask you, how's the business, what's the results look like? You're a GM cloud native business unit of a storage company that's transformed and transforming. >>Yeah, you know, it's interesting, we just hit the two year anniversary, right John? And so what we did was just kind of like step back and hey to, you know, we're running so hard, you just take a step back and we've tripled the business in the two years since the acquisition, the two years before and, and we were growing through proven. So, you know, that that's quite a fee. And we've tripled the number of people, the amount of engineering investments we have, the number of go to market investments have been, have been awesome. So business is going really well though, I will say. But I think, you know, we have, we can't be, we're watching the market closely. You know, as a former ceo, I, you have to kind of learn to read the tea leaves when you invest. And I think, you know, what I would say is we're proceeding with caution in the next two quarters. I view business transformation as not a cancelable activity. So that's the, that's the good news, right? Our customers are large, >>It's >>Right. Never gonna stop prices, right? All they're gonna do is say, Hey, they're gonna put their hand, their hand was always going right on the dial. Now they're kind of putting their hand on the dial going, hey, where, what is happening? But my, my own sense of this is that people who continue to invest through it, the question is at what level? And I also think that this is a six month kind of watch, the watch where, where we put the dial. So Q4 and q1 I think are kind of, you know, we have our, our watch kind of watch the market sign. But I have the highest confidence. What >>Does your gut tell you? You're an >>Entrepreneur. My, my gut says that we'll go through a little bit of a cautious investment period in the next six months. And after that I think we're gonna be back in, back full, full in the crazy growth that we've always been. Yeah. We're gonna grow by the way, in the next, I think >>It's corn style. I think I'm, I'm more bullish. I think it's gonna be some, you know, weeding out of some overinvestment, pre covid or pre bubble. But I think tech's gonna continue to grow. I don't see >>It's stopping. Yeah. And, and the investment is gonna be on these core platforms. See, back to the platform story, it's gonna be in these lower platforms and on unifying everything, let's consume it better rather than let's go kind of experiment with a whole bunch of things all over the map, right? So you'll see less experimentation and more kind of, let's harvest some of the investments we've made in the last couple >>Of years and actually be able to, to enable companies in, in the industry to truly be data companies because absolutely. We talked about as a service, we all have these expectations that any service we want, we can get it. Yes. There's no delay because patients has gone Yeah. From the pandemic. >>So it is kind of, you know, tightening up the screws on what they've built. They, you know, adding some polish to it, adding some more capability, like I said, a, a a, a combination of harvesting and new investing. It's a combination I think is what we're gonna see. >>Yeah. What are some of the things that you're looking forward to? You talked about some of the, the growth things in the investment, but as we round out Q4 and head into a new year, what are you excited about? >>Yeah, so, you know, I mentioned our, as a service kind of platform. The global two K for us has been a set of customers who we co-create stuff with. And so one of the other set of things that we are very excited about and announcing is because we're deployed at scale, we're, we're, we have upgraded our backend. So we have now the ability to go to million IOPS and more and, and for, for the right backends. And so Kubernetes is a add-on, which will not slow down your, your core base infrastructure. Second thing that that we, we have is added a bunch of capability in the disaster recovery business continuity front, you know, we always had like metro kind of distance Dr. We had long distance dr. We've added a near sync Dr. So now we can provide disaster recovery and business continuity for metro distances across continents and across the planet. Right? That's kind of a major change that we've done. The third thing is we've added the capability for file block and Object. So now by adding object, we're really a complete solution. So it is really that maturity of the business Yeah. That you start seeing as enterprises move to embracing a platform approach, deploying it much more widely. You talked about the early majority. Yeah. Right. And so what they require is more enterprise class capability and those are all the things that we've been adding and we're really looking forward to it. >>Well it sounds like tremendous evolution and maturation of Port Works in the two years since it's been with Pure Storage. You talked about the cultural alignment, Great stuff that you are achieving. Congratulations on that. Great stuff >>Ahead and having fun. Let's not forget that that's too life's too short to do. It is. You're right. >>Right. Thank you. We will definitely, as always on the cube, keep our eyes on this space. Mur. Meley, it's been great to have you back on the program. Thank you for joining, John. >>Great. Thank you so much. It's a pleasure. Our, >>For our guests and John Furrier, Lisa Martin here live in Detroit with the cube about Cob Con Cloud native Con at 22. We'll be back after a short break.

>>Hello and welcome back to the live coverage of the Cube here. Live in Detroit, Michigan for Cub Con, our seventh year covering all seven years. The cube has been here. M John Fur, host of the Cube, co-founder of the Cube. I'm here with Lisa Mart, my co-host, and our new host, Savannah Peterson. Great to see you guys. We're wrapping up day one of three days of coverage, and our guest analyst is Sario Wall, who's the cube analyst who's gonna give us his report. He's been out all day, ear to the ground in the sessions, peeking in, sneaking in, crashing him, getting all the data. Great to see you, Sarvi. Lisa Savannah, let's wrap this puppy up. >>I am so excited to be here. My first coupon with the cube and being here with you and Lisa has just been a treat. I can't wait to hear what you have to say in on the report side. And I mean, I have just been reflecting, it was last year's coupon that brought me to you, so I feel so lucky. So much can change in a year, folks. You never know where you're be. Wherever you're sitting today, you could be living your dreams in just a few >>Months. Lisa, so much has changed. I mean, just look at the past this year. Events we're back in person. Yeah. Yep. This is a big team here. They're still wearing masks, although we can take 'em off with a cube. But mask requirement. Tech has changed. Conversations are upleveling, skill gaps still there. So much has changed. >>So much has changed. There's so much evolution and so much innovation that we've also seen. You know, we started out the keynote this morning, standing room. Only thousands of people are here. Even though there's a mass requirement, the community that is CNCF Co Con is stronger than I, stronger than I saw it last year. This is only my second co con. But the collaboration, what they've done, their devotion to the maintainers, their devotion to really finding mentors for mentees was really a strong message this morning. And we heard a >>Lot of that today. And it's going beyond Kubernetes, even though it's called co con. I also call it cloud native con, which I think we'll probably end up being the name because at the end of day, the cloud native scaling, you're starting to see the pressure points. You're start to see where things are breaking, where automation's coming in, breaking in a good way. And we're gonna break it all down Again. So much going on again, I've overs gonna be in charge. Digital is transformation. If you take it to its conclusion, then you will see that the developers are running the business. It isn't a department, it's not serving the business, it is the business. If that's the case, everything has to change. And we're, we're happy to have Sarib here with us Cube analysts on the badge. I saw that with the press pass. Well, >>Thank you. Thanks for getting me that badge. So I'm here with you guys and >>Well, you got a rapport. Let's get into it. You, I >>Know. Let's hear what you gotta say. I'm excited. >>Yeah. Went around, actually attend some sessions and, and with the analysts were sitting in, in the media slash press, and I spoke to some people at their booth and the, there are a few, few patterns, you know, which are, some are the exaggeration of existing patterns or some are kind of new patterns emerging. So things are getting complex in open source. The lawn more projects, right. They have, the CNCF has graduated some projects even after graduation, they're, they're exploring, right? Kubernetes is one of those projects which has graduated. And on that front, just a side note, the new projects where, which are entering the cncf, they're the, we, we gotta see that process and the three stages and all that stuff. I tweeted all day long, if you wanna know what it is, you can look at my tweets. But when I will look, actually write right on that actually after, after the show ends, what, what I saw there, these new projects need to be curated properly. >>I think they need to be weed. There's a lot of noise in these projects. There's a lot of overlap. So the, the work is cut out for CNCF folks, by the way. They're sort of managerial committee or whatever you call that. The, the people who are leading it, they're try, I think they're doing their best and they're doing a good job of that. And another thing actually, I really liked in the morning's keynote was that lot of women on the stage and minorities represented. I loved it, to be honest with you. So believe me, I'm a minority even though I'm Indian, but from India, I'm a minority. So people who have Punjab either know that I'm a minority, so I, I understand their pain and how hard it is to, to break through the ceiling and all that. So I love that part as well. Yeah, the >>Activity is clear. Yeah. From day one. It's in the, it's in the dna. I mean, they'll reject anything that the opposite >>Representation too. I mean, it's not just that everyone's invited, it's they're celebrated and that's a very big difference. Yeah. It's, you see conferences offer discounts for women for tickets or minorities, but you don't necessarily see them put them running where their mouth is actually recruit the right women to be on stage. Right. Something you know a little bit about John >>Diversity brings better outcomes, better product perspectives. The product is better with all the perspectives involved. Percent, it might go a little slower, maybe a little debates, but it's all good. I mean, it's, to me, the better product comes when everyone's in. >>I hope you didn't just imply that women would make society. So >>I think John men, like slower means a slower, >>More diversity, more debate, >>The worst. Bringing the diversity into picture >>Wine. That's, that's how good groups, which is, which is >>Great. I mean, yeah, yeah, >>Yeah, yeah. I, I take that mulligan back and say, hey, you knows >>That's >>Just, it's gonna go so much faster and better and cheaper, but that not diversity. Absolutely. >>Yes. Well, you make better products faster because you have a variety >>Of perspectives. The bigger the group, there's more debate. More debate is key. But the key to success is aligning and committing. Absolutely. Once you have that, and that's what open sources has been about for. Oh God, yeah. Generations >>Has been a huge theme in the >>Show generations. All right, so, so, >>So you have to add another, like another important, so observation if you will, is that the security is, is paramount right. Requirement, especially for open source. There was a stat which was presented in the morning that 60% of the projects in under CNCF have more vulnerabilities today than they had last year. So that was, That's shocking actually. It's a big jump. It's a big jump. Like big jump means jump, jump means like it can be from from 40 to 60 or or 50 or 60. But still that percentage is high. What, what that means is that lot more people are contributing. It's very sort of di carmic or ironic that we say like, Oh this project has 10,000 contributors. Is that a good thing? Right. We do. Do we know the quality of that, where they're coming from? Are there any back doors being, you know, open there? How stringent is the process of rolling those things, which are being checked in, into production? You know, who is doing that? I've >>Wondered about that. Yeah. The quantity, quality, efficacy game. Yes. And what a balance that must be for someone like CNCF putting in the structure to try and >>That's >>Hard. Curate and regulate and, and you know, provide some bumpers on the bowling lane, so to speak, of, of all of these projects. Yeah. >>Yeah. We thought if anybody thought that the innovation coming from, or the number of services coming from AWS or Google Cloud or likes of them is overwhelming, look at open source, it's even more >>Overwhelming. What's your take on the supply chain discussion? More code more happening. What are you hearing there? >>The supply chain from the software? Yeah. >>Supply chain software, supply chain security pays. Are people talking about that? What are you >>Seeing? Yeah, actually people are talking about that. The creation, the curation, not creation. Curation of suppliers of software I think is best done in the cloud. Marketplaces Ive call biased or what, you know, but curation of open source is hard. It's hard to know which project to pick. It's hard to know which project will pan out. Many of the good projects don't see the day light of the day, but some decent ones like it becomes >>A marketing problem. Exactly. The more you have out there. Exactly. The more you gotta get above the noise. Exactly. And the noise echo that. And you got, you got GitHub stars, you got contributors, you have vanity metrics now coming in to this that are influencing what's real. But sometimes the best project could have smaller groups. >>Yeah, exactly. And another controversial thing a little bit I will say that is that there's a economics of the practitioner, right? I usually talk about that and economics of the, the enterprise, right? So practitioners in our world, in software world especially right in systems world, practitioners are changing jobs every two to three years. And number of developers doubles every three years. That's the stat I've seen from Uncle Bob. He's authority on that software side of things. Wow. So that means there's a lot more new entrance that means a lot of churn. So who is watching out for the enterprise enterprises economics, You know, like are we creating stable enterprises? How stable are our operations? On a side note to that, most of us see the software as like one band, which is not true. When we talk about all these roles and personas, somebody's writing software for, for core layer, which is the infrastructure part. Somebody's writing business applications, somebody's writing, you know, systems of bracket, some somebody's writing systems of differentiation. We talk about those things. We need to distinguish between those and have principle based technology consumption, which I usually write about in our Oh, >>So bottom line in Europe about it, in your opinion. Yeah. What's the top story here at coupon? >>Top story is >>Headline. Yeah, >>The, the headline. Okay. The open source cannot be ignored. That's a headline. >>And what should people be paying attention to if there's a trend coming out? See any kind of trends coming out or any kind of signal, What, what do you see that people should pay attention to here? The put top >>Two, three things. The signal is that, that if you are a big shop, like you'd need to assess your like capacity to absorb open source. You need to be certain size to absorb the open source. If you are below that threshold, I mean we can talk about that at some other time. Like what is that threshold? I will suggest you to go with the managed services from somebody, whoever is providing those managed services around open source. So manage es, right? So from, take it from aws, Google Cloud or Azure or IBM or anybody, right? So use open source as managed offering rather than doing it yourself. Because doing it yourself is a lot more heavy lifting. >>I I, >>There's so many thoughts coming, right? >>Mind it's, >>So I gotta ask you, what's your rapport? You have some swag, What's the swag look >>Like to you? I do. Just as serious of a report as you do on the to floor, but I do, so you know, I come from a marketing background and as I, I know that Lisa does as well. And one of the things that I think about that we touched on in this is, is you know, canceling the noise or standing out from the noise and, and on a show floor, that's actually a huge challenge for these startups, especially when you're up against a rancher or companies or a Cisco with a very large budget. And let's say you've only got a couple grand for an activation here. Like most of my clients, that's how I ended up in the CU County ecosystem, was here with the A client before. So there actually was a booth over there and I, they didn't quite catch me enough, but they had noise canceling headphones. >>So if you just wanted to take a minute on the show floor and just not hear anything, which I thought was a little bit clever, but gonna take you through some of my favorite swag from today and to all the vendors, you know, this is why you should really put some thought into your swag. You never know when you're gonna end up on the cube. So since most swag is injection molded plastic that's gonna end up in the landfill, I really appreciate that garden has given all of us a potable plant. And even the packaging is plantable, which is very exciting. So most sustainable swag goes to garden. Well done >>Rep replicated, I believe is their name. They do a really good job every year. They had some very funny pins that say a word that, I'm not gonna say live on television, but they have created, they brought two things for us, yet it's replicated little etch sketch for your inner child, which is very nice. And given that we are in Detroit, we are in Motor City, we are in the home of Ford. We had Ford on the show. I love that they have done the custom K eight s key chains in the blue oval logo. Like >>Fords right behind us by the way, and are on you >>Interviewed, we had 'em on earlier GitLab taking it one level more personal and actually giving out digital portraits today. Nice. Cool. Which is quite fun. Get lap house multiple booths here. They actually IPOed while they were on the show floor at CubeCon 2021, which is fun to see that whole gang again. And then last but not least, really embracing the ship wheel logo of a Kubernetes is the robusta accrue that is giving out bucket hats. And if you check out my Twitter at sabba Savvy, you can see me holding the ship wheel that they're letting everyone pose with. So we are all in on Kubernetes. That cove gone 2022, that's for sure. Yeah. >>And this is something, day one guys, we've got three. >>I wanna get one of those >>Hats. We we need to, we need a group photo >>By the end of Friday we will have a beverage and hats on to sign off. That's, that's my word. If I can convince John, >>Don, what's your takeaway? You guys did a great kind of kickoff about last week or so about what you were excited about, what your thoughts were going to be. We're only on day one, There's been thousands of people here, we've had great conversations with contributors, the community. What's your take on day one? What's your, what's your tagline? >>Well, Savannah and I had at we up, we, we were talking about what we might see and I think we, we were right. I think we had it right. There's gonna be a lot more people than there were last year. Okay, check. That's definitely true. We're in >>Person, which >>Is refreshing. I was very surprised about the mask mandate that kind of caught me up guard. I was major. Yeah. Cause I've been comfortable without the mask. I'm not a mask person, but I had to wear it and I was like, ah, mask. But I understand I support that. But whatever. It's >>Corporate travel policy. So you know, that's what it is. >>And then, you know, they, I thought that they did an okay job with the gates, but they wasn't slow like last time. But on the content side, definitely Kubernetes security, top line headline, Kubernetes at scale security, that's, that's to me the bumper sticker top things to pay attention to the supply chain and the role of docker and the web assembly was a surprise. You're starting to see containers ecosystem coming back to, I won't say tension growth in the functionality of containers cuz they have to solve the security problem in the container images. Okay, you got scanning technology so it's a little bit in the weeds, but there's a huge movement going on to fix that problem to scale it so it's not a problem area contain. And then Dr sent a great job with productivity interviews. Scott Johnston over a hundred million in revenue so far. That's my number. They have not publicly said that. That's what I'm reporting from sources extremely well financially. And they, and they love their business model. They make productivity for developers. That's a scoop. That's new >>Information. That's a nice scoop we just dropped there on the co casually. >>You're watching that. Pay attention to that. But that, that's proof. But guess what, Red Hat's got developers too. Yes. Other people have to, So developers gonna go where it's the best. Yeah. Developers are voting with their code, they're voting with their feet. You will see the winners with the developers and that's what we've talked about. >>Well and the companies are catering to the developers. Savannah and I had a great conversation with Ford. Yeah. You saw, you showed their fantastic swag was an E for Ev right behind us. They were talking about the, all the cultural changes that they've really focused on to cater towards the developers. The developers becoming the influencers as you say. But to see a company that is as, as historied as Ford Motor Company and what they're doing to attract and retain developer talent was impressive. And honestly that surprised me. Yeah. >>And their head of deb relations has been working for, for, for 29 years. Which I mean first of all, most companies on the show floor haven't been around for 29 years. Right. But what I love is when you put community first, you get employees to stick around. And I think community is one of the biggest themes here at Cuco. >>Great. My, my favorite story that surprised me and was cool was the Red Hat Lockheed Martin interview where they had edge deployments with micro edge, >>Micro shift, >>Micro >>Shift, new projects under, there's, there are three new projects under, >>Under that was so, so cool because it was an edge story in deployment for the military where lives are on the line, they actually had it working. That is a real world example of Kubernetes and tech orchestrating to deploy the industrial edge. And I think that's proof in my mind that Kubernetes and this ecosystem is gonna move faster through this next wave of growth. Because once things start clicking, you get hybrid on premise to super cloud and edge. That was, that was my favorite cause it was real. That was real >>Story that it can make is literally life and death on the battlefield. Yeah, that was amazing. With what they're doing and what >>They're talking check out the Lockheed Martin Red Hat edge story on Silicon Angle and then a press release all pillar. >>Yeah. Another actually it's impressive, which we knew this which is happening, but I didn't know that it was happening at this scale is the finops. The finops is, I saw your is a discipline which most companies are adopting bigger companies, which are spending like hundreds of millions dollars in cloud average. Si a team size of finops for finops is seven people. And average number of tools is I think 3.5 or around 3.7 or something like that. Average number of tools they use to control the cost. So finops is a very generic term for years. It's not financial operations, it's the financial operations for the cloud cost, you know, containing the cloud costs. So that's a finops that is a very emerging sort of discipline >>To keep an eye on. And well, not only is that important, I talked to, well one of the principles over there, it's growing and they have real big players in that foundation. Their, their events are highly attended. It's super important. It's just, it's the cost side of cloud. And, and of course, you know, everyone wants to know what's going on. No one wants to leave there. Their Amazon on Yeah, you wanna leave the lights on the cloud, as we always say, you never know what the bill's gonna look like. >>The cloud is gonna reach $3 billion in next few years. So we might as well control the cost there. Yeah, >>It was, it was funny to get the reaction I found, I don't know if I was, how I react, I dunno how I felt. But we, we did introduce Super Cloud to a couple of guests and a, there were a couple reactions, a couple drawn. There was a couple, right. There was a couple, couple reactions. And what I love about the super cloud is that some people are like, oh, cringing. And some people are like, yeah, go. So it's a, it's a solid debate. It is solid. I saw more in the segments that I did with you together. People leaning in. Yeah. Super fun. We had a couple sum up, we had a couple, we had a couple cringes, I'll say their names, but I'll go back and make sure I, >>I think people >>Get 'em later. I think people, >>I think people cringe on the, on the term not on the idea. Yeah. You know, so the whole idea is that we are building top of the cloud >>And then so I mean you're gonna like this, I did successfully introduce here on the cube, a new term called architectural list. He did? That's right. Okay. And I wanna thank Charles Fitzgerald for that cuz he called super cloud architectural list. And that's exactly the point of super cloud. If you have a great coding environment, you shouldn't have to do an architecture to do. You should code and let the architecture of the Super cloud make it happen. And of course Brian Gracely, who will be on tomorrow at his cloud cast said Super Cloud enables super services. Super Cloud enables what Super services, super service. The microservices underneath the covers have to be different. High performing, automated. So again, the debate and Susan, the goal is to keep it open. And that's our, that's our goal. But we had a lot of fun with that. It was fun to poke the bear a little bit. So >>What is interesting to see just how people respond to it too, with you throwing it out there so consistently, >>You wanna poke the bear, get a conversation going, you know, let let it go. We'll see, it's been positive so far. >>There, there I had a discussion outside somebody who is from Ford but not attending this conference and they have been there for a while. I, I just some moment hit like me, like I said, people, okay, technologists are horizontal, the codes are horizontal. They will go from four to GM to Chrysler to Bank of America to, you know, GE whatever, you know, like cross vertical within vertical different vendors. So, but the culture of a company is local, right? Right. Ford has been building cars for forever. They sort of democratize it. They commercialize it, right? But they have some intense culture. It's hard to change those cultures. And how do we bring in the new thinking? What is, what approach that should be? Is it a sandbox approach for like putting new sensors on the car? They have to compete with te likes our Tesla, right? Yeah. But they cannot, if they are afraid of deluding their existing market or they're afraid of failure there, right? So it's very >>Tricky. Great stuff. Sorry. Great to have you on as our cube analyst breaking down the stories. We'll document that, that we'll roll out a post on it. Lisa Savannah, let's wrap up the show for day one. We got day two and three. We'll start with you. What's your summary? Quick bumper sticker. What's today's show all about? >>I'm a community first gal and this entire experience is about community and it's really nice to see the community come together, celebrate that, share ideas, and to have our community together on stage. >>Yeah. To me, to me it was all real. It's happening. Kubernetes cloud native at scale, it's happening, it's real. And we see proof points and we're gonna have faster time to value. It's gonna accelerate faster from here. >>The proof points, the impact is real. And we saw that in some amazing stories. And this is just a one of the cubes >>Coverage. Ib final word on this segment was well >>Said Lisa. Yeah, I, I think I, I would repeat what I said. I got eight, nine years back at a rack space conference. Open source is amazing for one biggest reason. It gives the ability to the developing nations to be at somewhat at par where the dev develop nations and, and those people to lift up their masses through the automation. Cuz when automation happens, the corruption goes down and the economy blossoms. And I think it's great and, and we need to do more in it, but we have to be careful about the supply chains around the software so that, so our systems are secure and they are robust. Yeah, >>That's it. Okay. To me for SAR B and my two great co-host, Lisa Martin, Savannah Peterson. I'm John Furry. You're watching the Cube Day one in, in the Books. We'll see you tomorrow, day two Cuban Cloud Native live in Detroit. Thanks for watching.

>>Hello, welcome to the Cube here in Palo Alto, California for a special presentation on Cloud native at scale, enabling super cloud modern applications with Platform nine. I'm John Furr, your host of The Cube. We had a great lineup of three interviews we're streaming today. Meor Ma Makowski, who's the co-founder and VP of Product of Platform nine. She's gonna go into detail around Arlon, the open source products, and also the value of what this means for infrastructure as code and for cloud native at scale. Bickley the chief architect of Platform nine Cube alumni. Going back to the OpenStack days. He's gonna go into why Arlon, why this infrastructure as code implication, what it means for customers and the implications in the open source community and where that value is. Really great wide ranging conversation there. And of course, Vascar, Gort, the CEO of Platform nine, is gonna talk with me about his views on Super Cloud and why Platform nine has a scalable solutions to bring cloudnative at scale. So enjoy the program. See you soon. Hello everyone. Welcome to the cube here in Palo Alto, California for special program on cloud native at scale, enabling next generation cloud or super cloud for modern application cloud native developers. I'm John Furry, host of the Cube. A pleasure to have here, me Makoski, co-founder and VP of product at Platform nine. Thanks for coming in today for this Cloudnative at scale conversation. Thank >>You for having me. >>So Cloudnative at scale, something that we're talking about because we're seeing the, the next level of mainstream success of containers Kubernetes and cloud native develop, basically DevOps in the C I C D pipeline. It's changing the landscape of infrastructure as code, it's accelerating the value proposition and the super cloud as we call it, has been getting a lot of traction because this next generation cloud is looking a lot different, but kind of the same as the first generation. What's your view on super cloud as it fits to cloud native as scales up? >>Yeah, you know, I think what's interesting, and I think the reason why Super Cloud is a really good, in a really fit term for this, and I think, I know my CEO was chatting with you as well, and he was mentioning this as well, but I think there needs to be a different term than just multi-cloud or cloud. And the reason is because as cloud native and cloud deployments have scaled, I think we've reached a point now where instead of having the traditional data center style model where you have a few large distributions of infrastructure and workload at a few locations, I think the model is kind of flipped around, right? Where you have a large number of microsites, these microsites could be your public cloud deployment, your private on-prem infrastructure deployments, or it could be your edge environment, right? And every single enterprise, every single industry is moving in that direction. And so you gotta rougher that with a terminology that, that, that indicates the scale and complexity of it. And so I think supercloud is a, is an appropriate term for that. >>So you brought a couple of things I want to dig into. You mentioned edge nodes. We're seeing not only edge nodes being the next kind of area of innovation, mainly because it's just popping up everywhere. And that's just the beginning. Wouldn't even know what's around the corner. You got buildings, you got iot, ot, and IT kind of coming together, but you also got this idea of regions, global infras infrastructures, big part of it. I just saw some news around CloudFlare shutting down a site here. There's policies being made at scale, These new challenges there. Can you share because you can have edge. So hybrid cloud is a winning formula. Everybody knows that it's a steady state. Yeah. But across multiple clouds brings in this new un engineered area, yet it hasn't been done yet. Spanning clouds. People say they're doing it, but you start to see the toe in the water, it's happening, it's gonna happen. It's only gonna get accelerated with the edge and beyond globally. So I have to ask you, what is the technical challenges in doing this? Because there's something business consequences as well, but there are technical challenges. Can you share your view on what the technical challenges are for the super cloud or across multiple edges and regions? >>Yeah, absolutely. So I think, you know, in in the context of this, the, this, this term of super cloud, I think it's sometimes easier to visualize things in terms of two access, right? I think on one end you can think of the scale in terms of just pure number of nodes that you have deploy a number of clusters in the Kubernetes space. And then on the other axis you would have your distribution factor, right? Which is, do you have these tens of thousands of nodes in one site or do you have them distributed across tens of thousands of sites with one node at each site? Right? And if you have just one flavor of this, there is enough complexity, but potentially manageable. But when you are expanding on both these access, you really get to a point where that scale really needs some well thought out, well structured solutions to address it, right? A combination of homegrown tooling along with your, you know, favorite distribution of Kubernetes is not a strategy that can help you in this environment. It may help you when you have one of this or when you, when you scale, is not at the level. >>Can you scope the complexity? Because I mean, I hear a lot of moving parts going on there, the technology's also getting better. We we're seeing cloud native become successful. There's a lot to configure, there's a lot to install. Can you scope the scale of the problem? Because we're talking about at scale Yep. Challenges here. Yeah, >>Absolutely. And I think, you know, I I like to call it, you know, the, the, the problem that the scale creates, you know, there's various problems, but I think one, one problem, one way to think about it is, is, you know, it works on my cluster problem, right? So I, you know, I come from engineering background and there's a, you know, there's a famous saying between engineers and QA and the support folks, right? Which is, it works on my laptop, which is I tested this chain, everything was fantastic, it worked flawlessly on my machine, on production, It's not working. The exact same problem now happens and these distributed environments, but at massive scale, right? Which is that, you know, developers test their applications, et cetera within the sanctity of their sandbox environments. But once you expose that change in the wild world of your production deployment, right? >>And the production deployment could be going at the radio cell tower at the edge location where a cluster is running there, or it could be sending, you know, these applications and having them run at my customer site where they might not have configured that cluster exactly the same way as I configured it, or they configured the cluster, right? But maybe they didn't deploy the security policies, or they didn't deploy the other infrastructure plugins that my app relies on. All of these various factors are their own layer of complexity. And there really isn't a simple way to solve that today. And that is just, you know, one example of an issue that happens. I think another, you know, whole new ball game of issues come in the context of security, right? Because when you are deploying applications at scale in a distributed manner, you gotta make sure someone's job is on the line to ensure that the right security policies are enforced regardless of that scale factor. So I think that's another example of problems that occur. >>Okay. So I have to ask about scale, because there are a lot of multiple steps involved when you see the success of cloud native. You know, you see some, you know, some experimentation. They set up a cluster, say it's containers and Kubernetes, and then you say, Okay, we got this, we can figure it. And then they do it again and again, they call it day two. Some people call it day one, day two operation, whatever you call it. Once you get past the first initial thing, then you gotta scale it. Then you're seeing security breaches, you're seeing configuration errors. This seems to be where the hotspot is in when companies transition from, I got this to, Oh no, it's harder than I thought at scale. Can you share your reaction to that and how you see this playing out? >>Yeah, so, you know, I think it's interesting. There's multiple problems that occur when, you know, the two factors of scale, as we talked about, start expanding. I think one of them is what I like to call the, you know, it, it works fine on my cluster problem, which is back in, when I was a developer, we used to call this, it works on my laptop problem, which is, you know, you have your perfectly written code that is operating just fine on your machine, your sandbox environment. But the moment it runs production, it comes back with p zeros and pos from support teams, et cetera. And those issues can be really difficult to triage us, right? And so in the Kubernetes environment, this problem kind of multi folds, it goes, you know, escalates to a higher degree because you have your sandbox developer environments, they have their clusters and things work perfectly fine in those clusters because these clusters are typically handcrafted or a combination of some scripting and handcrafting. >>And so as you give that change to then run at your production edge location, like say your radio cell tower site, or you hand it over to a customer to run it on their cluster, they might not have not have configured that cluster exactly how you did, or they might not have configured some of the infrastructure plugins. And so the things don't work. And when things don't work, triaging them becomes nightmarishly hard, right? It's just one of the examples of the problem, another whole bucket of issues is security, which is, is you have these distributed clusters at scale, you gotta ensure someone's job is on the line to make sure that these security policies are configured properly. >>So this is a huge problem. I love that comment. That's not not happening on my system. It's the classic, you know, debugging mentality. Yeah. But at scale it's hard to do that with error prone. I can see that being a problem. And you guys have a solution you're launching. Can you share what Arlon is this new product? What is it all about? Talk about this new introduction. >>Yeah, absolutely. Very, very excited. You know, it's one of the projects that we've been working on for some time now because we are very passionate about this problem and just solving problems at scale in on-prem or at in the cloud or at edge environments. And what arlon is, it's an open source project, and it is a tool, it's a Kubernetes native tool for complete end to end management of not just your clusters, but your clusters. All of the infrastructure that goes within and along the site of those clusters, security policies, your middleware, plug-ins, and finally your applications. So what our LA you do in a nutshell is in a declarative way, it lets you handle the configuration and management of all of these components in at scale. >>So what's the elevator pitch simply put for what dissolves in, in terms of the chaos you guys are reigning in, what's the, what's the bumper sticker? Yeah, what >>Would it do? There's a perfect analogy that I love to reference in this context, which is think of your assembly line, you know, in a traditional, let's say, you know, an auto manufacturing factory or et cetera, and the level of efficiency at scale that that assembly line brings, right? Our line, and if you look at the logo we've designed, it's this funny little robot. And it's because when we think of online, we think of these enterprise large scale environments, you know, sprawling at scale, creating chaos because there isn't necessarily a well thought through, well structured solution that's similar to an assembly line, which is taking each component, you know, addressing them, manufacturing, processing them in a standardized way, then handing to the next stage. But again, it gets, you know, processed in a standardized way. And that's what arlon really does. That's like the deliver pitch. If you have problems of scale of managing your infrastructure, you know, that is distributed. Arlon brings the assembly line level of efficiency and consistency for >>Those. So keeping it smooth, the assembly on things are flowing. See c i CD pipe pipelining. Exactly. So that's what you're trying to simplify that ops piece for the developer. I mean, it's not really ops, it's their ops, it's coding. >>Yeah. Not just developer, the ops, the operations folks as well, right? Because developers, you know, there is, developers are responsible for one picture of that layer, which is my apps, and then maybe that middleware of applications that they interface with, but then they hand it over to someone else who's then responsible to ensure that these apps are secure properly, that they are logging, logs are being collected properly, monitoring and observability integrated. And so it solves problems for both >>Those teams. Yeah. It's DevOps. So the DevOps is the cloud needed developer's. That's right. The option teams have to kind of set policies. Is that where the declarative piece comes in? Is that why that's important? >>Absolutely. Yeah. And, and, and, and you know, ES really in introduced or elevated this declarative management, right? Because, you know, s clusters are Yeah. Or your, yeah, you know, specifications of components that go in Kubernetes are defined a declarative way, and Kubernetes always keeps that state consistent with your defined state. But when you go outside of that world of a single cluster, and when you actually talk about defining the clusters or defining everything that's around it, there really isn't a solution that does that today. And so Arlon addresses that problem at the heart of it, and it does that using existing open source well known solutions. >>And do I want to get into the benefits? What's in it for me as the customer developer? But I want to finish this out real quick and get your thoughts. You mentioned open source. Why open source? What's the, what's the current state of the product? You run the product group over at Platform nine, is it open source? And you guys have a product that's commercial? Can you explain the open source dynamic? And first of all, why open source? Yeah. And what is the consumption? I mean, open source is great, People want open source, they can download it, look up the code, but maybe wanna buy the commercial. So I'm assuming you have that thought through, can you share open source and commercial relationship? >>Yeah, I think, you know, starting with why open source? I think it's, you know, we as a company, we have, you know, one of the things that's absolutely critical to us is that we take mainstream open source technologies components and then we, you know, make them available to our customers at scale through either a SaaS model or on-prem model, right? But, so as we are a company or startup or a company that benefits, you know, in a massive way by this open source economy, it's only right, I think in my mind that we do our part of the duty, right? And contribute back to the community that feeds us. And so, you know, we have always held that strongly as one of our principles. And we have, you know, created and built independent products starting all the way with fision, which was a serverless product, you know, that we had built to various other, you know, examples that I can give. But that's one of the main reasons why opensource and also open source, because we want the community to really firsthand engage with us on this problem, which is very difficult to achieve if your product is behind a wall, you know, behind, behind a block box. >>Well, and that's, that's what the developers want too. And what we're seeing in reporting with Super Cloud is the new model of consumption is I wanna look at the code and see what's in there. That's right. And then also, if I want to use it, I'll do it. Great. That's open source, that's the value. But then at the end of the day, if I wanna move fast, that's when people buy in. So it's a new kind of freemium, I guess, business model. I guess that's the way that long. But that's, that's the benefit. Open source. This is why standards and open source is growing so fast. You have that confluence of, you know, a way for developers to try before they buy, but also actually kind of date the application, if you will. We, you know, Adrian Karo uses the dating met metaphor, you know, Hey, you know, I wanna check it out first before I get married. Right? And that's what open source, So this is the new, this is how people are selling. This is not just open source, this is how companies are selling. >>Absolutely. Yeah. Yeah. You know, I think, and you know, two things. I think one is just, you know, this, this, this cloud native space is so vast that if you, if you're building a close flow solution, sometimes there's also a risk that it may not apply to every single enterprises use cases. And so having it open source gives them an opportunity to extend it, expand it, to make it proper to their use case if they choose to do so, right? But at the same time, what's also critical to us is we are able to provide a supported version of it with an SLA that we, you know, that's backed by us, a SAS hosted version of it as well, for those customers who choose to go that route, you know, once they have used the open source version and loved it and want to take it at scale and in production and need, need, need a partner to collaborate with, who can, you know, support them for that production >>Environment. I have to ask you now, let's get into what's in it for the customer. I'm a customer. Yep. Why should I be enthused about Arla? What's in it for me? You know? Cause if I'm not enthused about it, I'm not gonna be confident and it's gonna be hard for me to get behind this. Can you share your enthusiastic view of, you know, why I should be enthused about Arlo? I'm a >>Customer. Yeah, absolutely. And so, and there's multiple, you know, enterprises that we talk to, many of them, you know, our customers, where this is a very kind of typical story that you hear, which is we have, you know, a Kubernetes distribution. It could be on premise, it could be public clouds, native Kubernetes, and then we have our C I C D pipelines that are automating the deployment of applications, et cetera. And then there's this gray zone. And the gray zone is well before you can you, your CS c D pipelines can deploy the apps. Somebody needs to do all of that groundwork of, you know, defining those clusters and yeah. You know, properly configuring them. And as these things, these things start by being done hand grown. And then as the, as you scale, what typically enterprises would do today is they will have their home homegrown DIY solutions for this. >>I mean, the number of folks that I talk to that have built Terra from automation, and then, you know, some of those key developers leave. So it's a typical open source or typical, you know, DIY challenge. And the reason that they're writing it themselves is not because they want to. I mean, of course technology is always interesting to everybody, but it's because they can't find a solution that's out there that perfectly fits the problem. And so that's that pitch. I think Ops FICO would be delighted. The folks that we've talk, you know, spoken with, have been absolutely excited and have, you know, shared that this is a major challenge we have today because we have, you know, few hundreds of clusters on ecos Amazon, and we wanna scale them to few thousands, but we don't think we are ready to do that. And this will give us the >>Ability to, Yeah, I think people are scared. Not sc I won't say scare, that's a bad word. Maybe I should say that they feel nervous because, you know, at scale small mistakes can become large mistakes. This is something that is concerning to enterprises. And, and I think this is gonna come up at co con this year where enterprises are gonna say, Okay, I need to see SLAs. I wanna see track record, I wanna see other companies that have used it. Yeah. How would you answer that question to, or, or challenge, you know, Hey, I love this, but is there any guarantees? Is there any, what's the SLAs? I'm an enterprise, I got tight, you know, I love the open source trying to free fast and loose, but I need hardened code. >>Yeah, absolutely. So, so two parts to that, right? One is Arlan leverages existing open source components, products that are extremely popular. Two specifically. One is Arlan uses Argo cd, which is probably one of the highest and used CD open source tools that's out there. Right's created by folks that are as part of into team now, you know, really brilliant team. And it's used at scale across enterprises. That's one. Second is Alon also makes use of Cluster api cappi, which is a Kubernetes sub-component, right? For lifecycle management of clusters. So there is enough of, you know, community users, et cetera, around these two products, right? Or, or, or open source projects that will find Arlan to be right up in their alley because they're already comfortable, familiar with Argo cd. Now Arlan just extends the scope of what City can do. And so that's one. And then the second part is going back to a point of the comfort. And that's where, you know, platform line has a role to play, which is when you are ready to deploy online at scale, because you've been, you know, playing with it in your DEF test environments, you're happy with what you get with it, then Platform nine will stand behind it and provide that >>Sla. And what's been the reaction from customers you've talked to Platform nine customers with, with that are familiar with, with Argo and then rlo? What's been some of the feedback? >>Yeah, I, I think the feedback's been fantastic. I mean, I can give you examples of customers where, you know, initially, you know, when you are, when you're telling them about your entire portfolio of solutions, it might not strike a card right away. But then we start talking about Arlan and, and we talk about the fact that it uses Argo adn, they start opening up, they say, We have standardized on Argo and we have built these components, homegrown, we would be very interested. Can we co-develop? Does it support these use cases? So we've had that kind of validation. We've had validation all the way at the beginning of our land before we even wrote a single line of code saying this is something we plan on doing. And the customer said, If you had it today, I would've purchased it. So it's been really great validation. >>All right. So next question is, what is the solution to the customer? If I asked you, Look it, I have, I'm so busy, my team's overworked. I got a skills gap. I don't need another project that's, I'm so tied up right now and I'm just chasing my tail. How does Platform nine help me? >>Yeah, absolutely. So I think, you know, one of the core tenets of Platform nine has always been been that we try to bring that public cloud like simplicity by hosting, you know, this in a lot of such similar tools in a SaaS hosted manner for our customers, right? So our goal behind doing that is taking away or trying to take away all of that complexity from customers' hands and offloading it to our hands, right? And giving them that full white glove treatment, as we call it. And so from a customer's perspective, one, something like arlon will integrate with what they have so they don't have to rip and replace anything. In fact, it will, even in the next versions, it may even discover your clusters that you have today and you know, give you an inventory. And that will, >>So if customers have clusters that are growing, that's a sign correct call you guys. >>Absolutely. Either they're, they have massive large clusters, right? That they wanna split into smaller clusters, but they're not comfortable doing that today, or they've done that already on say, public cloud or otherwise. And now they have management challenges. So >>Especially operationalizing the clusters, whether they want to kind of reset everything and remove things around and reconfigure Yep. And or scale out. >>That's right. Exactly. And >>You provide that layer of policy. >>Absolutely. >>Yes. That's the key value here. >>That's right. >>So policy based configuration for cluster scale up, >>Well profile and policy based declarative configuration and lifecycle management for clusters. >>If I asked you how this enables supercloud, what would you say to that? >>I think this is one of the key ingredients to super cloud, right? If you think about a super cloud environment, there's at least few key ingredients that that come to my mind that are really critical. Like they are, you know, life saving ingredients at that scale. One is having a really good strategy for managing that scale, you know, in a, going back to assembly line in a very consistent, predictable way so that our lot solves then you, you need to compliment that with the right kind of observability and monitoring tools at scale, right? Because ultimately issues are gonna happen and you're gonna have to figure out, you know, how to solve them fast. And arlon by the way, also helps in that direction, but you also need observability tools. And then especially if you're running it on the public cloud, you need some cost management tools. In my mind, these three things are like the most necessary ingredients to make Super Cloud successful. And you know, our alarm fills in >>One. Okay. So now the next level is, Okay, that makes sense. Is under the covers kind of speak under the hood. Yeah. How does that impact the app developers and the cloud native modern application workflows? Because the impact to me, seems the apps are gonna be impacted. Are they gonna be faster, stronger? I mean, what's the impact if you do all those things, as you mentioned, what's the impact of the apps? >>Yeah, the impact is that your apps are more likely to operate in production the way you expect them to, because the right checks and balances have gone through, and any discrepancies have been identified prior to those apps, prior to your customer running into them, right? Because developers run into this challenge to their, where there's a split responsibility, right? I'm responsible for my code, I'm responsible for some of these other plugins, but I don't own the stack end to end. I have to rely on my ops counterpart to do their part, right? And so this really gives them, you know, the right tooling for that. >>So this is actually a great kind of relevant point, you know, as cloud becomes more scalable, you're starting to see this fragmentation gone of the days of the full stack developer to the more specialized role. But this is a key point, and I have to ask you because if this RLO solution takes place, as you say, and the apps are gonna be stupid, they're designed to do, the question is, what did does the current pain look like of the apps breaking? What does the signals to the customer Yeah. That they should be calling you guys up into implementing Arlo, Argo and, and all the other goodness to automate? What are some of the signals? Is it downtime? Is it, is it failed apps, Is it latency? What are some of the things that Yeah, absolutely would be indications of things are effed up a little bit. Yeah. >>More frequent down times, down times that are, that take longer to triage. And so you are, you know, the, you know, your mean times on resolution, et cetera, are escalating or growing larger, right? Like we have environments of customers where they're, they have a number of folks on in the field that have to take these apps and run them at customer sites. And that's one of our partners. And they're extremely interested in this because they're the, the rate of failures they're encountering for this, you know, the field when they're running these apps on site, because the field is automating their clusters that are running on sites using their own script. So these are the kinds of challenges, and those are the pain points, which is, you know, if you're looking to reduce your meantime to resolution, if you're looking to reduce the number of failures that occur on your production site, that's one. And second, if you are looking to manage these at scale environments with a relatively small, focused, nimble ops team, which has an immediate impact on your budget. So those are, those are the signals. >>This is the cloud native at scale situation, the innovation going on. Final thought is your reaction to the idea that if the world goes digital, which it is, and the confluence of physical and digital coming together, and cloud continues to do its thing, the company becomes the application, not where it used to be supporting the business, you know, the back office and the maybe terminals and some PCs and handhelds. Now if technology's running, the business is the business. Yeah. Company's the application. Yeah. So it can't be down. So there's a lot of pressure on, on CSOs and CIOs now and boards is saying, How is technology driving the top line revenue? That's the number one conversation. Yep. Do you see that same thing? >>Yeah. It's interesting. I think there's multiple pressures at the CXO CIO level, right? One is that there needs to be that visibility and clarity and guarantee almost that, you know, that the, the technology that's, you know, that's gonna drive your top line is gonna drive that in a consistent, reliable, predictable manner. And then second, there is the constant pressure to do that while always lowering your costs of doing it, right? Especially when you're talking about, let's say retailers or those kinds of large scale vendors, they many times make money by lowering the amount that they spend on, you know, providing those goods to their end customers. So I think those, both those factors kind of come into play and the solution to all of them is usually in a very structured strategy around automation. >>Final question. What does cloudnative at scale look like to you? If all the things happen the way we want 'em to happen, The magic wand, the magic dust, what does it look like? >>What that looks like to me is a CIO sipping at his desk on coffee production is running absolutely smooth. And his, he's running that at a nimble, nimble team size of at the most, a handful of folks that are just looking after things, but things are >>Just taking care of the CIO doesn't exist. There's no ciso, they're at the beach. >>Yep. >>Thank you for coming on, sharing the cloud native at scale here on the cube. Thank you for your time. >>Fantastic. Thanks for >>Having me. Okay. I'm John Fur here for special program presentation, special programming cloud native at scale, enabling super cloud modern applications with Platform nine. Thanks for watching. Welcome back everyone to the special presentation of cloud native at scale, the cube and platform nine special presentation going in and digging into the next generation super cloud infrastructure as code and the future of application development. We're here with Bickley, who's the chief architect and co-founder of Platform nine Pick. Great to see you Cube alumni. We, we met at an OpenStack event in about eight years ago, or later, earlier when OpenStack was going. Great to see you and great to see congratulations on the success of platform nine. >>Thank you very much. >>Yeah. You guys have been at this for a while and this is really the, the, the year we're seeing the, the crossover of Kubernetes because of what happens with containers. Everyone now has realized, and you've seen what Docker's doing with the new docker, the open source Docker now just the success Exactly. Of containerization, right? And now the Kubernetes layer that we've been working on for years is coming, bearing fruit. This is huge. >>Exactly. Yes. >>And so as infrastructures code comes in, we talked to Bacar talking about Super Cloud, I met her about, you know, the new Arlon, our, our lawn, and you guys just launched the infrastructures code is going to another level, and then it's always been DevOps infrastructures code. That's been the ethos that's been like from day one, developers just code. Then you saw the rise of serverless and you see now multi-cloud or on the horizon, connect the dots for us. What is the state of infrastructure as code today? >>So I think, I think I'm, I'm glad you mentioned it, everybody or most people know about infrastructures code. But with Kubernetes, I think that project has evolved at the concept even further. And these dates, it's infrastructure is configuration, right? So, which is an evolution of infrastructure as code. So instead of telling the system, here's how I want my infrastructure by telling it, you know, do step A, B, C, and D instead with Kubernetes, you can describe your desired state declaratively using things called manifest resources. And then the system kind of magically figures it out and tries to converge the state towards the one that you specified. So I think it's, it's a even better version of infrastructures code. >>Yeah. And that really means it's developer just accessing resources. Okay. That declare, Okay, give me some compute, stand me up some, turn the lights on, turn 'em off, turn 'em on. That's kind of where we see this going. And I like the configuration piece. Some people say composability, I mean now with open source so popular, you don't have to have to write a lot of code, this code being developed. And so it's into integration, it's configuration. These are areas that we're starting to see computer science principles around automation, machine learning, assisting open source. Cuz you got a lot of code that's right in hearing software, supply chain issues. So infrastructure as code has to factor in these new dynamics. Can you share your opinion on these new dynamics of, as open source grows, the glue layers, the configurations, the integration, what are the core issues? >>I think one of the major core issues is with all that power comes complexity, right? So, you know, despite its expressive power systems like Kubernetes and declarative APIs let you express a lot of complicated and complex stacks, right? But you're dealing with hundreds if not thousands of these yamo files or resources. And so I think, you know, the emergence of systems and layers to help you manage that complexity is becoming a key challenge and opportunity in, in this space. >>That's, I wrote a LinkedIn post today was comments about, you know, hey, enterprise is a new breed. The trend of SaaS companies moving our consumer comp consumer-like thinking into the enterprise has been happening for a long time, but now more than ever, you're seeing it the old way used to be solve complexity with more complexity and then lock the customer in. Now with open source, it's speed, simplification and integration, right? These are the new dynamic power dynamics for developers. Yeah. So as companies are starting to now deploy and look at Kubernetes, what are the things that need to be in place? Because you have some, I won't say technical debt, but maybe some shortcuts, some scripts here that make it look like infrastructure is code. People have done some things to simulate or or make infrastructure as code happen. Yes. But to do it at scale Yes. Is harder. What's your take on this? What's your view? >>It's hard because there's a per proliferation of methods, tools, technologies. So for example, today it's very common for DevOps and platform engineering tools, I mean, sorry, teams to have to deploy a large number of Kubernetes clusters, but then apply the applications and configurations on top of those clusters. And they're using a wide range of tools to do this, right? For example, maybe Ansible or Terraform or bash scripts to bring up the infrastructure and then the clusters. And then they may use a different set of tools such as Argo CD or other tools to apply configurations and applications on top of the clusters. So you have this sprawl of tools. You, you also have this sprawl of configurations and files because the more objects you're dealing with, the more resources you have to manage. And there's a risk of drift that people call that where, you know, you think you have things under control, but some people from various teams will make changes here and there and then before the end of the day systems break and you have no idea of tracking them. So I think there's real need to kind of unify, simplify, and try to solve these problems using a smaller, more unified set of tools and methodologies. And that's something that we try to do with this new project. Arlon. >>Yeah. So, so we're gonna get into Arlan in a second. I wanna get into the why Arlon. You guys announced that at AR GoCon, which was put on here in Silicon Valley at the, at the community meeting by in two, they had their own little day over there at their headquarters. But before we get there, vascar, your CEO came on and he talked about Super Cloud at our in AAL event. What's your definition of super cloud? If you had to kind of explain that to someone at a cocktail party or someone in the industry technical, how would you look at the super cloud trend that's emerging? It's become a thing. What's your, what would be your contribution to that definition or the narrative? >>Well, it's, it's, it's funny because I've actually heard of the term for the first time today, speaking to you earlier today. But I think based on what you said, I I already get kind of some of the, the gist and the, the main concepts. It seems like super cloud, the way I interpret that is, you know, clouds and infrastructure, programmable infrastructure, all of those things are becoming commodity in a way. And everyone's got their own flavor, but there's a real opportunity for people to solve real business problems by perhaps trying to abstract away, you know, all of those various implementations and then building better abstractions that are perhaps business or applications specific to help companies and businesses solve real business problems. >>Yeah, I remember that's a great, great definition. I remember, not to date myself, but back in the old days, you know, IBM had a proprietary network operating system, so of deck for the mini computer vendors, deck net and SNA respectively. But T C P I P came out of the osi, the open systems interconnect and remember, ethernet beat token ring out. So not to get all nerdy for all the young kids out there, look, just look up token ring, you'll see, you've probably never heard of it. It's IBM's, you know, connection for the internet at the, the layer two is Amazon, the ethernet, right? So if T C P I P could be the Kubernetes and the container abstraction that made the industry completely change at that point in history. So at every major inflection point where there's been serious industry change and wealth creation and business value, there's been an abstraction Yes. Somewhere. Yes. What's your reaction to that? >>I think this is, I think a saying that's been heard many times in this industry and, and I forgot who originated it, but I think that the saying goes like, there's no problem that can't be solved with another layer of indirection, right? And we've seen this over and over and over again where Amazon and its peers have inserted this layer that has simplified, you know, computing and, and infrastructure management. And I believe this trend is going to continue, right? The next set of problems are going to be solved with these insertions of additional abstraction layers. I think that that's really a, yeah, it's gonna >>Continue. It's interesting. I just, when I wrote another post today on LinkedIn called the Silicon Wars AMD stock is down arm has been on a rise. We remember pointing for many years now that arm's gonna be hugely, it has become true. If you look at the success of the infrastructure as a service layer across the clouds, Azure, aws, Amazon's clearly way ahead of everybody. The stuff that they're doing with the silicon and the physics and the, the atoms, the pro, you know, this is where the innovation, they're going so deep and so strong at ISAs, the more that they get that gets come on, they have more performance. So if you're an app developer, wouldn't you want the best performance and you'd wanna have the best abstraction layer that gives you the most ability to do infrastructures, code or infrastructure for configuration, for provisioning, for managing services. And you're seeing that today with service MeSHs, a lot of action going on in the service mesh area in in this community of, of co con, which will be a covering. So that brings up the whole what's next? You guys just announced our lawn at Argo Con, which came out of Intuit. We've had Mariana Tessel at our super cloud event. She's the cto, you know, they're all in the cloud. So they contributed that project. Where did Arlon come from? What was the origination? What's the purpose? Why our lawn, why this announcement? >>Yeah, so the, the inception of the project, this was the result of us realizing that problem that we spoke about earlier, which is complexity, right? With all of this, these clouds, these infrastructure, all the variations around and, you know, compute storage networks and the proliferation of tools we talked about the Ansibles and Terraforms and Kubernetes itself. You can, you can think of that as another tool, right? We saw a need to solve that complexity problem, and especially for people and users who use Kubernetes at scale. So when you have, you know, hundreds of clusters, thousands of applications, thousands of users spread out over many, many locations, there, there needs to be a system that helps simplify that management, right? So that means fewer tools, more expressive ways of describing the state that you want and more consistency. And, and that's why, you know, we built our lawn and we built it recognizing that many of these problems or sub problems have already been solved. So Arlon doesn't try to reinvent the wheel, it instead rests on the shoulders of several giants, right? So for example, Kubernetes is one building block, GI ops, and Argo CD is another one, which provides a very structured way of applying configuration. And then we have projects like cluster API and cross plane, which provide APIs for describing infrastructure. So arlon takes all of those building blocks and builds a thin layer, which gives users a very expressive way of defining configuration and desired state. So that's, that's kind of the inception of, And >>What's the benefit of that? What does that give the, what does that give the developer, the user, in this case, >>The developers, the, the platform engineer, team members, the DevOps engineers, they get a a ways to provision not just infrastructure and clusters, but also applications and configurations. They get a way, a system for provisioning, configuring, deploying, and doing life cycle management in a, in a much simpler way. Okay. Especially as I said, if you're dealing with a large number of applications. >>So it's like an operating fabric, if you will. Yes. For them. Okay, so let's get into what that means for up above and below the the, this abstraction or thin layer below as the infrastructure. We talked a lot about what's going on below that. Yeah. Above our workloads. At the end of the day, you know, I talk to CXOs and IT folks that are now DevOps engineers. They care about the workloads and they want the infrastructures code to work. They wanna spend their time getting in the weeds, figuring out what happened when someone made a push that that happened or something happened. They need observability and they need to, to know that it's working. That's right. And is my workloads running effectively? So how do you guys look at the workload side of it? Cuz now you have multiple workloads on these fabric, >>Right? So workloads, so Kubernetes has defined kind of a standard way to describe workloads and you can, you know, tell Kubernetes, I want to run this container this particular way, or you can use other projects that are in the Kubernetes cloud native ecosystem like K native, where you can express your application in more at a higher level, right? But what's also happening is in addition to the workloads, DevOps and platform engineering teams, they need to very often deploy the applications with the clusters themselves. Clusters are becoming this commodity. It's, it's becoming this host for the application and it kind of comes bundled with it. In many cases it is like an appliance, right? So DevOps teams have to provision clusters at a really incredible rate and they need to tear them down. Clusters are becoming more, >>It's kinda like an EC two instance, spin up a cluster. We very, people used words like that. That's >>Right. And before arlon you kind of had to do all of that using a different set of tools as, as I explained. So with Armon you can kind of express everything together. You can say I want a cluster with a health monitoring stack and a logging stack and this ingress controller and I want these applications and these security policies. You can describe all of that using something we call a profile. And then you can stamp out your app, your applications and your clusters and manage them in a very, so >>Essentially standard creates a mechanism. Exactly. Standardized, declarative kind of configurations. And it's like a playbook. You deploy it. Now what's there is between say a script like I'm, I have scripts, I could just automate scripts >>Or yes, this is where that declarative API and infrastructures configuration comes in, right? Because scripts, yes you can automate scripts, but the order in which they run matters, right? They can break, things can break in the middle and, and sometimes you need to debug them. Whereas the declarative way is much more expressive and powerful. You just tell the system what you want and then the system kind of figures it out. And there are these things about controllers which will in the background reconcile all the state to converge towards your desire. It's a much more powerful, expressive and reliable way of getting things done. >>So infrastructure has configuration is built kind of on, it's as super set of infrastructures code because it's >>An evolution. >>You need edge's code, but then you can configure the code by just saying do it. You basically declaring and saying Go, go do that. That's right. Okay, so, alright, so cloud native at scale, take me through your vision of what that means. Someone says, Hey, what does cloud native at scale mean? What's success look like? How does it roll out in the future as you, not future next couple years? I mean people are now starting to figure out, okay, it's not as easy as it sounds. Could be nice, it has value. We're gonna hear this year coan a lot of this. What does cloud native at scale >>Mean? Yeah, there are different interpretations, but if you ask me, when people think of scale, they think of a large number of deployments, right? Geographies, many, you know, supporting thousands or tens or millions of, of users there, there's that aspect to scale. There's also an equally important a aspect of scale, which is also something that we try to address with Arran. And that is just complexity for the people operating this or configuring this, right? So in order to describe that desired state and in order to perform things like maybe upgrades or updates on a very large scale, you want the humans behind that to be able to express and direct the system to do that in, in relatively simple terms, right? And so we want the tools and the abstractions and the mechanisms available to the user to be as powerful but as simple as possible. So there's, I think there's gonna be a number and there have been a number of CNCF and cloud native projects that are trying to attack that complexity problem as well. And Arlon kind of falls in in that >>Category. Okay, so I'll put you on the spot road that CubeCon coming up and obviously this will be shipping this segment series out before. What do you expect to see at Coan this year? What's the big story this year? What's the, what's the most important thing happening? Is it in the open source community and also within a lot of the, the people jogging for leadership. I know there's a lot of projects and still there's some white space in the overall systems map about the different areas get run time and there's ability in all these different areas. What's the, where's the action? Where, where's the smoke? Where's the fire? Where's the piece? Where's the tension? >>Yeah, so I think one thing that has been happening over the past couple of cons and I expect to continue and, and that is the, the word on the street is Kubernetes is getting boring, right? Which is good, right? >>Boring means simple. >>Well, well >>Maybe, >>Yeah, >>Invisible, >>No drama, right? So, so the, the rate of change of the Kubernetes features and, and all that has slowed but in, in a, in a positive way. But there's still a general sentiment and feeling that there's just too much stuff. If you look at a stack necessary for hosting applications based on Kubernetes, there are just still too many moving parts, too many components, right? Too much complexity. I go, I keep going back to the complexity problem. So I expect Cube Con and all the vendors and the players and the startups and the people there to continue to focus on that complexity problem and introduce further simplifications to, to the stack. >>Yeah. Vic, you've had an storied career, VMware over decades with them obviously in 12 years with 14 years or something like that. Big number co-founder here at Platform. Now you guys have been around for a while at this game. We, man, we talked about OpenStack, that project you, we interviewed at one of their events. So OpenStack was the beginning of that, this new revolution. And I remember the early days it was, it wasn't supposed to be an alternative to Amazon, but it was a way to do more cloud cloud native. I think we had a cloud ERO team at that time. We would to joke we, you know, about, about the dream. It's happening now, now at Platform nine. You guys have been doing this for a while. What's the, what are you most excited about as the chief architect? What did you guys double down on? What did you guys tr pivot from or two, did you do any pivots? Did you extend out certain areas? Cuz you guys are in a good position right now, a lot of DNA in Cloud native. What are you most excited about and what does Platform nine bring to the table for customers and for people in the industry watching this? >>Yeah, so I think our mission really hasn't changed over the years, right? It's been always about taking complex open source software because open source software, it's powerful. It solves new problems, you know, every year and you have new things coming out all the time, right? OpenStack was an example when the Kubernetes took the world by storm. But there's always that complexity of, you know, just configuring it, deploying it, running it, operating it. And our mission has always been that we will take all that complexity and just make it, you know, easy for users to consume regardless of the technology, right? So the successor to Kubernetes, you know, I don't have a crystal ball, but you know, you have some indications that people are coming up of new and simpler ways of running applications. There are many projects around there who knows what's coming next year or the year after that. But platform will a, platform nine will be there and we will, you know, take the innovations from the the community. We will contribute our own innovations and make all of those things very consumable to customers. >>Simpler, faster, cheaper. Exactly. Always a good business model technically to make that happen. Yes. Yeah, I think the, the reigning in the chaos is key, you know, Now we have now visibility into the scale. Final question before we depart this segment. What is at scale, how many clusters do you see that would be a watermark for an at scale conversation around an enterprise? Is it workloads we're looking at or, or clusters? How would you, Yeah, how would you describe that? When people try to squint through and evaluate what's a scale, what's the at scale kind of threshold? >>Yeah. And, and the number of clusters doesn't tell the whole story because clusters can be small in terms of the number of nodes or they can be large. But roughly speaking when we say, you know, large scale cluster deployments, we're talking about maybe hundreds, two thousands. >>Yeah. And final final question, what's the role of the hyperscalers? You got AWS continuing to do well, but they got their core ias, they got a PAs, they're not too too much putting a SaaS out there. They have some SaaS apps, but mostly it's the ecosystem. They have marketplaces doing over $2 billion billions of transactions a year and, and it's just like, just sitting there. It hasn't really, they're now innovating on it, but that's gonna change ecosystems. What's the role the cloud play in the cloud native of its scale? >>The, the hyperscalers, >>Yeahs Azure, Google. >>You mean from a business perspective? Yeah, they're, they have their own interests that, you know, that they're, they will keep catering to, they, they will continue to find ways to lock their users into their ecosystem of services and, and APIs. So I don't think that's gonna change, right? They're just gonna keep, >>Well they got great I performance, I mean from a, from a hardware standpoint, yes, that's gonna be key, right? >>Yes. I think the, the move from X 86 being the dominant way and platform to run workloads is changing, right? That, that, that, that, and I think the, the hyperscalers really want to be in the game in terms of, you know, the the new risk and arm ecosystems and the platforms. >>Yeah, not joking aside, Paul Morritz, when he was the CEO of VMware, when he took over once said, I remember our first year doing the cube. Oh the cloud is one big distributed computer, it's, it's hardware and he got software and you got middleware and he kind over, well he's kind of tongue in cheek, but really you're talking about large compute and sets of services that is essentially a distributed computer. >>Yes, >>Exactly. It's, we're back on the same game. Vic, thank you for coming on the segment. Appreciate your time. This is cloud native at scale special presentation with Platform nine. Really unpacking super cloud Arlon open source and how to run large scale applications on the cloud Cloud Native Phil for developers and John Furrier with the cube. Thanks for Washington. We'll stay tuned for another great segment coming right up. Hey, welcome back everyone to Super Cloud 22. I'm John Fur, host of the Cuba here all day talking about the future of cloud. Where's it all going? Making it super multi-cloud clouds around the corner and public cloud is winning. Got the private cloud on premise and edge. Got a great guest here, Vascar Gorde, CEO of Platform nine, just on the panel on Kubernetes. An enabler blocker. Welcome back. Great to have you on. >>Good to see you >>Again. So Kubernetes is a blocker enabler by, with a question mark. I put on on that panel was really to discuss the role of Kubernetes. Now great conversation operations is impacted. What's interest thing about what you guys are doing at Platform nine? Is your role there as CEO and the company's position, kind of like the world spun into the direction of Platform nine while you're at the helm? Yeah, right. >>Absolutely. In fact, things are moving very well and since they came to us, it was an insight to call ourselves the platform company eight years ago, right? So absolutely whether you are doing it in public clouds or private clouds, you know, the application world is moving very fast in trying to become digital and cloud native. There are many options for you do on the infrastructure. The biggest blocking factor now is having a unified platform. And that's what we, we come into, >>Patrick, we were talking before we came on stage here about your background and we were gonna talk about the glory days in 2000, 2001, when the first as piece application service providers came out, kind of a SaaS vibe, but that was kind of all kind of cloudlike. >>It wasn't, >>And and web services started then too. So you saw that whole growth. Now, fast forward 20 years later, 22 years later, where we are now, when you look back then to here and all the different cycles, >>I, in fact you, you know, as we were talking offline, I was in one of those ASPs in the year 2000 where it was a novel concept of saying we are providing a software and a capability as a service, right? You sign up and start using it. I think a lot has changed since then. The tooling, the tools, the technology has really skyrocketed. The app development environment has really taken off exceptionally well. There are many, many choices of infrastructure now, right? So I think things are in a way the same but also extremely different. But more importantly now for any company, regardless of size, to be a digital native, to become a digital company is extremely mission critical. It's no longer a nice to have everybody's in the journey somewhere. >>Everyone is going digital transformation here. Even on a so-called downturn recession that's upcoming inflation's here. It's interesting. This is the first downturn in the history of the world where the hyperscale clouds have been pumping on all cylinders as an economic input. And if you look at the tech trends, GDPs down, but not tech. >>Nope. >>Cuz the pandemic showed everyone digital transformation is here and more spend and more growth is coming even in, in tech. So this is a unique factor which proves that that digital transformation's happening and company, every company will need a super cloud. >>Everyone, every company, regardless of size, regardless of location, has to become modernize their infrastructure. And modernizing Infras infrastructure is not just some new servers and new application tools, It's your approach, how you're serving your customers, how you're bringing agility in your organization. I think that is becoming a necessity for every enterprise to survive. >>I wanna get your thoughts on Super Cloud because one of the things Dave Ante and I want to do with Super Cloud and calling it that was we, I, I personally, and I know Dave as well, he can, I'll speak from, he can speak for himself. We didn't like multi-cloud. I mean not because Amazon said don't call things multi-cloud, it just didn't feel right. I mean everyone has multiple clouds by default. If you're running productivity software, you have Azure and Office 365. But it wasn't truly distributed. It wasn't truly decentralized, it wasn't truly cloud enabled. It didn't, it felt like they're not ready for a market yet. Yet public clouds booming on premise. Private cloud and Edge is much more on, you know, more, more dynamic, more real. >>Yeah. I think the reason why we think super cloud is a better term than multi-cloud. Multi-cloud are more than one cloud, but they're disconnected. Okay, you have a productivity cloud, you have a Salesforce cloud, you may have, everyone has an internal cloud, right? So, but they're not connected. So you can say okay, it's more than one cloud. So it's you know, multi-cloud. But super cloud is where you are actually trying to look at this holistically. Whether it is on-prem, whether it is public, whether it's at the edge, it's a store at the branch. You are looking at this as one unit. And that's where we see the term super cloud is more applicable because what are the qualities that you require if you're in a super cloud, right? You need choice of infrastructure, you need, but at the same time you need a single pain, a single platform for you to build your innovations on regardless of which cloud you're doing it on, right? So I think Super Cloud is actually a more tightly integrated orchestrated management philosophy we think. >>So let's get into some of the super cloud type trends that we've been reporting on. Again, the purpose of this event is to, as a pilots, to get the conversations flowing with with the influencers like yourselves who are running companies and building products and the builders, Amazon and Azure are doing extremely well. Google's coming up in third cloudworks in public cloud. We see the use cases on premises use cases. Kubernetes has been an interesting phenomenon because it's become from the developer side a little bit, but a lot of ops people love Kubernetes. It's really more of an ops thing. You mentioned OpenStack earlier. Kubernetes kind of came out of that open stack. We need an orchestration and then containers had a good shot with, with Docker. They re pivoted the company. Now they're all in an open source. So you got containers booming and Kubernetes as a new layer there. What's the, what's the take on that? What does that really mean? Is that a new defacto enabler? It >>Is here. It's for here for sure. Every enterprise somewhere else in the journey is going on. And you know, most companies are, 70 plus percent of them have won two, three container based, Kubernetes based applications now being rolled out. So it's very much here, it is in production at scale by many customers. And the beauty of it is, yes, open source, but the biggest gating factor is the skill set. And that's where we have a phenomenal engineering team, right? So it's, it's one thing to buy a tool >>And just be clear, you're a managed service for Kubernetes. >>We provide, provide a software platform for cloud acceleration as a service and it can run anywhere. It can run in public private. We have customers who do it in truly multi-cloud environments. It runs on the edge, it runs at this in stores are thousands of stores in a retailer. So we provide that and also for specific segments where data sovereignty and data residency are key regulatory reasons. We also un OnPrem as an air gap version. >>Can you give an example on how you guys are deploying your platform to enable a super cloud experience for your >>Customer? Right. So I'll give you two different examples. One is a very large networking company, public networking company. They have, I dunno, hundreds of products, hundreds of r and d teams that are building different, different products. And if you look at few years back, each one was doing it on a different platforms but they really needed to bring the agility and they worked with us now over three years where we are their build test dev pro platform where all their products are built on, right? And it has dramatically increased their agility to release new products. Number two, it actually is a light out operation. In fact the customer says like, like the Maytag service person cuz we provide it as a service and it barely takes one or two people to maintain it for them. >>So it's kinda like an SRE vibe. One person managing a >>Large 4,000 engineers building infrastructure >>On their tools, >>Whatever they want on their tools. They're using whatever app development tools they use, but they use our platform. >>What benefits are they seeing? Are they seeing speed? >>Speed, definitely. Okay. Definitely they're speeding. Speed uniformity because now they're building able to build, so their customers who are using product A and product B are seeing a similar set of tools that are being used. >>So a big problem that's coming outta this super cloud event that we're, we're seeing and we've heard it all here, ops and security teams cuz they're kind of too part of one theme, but ops and security specifically need to catch up speed wise. Are you delivering that value to ops and security? Right. >>So we, we work with ops and security teams and infrastructure teams and we layer on top of that. We have like a platform team. If you think about it, depending on where you have data centers, where you have infrastructure, you have multiple teams, okay, but you need a unified platform. Who's your buyer? Our buyer is usually, you know, the product divisions of companies that are looking at or the CTO would be a buyer for us functionally cio definitely. So it it's, it's somewhere in the DevOps to infrastructure. But the ideal one we are beginning to see now many large corporations are really looking at it as a platform and saying we have a platform group on which any app can be developed and it is run on any infrastructure. So the platform engineering teams, >>You working two sides of that coin. You've got the dev side and then >>And then infrastructure >>Side side, okay. >>Another customer like give you an example, which I would say is kind of the edge of the store. So they have thousands of stores. Retail, retail, you know food retailer, right? They have thousands of stores that are on the globe, 50,000, 60,000. And they really want to enhance the customer experience that happens when you either order the product or go into the store and pick up your product or buy or browse or sit there. They have applications that were written in the nineties and then they have very modern AIML applications today. They want something that will not have to send an IT person to install a rack in the store or they can't move everything to the cloud because the store operations has to be local. The menu changes based on, It's a classic edge. It's classic edge. Yeah. Right. They can't send it people to go install rack access servers then they can't sell software people to go install the software and any change you wanna put through that, you know, truck roll. So they've been working with us where all they do is they ship, depending on the size of the store, one or two or three little servers with instructions that >>You, you say little servers like how big one like a net box box, like a small little >>Box and all the person in the store has to do like what you and I do at home and we get a, you know, a router is connect the power, connect the internet and turn the switch on. And from there we pick it up. >>Yep. >>We provide the operating system, everything and then the applications are put on it. And so that dramatically brings the velocity for them. They manage >>Thousands of them. True plug and play >>Two, plug and play thousands of stores. They manage it centrally. We do it for them, right? So, so that's another example where on the edge then we have some customers who have both a large private presence and one of the public clouds. Okay. But they want to have the same platform layer of orchestration and management that they can use regardless of the location. So >>You guys got some success. Congratulations. Got some traction there. It's awesome. The question I want to ask you is that's come up is what is truly cloud native? Cuz there's lift and shift of the cloud >>That's not cloud native. >>Then there's cloud native. Cloud native seems to be the driver for the super cloud. How do you talk to customers? How do you explain when someone says what's cloud native, what isn't cloud native? >>Right. Look, I think first of all, the best place to look at what is the definition and what are the attributes and characteristics of what is truly a cloud native, is CNC foundation. And I think it's very well documented where you, well >>Con of course Detroit's >>Coming here, so, so it's already there, right? So, so we follow that very closely, right? I think just lifting and shifting your 20 year old application onto a data center somewhere is not cloud native. Okay? You can't put to cloud native, you have to rewrite and redevelop your application and business logic using modern tools. Hopefully more open source and, and I think that's what Cloudnative is and we are seeing a lot of our customers in that journey. Now everybody wants to be cloudnative, but it's not that easy, okay? Because it's, I think it's first of all, skill set is very important. Uniformity of tools that there's so many tools there. Thousands and thousands of tools you could spend your time figuring out which tool to use. Okay? So I think the complexities there, but the business benefits of agility and uniformity and customer experience are truly them. >>And I'll give you an example. I don't know how clear native they are, right? And they're not a customer of ours, but you order pizzas, you do, right? If you just watch the pizza industry, how dominoes actually increase their share and mind share and wallet share was not because they were making better pizzas or not, I don't know anything about that, but the whole experience of how you order, how you watch what's happening, how it's delivered. There were a pioneer in it. To me, those are the kinds of customer experiences that cloud native can provide. >>Being agility and having that flow to the application changes what the expectations of the, for the customer. >>Customer, the customer's expectations change, right? Once you get used to a better customer experience, you learn >>Best car. To wrap it up, I wanna just get your perspective again. One of the benefits of chatting with you here and having you part of the Super Cloud 22 is you've seen many cycles, you have a lot of insights. I want to ask you, given your career where you've been and what you've done and now the CEO platform nine, how would you compare what's happening now with other inflection points in the industry? And you've been, again, you've been an entrepreneur, you sold your company to Oracle, you've been seeing the big companies, you've seen the different waves. What's going on right now put into context this moment in time around Super >>Cloud. Sure. I think as you said, a lot of battles. Cars being been, been in an asp, been in a realtime software company, being in large enterprise software houses and a transformation. I've been on the app side, I did the infrastructure right and then tried to build our own platforms. I've gone through all of this myself with a lot of lessons learned in there. I think this is an event which is happening now for companies to go through to become cloud native and digitalize. If I were to look back and look at some parallels of the tsunami that's going on is a couple of paddles come to me. One is, think of it, which was forced to honors like y2k. Everybody around the world had to have a plan, a strategy, and an execution for y2k. I would say the next big thing was e-commerce. I think e-commerce has been pervasive right across all industries. >>And disruptive. >>And disruptive, extremely disruptive. If you did not adapt and adapt and accelerate your e-commerce initiative, you were, it was an existence question. Yeah. I think we are at that pivotal moment now in companies trying to become digital and cloudnative that know that is what I see >>Happening there. I think that that e-commerce was interesting and I think just to riff with you on that is that it's disrupting and refactoring the business models. I think that is something that's coming out of this is that it's not just completely changing the game, it's just changing how you operate, >>How you think, and how you operate. See, if you think about the early days of eCommerce, just putting up a shopping cart didn't made you an eCommerce or an E retailer or an e e customer, right? Or so. I think it's the same thing now is I think this is a fundamental shift on how you're thinking about your business. How are you gonna operate? How are you gonna service your customers? I think it requires that just lift and shift is not gonna work. >>Mascar, thank you for coming on, spending the time to come in and share with our community and being part of Super Cloud 22. We really appreciate, we're gonna keep this open. We're gonna keep this conversation going even after the event, to open up and look at the structural changes happening now and continue to look at it in the open in the community. And we're gonna keep this going for, for a long, long time as we get answers to the problems that customers are looking for with cloud cloud computing. I'm Sean Feer with Super Cloud 22 in the Cube. Thanks for watching. >>Thank you. Thank you, John. >>Hello. Welcome back. This is the end of our program, our special presentation with Platform nine on cloud native at scale, enabling the super cloud. We're continuing the theme here. You heard the interviews Super Cloud and its challenges, new opportunities around the solutions around like Platform nine and others with Arlon. This is really about the edge situations on the internet and managing the edge multiple regions, avoiding vendor lock in. This is what this new super cloud is all about. The business consequences we heard and and the wide ranging conversations around what it means for open source and the complexity problem all being solved. I hope you enjoyed this program. There's a lot of moving pieces and things to configure with cloud native install, all making it easier for you here with Super Cloud and of course Platform nine contributing to that. Thank you for watching.

>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all as we know, data is changing the world and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to "theCUBE"'s presentation of democratizing analytics across the enterprise, made possible by Alteryx. An Alteryx commissioned IDC info brief entitled, "Four Ways to Unlock Transformative Business Outcomes from Analytics Investments" found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special "CUBE" presentation, Jason Klein, product marketing director of Alteryx, will join me to share key findings from the new Alteryx commissioned IDC brief and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, chief data and analytics officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then in our final segment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who is the global head of tax technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, product marketing director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research, which spoke with about 1500 leaders, what nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees, and this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics, and we're able to focus on the behaviors driving higher ROI. >> So the info brief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the info brief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack, what's driving this demand, this need for analytics across organizations? >> Sure, well first there's more data than ever before, the data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins and to improve customer experiences. And analytics along with automation and AI is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> One of the things that the study also showed was that not all analytics spending is resulting in the same ROI. What are some of the discrepancies that the info brief uncovered with respect to the changes in ROI that organizations are achieving? >> Our research with IDC revealed significant roadblocks across people, processes, and technologies. They're preventing companies from reaping greater benefits from their investments. So for example, on the people side, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% from our survey, are still not using the full breadth of data types available. Yet data's never been this prolific, it's going to continue to grow, and orgs should be using it to their advantage. And lastly organizations, they need to provide the right analytics tools to help everyone unlock the power of data. >> So they- >> They instead rely on outdated spreadsheet technology. In our survey, nine out of 10 respondents said less than half of their knowledge workers are active users of analytics software beyond spreadsheets. But true analytic transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and be driving value. >> Should we retake that, since I started talking over Jason accidentally? >> Yep, absolutely we can do so. We'll just go, yep, we'll go back to Lisa's question. Let's just, let's do the, retake the question and the answer, that'll be able to. >> It'll be not all analytics spending results in the same ROI, what are some of the discrepancies? >> Yes, Lisa, so we'll go from your ISO, just so we get that clean question and answer. >> Okay. >> Thank you for that. On your ISO, we're still speeding, Lisa, so give it a beat in your head and then on you. >> Yet not all analytics spending is resulting in the same ROI. So what are some of the discrepancies that the info brief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes, and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead they're relying on outdated spreadsheet technology. Nine of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically, then what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value from their data and analytics and achieve more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So very strategic investments. Did the survey uncover any specific areas where most companies are falling short, like any black holes that organizations need to be aware of at the outset? >> It did, it did. So organizations, they need to build a data-centric culture. And this begins with people. But what the survey told us is that the people aspect of analytics is the most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone in the organization has access to the data and analytics technology they need. And then the organizations also have to align their investments with upskilling in data literacy to enjoy that higher ROI. Companies who did so experience higher ROI than companies who underinvested in analytics literacy. So among the high ROI achievers, 78% have a good or great alignment between analytics investment and workforce upskilling compared to only 64% among those without positive ROI. And as more orgs adopt cloud data warehouses or cloud data lakes, in order to manage the massively increasing workloads- Can I start that one over. >> Sure. >> Can I redo this one? >> Yeah. >> Of course, stand by. >> Tongue tied. >> Yep, no worries. >> One second. >> If we could do the same, Lisa, just have a clean break, we'll go your question. >> Yep, yeah. >> On you Lisa. Just give that a count and whenever you're ready. Here, I'm going to give us a little break. On you Lisa. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture and this begins with people, but we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources, compared to only 67% among the ROI laggards. >> So interesting that you mentioned people, I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand, we know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right, so analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively and letting them do so cross-functionally. >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side. And it's expected to spend more on analytics than other IT. What risks does this present to the overall organization, if IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this isn't because the lines of business have recognized the value of analytics and plan to invest accordingly, but a lack of alignment between IT and business. This will negatively impact governance, which ultimately impedes democratization and hence ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up in Alteryx environment, but also to take a look at your analytics stack and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process, and technologies. Jason, thank you so much for joining me today, unpacking the IDC info brief and the great nuggets in there. Lots that organizations can learn and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you, it's been a pleasure. >> In a moment, Alan Jacobson, who's the chief data and analytics officer at Alteryx is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching "theCUBE", the leader in tech enterprise coverage. >> Somehow many have come to believe that data analytics is for the few, for the scientists, the PhDs, the MBAs. Well, it is for them, but that's not all. You don't have to have an advanced degree to do amazing things with data. You don't even have to be a numbers person. You can be just about anything. A titan of industry or a future titan of industry. You could be working to change the world, your neighborhood, or the course of your business. You can be saving lives or just looking to save a little time. The power of data analytics shouldn't be limited to certain job titles or industries or organizations because when more people are doing more things with data, more incredible things happen. Analytics makes us smarter and faster and better at what we do. It's practically a superpower. That's why we believe analytics is for everyone, and everything, and should be everywhere. That's why we believe in analytics for all. (upbeat music) >> Hey, everyone. Welcome back to "Accelerating Analytics Maturity". I'm your host, Lisa Martin. Alan Jacobson joins me next. The chief of data and analytics officer at Alteryx. Alan, it's great to have you on the program. >> Thanks, Lisa. >> So Alan, as we know, everyone knows that being data driven is very important. It's a household term these days, but 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. What's your advice, your recommendations for organizations who are just starting out with analytics? >> You're spot on, many organizations really aren't leveraging the full capability of their knowledge workers. And really the first step is probably assessing where you are on the journey, whether that's you personally, or your organization as a whole. We just launched an assessment tool on our website that we built with the International Institute of Analytics, that in a very short period of time, in about 15 minutes, you can go on and answer some questions and understand where you sit versus your peer set versus competitors and kind of where you are on the journey. >> So when people talk about data analytics, they often think, ah, this is for data science experts like people like you. So why should people in the lines of business like the finance folks, the marketing folks, why should they learn analytics? >> So domain experts are really in the best position. They know where the gold is buried in their companies. They know where the inefficiencies are. And it is so much easier and faster to teach a domain expert a bit about how to automate a process or how to use analytics than it is to take a data scientist and try to teach them to have the knowledge of a 20 year accounting professional or a logistics expert of your company. Much harder to do that. And really, if you think about it, the world has changed dramatically in a very short period of time. If you were a marketing professional 30 years ago, you likely didn't need to know anything about the internet, but today, do you know what you would call that marketing professional if they didn't know anything about the internet, probably unemployed or retired. And so knowledge workers are having to learn more and more skills to really keep up with their professions. And analytics is really no exception. Pretty much in every profession, people are needing to learn analytics to stay current and be capable for their companies. And companies need people who can do that. >> Absolutely, it seems like it's table stakes these days. Let's look at different industries now. Are there differences in how you see analytics in automation being employed in different industries? I know Alteryx is being used across a lot of different types of organizations from government to retail. I also see you're now with some of the leading sports teams. Any differences in industries? >> Yeah, there's an incredible actually commonality between the domains industry to industry. So if you look at what an HR professional is doing, maybe attrition analysis, it's probably quite similar, whether they're in oil and gas or in a high tech software company. And so really the similarities are much larger than you might think. And even on the sports front, we see many of the analytics that sports teams perform are very similar. So McLaren is one of the great partners that we work with and they use Alteryx across many areas of their business from finance to production, extreme sports, logistics, wind tunnel engineering, the marketing team analyzes social media data, all using Alteryx, and if I take as an example, the finance team, the finance team is trying to optimize the budget to make sure that they can hit the very stringent targets that F1 Sports has, and I don't see a ton of difference between the optimization that they're doing to hit their budget numbers and what I see Fortune 500 finance departments doing to optimize their budget, and so really the commonality is very high, even across industries. >> I bet every Fortune 500 or even every company would love to be compared to the same department within McLaren F1. Just to know that wow, what they're doing is so incredibly important as is what we're doing. >> So talk- >> Absolutely. >> About lessons learned, what lessons can business leaders take from those organizations like McLaren, who are the most analytically mature? >> Probably first and foremost, is that the ROI with analytics and automation is incredibly high. Companies are having a ton of success. It's becoming an existential threat to some degree, if your company isn't going on this journey and your competition is, it can be a huge problem. IDC just did a recent study about how companies are unlocking the ROI using analytics. And the data was really clear, organizations that have a higher percentage of their workforce using analytics are enjoying a much higher return from their analytic investment, and so it's not about hiring two double PhD statisticians from Oxford. It really is how widely you can bring your workforce on this journey, can they all get 10% more capable? And that's having incredible results at businesses all over the world. An another key finding that they had is that the majority of them said that when they had many folks using analytics, they were going on the journey faster than companies that didn't. And so picking technologies that'll help everyone do this and do this fast and do it easily. Having an approachable piece of software that everyone can use is really a key. >> So faster, able to move faster, higher ROI. I also imagine analytics across the organization is a big competitive advantage for organizations in any industry. >> Absolutely the IDC, or not the IDC, the International Institute of Analytics showed huge correlation between companies that were more analytically mature versus ones that were not. They showed correlation to growth of the company, they showed correlation to revenue and they showed correlation to shareholder values. So across really all of the key measures of business, the more analytically mature companies simply outperformed their competition. >> And that's key these days, is to be able to outperform your competition. You know, one of the things that we hear so often, Alan, is people talking about democratizing data and analytics. You talked about the line of business workers, but I got to ask you, is it really that easy for the line of business workers who aren't trained in data science to be able to jump in, look at data, uncover and extract business insights to make decisions? >> So in many ways, it really is that easy. I have a 14 and 16 year old kid. Both of them have learned Alteryx, they're Alteryx certified and it was quite easy. It took 'em about 20 hours and they were off to the races, but there can be some hard parts. The hard parts have more to do with change management. I mean, if you're an accountant that's been doing the best accounting work in your company for the last 20 years, and all you happen to know is a spreadsheet for those 20 years, are you ready to learn some new skills? And I would suggest you probably need to, if you want to keep up with your profession. The big four accounting firms have trained over a hundred thousand people in Alteryx. Just one firm has trained over a hundred thousand. You can't be an accountant or an auditor at some of these places without knowing Alteryx. And so the hard part, really in the end, isn't the technology and learning analytics and data science, the harder part is this change management, change is hard. I should probably eat better and exercise more, but it's hard to always do that. And so companies are finding that that's the hard part. They need to help people go on the journey, help people with the change management to help them become the digitally enabled accountant of the future, the logistics professional that is E enabled, that's the challenge. >> That's a huge challenge. Cultural shift is a challenge, as you said, change management. How do you advise customers if you might be talking with someone who might be early in their analytics journey, but really need to get up to speed and mature to be competitive, how do you guide them or give them recommendations on being able to facilitate that change management? >> Yeah, that's a great question. So people entering into the workforce today, many of them are starting to have these skills. Alteryx is used in over 800 universities around the globe to teach finance and to teach marketing and to teach logistics. And so some of this is happening naturally as new workers are entering the workforce, but for all of those who are already in the workforce, have already started their careers, learning in place becomes really important. And so we work with companies to put on programmatic approaches to help their workers do this. And so it's, again, not simply putting a box of tools in the corner and saying free, take one. We put on hackathons and analytic days, and it can be great fun. We have a great time with many of the customers that we work with, helping them do this, helping them go on the journey, and the ROI, as I said, is fantastic. And not only does it sometimes affect the bottom line, it can really make societal changes. We've seen companies have breakthroughs that have really made great impact to society as a whole. >> Isn't that so fantastic, to see the difference that that can make. It sounds like you guys are doing a great job of democratizing access to Alteryx to everybody. We talked about the line of business folks and the incredible importance of enabling them and the ROI, the speed, the competitive advantage. Can you share some specific examples that you think of Alteryx customers that really show data breakthroughs by the lines of business using the technology? >> Yeah, absolutely, so many to choose from. I'll give you two examples quickly. One is Armor Express. They manufacture life saving equipment, defensive equipments, like armor plated vests, and they were needing to optimize their supply chain, like many companies through the pandemic. We see how important the supply chain is. And so adjusting supply to match demand is really vital. And so they've used Alteryx to model some of their supply and demand signals and built a predictive model to optimize the supply chain. And it certainly helped out from a dollar standpoint. They cut over a half a million dollars of inventory in the first year, but more importantly, by matching that demand and supply signal, you're able to better meet customer demand. And so when people have orders and are looking to pick up a vest, they don't want to wait. And it becomes really important to get that right. Another great example is British Telecom. They're a company that services the public sector. They have very strict reporting regulations that they have to meet and they had, and this is crazy to think about, over 140 legacy spreadsheet models that they had to run to comply with these regulatory processes and report, and obviously running 140 legacy models that had to be done in a certain order and length, incredibly challenging. It took them over four weeks each time that they had to go through that process. And so to save time and have more efficiency in doing that, they trained 50 employees over just a two week period to start using Alteryx and learn Alteryx. And they implemented an all new reporting process that saw a 75% reduction in the number of man hours it took to run in a 60% run time performance. And so, again, a huge improvement. I can imagine it probably had better quality as well, because now that it was automated, you don't have people copying and pasting data into a spreadsheet. And that was just one project that this group of folks were able to accomplish that had huge ROI, but now those people are moving on and automating other processes and performing analytics in other areas. So you can imagine the impact by the end of the year that they will have on their business, potentially millions upon millions of dollars. And this is what we see again and again, company after company, government agency after government agency, is how analytics are really transforming the way work is being done. >> That was the word that came to mind when you were describing the all three customer examples, transformation, this is transformative. The ability to leverage Alteryx, to truly democratize data and analytics, give access to the lines of business is transformative for every organization. And also the business outcome you mentioned, those are substantial metrics based business outcomes. So the ROI in leveraging a technology like Alteryx seems to be right there, sitting in front of you. >> That's right, and to be honest, it's not only important for these businesses. It's important for the knowledge workers themselves. I mean, we hear it from people that they discover Alteryx, they automate a process, they finally get to get home for dinner with their families, which is fantastic, but it leads to new career paths. And so knowledge workers that have these added skills have so much larger opportunity. And I think it's great when the needs of businesses to become more analytic and automate processes actually matches the needs of the employees, and they too want to learn these skills and become more advanced in their capabilities. >> Huge value there for the business, for the employees themselves to expand their skillset, to really open up so many opportunities for not only the business to meet the demands of the demanding customer, but the employees to be able to really have that breadth and depth in their field of service. Great opportunities there, Alan. Is there anywhere that you want to point the audience to go to learn more about how they can get started? >> Yeah, so one of the things that we're really excited about is how fast and easy it is to learn these tools. So any of the listeners who want to experience Alteryx, they can go to the website, there's a free download on the website. You can take our analytic maturity assessment, as we talked about at the beginning, and see where you are on the journey and just reach out. We'd love to work with you and your organization to see how we can help you accelerate your journey on analytics and automation. >> Alan, it was a pleasure talking to you about democratizing data and analytics, the power in it for organizations across every industry. We appreciate your insights and your time. >> Thank you so much. >> In a moment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who's the global head of tax technology at eBay, will join me. You're watching "theCUBE", the leader in high tech enterprise coverage. >> 1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops. >> Make that 2.3. >> Sector times out the wazoo. >> Way too much of this. >> Velocities, pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into winning insights, they turn to Alteryx. Alteryx, analytics automation. (upbeat music) >> Hey, everyone, welcome back to the program. Lisa Martin here, I've got two guests joining me. Please welcome back to "theCUBE" Paula Hansen, the chief revenue officer and president at Alteryx, and Jacqui Van der Leij Greyling joins us as well, the global head of tax technology at eBay. They're going to share with you how Alteryx is helping eBay innovate with analytics. Ladies, welcome, it's great to have you both on the program. >> Thank you, Lisa, it's great to be here. >> Yeah, Paula, we're going to start with you. In this program, we've heard from Jason Klein, we've heard from Alan Jacobson. They talked about the need to democratize analytics across any organization to really drive innovation. With analytics, as they talked about, at the forefront of software investments, how's Alteryx helping its customers to develop roadmaps for success with analytics? >> Well, thank you, Lisa. It absolutely is about our customers' success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts of course with our innovative technology and platform, but ultimately we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics, through things like enablement programs, skills assessments, hackathons, setting up centers of excellence to help their organization scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics maturity curve with proven technologies and best practices, so they can make better business decisions and compete in their respective industries. >> Excellent, sounds like a very strategic program, we're going to unpack that. Jacqui, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How Jacqui did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >> So I think the main thing for us is when we started out was is that, our, especially in finance, they became spreadsheet professionals instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and being more effective. So ultimately we really started very, very actively embedding analytics in our people and our data and our processes. >> Starting with people is really critical. Jacqui, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >> So I think eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year, so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and just finding those data sources and finding ways to connect to them to move forward. The other thing is that people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals. And there was no, we were not independent. You couldn't move forward, you would've put it on somebody else's roadmap to get the data and to get the information if you want it. So really finding something that everybody could access analytics or access data. And finally we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy, and that is not so daunting on somebody who's brand new to the field? And I would call those out as your major roadblocks, because you always have, not always, but most of the times you have support from the top, and in our case we have, but at the end of the day, it's our people that need to actually really embrace it, and making that accessible for them, I would say is definitely not per se, a roadblock, but basically a block you want to be able to move. >> It's really all about putting people first. Question for both of you, and Paula we'll start with you, and then Jacqui we'll go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone, should be for everyone. Let's talk now about how both of your organizations are empowering people, those in the organization that may not have technical expertise to be able to leverage data, so that they can actually be data driven. Paula. >> Yes, well, we leverage our platform across all of our business functions here at Alteryx. And just like Jacqui explained, at eBay finance is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jacqui mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO Kevin Rubin has been a key sponsor for using our own technology. We use Alteryx for forecasting all of our key performance metrics, for business planning, across our audit function, to help with compliance and regulatory requirements, tax, and even to close our books at the end of each quarter. So it's really going to remain across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other, to problem solve and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jacqui mentioned, it's really about ensuring that people feel comfortable, that they feel supported, that they have access to the training that they need, and ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >> That confidence is key. Jacqui, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >> Yeah, I think it means to what Paula has said in terms of getting people excited about it, but it's also understanding that this is a journey and everybody is at a different place in their journey. You have folks that's already really advanced who has done this every day. And then you have really some folks that this is brand new or maybe somewhere in between. And it's about how you get everybody in their different phases to get to the initial destination. I say initial, because I believe a journey is never really complete. What we have done is that we decided to invest, and built a proof of concept, and we got our CFO to sponsor a hackathon. We opened it up to everybody in finance in the middle of the pandemic. So everybody was on Zoom and we told people, listen, we're going to teach you this tool, it's super easy, and let's just see what you can do. We ended up having 70 entries. We had only three weeks. So and these are people that do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon from the 70 entries with people that have never, ever done anything like this before. And there you have the result. And then it just went from there. People had a proof of concept. They knew that it worked and they overcame the initial barrier of change. And that's where we are seeing things really, really picking up now. >> That's fantastic. And the business outcome that you mentioned there, the business impact is massive, helping folks get that confidence to be able to overcome sometimes the cultural barriers is key here. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you're empowering the next generation of data workers? Paula, we'll start with you. >> Absolutely, and Jacqui says it so well, which is that it really is a journey that organizations are on and we as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Alteryx to help address this skillset gap on a global level is through a program that we call SparkED, which is essentially a no-cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed just to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with SparkED. We started last May, but we currently have over 850 educational institutions globally engaged across 47 countries, and we're going to continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close the gap and empower more people with the necessary analytics skills to solve all the problems that data can help solve. >> So SparkED has made a really big impact in such a short time period. It's going to be fun to watch the progress of that. Jacqui, let's go over to you now. Talk about some of the things that eBay is doing to empower the next generation of data workers. >> So we basically wanted to make sure that we kept that momentum from the hackathon, that we don't lose that excitement. So we just launched the program called eBay Masterminds. And what it basically is, is it's an inclusive innovation in each other, where we firmly believe that innovation is for upskilling for all analytics roles. So it doesn't matter your background, doesn't matter which function you are in, come and participate in in this where we really focus on innovation, introducing new technologies and upskilling our people. We are, apart from that, we also said, well, we shouldn't just keep it to inside eBay. We have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use Alteryx. And we're working with, actually, we're working with SparkED and they're helping us develop that program. And we really hope that at, say, by the end of the year, we have a pilot and then also next year, we want to roll it out in multiple locations in multiple countries and really, really focus on that whole concept of analytics for all. >> Analytics for all, sounds like Alteryx and eBay have a great synergistic relationship there that is jointly aimed at especially going down the stuff and getting people when they're younger interested, and understanding how they can be empowered with data across any industry. Paula, let's go back to you, you were recently on "theCUBE"'s Supercloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating what is by default a multi-cloud world. How does the Alteryx Analytics Cloud platform enable CIOs to democratize analytics across their organization? >> Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last I checked, there was 2 million data scientists in the world, so that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CIOs, with business leaders is that they're integrating data analysis and the skillset of data analysis into virtually every job function, and that is what we think of when we think of analytics for all. And so our mission with Alteryx Analytics Cloud is to empower all of those people in every job function, regardless of their skillset, as Jacqui pointed out from people that are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Alteryx Analytics Cloud, and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze, and report out so that we can break down data silos across the enterprise and help drive real business outcomes as a result of unlocking the potential of data. >> As well as really lessening that skill gap. As you were saying, there's only 2 million data scientists. You don't need to be a data scientist, that's the beauty of what Alteryx is enabling and eBay is a great example of that. Jacqui, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where Alteryx fits in as that analytics maturity journey continues and what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >> When we're starting up and getting excited about things when it comes to analytics, I can go on all day, but I'll keep it short and sweet for you. I do think we are on the top of the pool of data scientists. And I really feel that that is your next step, for us anyways, is that how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's something completely different. And it's something that is in everybody in a certain extent. So again, partnering with Alteryx who just released the AI ML solution, allowing folks to not have a data scientist program, but actually build models and be able to solve problems that way. So we have engaged with Alteryx and we purchased the licenses, quite a few. And right now through our Masterminds program, we're actually running a four month program for all skill levels, teaching them AI ML and machine learning and how they can build their own models. We are really excited about that. We have over 50 participants without a background from all over the organization. We have members from our customer services. We have even some of our engineers are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I want to give you a quick example of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution where there is a checkout feedback functionality on the eBay side where sellers or buyers can verbatim add information. And she built a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we as a human even step in, and now instead of us or somebody going to verbatim and try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value, and it's a beautiful tool and I was very impressed when I saw the demo and definitely developing that sort of thing. >> That sounds fantastic. And I think just the one word that keeps coming to mind, and we've said this a number of times in the program today is empowerment. What you're actually really doing to truly empower people across the organization with varying degrees of skill level, going down to the high school level, really exciting. We'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I want to thank you so much for joining me on the program today and talking about how Alteryx and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you. >> Thank you, Lisa. >> Thank you so much. (cheerful electronic music) >> As you heard over the course of our program, organizations where more people are using analytics who have deeper capabilities in each of the four Es, that's everyone, everything, everywhere, and easy analytics, those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling and empowering line of business users to use analytics, not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We want to thank you so much for watching the program today. Remember you can find all of the content on thecube.net. You can find all of the news from today on siliconangle.com and of course alteryx.com. We also want to thank Alteryx for making this program possible and for sponsoring "theCUBE". For all of my guests, I'm Lisa Martin. We want to thank you for watching and bye for now. (upbeat music)

>>the Cube presents H P E discovered 2022. Brought to you by H P E. >>Hi, buddy Dave Balon and Jon Ferrier Wrapping up the cubes. Coverage of day two, hp Discover 2022. We're live from Las Vegas. Vishal Lall is here. He's the senior vice president and general manager for HP ES Green Lake Cloud Services Solutions. Michelle, good to see you again. >>Likewise. David, good to see you. It was about a year ago that we met here. Or maybe nine months >>ago. That's right. Uh, September of last year. A new role >>for you. Is that right? I was starting that new role when I last met you. Yeah, but it's been nine months. Three quarters? What have you learned so far? I mean, it's been quite a right, right? I mean, when I was starting off, I had, you know, about three priorities we've executed on on all of them. So, I mean, if you remember back then they we talked about, you know, improving a cloud experience. We talked about data and analytics being a focus area and then building on the marketplace. I think you heard a lot of that over the last couple of days here. Right? So we've enhanced our cloud experience. We added a private cloud, which was the big announcement yesterday or day before yesterday that Antonio made so that's been I mean, we've been testing that with customers. Great feedback so far. Right? And we're super excited about that. And, uh, you know, uh, down there, the test drive section people are testing that. So we're getting really, really good feedback. Really good acceptance from customers on the data and Analytics side. We you know, we launched the S three connector. We also had the analytics platform. And then we launched data fabric as a service a couple of days ago, right, which is kind of like back into that hybrid world. And then on the marketplace side, we've added a tonne of partners going deep with them about 80 plus partners now different SVS. So again, I think, uh, great. I think we've accomplished a lot over the last three quarters or so lot more to be done. Though >>the marketplace is really interesting to us because it's a hallmark of cloud. You've got to have a market price. Talk about how that's evolving and what your vision is for market. Yes, >>you're exactly right. I mean, having a broad marketplace provides a full for the platform, right? It's a chicken and egg. You need both. You need a good platform on which a good marketplace can set, but the vice versa as well. And what we're doing two things there, Right? One Is we expanding coverage of the marketplace. So we're adding more SVS into the marketplace. But at the same time, we're adding more capabilities into the marketplace. So, for example, we just demoed earlier today quickly deploy capabilities, right? So we have an I S p in the marketplace, they're tested. They are, uh, the work with the solution. But now you can you can collect to deploy directly on our infrastructure over time, the lad, commerce capabilities, licencing capabilities, etcetera. But again, we are super excited about that capability because I think it's important from a customer perspective. >>I want to ask you about that, because that's again the marketplace will be the ultimate arbiter of value creation, ecosystem and marketplace. Go hand in hand. What's your vision for what a successful ecosystem looks like? What's your expectation now that Green Lake is up and running. I stay up and running, but like we've been following the announcement, it just gets better. It's up to the right. So we're anticipating an ecosystem surge. Yeah. What are you expecting? And what's your vision for? How the ecosystem is going to develop out? Yeah. I >>mean, I've been meeting with a lot of our partners over the last couple of days, and you're right, right? I mean, I think of them in three or four buckets right there. I s V s and the I S P is coming to two forms right there. Bigger solutions, right? I think of being Nutanix, right, Home wall, big, bigger solutions. And then they are smaller software packages. I think Mom would think about open source, right? So again, one of them is targeted to developers, the other to the I t. Tops. But that's kind of one bucket, right? I s P s, uh, the second is around the channel partners who take this to market and they're asking us, Hey, this is fantastic. Help us understand how we can help you take this to market. And I think the other bucket system indicators right. I met with a few today and they're all excited about. They're like, Hey, we have some tooling. We have the manage services capabilities. How can we take your cloud? Because they build great practise around extent around. Sorry. Aws around? Uh, sure. So they're like, how can we build a similar practise around Green Lake? So again, those are the big buckets. I would say. Yeah, >>that's a great answer. Great commentary. I want to just follow up on that real quick. You don't mind? So a couple things we're seeing observing I want to get your reaction to is with a i machine learning. And the promise of that vertical specialisation is creating unique opportunities on with these platforms. And the other one is the rise of the managed service provider because expertise are hard to come by. You want kubernetes? Good luck finding talent. So managed services seem to be exploding. How does that fit into the buckets? Or is it all three buckets or you guys enable that? How do you see that coming? And then the vertical piece? >>A really good question. What we're doing is through our software, we're trying to abstract a lot of the complexity of take communities, right? So we are actually off. We have actually automated a whole bunch of communities functionality in our software, and then we provide managed services around it with very little. I would say human labour associated with it is is software manage? But at the same time we are. What we are trying to do is make sure that we enable that same functionality to our partners. So a lot of it is software automation, but then they can wrap their services around it, and that way we can scale the business right. So again, our first principle is automated as much as we can to software right abstract complexity and then as needed, uh, at the Manus Services. >>So you get some functionality for HP to have it and then encourage the ecosystem to fill it in or replicated >>or replicated, right? I mean, I don't think it's either or it should be both right. We can provide many services or we should have our our partners provide manage services. That's how we scale the business. We are the end of the day. We are product and product company, right, and it can manifest itself and services. That discussion was consumed, but it's still I p based. So >>let's quantify, you know, some of that momentum. I think the last time you call your over $800 million now in a are are you gotta You're growing at triple digits. Uh, you got a big backlog. Forget the exact number. Uh, give us a I >>mean, the momentum is fantastic Day. Right. So we have about $7 billion in total contract value, Right? Significant. We have 1600 customers now. Unique customers are running Green Lake. We have, um, your triple dip growth year over year. So the last quarter, we had 100% growth year over year. So again, fantastic momentum. I mean, the other couple, like one other metric I would like to talk about is the, um the stickiness factor associated tension in our retention, right? As renewal's is running in, like, high nineties, right? So if you think about it, that's a reflection of the value proposition of, like, >>that's that's kind of on a unit basis, if you will. That's the number >>on the revenue basis on >>revenue basis. Okay? >>And the 1600 customers. He's talking about the size and actually big numbers. Must be large companies that are. They're >>both right. So I'll give you some examples, right? So I mean, there are large companies. They come from different industries. Different geography is we're seeing, like, the momentum across every single geo, every single industry. I mean, just to take some examples. BMW, for example. Uh, I mean, they're running the entire electrical electric car fleet data collection on data fabric on Green Lake, right? Texas Children's Health on the on the healthcare side. Right On the public sector side, I was with with Carl Hunt yesterday. He's the CEO of County of Essex, New Jersey. So they are running the entire operations on Green Lake. So just if you look at it, Barclays the financial sector, right? I mean, they're running 100,000 workloads of three legs. So if you just look at the scale large companies, small companies, public sector in India, we have Steel Authority of India, which is the largest steel producer there. So, you know, we're seeing it across multiple industries. Multiple geography is great. Great uptake. >>Yeah. We were talking yesterday on our wrap up kind of dissecting through the news. I want to ask you the question that we were riffing on and see if we can get some clarity on it. If I'm a customer, CI or C so or buyer HP have been working with you or your team for for years. What's the value proposition? Finish this sentence. I work with HPV because blank because green like, brings new value proposition. What is that? Fill in that blank for >>me. So I mean, as we, uh, talked with us speaking with customers, customers are looking at alternatives at all times, right? Sometimes there's other providers on premises, sometimes as public cloud. And, uh, as we look at it, uh, I mean, we have value propositions across both. Right. So from a public cloud perspective, some of the challenges that our customers cr around latency around, uh, post predictability, right? That variability cost is really kind of like a challenge. It's around compliance, right? Uh, things of that nature is not open systems, right? I mean, sometimes, you know, they feel locked into a cloud provider, especially when they're using proprietary services. So those are some of the things that we have solved for them as compared to kind of like, you know, the other on premises vendors. I would say the marketplace that we spoke about earlier is huge differentiator. We have this huge marketplace. Now that's developing. Uh, we have high levels of automation that we have built, right, which is, uh, you know, which tells you about the TCO that we can drive for the customers. What? The other thing that is really cool that be introduced in the public in the private cloud is fungible itty across infrastructure. Right? So basically on the same infrastructure you can run. Um, virtual machines, containers, bare metals, any application he wants, you can decommission and commission the infrastructure on the fly. So what it does, is it no matter where it is? Uh, on premises, right? Yeah, earlier. I mean, if you think about it, the infrastructure was dedicated for a certain application. Now we're basically we have basically made it compose herbal, right? And that way, what? Really? Uh, that doesnt increases utilisation so you can get increased utilisation. High automation. What drives lower tco. So you've got a >>horizontal basically platform now that handle a variety of work and >>and these were close. Can sit anywhere to your point, right? I mean, we could have a four node workload out in a manufacturing setting multiple racks in a data centre, and it's all run by the same cloud prints, same software train. So it's really extensive. >>And you can call on the resources that you need for that particular workload. >>Exactly what you need them exactly. Right. >>Excellent. Give you the last word kind of takeaways from Discover. And where when we talk, when we sit down and talk next year, it's about where do you want to be? >>I mean, you know, I think, as you probably saw from discovered, this is, like, very different. Antonio did a live demo of our product, right? Uh, visual school, right? I mean, we haven't done that in a while, so I mean, you started. It >>didn't die like Bill Gates and demos. No, >>no, no, no. I think, uh, so I think you'll see more of that from us. I mean, I'm focused on three things, right? I'm focused on the cloud experience we spoke about. So what we are doing now is making sure that we increase the time for that, uh, make it very, you know, um, attractive to different industries to certifications like HIPAA, etcetera. So that's kind of one focus. So I just drive harder at that adoption of that of the private out, right across different industries and different customer segments. The second is more on the data and analytics I spoke about. You will have more and more analytic capabilities that you'll see, um, building upon data fabric as a service. And this is a marketplace. So that's like it's very specific is the three focus areas were driving hard. All right, we'll be watching >>number two. Instrumentation is really keen >>in the marketplace to I mean, you mentioned Mongo. Some other data platforms that we're going to see here. That's going to be, I think. Critical for Monetisation on the on on Green Lake. Absolutely. Uh, Michelle, thanks so much for coming back in the Cube. >>Thank you. Thanks for coming. All >>right, keep it right. There will be John, and I'll be back up to wrap up the day with a couple of heavies from I d. C. You're watching the cube. Mhm. Mm mm. Mhm.

>>The cube presents HPE discover 2022 brought to you by HPE. >>Hey everyone. Welcome back to the Cube's day one coverage of HPE discover 22 live from the Venetian in Las Vegas. I got a power panel here, Lisa Martin, with Dave Valante, John furrier, Holger Mueller also joins us. We are gonna wrap this, like you've never seen a rap before guys. Lot of momentum today, lot, lot of excitement, about 8,000 or so customers, partners, HPE leaders here. Holger. Let's go ahead and start with you. What are some of the things that you heard felt saw observed today on day one? >>Yeah, it's great to be back in person. Right? 8,000 people events are rare. Uh, I'm not sure. Have you been to more than 8,000? <laugh> yeah, yeah. Okay. This year, this year. I mean, historically, yes, but, um, >>Snowflake was 10. Yeah. >>So, oh, wow. Okay. So 8,000 was my, >>Cisco was, they said 15, >>But is my, my 8,000, my record, I let us down with 7,000 kind of like, but it's in the Florida swarm. It's not nicely. Like, and there's >>Usually what SFI, there's usually >>20, 20, 30, 40, 50. I remember 50 in the nineties. Right. That was a different time. But yeah. Interesting. Yeah. Interesting what people do and it depends how much time there is to come. Right. And know that it happens. Right. But yeah, no, I think it's interesting. We, we had a good two analyst track today. Um, interesting. Like HPE is kind of like back not being your grandfather's HPE to a certain point. One of the key stats. I know Dave always for the stats, right. Is what I found really interesting that over two third of GreenLake revenue is software and services. Now a love to know how much of that services, how much of that software. But I mean, I, I, I, provocate some, one to ones, the HP executives saying, Hey, you're a hardware company. Right. And they didn't even come back. Right. But Antonio said, no, two thirds is, uh, software and services. Right. That's interesting. They passed the one exabyte, uh, being managed, uh, as a, as a hallmark. Right. I was surprised only 120,000 users if I had to remember the number. Right, right. So that doesn't seem a terrible high amount of number of users. Right. So, but that's, that's, that's promising. >>So what software is in there, cuz it's gotta be mostly services. >>Right? Well it's the 70 plus cloud services, right. That everybody's talking about where the added eight of them shockingly back up and recovery, I thought that was done at launch. Right. >>Still who >>Keep recycling storage and you back. But now it's real. Yeah. >>But the company who knows the enterprise, right. HPE, what I've been doing before with no backup and recovery GreenLake. So that was kind of like, okay, we really want to do this now and nearly, and then say like, oh, by the way, we've been doing this all the time. Yeah. >>Oh, what's your take on the installed base of HP. We had that conversation, the, uh, kickoff or on who's their target, what's the target audience environment look like. It certainly is changing. Right? If it's software and services, GreenLake is resonating. Yeah. Um, ecosystems responding. What's their customers cuz managed services are up too Kubernetes, all the managed services what's what's it like what's their it transformation base look like >>Much of it is of course install base, right? The trusted 20, 30 plus year old HP customer. Who's keeping doing stuff of HP. Right. And call it GreenLake. They've been for so many name changes. It doesn't really matter. And it's kind of like nice that you get the consume pain only what you consume. Right. I get the cloud broad to me then the general markets, of course, people who still need to run stuff on premises. Right. And there's three reasons of doing this performance, right. Because we know the speed of light is relative. If you're in the Southern hemisphere and even your email servers in Northern hemisphere, it takes a moment for your email to arrive. It's a very different user experience. Um, local legislation for data, residency privacy. And then, I mean Charles Phillips who we all know, right. Former president of uh, info nicely always said, Hey, if the CIOs over 50, I don't have to sell qu. Right. So there is not invented. I'm not gonna do cloud here. And now I've kind of like clouded with something like HP GreenLake. That's the customers. And then of course procurement is a big friend, right? Yeah. Because when you do hardware refresh, right. You have to have two or three competitors who are the two or three competitors left. Right. There's Dell. Yeah. And then maybe Lenovo. Right? So, so like a >>Little bit channels, the strength, the procurement physicians of strength, of course install base question. Do you think they have a Microsoft opportunity where, what 365 was Microsoft had office before 365, but they brought in the cloud and then everything changed. Does HP have that same opportunity with kind of the GreenLake, you know, model with their existing stuff. >>It has a GreenLake opportunity, but there's not much software left. It's a very different situation like Microsoft. Right? So, uh, which green, which HP could bring along to say, now run it with us better in the cloud because they've been selling much of it. Most of it, of their software portfolio, which they bought as an HP in the past. Right. So I don't see that happening so much, but GreenLake as a platform itself course interesting because enterprise need a modern container based platform. >>I want, I want to double click on this a little bit because the way I see it is HP is going to its installed base. I think you guys are right on say, this is how we're doing business now. Yeah. You know, come on along. But my sense is, some customers don't want to do the consumption model. There are actually some customers that say, Hey, of course I got, I don't have a cash port problem. I wanna pay for it up front and leave me alone. >>I've been doing this since 50 years. Nice. As I changed it, now <laugh> two know >>Money's wants to do it. And I don't wanna rent because rental's more expensive and blah, blah, blah. So do you see that in the customer base that, that some are pushing back? >>Of course, look, I have a German accent, right? So I go there regularly and uh, the Germans are like worried about doing anything in the cloud. And if you go to a board in Germany and say, Hey, we can pay our usual hardware, refresh, CapEx as usual, or should we bug consumption? And they might know what we are running. <laugh> so not whole, no offense against the Germans out. The German parts are there, but many of them will say, Hey, so this is change with COVID. Right. Which is super interesting. Right? So the, the traditional boards non-technical have been hearing about this cloud variable cost OPEX to CapEx and all of a sudden there's so much CapEx, right. Office buildings, which are not being used truck fleets. So there's a whole new sensitivity by traditional non-technical boards towards CapEx, which now the light bulb went on and say, oh, that's the cloud thing about also. So we have to find a way to get our cost structure, to ramp up and ramp down as our business might be ramping up through COVID through now inflation fears, recession, fears, and so on. >>So, okay. HP's, HP's made the statement that anything you can do in the cloud you can do in GreenLake. Yes. And I've said you can't run on snowflake. You can't run Mongo Atlas, you can't run data bricks, but that's okay. That's fine. Let's be, I think they're talking about, there's >>A short list of things. I think they're talking about the, their >>Stuff, their, >>The operating experience. So we've got single sign on through a URL, right. Uh, you've got, you know, some level of consistency in terms of policy. It's unclear exactly what that is. You've got storage backup. Dr. What, some other services, seven other services. If you had to sort of take your best guess as to where HP is now and peg it toward where Amazon was in which year? >>20 14, 20 14. >>Yeah. Where they had their first conference or the second we invent here with 3000 people and they were thinking, Hey, we're big. Yeah. >>Yeah. And I think GreenLake is the building blocks. So they quite that's the >>Building. Right? I mean similar. >>Okay. Well, I mean they had E C, Q and S3 and SQS, right. That was the core. And then the rest of those services were, I mean, base stock was one of that first came in behind and >>In fairness, the industry has advanced since then, Kubernetes is further along. And so HPE can take advantage of that. But in terms of just the basic platform, I, I would agree. I think it's >>Well, I mean, I think, I mean the software, question's a big one. I wanna bring up because the question is, is that software is getting the world. Hardware is really software scales, everything, data, the edge story. I love their story. I think HP story is wonderful Aruba, you know, hybrid cloud, good story, edge edge. But if you look under the covers, it's weak, right? It's like, it's not software. They don't have enough software juice, but the ecosystem opportunity to me is where you plug and play. So HP knows that game. But if you look historically over the past 25 years, HP now HPE, they understand plug and play interoperability. So the question is, can they thread the needle >>Right. >>Between filling the gaps on the software? Yeah. With partners, >>Can they get the partners? Right. And which have been long, long time. Right. For a long time, HP has been the number one platform under ICP, right? Same thing. You get certified for running this. Right. I know from my own history, uh, I joined Oracle last century and the big thing was, let's get your eBusiness suite certified on HP. Right? Like as if somebody would buy H Oracle work for them, right. This 20 years ago, server >>The original exit data was HP. Oracle. >>Exactly. Exactly. So there's this thinking that's there. But I think the key thing is we know that all modern forget about the hardware form in the platforms, right? All modern software has to move to containers and snowflake runs in containers. You mentioned that, right? Yeah. If customers force snowflake and HPE to the table, right, there will be a way to make it work. Right. And which will help HPE to be the partner open part will bring the software. >>I, I think it's, I think that's an opportunity because that changes the game and agility and speed. If HP plays their differentiation, right. Which we asked on their opening segment, what's their differentiation. They got size scale channel, >>What to the enterprise. And then the big benefit is this workload portability thing. Right? You understand what is run in the public cloud? I need to run it local. For whatever reason, performance, local residency of data. I can move that. There that's the big benefit to the ISVs, the sales vendors as well. >>But they have to have a stronger data platform story in my that's right. Opinion. I mean, you can run Oracle and HPE, but there's no reason they shouldn't be able to do a deal with, with snowflake. I mean, we saw it with Dell. Yep. We saw it with, with, with pure and I, if our HPE I'd be saying, Hey, because the way the snowflake deal worked, you probably know this is your reading data into the cloud. The compute actually occurs in the cloud viral HB going snowflake saying we can separate compute and storage. Right. And we have GreenLake. We have on demand. Why don't we run the compute on-prem and make it a full class, first class citizen, right. For all of our customers data. And that would be really innovative. And I think Mongo would be another, they've got OnPrem. >>And the question is, how many, how many snowflake customers are telling snowflake? Can I run you on premise? And how much defo open years will they hear from that? Right? This is >>Why would they deal Dell? That >>Deal though, with that, they did a deal. >>I think they did that deal because the customer came to them and said, you don't exactly that deal. We're gonna spend the >>Snowflake >>Customers think crazy things happen, right? Even, even put an Oracle database in a Microsoft Azure data center, right. Would off who, what as >>Possible snowflake, >>Oracle. So on, Aw, the >>Snow, the snowflakes in the world have to make a decision. Dave on, is it all snowflake all the time? Because what the reality is, and I think, again, this comes back down to the, the track that HP could go up or down is gonna be about software. Open source is now the software industry. There's no such thing as proprietary software, in my opinion, relatively speaking, cloud scale and integrated, integrated integration software is proprietary. The workflows are proprietary. So if they can get that right with the partners, I would focus on that. I think they can tap open source, look at Amazon with open source. They sucked it up and they integrated it in. No, no. So integration is the deal, not >>Software first, but Snowflake's made the call. You were there, Lisa. They basically saying it's we have, you have to be in snowflake in order to get the governance and the scalability, all that other wonderful stuff. Oh, but we we'll do Apache iceberg. We'll we'll open it up. We'll do Python. Yeah. >>But you can't do it data clean room unless you are in snowflake. Exactly. Snowflake on snowflake. >>Exactly. >>But got it. Isn't that? What you heard from AWS all the time till they came out outposts, right? I mean, snowflake is a market leader for what they're doing. Right. So that they want to change their platform. I mean, kudos to them. They don't need to change the platform. They will be the last to change their platform to a ne to anything on premises. Right. But I think the trend already shows that it's going that way. >>Well, if you look at outpost is an signal, Dave, the success of outpost launched what four years ago, they announced it. >>What >>EKS is beating, what outpost is doing. Outpost is there. There's not a lot of buzz and talk to the insiders and the open source community, uh, EKS and containers. To your point mm-hmm <affirmative> is moving faster on, I won't say commodity hardware, but like could be white box or HP, Dell, whatever it's gonna be that scale differentiation and the edge story is, is a good one. And I think with what we're seeing in the market now it's the industrial edge. The back office was gen one cloud back office data center. Now it's hybrid. The focus will be industrial edge machine learning and AI, and they have it here. And there's some, some early conversations with, uh, I heard it from, uh, this morning, you guys interviewed, uh, uh, John Schultz, right? With the world economic 4k birth Butterfield. She was amazing. And then you had Justin bring up a Hoar, bring up quantum. Yes. That is a differentiator. >>HP. >>Yes. Yeah. You, they have the computing shops. They had the R and D can they bring it to the table >>As, as HPC, right. To what they Schultz for of uh, the frontier system. Right. So very impressed. >>So the ecosystem is the key for them is because that's how they're gonna fill the gaps. They can't, they can't only, >>They could, they could high HPC edge piece. I wouldn't count 'em out of that game yet. If you co-locate a box, I'll use the word box, particularly at a telco tower. That's a data center. Yep. Right. If done properly. Yep. So, you know, what outpost was supposed to do actually is a hybrid opportunity. Aruba >>Gives them a unique, >>But the key thing is right. It's a yin and yang, right? It's the ecosystem it's partners to bring those software workload. Absolutely. Right. But HPE has to keep the platform attractive enough. Right. And the key thing there is that you have this workload capability thing that you can bring things, which you've built yourself. I mean, look at the telcos right. Network function, visualization, thousands of man, years into these projects. Right. So if I can't bring it to your edge box, no, I'm not trying to get to your Xbox. Right. >>Hold I gotta ask you since in the Dave too, since you guys both here and Lisa, you know, I said on the opening, they have serious customers and those customers have serious problems, cyber security, ransomware. So yeah. I teach transformation now. Industrial transformation machine learning, check, check, check. Oh, sounds good. But at the end of the day, their customers have some serious problems. Right? Cyber, this is, this is high stakes poker. Yeah. What do you think HP's position for in the security? You mentioned containers, you got all this stuff, you got open source, supply chain, you have to left supply chain issues. What is their position with security? Cuz that's the big one. >>I, I think they have to have a mature attitude that customers expect from HPE. Right? I don't have to educate HP on security. So they have to have the partner offerings again. We're back at the ecosystem to have what probably you have. So bring your own security apart from what they have to have out of the box to do business with them. This is why the shocker this morning was back up in recovery coming. <laugh> it's kind like important for that. Right? Well >>That's, that's, that's more ransomware and the >>More skeleton skeletons in the closet there, which customers should check of course. But I think the expectations HP understands that and brings it along either from partner or natively. >>I, I think it's, I think it's services. I think point next is the point of integration for their security. That's why two thirds is software and services. A lot of that is services, right? You know, you need security, we'll help you get there. We people trust HP >>Here, but we have nothing against point next or any professional service. They're all hardworking. But if I will have to rely on humans for my cyber security strategy on a daily level, I'm getting gray hair and I little gray hair >>Red. Okay. I that's, >>But >>I think, but I do think that's the camera strategy. I mean, I'm sure there's a lot of that stuff that's beginning to be designed in, but I, my guess is a lot of it is services. >>Well, you got the Aruba. Part of the booth was packed. Aruba's there. You mentioned that earlier. Is that good enough? Because the word zero trust is kicked around a lot. On one hand, on the other hand, other conversations, it's all about trust. So supply chain and software is trusting trust, trust and verified. So you got this whole mentality of perimeter gone mentality. It's zero trust. And if you've got software trust, interesting thoughts there, how do you reconcile zero trust? And then I need trust. What's what's you? What are you seeing older on that? Because I ask people all the time, they're like, uh, I'm zero trust or is it trust? >>Yeah. The middle ground. Right? Trusted. The meantime people are man manipulating what's happening in your runtime containers. Right? So, uh, drift control is a new password there that you check what's in your runtime containers, which supposedly impenetrable, but people finding ways to hack them. So we'll see this cat and mouse game going on all the time. Yeah. Yeah. There's always gonna be the need for being in a secure, good environment from that perspective. Absolutely. But the key is edge has to be more than Aruba, right? If yeah. HV goes away and says, oh yeah, we can manage your edge with our Aruba devices. That's not enough. It's the virtual probability. And you said the important thing before it's about the data, right? Because the dirty secret of containers is yeah, I move the code, but what enterprise code works without data, right? You can't say as enterprise, okay, we're done for the day check tomorrow. We didn't persist your data, auditor customer. We don't have your data anymore. So filling a way to transport the data. And there just one last thought, right? They have a super interesting asset. They want break lands for the venerable map R right. Which wrote their own storage drivers and gives you the chance to potentially do something in that area, which I'm personally excited about. But we'll see what happens. >>I mean, I think the holy grail is can I, can I put my data into a cloud who's ever, you know, call it a super cloud and can I, is it secure? Is it governed? Can I share it and be confident that it's discoverable and that the, the person I give it to has the right to use it. Yeah. And, and it's the correct data. There's not like a zillion copies running. That's the holy grail. And I, I think the answer today is no, you can, you can do that maybe inside of AWS or maybe inside of Azure, look maybe certainly inside of snowflake, can you do that inside a GreenLake? Well, you probably can inside a GreenLake, but then when you put it into the cloud, is it cross cloud? Is it really out to the edge? And that's where it starts to break down, but that's where the work is to be done. That's >>The one Exide is in there already. Right. So men being men. Yeah. >>But okay. But it it's in there. Yeah. Okay. What do you do with it? Can you share that data? What can you actually automate governance? Right? Uh, is that data discoverable? Are there multiple copies of that data? What's the, you know, master copy. Here's >>A question. You guys, here's a question for you guys analyst, what do you think the psychology is of the CIO or CSO when HP comes into town with GreenLake, uh, and they say, what's your relationship with the hyperscalers? Cause I'm a CIO. I got my environment. I might be CapEx centric or Hey, I'm open model. Open-minded to an operating model. Every one of these enterprises has a cloud relationship. Yeah. Yeah. What's the dynamic. What do you think the psychology is of the CIO when they're rationalizing their, their trajectory, their architecture, cloud, native scale integration with HPE GreenLake or >>HP service. I think she or he hears defensiveness from HPE. I think she hears HPE or he hears HPE coming in and saying, you don't need to go to the cloud. You know, you could keep it right here. I, I don't think that's the right posture. I think it should be. We are your cloud. And we can manage whether it's OnPrem hybrid in AWS, Azure, Google, across those clouds. And we have an edge story that should be the vision that they put forth. That's the super cloud vision, but I don't hear it >>From these guys. What do you think psycho, do you agree with that? >>I'm totally to make, sorry to be boring, but I totally agree with, uh, Dave on that. Right? So the, the, the multi-cloud capability from a trusted large company has worked for anybody up and down the stack. Right? You can look historically for, uh, past layers with cloud Foundry, right? It's history vulnerable. You can look for DevOps of Hashi coop. You can look for database with MongoDB right now. So if HPE provides that data access, right, with all the problems of data gravity and egres cost and the workability, they will be doing really, really well, but we need to hear it more, right. We didn't hear much software today in the keynote. Right. >>Do they have a competitive offering vis-a-vis or Azure? >>The question is, will it be an HPE offering or will, or the software platform, one of the offerings and you as customer can plug and play, right. Will software be a differentiator for HP, right. And will be close, proprietary to the point to again, be open enough for it, or will they get that R and D format that, or will they just say, okay, ES MES here on the side, your choice, and you can use OpenShift or whatever, we don't matter. That's >>The, that's the key question. That's the key question. Is it because it is a competitive strategy? Is it highly differentiated? Oracle is a highly differentiated strategy, right? Is Dell highly differentiated? Eh, Dell differentiates based on its breadth. What? >>Right. Well, let's try for the control plane too. Dell wants to be an, >>Their, their vision is differentiated. Okay. But their execution today is not >>High. All right. Let me throw, let me throw this out at you then. I'm I'm, I'm sorry. I'm I'm HPE. I wanna be the glue layer. Is that, does that fly? >>What >>Do you mean? The group glue layer? I'll I wanna be, you can do Amazon, but I wanna be the glue layer between the clouds and our GreenLake will. >>What's the, what's the incremental value that, that glue provides, >>Provides comfort and reliability and control for the single pane of glass for AWS >>And comes back to the data. In my opinion. Yeah. >>There, there there's glue levels on the data level. Yeah. And there's glue levels on API level. Right. And there's different vendors in the different spaces. Right. Um, I think HPE will want to play on the data side. We heard lots of data stuff. We >>Hear that, >>But we have to see it. Exactly. >>Yeah. But it's, it's lacking today. And so, Hey, you know, you guys know better than I APIs can be fragile and they can be, there's a lot of diversity in terms of the quality of APIs and the documentation, how they work, how mature they are, what, how, what kind of performance they can provide and recoverability. And so just saying, oh wow. We are living the API economy. You know, the it's gonna take time to brew, chime in here. Hi. >><laugh> oh, so guys, you've all been covering HPE for a long time. You know, when Antonio stood up on stage three years ago and said by 2022, and here we are, we're gonna be delivering everything as a service. He's saying we've, we've done it, but, and we're a new company. Do you guys agree with that? >>Definitely. >>I, yes. Yes. With the caveat, I think, yes. The COVID pandemic slowed them down a lot because, um, that gave a tailwind to the hyperscalers, um, because of the, the force of massive O under forecasting working at home. I mean, everyone I talked to was like, no one forecasted a hundred percent work at home, the, um, the CapEx investments. So I think that was an opportunity that they'd be much farther along if there's no COVID people >>Thought it wasn't impossible. Yeah. But so we had the old work from home thing right. Where people trying to get people fired at IBM and Yahoo. Right. So I would've this question covering the HR side and my other hat on. Right. And I would ask CHS let's assume, because I didn't know about COVID shame on me. Right. I said, big California, earthquake breaks. Right. Nobody gets hurt, but all the buildings have to be retrofitted and checked for seism logic down. So everybody's working from home, ask CHS, what kind of productivity gap hit would you get by forcing everybody working from home with the office unsafe? So one, one gentleman, I won't know him, his name, he said 20% and the other one's going ha you're smoking. It's 40 50%. We need to be in the office. We need to meet it first night. And now we went for this exercise. Luckily not with the California. Right. Well, through the price of COVID and we've seen what it can do to, to productivity well, >>The productivity, but also the impact. So like with all the, um, stories we've done over two years, the people that want came out ahead were the ones that had good cloud action. They were already in the cloud. So I, I think they're definitely in different company in the sense of they, I give 'em a pass. I think they're definitely a new company and I'm not gonna judge 'em on. I think they're doing great. But I think pandemic definitely slowed 'em down that about >>It. So I have a different take on this. I think. So we've go back a little history. I mean, you' said this, I steal your line. Meg Whitman took one for the Silicon valley team. Right. She came in. I don't think she ever was excited that I, that you said, you said that, and I think you wrote >>Up, get tape on that one. She >>Had to figure out how do I deal with this mess? I have EDS. I got PC. >>She never should have spun off the PC, but >>Okay. But >>Me, >>Yeah, you can, you certainly could listen. Maybe, maybe Gerstner never should have gone all in on services and IBM would dominate something other than mainframes. They had think pads even for a while, but, but, but so she had that mess to deal with. She dealt with it and however, they dealt with it, Antonio came in, he, he, and he said, all right, we're gonna focus the company. And we're gonna focus the mission on not the machine. Remember those yeah. Presentations, but you just make your eyes glaze over. We're going all in on Azure service >>And edge. He was all on. >>We're gonna build our own cloud. We acquired Aruba. He made some acquisitions in HPC to help differentiate. Yep. And they are definitely a much more focused company now. And unfortunately I wish Antonio would CEO in 2015, cuz that's really when this should have started. >>Yeah. And then, and if you remember back then, Dave, we were interviewing Docker with DevOps teams. They had composability, they were on hybrid really early. I think they might have even coined the term hybrid before VMware tri-state credit for it. But they were first on hybrid. They had DevOps, they had infrastructure risk code. >>HPE had an HP had an awesome cloud team. Yeah. But, and then, and then they tried to go public cloud. Yeah. You know, and then, you know, just made them, I mean, it was just a mess. The focus >>Is there. I give them huge props. And I think, I think the GreenLake to me is exciting here because it's much better than it was two years ago. When, when we talked to, when we started, it's >>Starting to get real. >>It's, it's a real thing. And I think the, the tell will be partners. If they make that right, can pull their different >>Ecosystem, >>Their scale and their customers and fill the software gas with partners mm-hmm <affirmative> and then create that integration opportunity. It's gonna be a home run if they don't do that, they're gonna miss the operating, >>But they have to have their own to your point. They have to have their own software innovation. >>They have to good infrastructure ways to build applications. I don't wanna build with somebody else. I don't wanna take a Microsoft stack on open source stack. I'm not sure if it's gonna work with HP. So they have to have an app dev answer. I absolutely agree with that. And the, the big thing for the partners is, which is a good thing, right? Yep. HPE will not move into applications. Right? You don't have to have the fear of where Microsoft is with their vocal large. Right. If AWS kind of like comes up with APIs and manufacturing, right. Google the same thing with their vertical push. Right. So HPE will not have the CapEx, but >>Application, >>As I SV making them, the partner, the bonus of being able to on premise is an attractive >>Part. That's a great point. >>Hold. So that's an inflection point for next 12 months to watch what we see absolutely running on GreenLake. >>Yeah. And I think one of the things that came out of the, the last couple events this past year, and I'll bring this up, we'll table it and we'll watch it. And it's early in this, I think this is like even, not even the first inning, the machine learning AI impact to the industrial piece. I think we're gonna see a, a brand new era of accelerated digital transformation on the industrial physical world, back office, cloud data center, accounting, all the stuff. That's applications, the app, the real world from space to like robotics. I think that HP edge opportunity is gonna be visible and different. >>So guys, Antonio Neri is on tomorrow. This is only day one. If you can imagine this power panel on day one, can you imagine tomorrow? What is your last question for each of you? What is your, what, what question would you want to ask him tomorrow? Hold start with you. >>How is HPE winning in the long run? Because we know their on premise market will shrink, right? And they can out execute Dell. They can out execute Lenovo. They can out Cisco and get a bigger share of the shrinking market. But that's the long term strategy, right? So why should I buy HPE stock now and have a good return put in the, in the safe and forget about it and have a great return 20 years from now? What's the really long term strategy might be unfair because they, they ran in survival mode to a certain point out of the mass post equipment situation. But what is really the long term strategy? Is it more on the hardware side? Is it gonna go on the HPE, the frontier side? It's gonna be a DNA question, which I would ask Antonio. >>John, >>I would ask him what relative to the macro conditions relative to their customer base, I'd say, cuz the customers are the scoreboard. Can they create a value proposition with their, I use the Microsoft 365 example how they kind of went to the cloud. So my question would be Antonio, what is your core value proposition to CIOs out there who want to transform and take a step function, increase for value with HPE? Tell me that story. I wanna hear. And I don't want to hear, oh, we got a portfolio and no, what value are you enabling your customers to do? >>What and what should that value be? >>I think it's gonna be what we were kind of riffing on, which is you have to provide either what their product market fit needs are, which is, are you solving a problem? Is it a pain point is a growth driver. Uh, and what's the, what's that tailwind. And it's obviously we know at cloud we know edge. The story is great, but what's the value proposition. But by going with HPE, you get X, Y, and Z. If they can explain that clearly with real, so qualitative and quantitative data it's home >>Run. He had a great line of the analyst summit today where somebody asking questions, I'm just listening to the customer. So be ready for this Steve jobs photo, listening to the customer. You can't build something great listening to the customer. You'll be good for the next quarter. The next exponential >>Say, what are the customers saying? <laugh> >>So I would make an observation. And my question would, so my observation would be cloud is growing collectively at 35%. It's, you know, it's approaching 200 billion with a big, big four. If you include Alibaba, IBM has actually said, Hey, we're gonna gr they've promised 6% growth. Uh, Cisco I think is at eight or 9% growth. Dow's growing in double digits. Antonio and HPE have promised three to 4% growth. So what do you have to do to actually accelerate growth? Because three to 4%, my view, not enough to answer Holger's question is why should I buy HPE stock? Well, >>If they have product, if they have customer and there's demand and traction to me, that's going to drive the growth numbers. And I think the weak side of the forecast means that they don't have that fit yet. >>Yeah. So what has to happen for them to get above five, 6% growth? >>That's what we're gonna analyze. I mean, I, I mean, I don't have an answer for that. I wish I had a better answer. I'd tell them <laugh> but I feel, it feels, it feels like, you know, HP has an opportunity to say here's the new HPE. Yeah. Okay. And this is what we stand for. And here's the one thing that we're going to do that consistently drives value for you, the customer. And that's gonna have to come into some, either architectural cloud shift or a data thing, or we are your store for blank. >>All of the above. >>I guess the other question is, would, would you know, he won't answer a rude question, would suspending things like dividends and stock buybacks and putting it into R and D. I would definitely, if you have confidence in the market and you know what to do, why wouldn't you just accelerate R and D and put the money there? IBM, since 2007, IBM spent is the last stat. And I'm looking go in 2007, IBM way, outspent, Google, and Amazon and R and D and, and CapEx two, by the way. Yep. Subsequent to that, they've spent, I believe it's the numbers close to 200 billion on stock buyback and dividends. They could have owned cloud. And so look at this business, the technology business by and large is driven by innovation. Yeah. And so how do you innovate if >>You have I'm buying, I'm buying HP because they're reliable high quality and they have the outcomes that I want. Oh, >>Buy their products and services. I'm not sure I'd buy the stock. Yeah. >>Yeah. But she has to answer ultimately, because a public company. Right. So >>Right. It's this job. Yeah. >>Never a dull moment with the three of you around <laugh> guys. Thank you so much for sharing your insights, your, an analysis from day one. I can't imagine what day two is gonna bring tomorrow. Debut and I are gonna be anchoring here. We've got a jam packed day, lots going on, hearing from the ecosystem from leadership. As we mentioned, Antonio is gonna be Tony >>Alma Russo. I'm dying. Dr. >>EDMA as well as on the CTO gonna be another action pack day. I'm excited for it, guys. Thanks so much for sharing your insights and for letting me join this power panel. >>Great. Great to be here. >>Power panel plus me. All right. For Holger, John and Dave, I'm Lisa, you're watching the cube our day one coverage of HPE discover wraps right now. Don't go anywhere, cuz we'll see you tomorrow for day two, live from Vegas, have a good night.

>>Welcome everyone to this cube conversation featuring unit Q. I'm your host, Lisa Martin. And we are excited to be joined by Christian Vickle, the founder and CEO of unit Q Christian. Thank you so much for joining me today. >>Thank you so much, Lisa pleasure to be here. >>Let's talk a little bit about unit Q. You guys were founded in 2018, so pretty recent. What is it that unit Q does. And what were some of the gaps in the market that led you to founding the company? >>Yep. So me and my co-founder Nick, we're actually doing our second company now is the unit Q is number two, and our first company was called scout years ago. We were back ES wicks and it was very different from unit Q. It's a social network for meeting people. And it was really during that experience where we saw the impact that quality of the experience quality of the product can have on your growth trajectory and the challenges we faced. How do we test everything before we ship it? And in reality, a modern company will have, let's say, 20 languages supported you support Android, Iowas, web big screen, small screen, you have 20 plus integrations and you have lots of different devices out there that might run your binary a little differently. So who is the ultimate test group of all of these different permutation and that's the end user. >>And we, we saw the, the big gap in the market, sort of the dream platform for us was unit queue. So if, if this would've existed back in the day, we would've been a, a happy purchaser and customer, and it really comes down to how do we, how do we harness the power of user feedback? You know, the end user, that's testing your product every single day in all different configurations. And then they're telling you that, Hey, something didn't work for me. I got double build or the passive recent link didn't work, or I couldn't, you know, when music, when the ad is finished playing on, on my app, the music doesn't resume. So how do we capture those signals into something that the company and different teams can align on? So that's where, you know, unit Q the, the vision here is to build a quality company, to help other companies build higher quality products. >>So really empowering companies to take a data driven approach to product quality. I was looking on your website and noticed that Pandora is one of your customers, but talk to me a little bit about a customer example that you think really articulates the value of what Q unit he was delivering. >>Right? So maybe we should just go back one little step and talk about what is quality. And I think quality is something that is, is a bit subjective. It's something that we live and breathe every day. It's something that can be formed in an instant first impressions. Last it's something that can be built over time that, Hey, I'm using this product and it's just not working for me. Maybe it's missing features. Maybe there are performance related bots. Maybe there is there's even fulfillment related issues. Like we work with Uber and hello, fresh and, and other types of more hybrid type companies in addition to the Pandoras and, and Pinterest and, and Spotify, and these more digital, only products, but the, the end users I'm producing this data, the reporting, what is working and not working out there in many different channels. So they will leave app produce. >>They will write into support. They might engage with a chat support bot. They will post stuff on Reddit on Twitter. They will comment on Facebook ads. So like this data is dispersed everywhere. The end user is not gonna fill out a perfect bug report in a form somewhere that gets filed into gr like they're, they're producing this content everywhere in different languages. So the first value of what we do is to just ingest all of that data. So all the entire surface area of use of feedback, we ingest into a machine and then we clean the data. We normalize it, and then we translate everything into English. And it was actually a surprise to us when we started this company, that there are quite a few companies out there that they're only looking at feedback in English. So what about my Spanish speaking users? What about my French speaking users? >>And when, when, when that is done, like when all of that data is, is need to organized, we extract signals from that around what is impacting the user experience right now. So we break these, all of this data down into something called quality monitors. So quality monitor is basically a topic which can be again, passive reset, link noting, or really anything that that's impacting the end user. And the important part here is that we need to have specific actionable data. For instance, if I tell you, Hey, Lisa music stops playing is a growing trend that our users are reporting. You will tell me, well, what can I do with that? Like what specifically is breaking? So we deploy up to 1500 unique quality monitors per customer. So we can then alert different teams inside of the organization of like, Hey, something broke and you should take a look at it. >>So it's really breaking down data silos within the company. It aligns cross-functional teams to agree on what should be fixed next. Cause there's typically a lot of confusion, you know, marketing, they might say, Hey, we want this fixed engineering. They're like, well, I can't reproduce, or that's not a high priority for us. The support teams might also have stuff that they want to get fixed. And what we've seen is that these teams, they struggle to communicate. So how do we align them around the single source of truth? And I think that's for unit two is early identification of stuff. That's not working in production and it's also aligning the teams so they can quickly triage and say, yes, we gotta fix this right before it snowballs into something. We say, you know, we wanna, we wanna cap catch issues before you go into crisis PR mode, right? So we want to get this, we wanna address it early in the cycle. >>Talk to me about when you're in customer conversations, Christian, the MarTech landscape is competitive. There's nearly 10,000 different solutions out there, and it's growing really quickly quality monitors that you just described is that one of the key things that, that you talk to customers about, that's a differentiator for unit Q. >>Yeah. So I mean, it, it, it comes down to, as you're building your product, right, you, you have, you have a few different options. One is to build new features and we need to build new features and innovate and, and, and that's all great. We also need to make sure that the foundation of the product is working and that we keep improving quality and what, what we see with, with basically every customer that we work with, that, that when quality goes up, it's supercharges the growth machine. So quality goes up, you're gonna see less support tickets. You're gonna see less one star reviews, less one star reviews is of course good for making the store front convert better. You know, I, I want install a 4.5 star app, not a 3.9 star app. We also see that sentiment. So for those who are interested in getting that NPS score up for the next time we measure it, we see that quality is of course a very important piece of that. >>And maybe even more importantly, so sort of inside of the product machine, the different conversion steps, let's say sign up to activate it to coming back in second day, 30 day, 90 day, and so forth. We see a dramatic impact on how quality sort of moves that up and down the retention function, if you will. So it, it really, if you think about a modern company, like the product is sort of the center of the existence of the company, and if the product performs really well, then you can spend more money in marketing because it converts really good. You can hire more engineers, you can hire, you can hire more support people and so forth. So it's, it's really cool to see that when quality improves its supercharges, everything else I think for marketing it's how do you know if you're spending into a broken product or not? >>And I, and I, I feel like marketing has, they have their insights, but it's, it's not deep enough where they can go to engineering and say, Hey, these 10 issues are impacting my MPS score and they're impacting my conversion and I would love for you to fix it. And when you can bring tangible impact, when you can bring real data to, to engineering and product, they move on it cause they also wanna help build the company. And, and so I think that's, that's how we stand out from the more traditional MarTech, because we need to fix the core of, of sort of this growth engine, which is the quality of the product >>Quality of the product. And obviously that's directly related to the customer experience. And we know these days, one of the things I think that's been in short supply the last couple of years is patience. We know when customers are unhappy with the product or service, and you talked about it a minute ago, they're gonna go right to, to Reddit or other sources to complain about that. So being able to, for uniq, to help companies to improve the customer experience, isn't I think table stakes for businesses it's mission critical these days. Yeah, >>It is mission critical. So if you look at the, let's say that we were gonna start a, a music app. Okay. So how do we, how do we compete as a music app? Well, if you, if you were to analyze all different music apps out there, they have more or less the same features app. Like they, the feature differentiation is minimal. And, and if you launch a new cool feature than your competitor will probably copy that pretty quickly as well. So competing with features is really hard. What about content? Well, I'm gonna get the same content on Spotify as apple SD. So competing with content is also really hard. What about price? So it turns out you'll pay 9 99 a month for music, but there's no, there's no 1 99. It's gonna be 9 99. So quality of the experience is one of the like last vectors or areas where you can actually compete. >>And we see consistently that if you' beating your competition on quality, you will do better. Like the best companies out there also have the highest quality experience. So it's, it's been, you know, for us at our last company, measuring quality was something that was very hard. How do we talk about it? And when we started this company, I went out and talked to a bunch of CEOs and product leaders and board members. And I said, how do you talk about quality in a board meeting? And they were, they said, well, we don't, we don't have any metrics. So actually the first thing we did was to define a metrics. We have, we have this thing called this unit Q score, which is on our website as well, where we can base it's like the credit score. So you can see your score between zero and a hundred. >>And if your score is 100, it means that we're finding no quality issues in the public domain. If your score is 90, it means that 10% of the data we look at refers to a quality issue. And the definition of a quality issue is quite simple. It is when the user experience doesn't match the user expectation. There is a gap in between, and we've actually indexed the 5,000 largest apps out there. So we're then looking at all the public review. So on our website, you can go in and, and look up the unit Q score for the 5,000 largest products. And we republish these every night. So it's an operational metric that changes all the time. >>Hugely impactful. Christian, thank you so much for joining me today, talking to the audience about unit Q, how you're turning qualitative feedback into pretty significant product improvements for your customers. We appreciate your insights. >>Thank you, Lisa, have a great day. >>You as well, per Christian Lin, I'm Lisa Martin. You're watching a cube conversation.

>>The cube presents, Coon and cloud native con Europe, 2022. Brought to you by red hat, the cloud native computing foundation and its ecosystem partners. >>Welcome to Valencia Spain and cuon cloud native con 20 Europe, 2022. I'm your host Keith towns alongside a new hope en Rico, senior reti, senior editor. I'm sorry, senior it analyst at <inaudible> Enrique. Welcome to the program. >>Thank you very much. And thank you for having me. It's exciting. >>So thoughts, high level thoughts of CU con first time in person again in couple years? >>Well, this is amazing for several reasons. And one of the reasons is that yeah, I had the chance to meet, uh, with, uh, you know, people like you again. I mean, we, we met several times over the internet over zoom calls. I, I started to eat these zoom codes. <laugh> because they're really impersonal in the end. And like last night we, we are together group of friends, industry folks. It's just amazing. And a part of that, I mean, the event is, uh, is a really cool, it's really cool. There are a lot from people interviews and, you know, real people doing real stuff, not just, uh, you know, again, in personal calls, you don't even know if they're telling the truth, but when you can, you know, look in their eyes, what they're doing, I, I think that's makes a difference. >>So speaking about real people, meeting people for the first time, new jobs, new roles, Greg Moscarella, enterprise container management and general manager at SUSE. Welcome to the show, welcome back clue belong. >>Thank you very much. It's awesome to be here. It's awesome to be back in person. And I completely agree with you. Like there's a certain fidelity to the conversation and a certain, uh, ability to get to know people a lot more. So it's absolutely fantastic to be here. >>So Greg, tell us about your new role and what SUSE has gone on at KU coupon. >>Sure. So I joined SA about three months ago to lead the rancher business unit, right? So our container management pieces and, you know, it's a, it's a fantastic time. Cause if you look at the transition from virtual machines to containers and to moving to microservices, right alongside that transition from on-prem to cloud, like this is a very exciting time to be in this industry. And rancher has been setting the stage. And again, I'm go back to being here. Rancher's all about the community, right? So this is a very open, independent, uh, community driven product and project. And so this, this is kinda like being back to our people, right. And being able to reconnect here. And so, you know, doing it, digital is great, but, but being here is changes the game for us. So we, we feed off that community. We feed off the energy. So, uh, and again, going back to the space and what's happening in it, great time to be in this space. And you guys have seen the transitions you've seen, I mean, we've seen just massive adoption, uh, of containers and Kubernetes overall and ranchers been been right there with some amazing companies doing really interesting things that I'd never thought of before. Uh, so I'm, I'm still learning on this, but, um, but it's been great so far. >>Yeah. And you know, when we talk about strategy about Kubernetes today, we are talking about very broad strategies. I mean, not just the data center or the cloud with, you know, maybe smaller organization adopting Kubernetes in the cloud, but actually large organization thinking guide and more and more the edge. So what's your opinion on this, you know, expansion of Kubernetes towards the edge. >>So I think you're, I think you're exactly right. And that's actually a lot of meetings I've been having here right now is these are some of these interesting use cases. So people who, uh, whether it be, you know, ones that are easy to understand in the telco space, right? Especially the adoption of 5g and you have all these space stations, new towers, and they have not only the core radio functions or network functions that they're trying to do there, but they have other applications that wanna run on that same environment. Uh, I spoke recently with some of our, our good friends at a major automotive manufacturer, doing things in their factories, right. That can't take the latency of being somewhere else. Right. So they have robots on the factory floor, the latency that they would experience if they tried to run things in the cloud meant that robot would've moved 10 centimeters. >>By the time, you know, the signal got back, it may not seem like a lot to you, but if, if, if you're an employee, you know, there, you know, uh, a big 2000 pound robot being 10 centimeters closer to you may not be what you, you really want. Um, there's, there's just a tremendous amount of activity happening out there on the retail side as well. So it's, it's amazing how people are deploying containers in retail outlets. You know, whether it be fast food and predicting, what, what, how many French fries you need to have going at this time of day with this sort of weather. Right. So you can make sure those queues are actually moving through. It's, it's, it's really exciting and interesting to look at all the different applications that are happening. So yes, on the edge for sure, in the public cloud, for sure. In the data center and we're finding is people want a common platform across those as well. Right? So for the management piece too, but also for security and for policies around these things. So, uh, it really is going everywhere. >>So talk to me, how do, how are we managing that as we think about pushing stuff out of the data center, out of the cloud cloud, closer to the edge security and life cycle management becomes like top of mind thought as, as challenges, how is rancher and sushi addressing >>That? Yeah. So I, I think you're, again, spot on. So it's, it starts off with the think of it as simple, but it's, it's not simple. It's the provisioning piece. How do we just get it installed and running right then to what you just asked the management piece of it, everything from your firmware to your operating system, to the, the cluster, uh, the Kubernetes cluster, that's running on that. And then the workloads on top of that. So with rancher, uh, and with the rest of SUSE, we're actually tacking all those parts of the problems from bare metal on up. Uh, and so we have lots of ways for deploying that operating system. We have operating systems that are, uh, optimized for the edge, very secure and ephemeral container images that you can build on top of. And then we have rancher itself, which is not only managing your ES cluster, but can actually start to manage the operating system components, uh, as well as the workload components. >>So all from your single interface, um, we mentioned policy and security. So we, yeah, we'll probably talk about it more, um, uh, in a little bit, but, but new vector, right? So we acquired a company called new vector, just open sourced, uh, that here in January, that ability to run that level of, of security software everywhere again, is really important. Right? So again, whether I'm running it on, whatever my favorite public cloud providers, uh, managed Kubernetes is, or out at the edge, you still have to have security, you know, in there. And, and you want some consistency across that. If you have to have a different platform for each of your environments, that's just upping the complexity and the opportunity for error. So we really like to eliminate that and simplify our operators and developers' lives as much as possible. >>Yeah. From this point of view, are you implying that even you, you are matching, you know, self, uh, let's say managed clusters at the, at the very edge now with, with, you know, added security, because these are the two big problems lately, you know, so having something that is autonomous somehow easier to manage, especially if you are deploying hundreds of these that's micro clusters. And on the other hand, you need to know a policy based security that is strong enough to be sure again, if you have these huge robots moving too close to you, because somebody act the, the, the class that is managing them, that is, could be a huge problem. So are you, you know, approaching this kind of problems? I mean, is it, uh, the technology that you are acquired, you know, ready to, to do this? >>Yeah. I, I mean, it, it really is. I mean, there's still a lot of innovation happening. Don't, don't get me wrong. We're gonna see a lot of, a lot more, not just from, from SA and ranch here, but from the community, right. There's a lot happening there, but we've come a long way and we solved a lot of problems. Uh, if I think about, you know, how do you have this distributed environment? Uh, well, some of it comes down to not just, you know, all the different environments, but it's also the applications, you know, with microservices, you have very dynamic environment now just with your application space as well. So when we think about security, we really have to evolve from a fairly static policy where like, you might even be able to set an IP address and a port and some configuration on that. >>It's like, well, your workload's now dynamically moving. So not only do you have to have that security capability, like the ability to like, look at a process or look at a network connection and stop it, you have to have that, uh, manageability, right? You can't expect an operator or someone to like go in and manually configure a YAML file, right? Because things are changing too fast. It needs to be that combination of convenient, easy to manage with full function and ability to protect your, your, uh, your resources. And I think that's really one of the key things that new vector really brings is because we have so much intelligence about what's going on there. Like the configuration is pretty high level, and then it just runs, right? So it's used to this dynamic environment. It can actually protect your workloads wherever it's going from pod to pod. Uh, and it's that, that combination, again, that manageability with that high functionality, um, that, that is what's making it so popular. And what brings that security to those edge locations or cloud locations or your data center. >>So one of the challenges you're kind of, uh, touching on is this abstraction on, upon abstraction. When I, I ran my data center, I could put, uh, say this IP address, can't talk to this IP address on this port. Then I got next generation firewalls where I could actually do, uh, some analysis. Where are you seeing the ball moving to when it comes to customers, thinking about all these layers of abstraction IP address doesn't mean anything anymore in cloud native it's yes, I need one, but I'm not, I'm not protecting based on IP address. How are customers approaching security from the name space perspective? >>Well, so it's, you're absolutely right. In fact, even when you go to IPV six, like, I don't even recognize IP addresses anymore. <laugh> yeah. >>That doesn't mean anything like, oh, just a bunch of, yeah. Those are numbers, alpha Ric >>And colons. Right. You know, it's like, I don't even know anymore. Right. So, um, yeah, so it's, it comes back to that, moving from a static, you know, it's the pets versus cattle thing. Right? So this static thing that I can sort of know and, and love and touch and kind of protect to this almost living, breathing thing, which is moving all around, it's a swarm of, you know, pods moving all over the place. And so, uh, it, it is, I mean, that's what Kubernetes has done for the workload side of it is like, how do you get away from, from that, that pet to a declarative approach to, you know, identifying your workload and the components of that workload and what it should be doing. And so if we go on the security side some more like, yeah, it's actually not even namespace namespace. >>Isn't good enough if we wanna get, if we wanna get to zero trust, it's like, just cuz you're running in my namespace doesn't mean I trust you. Right. So, and that's one of the really cool things about new vectors because of the, you know, we're looking at protocol level stuff within the network. So it's pod to pod, every single connection we can look at and it's at the protocol layer. So if you say you're on my SQL database and I have a mye request going into it, I can confirm that that's actually a mye protocol being spoken and it's well formed. Right. And I know that this endpoint, you know, which is a, uh, container image or a pod name or some, or a label, even if it's in the same name, space is allowed to talk to and use this protocol to this other pod that's running in my same name space. >>Right. So I can either allow or deny. And if I can, I can look into the content that request and make sure it's well formed. So I'll give you an example is, um, do you guys remember the log four J challenges from not too long ago, right. It was a huge deal. So if I'm doing something that's IP and port based and name space based, so what are my protections? What are my options for something that's got logged four J embedded in like, I either run the risk of it running or I shut it down. Those are my options. Like those neither one of those are very good. So we can do, because again, we're at the protocol layer. It's like, ah, I can identify any log for J protocol. I can look at whether it's well formed, you know, or if it's malicious and it's malicious, I can block it. If it's well formed, I can let it go through. So I can actually look at those, those, um, those vulnerabilities. I don't have to take my service down. I can run and still be protected. And so that, that extra level, that ability to kind of peek into things and also go pod to pod, you know, not just same space level is one of the key differences. So I talk about the evolution or how we're evolving with, um, with the security. Like we've grown a lot, we've got a lot more coming. >>So let's talk about that a lot more coming what's in the pipeline for SUSE. >>Well, probably before I get to that, we just announced new vector five. So maybe I can catch us up on what was released last week. Uh, and then we can talk a little bit about going, going forward. So new vector five, introduce something called um, well, several things, but one of the things I can talk in more detail about is something called zero drift. So I've been talking about the network security, but we also have run time security, right? So any, any container that's running within your environment has processes that are running that container. What we can do is actually comes back to that manageability and configuration. We can look at the root level of trust of any process that's running. And as long as it has an inheritance, we can let that process run without any extra configuration. If it doesn't have a root level of trust, like it didn't spawn from whatever the, a knit, um, function was in that container. We're not gonna let it run. Uh, so the, the configuration that you have to put in there is, is a lot simpler. Um, so that's something that's in, in new vector five, um, the web application firewall. So this layer seven security inspection has gotten a lot more granular now. So it's that pod Topo security, um, both for ingress egress and internal on the cluster. Right. >>So before we get to what's in the pipeline, one question around new vector, how is that consumed and deployed? >>How is new vector consumed, >>Deployed? And yeah, >>Yeah, yeah. So, uh, again with new vector five and, and also rancher 2 65, which just were released, there's actually some nice integration between them. So if I'm a rancher customer and I'm using 2 65, I can actually deploy that new vector with a couple clicks of the button in our, uh, in our marketplace. And we're actually tied into our role-based access control. So an administrator who has that has the rights can just click they're now in a new vector interface and they can start setting those policies and deploying those things out very easily. Of course, if you aren't using, uh, rancher, you're using some other, uh, container management platform, new vector still works. Awesome. You can deploy it there still in a few clicks. Um, you're just gonna get into, you have to log into your new vector, uh, interface and, and use it from there. >>So that's how it's deployed. It's, it's very, it's very simple to use. Um, I think what's actually really exciting about that too, is we've opensourced it? Um, so it's available for anyone to go download and try, and I would encourage people to give it a go. Uh, and I think there's some compelling reasons to do that now. Right? So we have pause security policies, you know, depreciated and going away, um, pretty soon in, in Kubernetes. And so there's a few things you might look at to make sure you're still able to run a secure environment within Kubernetes. So I think it's a great time to look at what's coming next, uh, for your security within your Kubernetes. >>So Paul, we appreciate chief stopping by from ity of Spain, from Spain, I'm Keith Townsend, along with en Rico Sinte. Thank you. And you're watching the, the leader in high tech coverage.

>>The cube presents, Coon and cloud native con Europe 22, brought to you by the cloud native computing foundation. >>Welcome to the cube coverage of CubeCon 2022 EU. I'm here with my cohost Paul Gill. Please work with you, Keith. Nice to work with you, Paul. And we have our first two guests. The cube is hot. I'm telling you we are having interviews before the start of even the show floor I have with me. We gotta start with the customers first enterprise architect, a non-con Aon con. Welcome to the show. >>Thank you so >>Much. Cube time cube time. First now you're at cube alumni. Yep. <laugh> and, and, uh, has Havani CEO. Arai welcome back. Nice to, >>Uh, >>Talk to you again today. So we're talking all things Kubernetes and we're super excited to talk to MoneyGram about their journey to Kubernetes. First question I have for Anon. Talk to us about what your pre Kubernetes landscape looked like. >>Yeah, certainly. Uh, Keith, so, um, we had a, uh, you know, a traditional mix of legacy applications and modern applications. Uh, you know, a few years ago we made the decision to move to a microservices architecture. Um, and this was all happening while we were still on prem. Right? So your traditional VMs, um, and you know, we started 20, 30 microservices, but with the microservices packing, you know, you quickly expand to hundreds of microservices. Um, and we started getting to that stage where managing them without sort of an orchestration platform, uh, and just as traditional VMs was getting to be really challenging, right. Uh, especially from a day two operational, uh, you know, you can manage 10, 15 microservices, but when you start having 50 and so forth, um, all those concerns around, uh, you know, high availability, operational performance. Um, so we started looking at some open source projects, you know, spring cloud. Uh, we are predominantly a Java, um, shop. So we looked at the spring cloud projects. Uh, they give you a number, uh, you know, of initiatives, um, for doing some of those, um, management and what we realized again, to manage those components, um, without sort of a platform was really challenging. So that, that kind of led us to sort of Kubernetes where, um, along with our journey cloud, uh, it was the platform that could help us with a lot of those management operational concerns. >>So as you talk about some of those challenges, pre Kubernetes, what were some of the operational issues that you folks experienced? >>Yeah. You know, uh, certain things like auto scaling is, is number one, right? I mean, that's a fundamental concept of cloud native, right. Is, um, how do you auto scale VMs? Right. Uh, you can put in some old methods and stuff, but, uh, it was really hard to do that automatically. Right. So, uh, Kubernetes with like HPA gives you those out of the box, right? Provided you set the right policies. Uh, you can have auto scaling, uh, where it can scale up and scale back. So we were doing that manually. Right. So before, uh, you know, MoneyGram, obviously, you know, holiday season, people are sending more money mother's day. Um, our ops team would go in basically manually scale, uh, VMs. Right. So we'd go from four instances to maybe eight instances. Right. Uh, but, but that entailed outages. Right. Um, and just to plan around doing that manually and then sort of scale them back was a lot of overhead, a lot of administration overhead. Right. So, uh, we wanted something that could help us do that automatically right. In a, in an efficient, uh, unintrusive way. So, so, you know, that was one of the things, uh, monitoring, um, and, and management, uh, operations, you know, just kind of visibility into how those applications were during, what were the status of your, um, workloads was also a challenge, right. Uh, to do that. >>So, cause see, I gotta ask the question. If someone would've came to me with that problem, I'd just say, you know, what, go to the plug, the cloud, what, how does, uh, your group help solve some of these challenges? What do you guys do? >>Yeah. What, what do we do? So here's my perspective on the market as it's playing out. So I see a bifurcation happening in the Kubernetes space, but there's the Kubernetes run time. So Amazon is EKS Azure as EKS, you know, there's enough of these available. They're not managed services. They're actually really good, frankly. Right? In fact, retail customers, if you're an Amazon, why would you spin up your own? Just use EK. It's awesome. But then there's an operational layer that is needed to run Kubernetes. Uh, my perspective is that, you know, 50,000 enterprises are adopting Kubernetes over the next five to 10 years. And they're all gonna go through the same exact journey and they're all gonna end up, you know, potentially making the same mistake, which is, they're gonna assume that Kubernetes is easy. <laugh> they're gonna say, well, this is not hard. I got this up and running on my laptop. >>This is so easy. No worries. Right. I can do key gas, but then, okay. Can you consistently spin up these things? Can you scale them consistently? Do you have the right blueprints in place? Do you have the right access management in place? Do you have the right policies in place? Can you deploy applications consistently? Do you have monitoring and visibility into those things? Do your developers have access to when they need it? Do you have the right networking layer in place? Do you have the right chargebacks in place? Remember you have multiple teams and by the way, nobody has a single cluster. So you gotta do this across multiple clusters. And some of them have multiple clouds, not because they wanna be multiple clouds because, but sometimes you buy a company and they happen to be in Azure. How many dashboards do you have now across all the open source technologies that you have identified to solve these problems? >>This is where pain lies. So I think that Kubernetes is fundamentally a solve problem. Like our friends at AWS and Azure they've solved this problem. It's like a KSKS et cetera, GK for that matter. They're they're great. And you should use them and don't even think about spinning up Q B and a best clusters. Don't do it. Use the platforms that exist and commensurately on premises. OpenShift is pretty awesome, right? If you like it, use it. But then when it comes to the operations layer, right, that's where today we end up investing in a DevOps team and then an SRE organization that need to become experts in Kubernetes. And that is not tenable, right? Can you let's say unlimited capital unlimited budgets. Can you hire 20 people to do Kubernetes today? >>If you could find them, if >>You can find 'em right. So even if you could, the point is that see, five years ago, when your competitors were not doing Kubernetes, it was a competitive advantage to go build a team to do Kubernetes. So you could move faster today. You know, there's a high chance that your competitors are already buying from a Rafa or somebody like Rafa. So now it's better to take these really, really sharp engineers and have them work on things that make the company money, writing operations for Kubernetes. This is a commodity. Now >>How confident are you that the cloud providers won't get in and do what you do and put you out of business? >>Yeah, I mean, absolutely. I think, I mean, in fact, I, I had a conversation with somebody from HBS this morning and I was telling them, I don't think you have a choice. You have to do this right. Competition is not a bad thing. Right? This, the, >>If we are the only company in a space, this is not a space, right. The bet we are making is that every enterprise has, you know, they have an on-prem strategy. They have at least a handful of, everybody's got at least two clouds that they're thinking about. Everybody starts with one cloud and then they have some other cloud that they're also thinking about, um, for them to only rely on one cloud's tools to solve for on-prem plus that second cloud, they potentially, they may have, that's a tough thing to do. Um, and at the same time we as a vendor, I mean the only real reason why startups survive is because you have technology that is truly differentiated, right. Otherwise, right. I mean, you gotta build something that is materially. Interesting. Right. We seem to have, sorry, go ahead. >>No, I was gonna ask you, you actually had me thinking about something, a non yes. MoneyGram big, well known company, a startup, adding, working in a space with Google, VMware, all the biggest names. What brought you to Rafi to solve this operational challenge? >>Yeah. Good question. So when we started out sort of in our Kubernetes, um, you know, we had heard about EKS, uh, and, and we are an AWS shop. So, uh, that was the most natural path. And, and we looked at, um, EKS and, and used that to, you know, create our clusters. Um, but then we realized very quickly that yes, toe's point AWS manages the control plane for you. It gives you the high availability. So you're not managing those components, which is some really heavy lifting. Right. Uh, but then what about all the other things like, you know, centralized dashboard, what about, we need to provision, uh, Kubernetes clusters on multi-cloud right. We have other clouds that we use, uh, or also on prem. Right. Um, how do you do some of that stuff? Right. Um, we, we also, at that time were looking at, uh, other, uh, tools also. >>And I had, I remember come up with an MVP list that we needed to have in place for day one or day two, uh, operations, right. To before we even launch any single applications into production. Um, and my ops team looked at that list. Um, and literally there was only one or two items that they could check, check off with S you know, they they've got the control plane, they've got the cluster provision, but what about all those other components? Uh, and some of that kind of led us down the path of, uh, you know, looking at, Hey, what's out there in this space. And, and we realized pretty quickly that there weren't too many, there were some large providers and capabilities like Antos, but we felt that it was, uh, a little too much for what we were trying to do. You know, at that point in time, we wanted to scale slowly. We wanted to minimize our footprint. Um, and, and Rafa seemed to sort of, uh, was, was a nice mix, uh, you know, uh, from all those different angles, how >>Was, how was the situation affecting your developer experience? >>So, um, so that's a really good question also. So operations was one aspect of, to it, right? The other part is the application development, right? We've got, uh, you know, Moneygrams when a lot of organizations have a plethora of technologies, right? From, from Java to.net to no GS, what have you, right. Um, now as you start saying, okay, now we're going cloud native, and we're gonna start deploying to Kubernetes. Um, there's a fair amount of overhead because a tech stack, all of a sudden goes from, you know, just being Java or just being.net to things like Docker, right? All these container orchestration and deployment concerns, Kubernetes, uh, deployment artifacts, right. I gotta write all this YAML, uh, as my developer say, YAML, hell right. <laugh>, uh, I gotta learn Docker files. I need to figure out, um, a package manager like helm, uh, on top of learning all the Kubernetes artifacts. >>Right. So, um, initially we went with sort of, okay, you know, we can just train our developers. Right. Um, and that was wrong. Right. I mean, you can't assume that everyone is gonna sort of learn all these deployment concerns, uh, and we'll adopt them. Right. Um, uh, there's a lot of stuff that's outside of their sort of core dev domain, uh, that you're putting all this burden on them. Right. So, um, we could not rely on them and to be sort of cube cuddle experts, right. That that's a fair amount, overhead learning curve there. Um, so Rafa again, from their dashboard perspective, right? So the managed cube cuddle gives you that easy access for devs, right. Where they can go and monitor the status of their workloads. Um, they can, they don't have to figure out, you know, configuring all these tools locally just to get it to work. >>Uh, we did some things from a DevOps perspective to basically streamline and automate that process. But then also office order came in and helped us out, uh, on kind of that providing that dashboard. They don't have to worry. They can basically get on through single sign on and have visibility into the status of their deployment. Uh, they can do troubleshooting diagnostics all through a single pane of glass. Right. Which was a key key item. Uh, initially before Rafa, we were doing that command line. Right. And again, just getting some of the tools configured was, was huge. Right. Took us days just to get that. And then the learning curve for development teams, right? Oh, now you gotta, you got the tools now you gotta figure out how to use it. Right. Um, so >>See, talk to me about the, the cloud native infrastructure. When I look at that entire landscaping number, I'm just overwhelmed by it. As a customer, I look at it, I'm like, I, I don't know where to start I'm sure. Or not, you, you folks looked at it and said, wow, there's so many solutions. How do you engage with the ecosystem? You have to be at some level opinionated, but flexible enough to, uh, meet every customer's needs. How, how do you approach that? >>Yeah. So it's a, it's a really tough problem to solve because, so, so the thing about abstraction layers, you know, we all know how that plays out, right? So abstraction layers are fundamentally never the right answer because they will never catch up. Right. Because you're trying to write and layer on top. So then we had to solve the problem, which was, well, we can't be an abstraction layer, but then at the same time, we need to provide some sort of, sort of like centralization standardization. Right. So, so we sort of have this, the following dissonance in our platform, which is actually really important to solve the problem. So we think of a, of a stack as sort of four things. There's the, there's the Kubernetes layer infrastructure layer, um, and EKS is different from ES and it's okay. Mm-hmm <affirmative>, if we try to now bring them all together and make them behave as one, our customers are gonna suffer because there are features in ESS that I really want. >>But then if you write an AB obsession layer, I'm not gonna get 'em so not. Okay. So treat them as individual things. And we logic that we now curate. So every time S for example, goes from 1 22 to 1 23, rewrite a new product, just so my customer can press a button and upgrade these clusters. Similarly, we do this fors, we do this for GK. We it's a really, really hard job, but that's the job. We gotta do it on top of that, you have these things called. Add-ons like my network policy, my access management policy, my et cetera. Right. These things are all actually the same. So whether I'm Anek or a Ks, I want the same access for Keith versus a none. Right. So then those components are sort of the same across doesn't matter how many clusters does money clouds on top of that? You have applications. And when it comes to the developer, in fact, I do the following demo a lot of times because people ask the question, right? Mean, I, I, I, people say things like, I wanna run the same Kubernetes distribution everywhere, because this is like Linux, actually, it's not. So I, I do a demo where I spin up a access to an OpenShift cluster and an EKS cluster and an AKs cluster. And I say, log in, show me which one is, which they're all the same. >>So Anan get, put, make that real for me, I'm sure after this amount of time, developers groups have come to you with things that are snowflakes and you, and as a enterprise architect, you have to make it work within your framework. How has working with RAI made that possible? >>Yeah. So, um, you know, I think one of the very common concerns is right. The whole deployment, right. Uh, toe's point, right. Is you are from an, from a deployment perspective. Uh, it's still using helm. It's still using some of the same tooling, um, right. But, um, how do you Rafa gives us, uh, some tools, you know, they have a, a command line, art cuddle API that essentially we use. Um, we wanted parody, um, across all our different environments, different clusters, you know, it doesn't matter where you're running. Um, so that gives us basically a consistent API for deployment. Um, we've also had, um, challenges, uh, with just some of the tooling in general, that we worked with RA actually to actually extend their, our cuddle API for us, so that we have a better deployment experience for our developers. So, >>Uh Huie how long does this opportunity exist for you? At some point, do the cloud providers figure this out or does the open source community figure out how to do what you've done and, and this opportunity is gone. >>So, so I think back to a platform that I, I think very highly of, which is a highly off, which has been around a long time and continues to live vCenter, I think vCenter is awesome. And it's, it's beautiful. VMware did an incredible job. Uh, what is the job? Its job is to manage VMs, right? But then it's for access. It's also storage. It's also networking and a sex, right? All these things got done because to solve a real problem, you have to think about all the things that come together to solve, help you solve that problem from an operations perspective. Right? My view is that this market needs essentially a vCenter, but for Kubernetes, right. Um, and that is a very broad problem, right. And it's gonna spend, it's not about a cloud, right? I mean, every cloud should build this. I mean, why would they not? It makes sense, Anto success, right. Everybody should have one. But then, you know, the clarity in thinking that the Rafa team seems to have exhibited till date seems to merit an independent company. In my opinion, I think like, I mean, from a technical perspective, this products awesome. Right? I mean, you know, we seem to have, you know, no real competition when it comes to this broad breadth of capabilities, will it last, we'll see, right. I mean, I keep doing Q shows, right? So every year you can ask me that question again. Well, you're >>You make a good point though. I mean, you're up against VMware, you're up against Google. They're both trying to do sort of the same thing you're doing. What's why are you succeeding? >>Maybe it's focus. Maybe it's because of the right experience. I think startups only in hindsight, can one tell why a startup was successful? In all honesty. I, I, I've been in a one or two service in the past. Um, and there's a lot of luck to this. There's a lot of timing to this. I think this timing for a com product like this is perfect. Like three, four years ago, nobody would've cared. Like honestly, nobody would've cared. This is the right time to have a product like this in the market because so many enterprises are now thinking of modernization. And because everybody's doing this, this is like the boots storm problem in HCI. Everybody's doing it. But there's only so many people in the industry who actually understand this problem. So they can't even hire the people. And the CTO said, I gotta go. I don't have the people. I can't fill the, the seats. And then they look for solutions and we are that solution that we're gonna get embedded. And when you have infrastructure software like this embedded in your solution, we're gonna be around with the assuming, obviously we don't score up, right. We're gonna be around with these companies for some time. We're gonna have strong partners for the long term. >>Well, vCenter for Kubernetes, I love to end on that note, intriguing conversation. We could go on forever on this topic, cuz there's a lot of work to do. I think, uh, I don't think this will over be a solve problem for the Kubernetes of cloud native solution. So I think there's a lot of opportunity in that space. Hi, thank you for rejoining the cube. I non con welcome becoming a cube alum. <laugh> I awesome. Thank you. Get your much your profile on the, on the Ken's. Website's really cool from Valencia Spain. I'm Keith Townsend, along with my whole Paul Gillon and you're watching the cube, the leader in high tech coverage.