(Gentle music) >> Hello and welcome to the Cube here in Palo Alto, California. I'm John Furrier here, joined by Platform nine, Amelia Bell the Chief Revenue Officer, really digging into the conversation around Kubernetes Cloud native and the journey this next generation cloud. Amelia, thanks for coming in and joining me today. >> Thank you, thank you. Great pleasure to be here. >> So, CRO, chief Revenue Officer. So you're mainly in charge of serving the customers, making sure they're they're happy with the solution you guys have. >> That's right. >> And this market must be pretty exciting. >> Oh, it's very exciting and we are seeing a lot of new use cases coming up all the time. So part of my job is to obtain new customers but then of course, service our existing customers and then there's a constant evolution. Nothing is standing still right now. >> We've had all your co-founders on, on the show here and we've kind of talked about the trends and where you guys have come from, where you guys are going now. And it's interesting, if you look at the cloud native market, the scale is still huge. You seeing now this next wave of AI coming on, which I call that's the real web three in my mind in terms of like the next experiences really still points to data infrastructure scale. These next gen apps are coming. And so that's being built on the previous generation of DevSecOps. >> Right >> And so a lot of enterprises are having to grow up really, really fast >> Right. >> And figure out, okay, I got to have scale I got large scale data, I got horizontal scalability I got to apply machine learning now the new software engineering practice. And then, oh, by the way I got the Kubernetes clusters I got to manage >> Right. >> I got what's containers weather, the security problems. This is a really complicated but important area of build out right now in the marketplace. >> Right. What are you seeing? >> So it's, it's really important that the infrastructure is not the hindrance in these cases. And we, one of our customers is in fact a large AI company and we, I met with them yesterday and asked them, you know, why are you giving that to us? You've got really smart engineers. They can run and create the infrastructure, you know in a custom way that you want it. And they said, we've got to be core to our business. There's plenty of work to do just on delivering the AI capabilities, and there's plenty of work to do. We can't get bogged down in the infrastructure. We don't want to have people running the engine we want them driving the car. We want them creating value on top of that. so they can't have the infrastructure being the bottleneck for them. >> It's interesting, the AI companies, that's their value proposition to their customers is that they don't want the technical talent. >> Right. >> Working on, you know, non-differentiated heavy lifting things. >> Right. >> And automate those and scale it up. Can you talk about the problem that you guys are solving? Because there's a lot going on here. >> Yeah. >> You can look at all aspects of the DevOps scale. There's a lot of little problems, some big problems. What are you guys focusing on? What's the bullseye for Platform known? >> Okay, so the bullseye is that Kubernetes infrastructure is really hard, right? It's really hard to create and run. So we introduce a time to market efficiency, let's get this up and running and let's get you into production and and producing results for your customers fast. But at the same time, let's reduce your cost and complexity and increase reliability. So, >> And what are some of the things that they're having problems with that are breaking? Is it more of updates on code? Is it size of the, I mean clusters they have, what what is it more operational? What are the, what are some of the things that are that kind of get them to call you guys up? What's the main thing? >> It's the operations. It's all operations. So what, what happens is that if you have a look at Kubernetes platform it's made up of many, many components. And that's where it gets complex. It's not just Kubernetes. There's load balances, networking, there's observability. All these things have to operate together. And all the piece parts have to be upgraded and maintained. The integrations need to work, you need to have probes into the system to predict where problems can be coming. So the operational part of it is complex. So you need to be observing not only your clusters in the health of the clusters and the nodes and so on but the health of the platform itself. >> We're going to get Peter Frey in on here after I talk about some of the technical issues on deployments. But what's the, what's the big decision for the customer? Because there's kind of, there's two schools of thought. One is, I'm going to build my own and have my team build it or I'm going to go with a partner >> Right. >> Say platform nine, what's the trade offs there? Because it seems to me that, that there's a there's a certain area of where it's core competency but I can outsource it or partner with it and, and work with platform nine versus trying to take it all on internally >> Right. >> Of which requires more costs. So there's a, there's a line where you kind of like figure out that customers have to figure out that, that piece >> Right >> What do, what's your view on that? Because I'm hearing that more people are saying, hey I want to, I want to focus my people on solutions. The app side, not so much the ops >> Right. >> What's the trade off? How do you talk about? >> It's a really interesting question because most companies think they have two options. It's either a DIY option and they love that engineers love playing with the new and on the latest. And then they think the other option is going to cloud, public cloud and have it semi managed by them. And you get very different out of those. So in the DIY you get flexibility coz you get to choose your infrastructure but then you've got all the complexities of the DIY piece. You've got to not only choose all your components but you've got to keep them working. Now if you go to public cloud option, you lose flexibility because a lot of those choices are made for you but you gain agility because quite frankly it's really easy to spin up clusters. So what we are, is that in the middle we bring the agility and the flexibility because we bring the control plane that allows you to spin up clusters and and lifecycle manage them very quickly. So the agility's there but you can do it on the infrastructure of your choice. And in the DIY culture, one of the hardest things to do actually is to convince them they don't have to do it themselves. They can focus on higher value activities, which are more focused on delivering outcomes to their customers. >> So you provide the solution that allows them to feel like they're billing it themselves. >> Correct. >> And get these scale and speed and the efficiencies of the op side. So it's kind of the best of both worlds. It's not a full outsource. >> Right, right. >> You're bringing them in to make their jobs easier >> Right, That's right. So they get choices. >> Yeah. >> We, we, they get choices on how they build it and then we run and operate it for them. But they, they have all the observability. The benefit is that if we are managing their operations and most of our customers choose the managed operations piece of it, then they don't. If something goes wrong, we fix that and they, they they get told, oh, by the way, you had a problem. We've dealt with it. But in the other model is they've got to create all that observability themselves and they've got to get ahead of the issues themselves, and then they've got to raise tickets to whoever they need to raise tickets to. Whereas we have things like auto ticket generation and so on where, look, just drive the car let us worry about the engine and all of that. Let us deal with that. And you can choose whatever you want about the engine but let us manage it for you. So >> What do you, what do you say to folks out there that are may have a need for platform nine? What's the signals inside their company that they should be calling you guys up and, and leaning in with platform nine? >> Right. >> Is it more sprawl on on clusters? Is it more errors? Is it more tickets? Is it more hassle? What are some of the signs? If someone's watching this say, hey I have, I have an issue with this. >> I would say, if there's operational inefficiencies you can't get things to market fast enough because you are building this and it's just taking too long you're spending way too much time operationally on the infrastructure, then you are, you are not using your resources where they should best be used. And, and that is delivering services to the customer. >> Ed me Hora on for International Women's Day. And she was talking about how they love to solve complex problems on the engineering team at Platform nine. It's going to get pretty complex with the edge emerging >> Indeed >> and cloud native on-premises distributed computing. >> Indeed. >> essentially is what it is. That's kind of the core DNA of the team. >> Yeah. >> What, how does that translate to the customers? Because IT seems to be, okay, I have virtual machines were great, now I got to scale up and and convert over a transform to containers, Kubernetes >> Right. >> And then large scale app, app applications. >> Right, so when it comes to Edge it gets complex pretty fast because it's highly distributed. So how do you have standardization and governance across all the different edge locations? So what we bring into play is an ability to, um, at each edge, location eh, provision from bare metal up all the way up to the application. So let's say you have thousands of stores and you want to modernize those stores, you know rather than having a server being sent somewhere to have an image loaded up and then sent that and then you've got to send a technical guide to the store and you've got to implement it all there. Forget all that. That's just, that's just a ridiculous waste of time. So what we've done is we've created the ability where the server can just be sent to the store. You can get your barista or your chef just to plug it in, right? You don't need to send any technical person over there. As long as we have access to it, we get access to it and we provision the whole thing from bare metal up and then we can maintain it according to the standards that are needed and upgrade accordingly. And that gives standardization across all your stores or edge locations or 5G towers or whatever it is, distribution centers. And we can create nice governance and good standardization which allows them to innovate fast as well. >> So this is a real opportunity for you guys. >> Yeah. >> This is an advantage from your expertise. >> Yes. >> The edge piece, dropping in a box, self-provisioning. >> That's right. So yeah. >> Can people do that? What's the, >> No, actually it, it's, it's very difficult to do. I I, from my understanding, we're the only people that can provision it from bare metal up, right? So if anyone has a different story, I'd love to hear about that. But that's my understanding today. >> That's a good value purpose. So talk about the value of the customer. What kind of scope do you got? Can you scope some of the customer environments you have from >> Sure. >> From, you know, small to the large, how give us an idea of the order of magnitude of the >> Yeah, so, so small customers may have 20 clusters or something like that. 20 nodes, I beg your pardon. Our large customers, like we're we are scaling one particular distributed environment from 2200 nodes to 10,000 nodes by the end of this year and 26,000 nodes next year. We have another customer that's scaling up to 10,000 nodes this year as well. So we have some very large scale, but some smaller ones too. And we're, we're happy to work with either end. >> Okay, so pretend I'm a customer. I'm really, I got pain and Kubernetes like I want to, I can't hire enough people. I want to have my all focus. What's the pitch? >> Okay. So skill shortage is something that that everyone is facing right now. And if, if you've got skill shortage it's going to be really hard to hire if you are competing against really, you know, high salary you know, offering companies that are out there. So the pitch is, let us do it for you. We have, we have a team of excellent probably the best Kubernetes engineers on the planet. We will create your environment for you. We will get it up and running. We will allow you to, you know, run your applica, just consume the platform, we'll run it for you. We'll have SLAs and up times guaranteed and you can just focus on delivering the software and the value needed to your customers. >> What are some of the testimonials that you get from people? Just anecdotally, what do they say? Oh my god, you guys save. >> Yeah. >> Our butts. >> Yeah. >> This is amazing. We just shipped our code out much faster. >> Yeah. >> What are some of the things that you hear? >> So, so the number one thing I hear is it just works right? It's, we don't have to worry about it, it just works. So that, that's a really great feedback that we get. The other thing I hear is if we do have issues that your team are amazing, they they fix things, they're proactive, you know, they're we really enjoy working with you. So from, from that perspective, that's great. But the other side of it is we hear things like if we were to do that ourselves we would've taken six to 12 months to build that. And you guys have just saved us six to 12 months. The other thing that we hear is with the same two engineers we started on, you know, a hundred nodes we're now running thousands of nodes. We have not had to increase the size of the team and expand and scale exponentially. >> Awesome. What's next for you guys? What's on your, your plate? >> Yeah. >> With CRO, what's some of the goals you have? >> Yeah, so growth of course as a CRO, you don't get away from that. We've got some very exciting, actually, initiatives coming up. One of the things that we are seeing a lot of demand for and is, is in the area of virtualization bringing virtual machine, virtual virtual containers, sorry I'm saying that all wrong. Bringing virtual machine, the virtual machines onto the cloud native infrastructure using Kubernetes technology. So that provides a, an excellent stepping stone for those guys who are in the virtualization world. And they can't move to containers, they can't refactor their applications and workloads fast enough. So just bring your virtual machine and put it onto the container infrastructure. So we're seeing a lot of demand for that, because it provides an excellent stepping stone. Why not use Kubernetes to orchestrate virtual the virtual world? And then we've got some really interesting cost optimization. >> So a lot of migration kind of thinking around VMs and >> Oh, tremendous. The, the VM world is just massively bigger than the container world right now. So you can't ignore that. So we are providing basically the evolution, the the journey for the customers to utilize the greatest of technologies without having to do that in a, in a in a way that just breaks the bank and they can't get there fast enough. So we provide those stepping stones for them. Yeah. >> Amelia thank you for coming on. Sharing. >> Thank you. >> The update on platform nine. Congratulations on your big accounts you have and >> thank you. >> And the world could get more complex, which Means >> indeed >> have more customers. >> Thank you, thank you John. Appreciate that. Thank you. >> I'm John Furry. You're watching Platform nine and the Cube Conversations here. Thanks for watching. (gentle music)

>> theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (ambient music) >> Lisa: Hey everyone, welcome back to Barcelona, Spain. It's theCUBE Live at MWC 23. Lisa Martin with Dave Vellante. This is day two of four days of cube coverage but you know that, because you've already been watching yesterday and today. We're going to have a great conversation next with EnterpriseWeb and Red Hat. We've had great conversations the last day and a half about the Telco industry, the challenges, the opportunities. We're going to unpack that from this lens. Please welcome Dave Duggal, founder and CEO of EnterpriseWeb and Azhar Sayeed is here, Senior Director Solution Architecture at Red Hat. >> Guys, it's great to have you on the program. >> Yes. >> Thank you Lisa, >> Great being here with you. >> Dave let's go ahead and start with you. Give the audience an overview of EnterpriseWeb. What kind of business is it? What's the business model? What do you guys do? >> Okay so, EnterpriseWeb is reinventing middleware, right? So the historic middleware was to build vertically integrated stacks, right? And those stacks are now such becoming the rate limiters for interoperability for so the end-to-end solutions that everybody's looking for, right? Red Hat's talking about the unified platform. You guys are talking about Supercloud, EnterpriseWeb addresses that we've built middleware based on serverless architecture, so lightweight, low latency, high performance middleware. And we're working with the world's biggest, we sell through channels and we work through partners like Red Hat Intel, Fortnet, Keysight, Tech Mahindra. So working with some of the biggest players that have recognized the value of our innovation, to deliver transformation to the Telecom industry. >> So what are you guys doing together? Is this, is this an OpenShift play? >> Is it? >> Yeah. >> Yeah, so we've got two projects right her on the floor at MWC throughout the various partners, where EnterpriseWeb is actually providing an application layer, sorry application middleware over Red Hat's, OpenShift and we're essentially generating operators so Red Hat operators, so that all our vendors, and, sorry vendors that we onboard into our catalog can be deployed easily through the OpenShift platform. And we allow those, those vendors to be flexibly composed into network services. So the real challenge for operators historically is that they, they have challenges onboarding the vendors. It takes a long time. Each one of them is a snowflake. They, you know, even though there's standards they don't all observe or follow the same standards. So we make it easier using models, right? For, in a model driven process to on boards or streamline that onboarding process, compose functions into services deploy those services seamlessly through Red Hat's OpenShift, and then manage the, the lifecycle, like the quality of service and the SLAs for those services. >> So Red Hat obviously has pretty prominent Telco business has for a while. Red Hat OpenStack actually is is pretty popular within the Telco business. People thought, "Oh, OpenStack, that's dead." Actually, no, it's actually doing quite well. We see it all over the place where for whatever reason people want to build their own cloud. And, and so, so what's happening in the industry because you have the traditional Telcos we heard in the keynotes that kind of typical narrative about, you know, we can't let the over the top vendors do this again. We're, we're going to be Apifi everything, we're going to monetize this time around, not just with connectivity but the, but the fact is they really don't have a developer community. >> Yes. >> Yet anyway. >> Then you have these disruptors over here that are saying "Yeah, we're going to enable ISVs." How do you see it? What's the landscape look like? Help us understand, you know, what the horses on the track are doing. >> Sure. I think what has happened, Dave, is that the conversation has moved a little bit from where they were just looking at IS infrastructure service with virtual machines and OpenStack, as you mentioned, to how do we move up the value chain and look at different applications. And therein comes the rub, right? You have applications with different requirements, IT network that have various different requirements that are there. So as you start to build those cloud platform, as you start to modernize those set of applications, you then start to look at microservices and how you build them. You need the ability to orchestrate them. So some of those problem statements have moved from not just refactoring those applications, but actually now to how do you reliably deploy, manage in a multicloud multi cluster way. So this conversation around Supercloud or this conversation around multicloud is very >> You could say Supercloud. That's okay >> (Dave Duggal and Azhar laughs) >> It's absolutely very real though. The reason why it's very real is, if you look at transformations around Telco, there are two things that are happening. One, Telco IT, they're looking at partnerships with hybrid cloud, I mean with public cloud players to build a hybrid environment. They're also building their own Telco Cloud environment for their network functions. Now, in both of those spaces, they end up operating two to three different environments themselves. Now how do you create a level of abstraction across those? How do you manage that particular infrastructure? And then how do you orchestrate all of those different workloads? Those are the type of problems that they're actually beginning to solve. So they've moved on from really just putting that virtualizing their application, putting it on OpenStack to now really seriously looking at "How do I build a service?" "How do I leverage the catalog that's available both in my private and public and build an overall service process?" >> And by the way what you just described as hybrid cloud and multicloud is, you know Supercloud is what multicloud should have been. And what, what it originally became is "I run on this cloud and I run on this cloud" and "I run on this cloud and I have a hybrid." And, and Supercloud is meant to create a common experience across those clouds. >> Dave Duggal: Right? >> Thanks to, you know, Supercloud middleware. >> Yeah. >> Right? And, and so that's what you guys do. >> Yeah, exactly. Exactly. Dave, I mean, even the name EnterpriseWeb, you know we started from looking from the application layer down. If you look at it, the last 10 years we've looked from the infrastructure up, right? And now everybody's looking northbound saying "You know what, actually, if I look from the infrastructure up the only thing I'll ever build is silos, right?" And those silos get in the way of the interoperability and the agility the businesses want. So we take the perspective as high level abstractions, common tools, so that if I'm a CXO, I can look down on my environments, right? When I'm really not, I honestly, if I'm an, if I'm a CEO I don't really care or CXO, I don't really care so much about my infrastructure to be honest. I care about my applications and their behavior. I care about my SLAs and my quality of service, right? Those are the things I care about. So I really want an EnterpriseWeb, right? Something that helps me connect all my distributed applications all across all of the environments. So I can have one place a consistency layer that speaks a common language. We know that there's a lot of heterogeneity down all those layers and a lot of complexity down those layers. But the business doesn't care. They don't want to care, right? They want to actually take their applications deploy them where they're the most performant where they're getting the best cost, right? The lowest and maybe sustainability concerns, all those. They want to address those problems, meet their SLAs meet their quality service. And you know what, if it's running on Amazon, great. If it's running on Google Cloud platform, great. If it, you know, we're doing one project right here that we're demonstrating here is with with Amazon Tech Mahindra and OpenShift, where we took a disaggregated 5G core, right? So this is like sort of latest telecom, you know net networking software, right? We're deploying pulling elements of that network across core, across Amazon EKS, OpenShift on Red Hat ROSA, as well as just OpenShift for cloud. And we, through a single pane of deployment and management, we deployed the elements of the 5G core across them and then connected them in an end-to-end process. That's Telco Supercloud. >> Dave Vellante: So that's an O-RAN deployment. >> Yeah that's >> So, the big advantage of that, pardon me, Dave but the big advantage of that is the customer really doesn't care where the components are being served from for them. It's a 5G capability. It happens to sit in different locations. And that's, it's, it's about how do you abstract and how do you manage all those different workloads in a cohesive way? And that's exactly what EnterpriseWeb is bringing to the table. And what we do is we abstract the underlying infrastructure which is the cloud layer. So if, because AWS operating environment is different then private cloud operating environment then Azure environment, you have the networking is set up is different in each one of them. If there is a way you can abstract all of that and present it in a common operating model it becomes a lot easier than for anybody to be able to consume. >> And what a lot of customers tell me is the way they deal with multicloud complexity is they go with mono cloud, right? And so they'll lose out on some of the best services >> Absolutely >> If best of, so that's not >> that's not ideal, but at the end of the day, agree, developers don't want to muck with all the plumbing >> Dave Duggal: Yep. >> They want to write code. >> Azhar: Correct. >> So like I come back to are the traditional Telcos leaning in on a way that they're going to enable ISVs and developers to write on top of those platforms? Or are there sort of new entrance and disruptors? And I know, I know the answer is both >> Dave Duggal: Yep. >> but I feel as though the Telcos still haven't, traditional Telcos haven't tuned in to that developer affinity, but you guys sell to them. >> What, what are you seeing? >> Yeah, so >> What we have seen is there are Telcos fall into several categories there. If you look at the most mature ones, you know they are very eager to move up the value chain. There are some smaller very nimble ones that have actually doing, they're actually doing something really interesting. For example, they've provided sandbox environments to developers to say "Go develop your applications to the sandbox environment." We'll use that to build an net service with you. I can give you some interesting examples across the globe that, where that is happening, right? In AsiaPac, particularly in Australia, ANZ region. There are a couple of providers who have who have done this, but in, in, in a very interesting way. But the challenges to them, why it's not completely open or public yet is primarily because they haven't figured out how to exactly monetize that. And, and that's the reason why. So in the absence of that, what will happen is they they have to rely on the ISV ecosystem to be able to build those capabilities which they can then bring it on as part of the catalog. But in Latin America, I was talking to one of the providers and they said, "Well look we have a public cloud, we have our own public cloud, right?" What we want do is use that to offer localized services not just bring everything in from the top >> But, but we heard from Ericson's CEO they're basically going to monetize it by what I call "gouge", the developers >> (Azhar laughs) >> access to the network telemetry as opposed to saying, "Hey, here's an open platform development on top of it and it will maybe create something like an app store and we'll take a piece of the action." >> So ours, >> to be is a better model. >> Yeah. So that's perfect. Our second project that we're showing here is with Intel, right? So Intel came to us cause they are a reputation for doing advanced automation solutions. They gave us carte blanche in their labs. So this is Intel Network Builders they said pick your partners. And we went with the Red Hat, Fort Net, Keysite this company KX doing AIML. But to address your DevX, here's Intel explicitly wants to get closer to the developers by exposing their APIs, open APIs over their infrastructure. Just like Red Hat has APIs, right? And so they can expose them northbound to developers so developers can leverage and tune their applications, right? But the challenge there is what Intel is doing at the low level network infrastructure, right? Is fundamentally complex, right? What you want is an abstraction layer where develop and this gets to, to your point Dave where you just said like "The developers just want to get their job done." or really they want to focus on the business logic and accelerate that service delivery, right? So the idea here is an EnterpriseWeb they can literally declaratively compose their services, express their intent. "I want this to run optimized for low latency. I want this to run optimized for energy consumption." Right? And that's all they say, right? That's a very high level statement. And then the run time translates it between all the elements that are participating in that service to realize the developer's intent, right? No hands, right? Zero touch, right? So that's now a movement in telecom. So you're right, it's taking a while because these are pretty fundamental shifts, right? But it's intent based networking, right? So it's almost two parts, right? One is you have to have the open APIs, right? So that the infrastructure has to expose its capabilities. Then you need abstractions over the top that make it simple for developers to take, you know, make use of them. >> See, one of the demonstrations we are doing is around AIOps. And I've had literally here on this floor, two conversations around what I call as network as a platform. Although it sounds like a cliche term, that's exactly what Dave was describing in terms of exposing APIs from the infrastructure and utilizing them. So once you get that data, then now you can do analytics and do machine learning to be able to build models and figure out how you can orchestrate better how you can monetize better, how can how you can utilize better, right? So all of those things become important. It's just not about internal optimization but it's also about how do you expose it to third party ecosystem to translate that into better delivery mechanisms or IOT capability and so on. >> But if they're going to charge me for every API call in the network I'm going to go broke (team laughs) >> And I'm going to get really pissed. I mean, I feel like, I'm just running down, Oracle. IBM tried it. Oracle, okay, they got Java, but they don't they don't have developer jobs. VMware, okay? They got Aria. EMC used to have a thing called code. IBM had to buy Red Hat to get to the developer community. (Lisa laughs) >> So I feel like the telcos don't today have those developer shops. So, so they have to partner. [Azhar] Yes. >> With guys like you and then be more open and and let a zillion flowers bloom or else they're going to get disrupted in a big way and they're going to it's going to be a repeat of the over, over the top in, in in a different model that I can't predict. >> Yeah. >> Absolutely true. I mean, look, they cannot be in the connectivity business. Telcos cannot be just in the connectivity business. It's, I think so, you know, >> Dave Vellante: You had a fry a frozen hand (Dave Daggul laughs) >> off that, you know. >> Well, you know, think about they almost have to go become over the top on themselves, right? That's what the cloud guys are doing, right? >> Yeah. >> They're riding over their backbone that by taking a creating a high level abstraction, they in turn abstract away the infrastructure underneath them, right? And that's really the end game >> Right? >> Dave Vellante: Yeah. >> Is because now, >> they're over the top it's their network, it's their infrastructure, right? They don't want to become bid pipes. >> Yep. >> Now you, they can take OpenShift, run that in any cloud. >> Yep. >> Right? >> You can run that in hybrid cloud, enterprise web can do the application layer configuration and management. And together we're running, you know, OSI layers one through seven, east to west, north to south. We're running across the the RAN, the core and the transport. And that is telco super cloud, my friend. >> Yeah. Well, >> (Dave Duggal laughs) >> I'm dominating the conversation cause I love talking super cloud. >> I knew you would. >> So speaking of super superpowers, when you're in customer or prospective customer conversations with providers and they've got, obviously they're they're in this transformative state right now. How, what do you describe as the superpower between Red Hat and EnterpriseWeb in terms of really helping these Telcos transforms. But at the end of the day, the connectivity's there the end user gets what they want, which is I want this to work wherever I am. >> Yeah, yeah. That's a great question, Lisa. So I think the way you could look at it is most software has, has been evolved to be specialized, right? So in Telcos' no different, right? We have this in the enterprise, right? All these specialized stacks, all these components that they wire together in the, in you think of Telco as a sort of a super set of enterprise problems, right? They have all those problems like magnified manyfold, right? And so you have specialized, let's say orchestrators and other tools for every Telco domain for every Telco layer. Now you have a zoo of orchestrators, right? None of them were designed to work together, right? They all speak a specific language, let's say quote unquote for doing a specific purpose. But everything that's interesting in the 21st century is across layers and across domains, right? If a siloed static application, those are dead, right? Nobody's doing those anymore. Even developers don't do those developers are doing composition today. They're not doing, nobody wants to hear about a 6 million lines of code, right? They want to hear, "How did you take these five things and bring 'em together for productive use?" >> Lisa: Right. How did you deliver faster for my enterprise? How did you save me money? How did you create business value? And that's what we're doing together. >> I mean, just to add on to Dave, I was talking to one of the providers, they have more than 30,000 nodes in their infrastructure. When I say no to your servers running, you know, Kubernetes,running open stack, running different components. If try managing that in one single entity, if you will. Not possible. You got to fragment, you got to segment in some way. Now the question is, if you are not exposing that particular infrastructure and the appropriate KPIs and appropriate things, you will not be able to efficiently utilize that across the board. So you need almost a construct that creates like a manager of managers, a hierarchical structure, which would allow you to be more intelligent in terms of how you place those, how you manage that. And so when you ask the question about what's the secret sauce between the two, well this is exactly where EnterpriseWeb brings in that capability to analyze information, be more intelligent about it. And what we do is provide an abstraction of the cloud layer so that they can, you know, then do the right job in terms of making sure that it's appropriate and it's consistent. >> Consistency is key. Guys, thank you so much. It's been a pleasure really digging through EnterpriseWeb. >> Thank you. >> What you're doing >> with Red Hat. How you're helping the organization transform and Supercloud, we can't forget Supercloud. (Dave Vellante laughs) >> Fight Supercloud. Guys, thank you so much for your time. >> Thank you so much Lisa. >> Thank you. >> Thank you guys. >> Very nice. >> Lisa: We really appreciate it. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live tech coverage coming to you live from MWC 23. We'll be back after a short break.

(light music) >> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to Spain everyone. Lisa Martin here with theCUBE Dave Vellante, my co-host for the next four days. We're live in Barcelona, covering MWC23. This is only day one, but I'll tell you the theme of this conference this year is velocity. And I don't know about you Dave, but this day is flying by already. This is ecosystem day. We're going to have a great discussion on the ecosystem next. >> Well we're seeing the disaggregation of the hardened telco stack, and that necessitates an ecosystem open- we're going to talk about Open RAN, we've been talking about even leading up to the show. It's a critical technology enabler and it's compulsory to have an ecosystem to support that. >> Absolutely compulsory. We've got two guests here joining us, Gautam Bhagra, Vice President partnerships at Dell, and Scott Walker, Vice President of global Telco ecosystem at Wind River. Guys, welcome to the program. >> Nice to be here. >> Thanks For having us. >> Thanks for having us. >> So you've got some news, this is day one of the conference, there's some news, Gautam, and let's start with you, unpack it. >> Yeah, well there's a lot of news, as you know, on Dell World. One of the things we are very excited to announce today is the launch of the Open Telecom Ecosystems Community. I think Dave, as you mentioned, getting into an Open RAN world is a challenge. And we know some of the challenges that our customers face. To help solve for those challenges, Dell wants to work with like-minded partners and customers to build innovative solutions, and join go-to-market. So we are launching that today. Wind River is one of our flagship partners for that, and I'm excited to be here to talk about that as well. >> Can you guys talk a little bit about the partnership, maybe a little bit about Wind River so the audience gets that context? >> Sure, absolutely, and the theme of the show, Velocity, is what this partnership is all about. We create velocity for operators if they want to adopt Open RAN, right? We simplify it. Wind River as a company has been around for 40 years. We were part of Intel at one point, and now we're independent, owned by a company called Aptiv. And with that we get another round of investment to help continue our acceleration into this market. So, the Dell partnership is about, like I said, velocity, accelerating the adoption. When we talk to operators, they have told us there are many roadblocks that they face, right? Like systems integration, operating at scale. 'Cause when you buy a traditional radio access network solution from a single supplier, it's very easy. It's works, it's been tested. When you break these components apart and disaggregate 'em, as we talked about David, it creates integration points and support issues, right? And what Dell and Wind River have done together is created a cloud infrastructure solution that could host a variety of RAN workloads, and essentially create a two layer cake. What we're, overall, what we're trying to do is create a traditional RAN experience, with the innovation agility and flexibility of Open RAN. And that's really what this partnership does. >> So these work, this workload innovation is interesting to me because you've got now developers, you know, the, you know, what's the telco developer look like, you know, is to be defined, right? I mean it's like this white sheet of paper that can create all this innovation. And to do that, you've got to have, as I said earlier, an ecosystem. But you've got now, I'm interested in your Open RAN agenda and how you see that sort of maturity model taking place. 'Cause today, you got disruptors that are going to lean right in say "Hey, yeah, that's great." The traditional carriers, they have to have a, you know, they have to migrate, they have to have a hybrid world. We know that takes time. So what's that look like in the marketplace today? >> Yeah, so I mean, I can start, right? So from a Dell's perspective, what we see in the market is yes, there is a drive towards, everyone understands the benefits of being open, right? There's the agility piece, the innovation piece. That's a no-brainer. The question is how do we get there? And I think that's where partnerships become critical to get there, right? So we've been working with partners like Wind River to build solutions that make it easier for customers to start adopting some of the foundational elements of an open network. The, one of the purposes in the agenda of building this community is to bring like-minded developers, like you said like we want those guys to come and work with the customers to create new solutions, and come up with something creative, which no one's even thought about, that accelerates your option even quicker, right? So that's exactly what we want to do as well. And that's one of the reasons why we launched the community. >> Yeah, and what we find with a lot of carriers, they are used to buying, like I said, traditional RAN solutions which are provided from a single provider like Erickson or Nokia and others, right? And we break this apart, and you cloudify that network infrastructure, there's usually a skills gap we see at the operator level, right? And so from a developer standpoint, they struggle with having the expertise in order to execute on that. Wind River helps them, working with companies like Dell, simplify that bottom portion of the stack, the infrastructure stack. So, and we lifecycle manage it, we test- we're continually testing it, and integrating it, so that the operator doesn't have to do that. In addition to that, wind River also has a history and legacy of working with different RAN vendors, both disruptors like Mavenir and Parallel Wireless, as well as traditional RAN providers like Samsung, Erickson, and others soon to be announced. So what we're doing on the northbound side is making it easy by integrating that, and on the southbound side with Dell, so that again, instead of four or five solutions that you need to put together, it's simply two. >> And you think about today how we- how you consume telco services are like there's these fixed blocks of services that you can buy, that has to change. It's more like the, the app stores. It's got to be an open marketplace, and that's where the innovation's going to come in, you know, from the developers, you know, top down maybe. I don't know, how do you see that maturity model evolving? People want to know how long it's going to take. So many questions, when will Open RAN be as reliable. Does it even have to be? You know, so many interesting dynamics going on. >> Yeah, and I think that's something we at Dell are also trying to find out, right? So we have been doing a lot of good work here to help our customers move in that direction. The work with Dish is an example of that. But I think we do understand the challenges as well in terms of getting, adopting the technologies, and adopting the innovation that's being driven by Open. So one of the agendas that we have as a company this year is to work with the community to drive this a lot further, right? We want to have customers adopt the technology more broadly with the tier one, tier two telcos globally. And our sales organizations are going to be working together with Wind Rivers to figure out who's the right set of customers to have these conversations with, so we can drop, drive, start driving this agenda a lot quicker than what we've seen historically. >> And where are you having those customer conversations? Is that at the operator level, is it higher, is it both? >> Well, all operators are deploying 5G in preparation for 6G, right? And we're all looking for those killer use cases which will drive top line revenue and not just make it a TCO discussion. And that starts at a very basic level today by doing things like integrating with Juniper, for their cloud router. So instead of at the far edge cell site, having a separate device that's doing the routing function, right? We take that and we cloudify that application, run it on the same server that's hosting the RAN applications, so you eliminate a device and reduce TCO. Now with Aptiv, which is primarily known as an automotive company, we're having lots of conversations, including with Dell and Intel and others about vehicle to vehicle communication, vehicle to anything communication. And although that's a little bit futuristic, there are shorter term use cases that, like, vehicle to vehicle accident avoidance, which are going to be much nearer term than autonomous driving, for example, which will help drive traffic and new revenue streams for operators. >> So, oh, that's, wow. So many other things (Scott laughs) that's just opened up there too. But I want to come back to, sort of, the Open RAN adoption. And I think you're right, there's a lot of questions that that still have to be determined. But my question is this, based on your knowledge so far does it have to be as hardened and reliable, obviously has to be low latency as existing networks, or can flexibility, like the cloud when it first came out, wasn't better than enterprise IT, it was just more flexible and faster, and you could rent it. And, is there a similar dynamic here where it doesn't have to replicate the hardened stack, it can bring in new benefits that drive adoption, what are your thoughts on that? >> Well there's a couple of things on that, because Wind River, as you know, where our legacy and history is in embedded devices like F-15 fighter jets, right? Or the Mars Rover or the James Web telescope, all run Wind River software. So, we know about can't fail ultra reliable systems, and operators are not letting us off the hook whatsoever. It has to be as hardened and locked down, as secure as a traditional RAN environment. Otherwise they will (indistinct). >> That's table stakes. >> That's table stakes that gets us there. And when River, with our legacy and history, and having operator experience running live commercial networks with a disaggregated stack in the tens of thousands of nodes, understand what this is like because they're running live commercial traffic with live customers. So we can't fail, right? And with that, they want their cake and eat it too, right? Which is, I want ultra reliable, I want what I have today, but I want the agility and flexibility to onboard third party apps. Like for example, this JCNR, this Juniper Cloud-Native Router. You cannot do something as simple as that on a traditional RAN Appliance. In an open ecosystem you can take that workload and onboard it because it is an open ecosystem, and that's really one of the true benefits. >> So they want the mainframe, but they want (Scott laughs) the flexibility of the developer cloud, right? >> That's right. >> They want their, have their cake eat it too and not gain weight. (group laughs) >> Yeah I mean David, I come from the public cloud world. >> We all don't want to do that. >> I used to work with a public cloud company, and nine years ago, public cloud was in the same stage, where you would go to a bank, and they would be like, we don't trust the cloud. It's not secure, it's not safe. It was the digital natives that adopted it, and that that drove the industry forward, right? And that's where the enterprises that realized that they're losing business because of all these innovative new companies that came out. That's what I saw over the last nine years in the cloud space. I think in the telco space also, something similar might happen, right? So a lot of this, I mean a lot of the new age telcos are understanding the value, are looking to innovate are adopting the open technologies, but there's still some inertia and hesitancy, for the reasons as Scott mentioned, to go there so quickly. So we just have to work through and balance between both sides. >> Yeah, well with that said, if there's still some inertia, but there's a theme of velocity, how do you help organizations balance that so they trust evolving? >> Yeah, and I think this is where our solution, like infrastructure block, is a foundational pillar to make that happen, right? So if we can take away the concerns that the organizations have in terms of security, reliability from the fundamental elements that build their infrastructure, by working with partners like Wind River, but Dell takes the ownership end-to-end to make sure that service works and we have those telco grade SLAs, then the telcos can start focusing on what's next. The applications and the customer services on the top. >> Customer service customer experience. >> You know, that's an interesting point Gautam brings up, too, because support is an issue too. We all talk about when you break these things apart, it creates integration points that you need to manage, right? But there's also, so the support aspect of it. So imagine if you will, you had one vendor, you have an outage, you call that one vendor, one necktie to choke, right, for accountability for the network. Now you have four or five vendors that you have to work. You get a lot of finger pointing. So at least at the infrastructure layer, right? Dell takes first call support for both the hardware infrastructure and the Wind River cloud infrastructure for both. And we are training and spinning them up to support, but we're always behind them of course as well. >> Can you give us a favorite customer example of- that really articulates the value of the partnership and the technologies that it's delivering to customers? >> Well, Infra Block- >> (indistinct) >> Is quite new, and we do have our first customer which is LG U plus, which was announced yesterday. Out of Korea, small customer, but a very important one. Okay, and I think they saw the value of the integrated system. They don't have the (indistinct) expertise and they're leveraging Dell and Wind River in order to make that happen. But I always also say historically before this new offering was Vodafone, right? Vodafone is a leader in Europe in terms of Open RAN, been very- Yago and Paco have been very vocal about what they're doing in Open RAN, and Dell and Wind River have been there with them every step of the way. And that's what I would say, kind of, led up to where we are today. We learned from engagements like Vodafone and I think KDDI as well. And it got us where we are today and understanding what the operators need and what the impediments are. And this directly addresses that. >> Those are two very different examples. You were talking about TCO before. I mean, so the earlier example is, that's an example to me of a disruptor. They'll take some chances, you know, maybe not as focused on TCO, of course they're concerned about it. Vodafone I would think very concerned about TCO. But I'm inferring from your comments that you're trying to get the industry, you're trying to check the TCO box, get there. And then move on to higher levels of value monetization. The TCO is going to come down to how many humans it takes to run the network, is it not, is that- >> Well a lot of, okay- >> Or is it devices- >> So the big one now, particularly with Vodafone, is energy cost, right? >> Of course, greening the network. >> Two-thirds of the energy consumption in RAN is the the Radio Access Network. Okay, the OPEX, right? So any reductions, even if they're 5% or 10%, can save tens or hundreds of millions of dollars. So we do things creatively with Dell to understand if there's a lot of traffic at the cell site and if it's not, we will change the C state or P state of the server, which basically spins it down, so it's not consuming power. But that's just at the infrastructure layer. Where this gets really powerful is working with the RAN vendors like Samsung and Ericson and others, and taking data from the traffic information there, applying algorithms to that in AI to shut it down and spin it back up as needed. 'Cause the idea is you don't want that thing powered up if there's no traffic on it. >> Well there's a sustainability, ESG, benefit to that, right? >> Yes. >> And, and it's very compute intensive. >> A hundred percent. >> Which is great for Dell. But at the same time, if you're not able to manage that power consumption, the whole thing fails. I mean it's, because there's going to be so much data, and such a intense requirement. So this is a huge issue. Okay, so Scott, you're saying that in the TCO equation, a big chunk is energy consumption? >> On the OPEX piece. Now there's also the CapEx, right? And Open RAN solutions are now, what we've heard from our customers today, are they're roughly at parity. 'Cause you can do things like repurpose servers after the useful life for a lower demand application which helps the TCO, right? Then you have situations like Juniper, where you can take, now software that runs on the same device, eliminating at a whole other device at the cell site. So we're not just taking a server and software point of view, we're taking a whole cell site point of view as it relates to both CapEx and OPEX. >> And then once that infrastructure it really gets adopted, that's when the innovation occurs. The ecosystem comes in. Developers now start to think of new applications that we haven't thought of yet. >> Gautam: Exactly. >> And that's where, that's going to force the traditional carriers to respond. They're responding, but they're doing so very carefully right now, it's understandable why. >> Yeah, and I think you're already seeing some news in the, I mean Nokia's announcement yesterday with the rebranding, et cetera. That's all positive momentum in my opinion, right? >> What'd you think of the logo? >> I love the logo. >> I liked it too. (group laughs) >> It was beautiful. >> I thought it was good. You had the connectivity down below, You need pipes, right? >> Exactly. >> But you had this sort of cool letters, and then the the pink horizon or pinkish, it was like (Scott laughs) endless opportunity. It was good, I thought it was well thought out. >> Exactly. >> Well, you pick up on an interesting point there, and what we're seeing, like advanced carriers like Dish, who has one of the true Open RAN networks, publishing APIs for programmers to build in their 5G network as part of the application. But we're also seeing the network equipment providers also enable carriers do that, 'cause carriers historically have not been advanced in that way. So there is a real recognition that in order for these networks to monetize new use cases, they need to be programmable, and they need to publish standard APIs, so you can access the 5G network capabilities through software. >> Yeah, and the problem from the carriers, there's not enough APIs that the carriers have produced yet. So that's where the ecosystem comes in, is going to >> A hundred percent >> I think there's eight APIs that are published out of the traditional carriers, which is, I mean there's got to be 8,000 for a marketplace. So that's where the open ecosystem really has the advantage. >> That's right. >> That's right. >> That's right. >> Yeah. >> So it all makes sense on paper, now you just, you got a lot of work to do. >> We got to deliver. Yeah, we launched it today. We got to get some like-minded partners and customers to come together. You'll start seeing results coming out of this hopefully soon, and we'll talk more about it over time. >> Dave: Great Awesome, thanks for sharing with us. >> Excellent. Guys, thank you for sharing, stopping by, sharing what's going on with Dell and Wind River, and why the opportunity's in it for customers and the technological evolution. We appreciate it, you'll have to come back, give us an update. >> Our pleasure, thanks for having us. (Group talks over each other) >> All right, thanks guys >> Appreciate it. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, Live from MWC23 in Barcelona. theCUBE is the leader in live tech coverage. (upbeat music)

(cheerful music) >> Welcome to theCUBE's continuing coverage of AMD's 4th Generation EPYC launch. I'm Dave Nicholson, and I'm here in our Palo Alto studios talking to Greg Gibby, senior product manager, data center products from AMD, and Mohan Rokkam, technical marketing engineer at Dell. Welcome, gentlemen. >> Mohan: Hello, hello. >> Greg: Thank you. Glad to be here. >> Good to see each of you. Just really quickly, I want to start out. Let us know a little bit about yourselves. Mohan, let's start with you. What do you do at Dell exactly? >> So I'm a technical marketing engineer at Dell. I've been with Dell for around 15 years now and my goal is to really look at the Dell powered servers and see how do customers take advantage of some of the features we have, especially with the AMD EPYC processors that have just come out. >> Greg, and what do you do at AMD? >> Yeah, so I manage our software-defined infrastructure solutions team, and really it's a cradle to grave where we work with the ISVs in the market, so VMware, Nutanix, Microsoft, et cetera, to integrate the features that we're putting into our processors and make sure they're ready to go and enabled. And then we work with our valued partners like Dell on putting those into actual solutions that customers can buy and then we work with them to sell those solutions into the market. >> Before we get into the details on the 4th Generation EPYC launch and what that means and why people should care. Mohan, maybe you can tell us a little about the relationship between Dell and AMD, how that works, and then Greg, if you've got commentary on that afterwards, that'd be great. Yeah, Mohan. >> Absolutely. Dell and AMD have a long standing partnership, right? Especially now with EPYC series. We have had products since EPYC first generation. We have been doing solutions across the whole range of Dell ecosystem. We have integrated AMD quite thoroughly and effectively and we really love how performant these systems are. So, yeah. >> Dave: Greg, what are your thoughts? >> Yeah, I would say the other thing too is, is that we need to point out is that we both have really strong relationships across the entire ecosystem. So memory vendors, the software providers, et cetera, we have technical relationships. We're working with them to optimize solutions so that ultimately when the customer buys that, they get a great user experience right out of the box. >> So, Mohan, I know that you and your team do a lot of performance validation testing as time goes by. I suspect that you had early releases of the 4th Gen EPYC processor technology. What have you been seeing so far? What can you tell us? >> AMD has definitely knocked it out of the park. Time and again, in the past four generations, in the past five years alone, we have done some database work where in five years, we have seen five exit performance. And across the board, AMD is the leader in benchmarks. We have done virtualization where we would consolidate from five into one system. We have world records in AI, we have world records in databases, we have world records in virtualization. The AMD EPYC solutions has been absolutely performant. I'll leave you with one number here. When we went from top of Stack Milan to top of Stack Genoa, we saw a performance bump of 120%. And that number just blew my mind. >> So that prompts a question for Greg. Often we, in industry insiders, think in terms of performance gains over the last generation or the current generation. A lot of customers in the real world, however, are N - 2. They're a ways back, so I guess two points on that. First of all, the kinds of increases the average person is going to see when they move to this architecture, correct me if I'm wrong, but it's even more significant than a lot of the headline numbers because they're moving two generations, number one. Correct me if I'm wrong on that, but then the other thing is the question to you, Greg. I like very long complicated questions, as you can tell. The question is, is it okay for people to skip generations or make the case for upgrades, I guess is the problem? >> Well, yeah, so a couple thoughts on that first too. Mohan talked about that five X over the generation improvements that we've seen. The other key point with that too is that we've made significant process improvements along the way moving to seven nanocomputer to now five nanocomputer and that's really reducing the total amount of power or the performance per watt the customers can realize as well. And when we look at why would a customer want to upgrade, right? And I want to rephrase that as to why aren't you? And there is a real cost of not upgrading. And so when you look at infrastructure, the average age of a server in the data center is over five years old. And if you look at the most popular processors that were sold in that timeframe, it's 8, 10, 12 cores. So now you've got a bunch of servers that you need in order to deliver the applications and meet your SLAs to your end users, and all those servers pull power. They require maintenance. They have the opportunity to go down, et cetera. You got to pay licensing and service and support costs and all those. And when you look at all the costs that roll up, even though the hardware is paid for just to keep the lights on, and not even talking about the soft costs of unplanned downtime, and, "I'm not meeting your SLAs," et cetera, it's very expensive to keep those servers running. Now, if you refresh, and now you have processors that have 32, 64, 96 cores, now you can consolidate that infrastructure and reduce your total power bill. You can reduce your CapEx, you reduce your ongoing OpEx, you improve your performance, and you improve your security profile. So it really is more cost effective to refresh than not to refresh. >> So, Mohan, what has your experience been double clicking on this topic of consolidation? I know that we're going to talk about virtualization in some of the results that you've seen. What have you seen in that regard? Does this favor better consolidation and virtualized environments? And are you both assuring us that the ROI and TCO pencil out on these new big, bad machines? >> Greg definitely hit the nail on the head, right? We are seeing tremendous savings really, if you're consolidating from two generations old. We went from, as I said, five is to one. You're going from five full servers, probably paid off down to one single server. That itself is, if you look at licensing costs, which again, with things like VMware does get pretty expensive. If you move to a single system, yes, we are at 32, 64, 96 cores, but if you compare to the licensing costs of 10 cores, two sockets, that's still pretty significant, right? That's one huge thing. Another thing which actually really drives the thing is we are looking at security, and in today's environment, security becomes a major driving factor for upgrades. Dell has its own setups, cyber-resilient architecture, as we call it, and that really is integrated from processor all the way up into the OS. And those are some of the features which customers really can take advantage of and help protect their ecosystems. >> So what kinds of virtualized environments did you test? >> We have done virtualization across primary codes with VMware, but the Azure Stack, we have looked at Nutanix. PowerFlex is another one within Dell. We have vSAN Ready Nodes. All of these, OpenShift, we have a broad variety of solutions from Dell and AMD really fits into almost every one of them very well. >> So where does hyper-converged infrastructure fit into this puzzle? We can think of a server as something that contains not only AMD's latest architecture but also latest PCIe bus technology and all of the faster memory, faster storage cards, faster nicks, all of that comes together. But how does that play out in Dell's hyper-converged infrastructure or HCI strategy? >> Dell is a leader in hyper-converged infrastructure. We have the very popular VxRail line, we have the PowerFlex, which is now going into the AWS ecosystem as well, Nutanix, and of course, Azure Stack. With all these, when you look at AMD, we have up to 96 cores coming in. We have PCIe Gen 5 which means you can now connect dual port, 100 and 200 gig nicks and get line rate on those so you can connect to your ecosystem. And I don't know if you've seen the news, 200, 400 gig routers and switchers are selling out. That's not slowing down. The network infrastructure is booming. If you want to look at the AI/ML side of things, the VDI side of things, accelerator cards are becoming more and more powerful, more and more popular. And of course they need that higher end data path that PCIe Gen 5 brings to the table. GDDR5 is another huge improvement in terms of performance and latencies. So when we take all this together, you talk about hyper-converged, all of them add into making sure that A, with hyper-converged, you get ease of management, but B, just 'cause you have ease of management doesn't mean you need to compromise on anything. And the AMD servers effectively are a no compromise offering that we at Dell are able to offer to our customers. >> So Greg, I've got a question a little bit from left field for you. We covered Supercompute Conference 2022. We were in Dallas a couple of weeks ago, and there was a lot of discussion of the current processor manufacturer battles, and a lot of buzz around 4th Gen EPYC being launched and what's coming over the next year. Do you have any thoughts on what this architecture can deliver for us in terms of things like AI? We talk about virtualization, but if you look out over the next year, do you see this kind of architecture driving significant change in the world? >> Yeah, yeah, yeah, yeah. It has the real potential to do that from just the building blocks. So we have our chiplet architecture we call it. So you have an IO die and then you have your core complexes that go around that. And we integrate it all with our infinity fabric. That architecture allows you, if we wanted to, replace some of those CCDs with specific accelerators. And so when we look two, three, four years down the road, that architecture and that capability already built into what we're delivering and can easily be moved in. We just need to make sure that when you look at doing that, that the power that's required to do that and the software, et cetera, and those accelerators actually deliver better performance as a dedicated engine versus just using standard CPUs. The other things that I would say too is if you look at emerging workloads. So data center modernization is one of the buzzwords in cloud native, right? And these container environments, well, AMD'S architecture really just screams support for those type of environments, right? Where when you get into these larger core accounts and the consolidation that Mohan talked about. Now when I'm in a container environment, that blast radius so a lot of customers have concerns around, "Hey, having a single point of failure and having more than X number of cores concerns me." If I'm in containers, that becomes less of a concern. And so when you look at cloud native, containerized applications, data center modernization, AMD's extremely well positioned to take advantage of those use cases as well. >> Yeah, Mohan, and when we talk about virtualization, I think sometimes we have to remind everyone that yeah, we're talking about not only virtualization that has a full-blown operating system in the bucket, but also virtualization where the containers have microservices and things like that. I think you had something to add, Mohan. >> I did, and I think going back to the accelerator side of business, right? When we are looking at the current technology and looking at accelerators, AMD has done a fantastic job of adding in features like AVX-512, we have the bfloat16 and eight features. And some of what these do is they're effectively built-in accelerators for certain workloads especially in the AI and media spaces. And in some of these use cases we look at, for example, are inference. Traditionally we have used external accelerator cards, but for some of the entry level and mid-level use cases, CPU is going to work just fine especially with the newer CPUs that we are seeing this fantastic performance from. The accelerators just help get us to the point where if I'm at the edge, if I'm in certain use cases, I don't need to have an accelerator in there. I can run most of my inference workloads right on the CPU. >> Yeah, yeah. You know the game. It's an endless chase to find the bottleneck. And once we've solved the puzzle, we've created a bottleneck somewhere else. Back to the supercompute conversations we had, specifically about some of the AMD EPYC processor technology and the way that Dell is packaging it up and leveraging things like connectivity. That was one of the things that was also highlighted. This idea that increasingly connectivity is critically important, not just for supercomputing, but for high-performance computing that's finding its way out of the realms of Los Alamos and down to the enterprise level. Gentlemen, any more thoughts about the partnership or maybe a hint at what's coming in the future? I know that the original AMD announcement was announcing and previewing some things that are rolling out over the next several months. So let me just toss it to Greg. What are we going to see in 2023 in terms of rollouts that you can share with us? >> That I can share with you? Yeah, so I think look forward to see more advancements in the technology at the core level. I think we've already announced our product code name Bergamo, where we'll have up to 128 cores per socket. And then as we look in, how do we continually address this demand for data, this demand for, I need actionable insights immediately, look for us to continue to drive performance leadership in our products that are coming out and address specific workloads and accelerators where appropriate and where we see a growing market. >> Mohan, final thoughts. >> On the Dell side, of course, we have four very rich and configurable options with AMD EPYC servers. But beyond that, you'll see a lot more solutions. Some of what Greg has been talking about around the next generation of processors or the next updated processors, you'll start seeing some of those. and you'll definitely see more use cases from us and how customers can implement them and take advantage of the features that. It's just exciting stuff. >> Exciting stuff indeed. Gentlemen, we have a great year ahead of us. As we approach possibly the holiday seasons, I wish both of you well. Thank you for joining us. From here in the Palo Alto studios, again, Dave Nicholson here. Stay tuned for our continuing coverage of AMD's 4th Generation EPYC launch. Thanks for joining us. (cheerful music)

>>And good afternoon. Welcome back here on the Cub as to continue our coverage at aws Reinvent 22, win the Venetian here in Las Vegas, day two, it's Wednesday. Thanks. Still rolling. Quite a along. We have another segment for you as part of the Global Startup program, which is under the AWS Startup Showcase. I'm joined now by Vink at Viera, who is the CEO and co-founder of R Set. And good to see you, >>Sir. Thanks for having me here. Yeah, >>No, a real pleasure. Looking forward to it. So first off, for some of, for yours who might not be familiar with Roxette, I know you've been on the cube a little bit, so you're, you're an alum, but, but why don't you set the stage a little bit for Rock set and you know, where you're engaged with in terms of, with aws? >>Definitely. Rock Set is a realtime analytics database that is built for the cloud. You know, we make realtime applications possible in the cloud. You know, realtime applications need high concurrency, low latency query processing data needs to be fresh, your analytic needs to be fast. And, you know, we built on aws and that's why we are here. We are very, very proud partners of aws. We are in the AWS Accelerate program, and also we are in the startup program of aws. We are strategic ISV partner. And so yeah, we make real time analytics possible without all the cost and complexity barriers that are usually associated with it. And very, very happy to be part of this movement from batch to real time that is happening in the world. >>Right. Which is certainly an exciting trend. Right. I know great news for you, you made news yesterday, had an announcement involved with the intel with aws, who wants to share some of that >>With us too? Definitely. So, you know, one, one question that I always ask people is like, you know, if you go perspective that I share is like, if you go ask a hundred people, do you want fast analytics on fresh data or slow analytics on stale data? You know, a hundred out of a hundred would say fast and fresh, right? Sure. So then the question is, why hasn't this happened already? Why is this still a new trend that is emerging as opposed to something that everybody's taking for granted? It really comes down to compute efficiency, right? I think, you know, at the end of the day, real time analytics was always in using, you know, technologies that are, let's say 10 years ago using let's say processors that were available 10 years ago to, you know, three cloud, you know, days. There was a lot of complexity barriers associated with realtime analytics and also a lot of cost and, and performance barriers associated with it. >>And so Rox said from the, you know, from the very beginning, has been obsessing about building the most compute efficient realtime database in the world. And, you know, AWS on one hand, you know, allows us to make a consumption based pricing model. So you only pay for what you use. Sure. And that shatters all the cost barriers. But in terms of computer efficiency, what we announced yesterday is the Intel's third generation Zon scalable processors, it's code named Intel Ice Lake. When we port it over Rock said to that architecture, taking advantage of some of the instructions sets that Intel has, we got an 84% performance boost, 84, 84, 84. >>It's, it's incredible, right? >>It's, it's an incredible charts, it's an incredible milestone. It reduces the barrier even more in terms of cost and, you know, and, and pushes the efficiency and sets a, a really new record for how efficient realtime, you know, data processing can be in the cloud. And, and it's very, very exciting news. And so we used to benchmark ourselves against some of our other, you know, realtime, you know, did up providers and we were already faster and now we've set a, a much, much higher bar for other people to follow. >>Yep. And, and so what is, or what was it about real time that, that, you know, was such a barrier because, and now you've got the speed of, of course, obviously, and maybe that's what it was, but I think cost is probably part of that too, right? That's all part of that equation. I mean, real time, so elusive. >>Yeah. So real time has this inherent pattern that your data never stops coming. And when your data never stops coming, and you can now actually do analytics on that. Now, initially people start with saying, oh, I just want a real time dashboard. And then very quickly they realize, well, the dashboard is actually in real time. I'm not gonna be staring at the 24 7. Can you tap on my shoulder when something is off, something needs to be looked at. So in which case you're constantly also asking the question, is everything okay? Is everything all right? Do I need to, is is that something that I need to be, you know, double clicking on and, and following up on? So essentially very quickly in real time analytics, what happens is your queries never stop. The questions that you're asking on your data never stops. And it's often a program asking the question to detect anomalies and things like that. >>And your data never stops coming. And so compute is running 24 7. If you look at traditional data warehouses and data lakes, they're not really optimized for these kinds of workloads. They're optimized to store massive volumes of data and in a storage efficient format. And when an analyst comes and asks a question to generate a report, you can spin up a whole bunch of compute, generate the report and tear it all down when you're done. Well, that is not compute running 24 7 to continuously, you know, you know, keep ingesting the data or continuously keep answering questions. So the compute efficiency that is needed is, is much, much, much higher. Right? And that is why, you know, Rox was born. So from the very beginning, we're only built, you know, for these use cases, we have a, an extremely powerful SQL engine that can give you full feature SQL analytics in a very, very compute efficient way in the cloud. >>Right. So, so let's talk about the leap that you've made, say in the last two years and, and, and what's been the spur of that? What has been allowed you to, to create this, you know, obviously a, a different kind of an array for your customers from which to choose, but, but what's been the spark you think >>We touched upon this a little earlier, right? This spark is really, you know, the world going from batch to real time. So if you look at mainstream adoption of technologies like Apache, Kafka and Confluent doing a really good job at that. In, in, in growing that community and, and use cases, now businesses are now acquiring business data, really important business data in real time. Now they want to operationalize it, right? So, you know, extract based static reports and bi you know, business intelligence is getting replaced in all modern enterprises with what we call operational intelligence, right? Don't tell me what happened last quarter and how to plan this quarter better. Tell me what's happening today, what's happening right now. And it's, it's your business operations using data to make day to day decisions better that either grows your top line, compresses your bottom line, eliminates risk that are inherently creeping up in your business. >>Sure. You know, eliminate potential churn from a customer or fraud, you know, deduction and, and getting on top of, you know, that, you know, a minute into this, into, into an outage as opposed to an hour into the outage. Right? And so essentially I think businesses are now realizing that operational intelligence and operational analytics really, you know, allows them to leverage data and especially real time data to make their, you know, to grow their businesses faster and more efficiently. And especially in this kind of macro environment that is, you know, more important to have better unit economics in your business than ever before. Sure. And so that is really, I think that is the real market movement happening. And, and we are here to just serve that market. We are making it much, much easier for companies that have already adopted, you know, streaming technologies like Kafka and, and, and knows Canis MSK and all these technologies. Now businesses are acquiring these data in real time now. They can also get realtime analytics on the other end of it. Sure. >>You know, you just touched on this and, and I'd like to hear your thoughts about this, about, about the economic environment because it does drive decisions, right? And it does motivate people to look for efficiencies and maybe costs, you know, right. Cutting costs. What are you seeing right now in terms of that, that kind of looming influence, right? That the economy can have in terms of driving decisions about where investments are being made and what expectations are in terms of delivering value, more value for the buck? >>Exactly. I think we see across the board, all of our customers come back and tell us, we don't want to manage data infrastructure and we don't want to do kind of DIY open source clusters. We don't wanna manage and scale and build giant data ops and DevOps teams to manage that, because that is not really, you know, in their business. You know, we have car rental companies want to be better at car rentals, we want airlines to be a better airline, and they don't, don't want their, you know, a massive investment in DevOps and data ops, which is not really their core business. And they really want to leverage, you know, you know, fully managed and, you know, cloud offerings like Rock said, you know, built on aws, massively scalable in the cloud with zero operational overhead, very, very easy to get started and scale. >>And so that completely removes all the operational overhead. And so they can invest the resources they have, the manpower, they have, the calories that they have on actually growing their businesses because that is what really gonna allow them to have better unit economics, right? So everybody that is on my payroll is helping me grow my top line or shrink my bottom line, eliminate risk in my business and, and, and, and churn and, and fraud and other, and eliminate all those risks that are inherent in my business. So, so that is where I think a lot of the investments going. So gone are the days where, you know, you're gonna have these in like five to 10% team managing a very hard to operate, you know, open source data management clusters on EC two nodes in, in AWS and, and kind of DIYing it their way because those 10 people, you know, if all they do is just operational maintenance of infrastructure, which is a means to an end, you're way better off, you know, using a cloud, you know, a bond in the cloud built for the cloud solution like rock and eliminate all that cost and, and replace that with an operationally much, much simpler, you know, system to op, you know, to to work with such as, such as rock. >>So that is really the big trend that we are seeing why, you know, not only real time is going more and more mainstream cloud native solutions or the real future even when it comes to real time because the complexity barrier needs to be shattered and only cloud native solutions can actually, >>You get the two Cs cost and complexity, right. That you, you need to address. Exactly. Yeah, for sure. You know, what is it about building trust with your, with your clients, with your partners? Because you, you're talking about this cloud environment that, that everyone is talking about, right? Not everyone's made that commitment. There are still some foot draggers out there. How are you going about establishing confidence and establishing trust and, and, and providing them with really concrete examples of the values and the benefits that you can provide, you know, with, with these opportunities? >>So, you know, I grew up, so there's a few ways to to, to answer this question. I'll, I'll, I'll come, I'll cover all the angles. So in, in order to establish trust, you have to create value. They, you know, your customer has to see that with you. They were able to solve the problem faster, better, cheaper, and they're able to, you know, have a, the business impact they were looking for, which is why they started the project in the first place. And so establishing that and proving that, I think there's no equivalence to that. And, you know, I grew up at, at, you know, at Facebook back in the day, you know, I was managing online data infrastructure, okay. For Facebook from 2007 and 2015. And internally we always had this kind of culture of all the product teams building on top of the infrastructure that my team was responsible for. >>And so they were not ever, there was never a, a customer vendor relationship internally within Facebook that we're all like, we're all part of the same team. We're partnering here to have you, you know, to help you have a successful product launch. There's a very similar DNA that, that exists in Rock said, when our customers work with us and they come to us and we are there to make them successful, our consumption based pricing model also forces us to say they're not gonna really use Rock said and consume more. I mean, we don't make money until they consume, right? And so their success is very much integral part of our, our success. And so that I think is one really important angle on, you know, give us a shot, come and do an evaluation, and we will work with you to build the most efficient way to solve your problem. >>And then when you succeed, we succeed. So that I think is a very important aspect. The second one is AWS partnership. You know, we are an ISV partner, you know, AWS a lot of the time. That really helps us establish trust. And a lot of the time, one of the, the, the people that they look up to, when a customer comes in saying, Hey, what is, who is Rock? Said? You know, who are your friends? Yeah. Who are your friends? And then, you know, and then the AWS will go like, oh, you know, we'll tell you, you know, all these other successful case studies that R has, you know, you know, built up on, you know, the world's largest insurance provider, Europe's largest insurance provider. We have customers like, you know, JetBlue Airlines to Klarna, which is a big bator company. And so, so all these case studies help and, and, and, and platform and partners like AWS helps us, helps you amplify that, that, you know, and, and, and, and, and give more credibility. And last but not least, compliance matters. You know, being Soto type two compliant is, is a really important part of establishing trust. We are hip hop compliant now so that, you know, we can, you know, pi I phi data handling that. And so I think that will continue to be a part, a big part of our focus in improving the security, you know, functionality and, and capabilities that R set has in the cloud, and also compliance and, and the set of com, you know, you know, standards that we are gonna be compliant against. >>Well, I'm glad you hit on the AWS too, cause I did wanna bring that up. I, I appreciate that and I know they appreciate the relationship as well. Thanks for the time here. It's been a pleasure. Awesome. Learning about Rockette and what you're up to. Thank you. >>You bet. >>It's a pleasure. Thank you. Vi ka. All right. You are watching the cube coverage here at AWS Reinvent 22. And on the cube, of course, the leader, the leader in high tech coverage.

(techno intro music) >> Hey everyone, good afternoon from sin city. This is Lisa Martin with Dave Vellante. We are in full swing of theCUBE's four days of coverage of AWS re:invent 2022. North of 50,000 people are here. We're nearing hundreds of thousands online. Dave, this has been, this is a great event. We've had great conversations. We're going to be having more conversations. One of the things we love talking about on theCUBE is AWS and its ecosystem of partners, and we are going to do just that right now. Brian Henderson joins us, Director of Marketing at Dell Technologies. Marc Trimuschat, Director of Worldwide Storage Specialists at AWS is also here. Guys, it's great to have you. >> Great to be here. >> Great to be here, yeah. Feeling the energy of the show. >> Isn't it great? >> Mark: I know, amazing. >> It's amazing. It started out high and it has not dropped since Monday night. Brian, talk a little bit about Dell, what you're doing with customers on their Cloud journeys. Every customer, every industry is on one at different points in their journey, but what's Dell helping out with there? >> What we're here to talk about is the progression that we've seen, right, Cloud has changed a lot over the years and Dell has really put out a strategy to help people with their Cloud journey, kind of wherever they are. So a lot of people have moved full shift. A lot of people see that as another location, and what we're showing at the booth is the idea of taking these enterprise capabilities that people know and trust from Dell, courting them to the Cloud. In some cases not courting, but just delivering that software in the Cloud, as well as taking some of the Kubernetes integrations, EKS Anywhere, bringing that on-prem. So we've got some storage, data protection, and our Kubernetes integration to talk about at the show. >> Awesome, Mark, talk about the role from Amazon's point of view that third party vendors like Dell Technologies plays in AWS's expanding vision of Cloud. >> Great, well, we're really excited to be partnering with Dell. What we see that historically is, you know, AWS is focused on builders, people, and really the developer community who are building those components themselves, putting together really resilient infrastructure and applications. What we're seeing today is a shift also to the type of customers that we're seeing, more traditional enterprise customers, who are demanding really performance, the scalability, also the resiliency of what they had on-premises, and they want that on the Cloud as well. So with Dell, and we've got some great solutions that we're partnering on, including Dell PowerFlex that provides that linear scalability and some of the high performance capabilities that customers are demanding. And also, another big trend that we're seeing is customers being affected by things like unfortunately malware events, right, and data protection. So Dell provides some great solutions in both those areas that allow enterprise customers to really experience that mission critical capability and resiliency that they have on-premises in the Cloud. >> You know, Brian, we've been at this a long time. >> Brian: Oh yeah, great to see you again. >> And I've been hearing my whole career that storage is going to get commoditized. And I guess if you're talking about spinning discs or flash drives, it's probably true, but as Mark was just saying if you want resilient storage and things that are recoverable, that don't go down all the time, they're not commodities. >> Brian: Yeah. >> It's real engineering. And you built the stack up, so talk about how that connection, what value you bring to the Cloud and your customers. >> Yeah, so what we see is people are always looking out for enterprise grade capabilities. So there's going to be a set of offerings, and AWS has a fantastic foundation for building on top of with the marketplace. So what we're able to do is really bring, in some cases, decades worth of investment in software engineering and put these advanced capabilities, whether it be PowerFlex with its linear scale. We'll have a file offering very soon. These products have been built from the ground up to do a very unique purpose. Giving that to people in the Cloud is just another location for us, AWS being the market leader. We're the market leader in storage. So us working together for the benefit of customers is really where it's at. >> Can you double click on that, Brian, what Dell and AWS? Give us all those juicy details. >> Sure, sure, sure, so what we've done right before this show is we put a product called PowerFlex, if you go back to 2018 scale IO, and you're taking this really linear scaling software defined architecture, and you're putting that in the Cloud. What that allows you to do is get that really advanced linear scale performance. You can even span clusters across AWS regions, as well as zones. So it's a really unique capability that allows us to be able to check in and do that. And in the data protection space, it's a whole separate category. We've been at this actually quite a while. We've got about 14 exo bytes of data that's already being protected on the AWS Cloud. So we've been at that for quite a while. And the two levels are really, do you want to back that up? Do you want to take a traditional back up application, maybe it's a lift and shift, and I want to back it up the way I used to, and you can do that in the Cloud now. Or we're seeing cyber resiliency come up a lot more, and we were just talking right before, it's a question of when, not if, and so we have to give our customers the option to not only detect that failure event early, but also to separate that copy with a logical air gap. >> The cyber resiliency is a topic we are talking more and more about. It's absolutely critical. We've seen the threat landscape change dramatically in the last couple of years. To your point, Brian, it's no longer, when we think of ransomware, it's no longer are we going to get hit? It's when, it's how often. What's the damage going to be? I think I saw a stat recently that there's one ransomware attack every 11 seconds. The average cost of reaches is in the millions, so what you're doing together on cyber resiliency for businesses in any industry is table stakes. >> Yeah, we just saw a survey that, it was done earlier this year survey, 66% unfortunately of corporations have experienced a malware attack. And that's an 80% increase from last year. >> Lisa: Wow. >> So again, I think that's an opportunity. It's a threat, but an opportunity, and so the partnership with Dell really helps bridge that and helps our customers, our mutual customers, recover from those incidents. >> A lot of people might say, this is interesting. A storage guy from Amazon, a storage guy from Dell, two leaders. And one might think, why didn't they just throw in a dash three, right, but you guys are both customer driven, customer obsessed. In the field, what are customers saying to you in terms of how they want you to work together? >> Well I think there's a place for everything. When you say throw in to S3, so S3 today, one of the big trends when you're looking here is just the amount of data, you know, we hear that rhetoric, you know, we've been in storage for many years, and the data has all increased up and to the right. But, you know, AWSI, S3 today, we have over 280 trillion objects in our, driving a hundred million transactions per second right now, so that's scale. So there's always a place for those really, we have hundreds of thousands of customers running their data links, so that's always going to be that really, you know, highly reliable, highly durable, high available solution for data links. But customers, there's a lot of different applications out there. So where customers are asking are those enterpise. So we have EBS, for example, which is our great, you know, scalable block search, elastic block store. We introduced some new volume types, like GP2, GP2, and IO2VX, which will have that performance. But there's still single availability zone. So what customers have done historically is they maybe the application layer, they put an application layer replication or resiliency across, but customers on-prem, they've relied on storage layers to do that work for them. So, with PowerFlex, that'll stand either using instant storage or EBS, building on that really strong foundation, but provide that additional layer to make it easy for customers to get that resiliency and that scalability that Brian talked about. >> Yep, yep. >> Anything you can add to that? >> Yeah, I mean to your question, how do we work together is really, it's all customer driven. So we see customers that are shifting workloads in the Cloud for the first time. And it might make sense to take an object, like PowerFlex or another storage technology, maybe you want to compress it a little bit before you send it to the Cloud. Maybe you don't want to lift and shift everything. So we have a team of people that works very closely with AWS to be able to determine how are you going to shift that workload out there? Does this make the right sense for you? So it's a very collaborative relationship. And it's all very customer driven because our customers are saying, I've got assets in the public Cloud, and I want them to be managed in a similar fashion to how I'm doing that on-prem. >> So customer obsession is clearly on both sides there. We know that. >> It's where it starts. >> Exactly, exactly. Going back to PowerFlex for a second, Brian, and I'd love to get an example of a joint customer that really is showing the value of what Dell and AWS are doing together. The question for you on PowerFlex, talk about the value that it offers to the public Cloud. And why should customers start there if they are early in this journey? >> All right, yeah, so the two angles are basically, are you coming from PowerFlex or you're coming from Cloud. If you're Cloud native, the advantage would be things like a really, really advanced block file system that has been built from the ground up to be software defined and pretty much Cloud native. What you're getting is that really linear scale up to about 1,000 nodes. You can span that across regions, across availability zones, so it's highly resilient. So if there's a node failure in one site, you're going to rebuild really fast, depending on the size of that cluster. So it's a very advanced architecture that's been built to run, you know, we didn't have to change a single line of code to run this product in the Cloud because it was Cloud native by default, so. >> Well that's the thing. We also see, and you've seen that with some of the other solutions, but customers really want that. Enterprise customers are, they want us to make sure those mission critical applications are working and stay up. So they also want to use the same environment. So we were talking before, we also see use cases where maybe they're using PowerFlex on-premises today and they want to be able to replicate that to PowerFlex that's in the Cloud. So we're seeing those, and the familiarity with that infrastructure really is that easy path, if you will, for those more conservative mission critical customers. >> We've learned a lot over the years from AWS's entry into the marketplace. Two recent teams working backwards. We talk about customer obsession. And also the Cloud experience. It brings me to APEX. >> Oh yeah. >> Dave: How does APEX fit in here? >> Yeah, so APEX is the categorization for all the things that we're doing around a modern Cloud experience for Dell customers. So we're taking them also on a journey, kind of as a service model. There's a do-it-yourself model. And anything that we do that touches Cloud is now being kind of put under that APEX moniker. So everything that we're doing around Project Alpine, enterprise software capabilities in the Cloud. Do you want someone else to manage it for you? Do you want it in a polo? That might be the right fit for you. It's all under that APEX umbrella and journey. So we're kind of still just getting started there, but we're seeing a lot of great traction. People want to pay as they go, you know, it's a very popular model that AWS has pretty much set the foundation for. So pay as you go, utility based pricing, this is all things our customers have been asking for. >> Yeah, so APEX, you basically set a baseline. You can dial it up, dial it down, very much pay by the drink. >> Absolutely. >> And, you know, like you said, it's early days. >> Brian: Yeah. >> But that's, again, AWS has influenced the business in a lot of different ways. >> Again, with the Dell, you know, the trust customers that Dell has built over the years and having those customers come in. We obviously are getting, again, it's an accelerated option for financial services to healthcare and all these customers that have relied on Dell for years, moving to the Cloud, having that trusted name and also that infrastructure that's similar and familiar to them. And then the resilience of the foundation that we have at AWS, I think it works really well together for those customers. >> I think it underscores to the majority of both AWS and in a lot of ways Dell, right. In the early days of Cloud, it was like uh oh, and now it's like oh, actually big market. Customers are demanding this. There's new value that we can create working together. Let's do it. >> Yeah, I mean, it didn't take us that long to get to it, but I'd say we had little fits and starts over the years, and now we've recognized like, this is where the future is. It's going to be Cloud, it's going to be on-prem, it's going to be Edge, it's going to be everything. It's going to be an and world. And so just doing the right thing for customers I think is exactly where we landed. It's a great partnership. >> Do you have a favorite customer story that you think really shines the light on the value of the Dell AWS partnership in terms of the business impact they're making? >> We have several large customers that I can't always like drop the names, but one of them is a very large video game production company. And we do a lot of work together where they're rendering maybe in house, they're sending to a shared location. They're copying data over to S3. They're able to let all their editors access that. They bring it back when it's compressed down a little bit and deliver that. We're also doing a lot of work with, I think I can say this, Amazon Thursday night football games. So what they've done there, it's a partner of ours working with AWS. All the details inside of that roaming truck that they drive around, there's a lot of Dell gear within there, and then everything connects back to AWS for that exact same kind of model. We need to get to the editors on a nightly basis. They're also streaming directly form that truck while they're enabling the editors to access a shared copy of it, so it's really powerful stuff. >> Thursday night prime is pretty cool. You know, some people are complaining cause I can't just switch channels during the commercials. It's like, first of all, you can. Second of all, the stats are unbelievable, right. You can just do your own replay when you want to. There's some cool innovations there. >> Oh yeah, absolutely. >> Very cool innovations. I've got one more question for each of you before we wrap. Marc, a question for you, we're making a fun Instagram reel. So think about a sizzle reel of if you were to summarize the show so far, what is AWS's message to its massive audience this year? >> Well, that's a big question. Because we have such a wide, as we mentioned, such a wide ranging audience. I really see a couple key trends that we're trying to address. One is, again don't forget, I'm a storage guy, so it's going to come from an angle from data, right. So, I think it's just this volume of data and that customers are bringing into the Cloud, either moving in from enterprises today or organically, just growing. You know, a couple years ago, megabytes were a lot, and now, you know, we're talking about petabytes every day. Soon it's going to be exo bytes are going to become the norm. So the big, I'd say, point one is the trend that I see is just the volume of data. And so what we're doing to address that is obviously we talked a little bit about S3 and being able to manage volumes of data, but also things like DataZone that we introduced because customers are looking to make sure that the right governance and controls to be able to access that data. So I think that's one big thing that I see the theme for the show today. The second thing is around, as I said, really these enterprise customers really wanted to move in these mission critical applications into the Cloud, and having that infrastructure to be able to support that easily from what they're doing today and move in quickly. The third area is around data protection, making sure the data protection and malware recovery, that's the theme that we see is really unfortunately that's today. But being able to recover quickly, both having native services and native offerings just built in resiliency into the core platforms, like S3 with object application, et cetera. And also partnering with Dell with cyber recovery and some of the solutions with Dell. >> Excellent, and Brian, last question for you. A bumper sticker that succinctly and powerfully describes why Dell and AWS are such awesome partners for customer issues. >> Best of both worlds, right? >> Lisa: Mic drop. >> Mic drop, done. >> That's awesome. You said that a lot more succinctly. (people laughing) >> Enterprise in Cloud, Cloud comin' to enterprise. >> Yeah, leader meets leader, right? >> Yeah, right. >> Love it, leader meets leader. Guys, it's been a pleasure having you on theCUBE. We appreciate hearing the latest from AWS and Dell from a storage perspective and from a Cloud perspective and how you're helping customers manage the explosion of data that's not going to slow down. We really appreciate you coming by the set. >> Thank you. >> Great, thanks so much, appreciate it. >> My pleasure. For our guests and Dave Vellante, I'm Lisa Martin, you're watching theCUBE, the leader in live enterprise and emerging tech coverage. (techno music)

(upbeat music) >> Good morning and welcome back to fabulous Las Vegas, Nevada. We're here at AWS re:Invent with wall-to-wall coverage all day long on theCUBE. My name is Savannah Peterson and I am joined this morning by the beautiful Lisa Martin. Lisa, good morning. >> Good morning. Good. >> How you feeling day three? >> Day three is we are going to be shot out of a cannon today. The amount of content coming at you from theCUBE today- >> Get ready, you all. >> Us two gals, is a lot. We're going to have some great conversations. >> And we're starting with a really great one with a Cube Alumni to the max. You've been on the show multiple times. >> John: Yeah. >> Very excited to welcome John, the CMO of Couchbase. Welcome. How you doing this morning? >> Thanks. I'm doing great. Great to be here with you. >> How do you feel about the show so far? What's your pulse? >> The show has been great. I say the energy is great. The traffic at our booth, the conversations that we're having, both with prospective customers and even just partners, right? They're all here. The ecosystem is here >> And everyone's finally back in person and it feels so good. >> John: It does. >> So, we're going to dig in a little bit but just in case the audience isn't familiar, tell us about Couchbase. >> Sure. Couchbase is a publicly traded database company. We have a cloud database platform called Capella which is hosted on AWS and GCP. It is used for building mission-critical applications. So, we have great customers, we're building apps that really matter and are using to drive their business. So, we're very excited about that. 30% of the Fortune 100 are Couchbase customers. >> Nice. Talk a little bit about the AWS relationship. >> Mm-hm. Yeah, so we have a great AWS relationship. In fact, yesterday we announced a deepening of that relationship, a strategic collaboration agreement. We're very excited. It's a multi-year agreement. It's focused on go-to market, from a sales and marketing standpoint. We're going to target, you know, various verticals and, you know, really generate joint business between the two of us. So, it's a deepening of a already strong relationship and we're really excited about that. >> Savannah: Yeah. Go ahead. >> What are some of the industry verticals that you're going to be tackling together? >> Well, gaming for one, right? Manufacturing, the workloads that Couchbase is good for are these mission-critical workloads are ones that are really suited for us to be used with AWS. So, we've done some work with them already in those areas and I'm sure we'll be digging in even deeper. >> That's exciting. Speaking of digging in deeper, tell us a little bit more about Capella. >> Capella. It's a cloud databases services I mentioned. We launched it last October and we are super excited by the uptake, the interest that we're seeing. We have a free 30 day trial, so, you know, people can come and try it and get their hands dirty just getting experience with the product and then, you know, become a customer after that. And we're seeing very strong interest from our existing customer base as well. So, we're really excited about how things are going. >> Talk about Capella and the latest release and how it's really enabling Couchbase to invest deeper into the developer experience. >> Yeah, so, at the end of October, we announced a revamp of our user interface, our user experience for Capella really focused on developers. And what we've done is make it so that it's familiar to developers, right? It's a GitHub-like experience. So, developer comes in, they're very familiar, of course, with GitHub, they are familiar with how the Couchbase Capella interface will work. And so that's something that, you know, we've really invested, in fact, we've invested in developers quite a bit. We announced a Couchbase community hub and a Couchbase ambassadors program, both focused on developers and getting out there and building our community. >> A community is a big topic that we've been talking about at all the conferences this year. We're all back in person, in community. How often are you communicating with your community to get feedback on what that experience should be like? >> Yeah, I mean, we actually have a Discord server, so we're in constant communication. (Savannah laughing) >> Savannah: Yes. (John laughing) 24/7. (laughing) >> Basically, you know, we have staff who's dedicated to making sure that the users on there are getting their answers and giving us feedback on the experience. The ambassadors are somebody who have a really strong relationship, who get early insight and give us feedback before we even release a product. So, it gives us a chance to really test-drive it with core developers and get the insight we need before we get it in the market. >> Yeah. It matters so much. You can build it, but they won't come if it's not fantastic. >> John: Exactly. >> Lisa: Right. >> Let's shift a little bit and talk about customers. How, and price, how do you guys compare? >> Customers and? >> And price, your price performance? >> Price, oh. So, customers, we also announced this week a joint customer Arthrex with AWS. Arthrex is a orthopedics medical devices company and they use our Edge capabilities along with running Couchbase on AWS. So, you think of the kinds of surgeries that orthopedic surgeons do, it's scopes and they are often inside. So, what it does is it collects the data, the video data and all of that on a medical devices and then brings it back to a centralized app for the doctors to use sort of in post when they're actually doing further medical recommendations. >> Savannah: It's so cool. >> So, it's cool, the thing about it is it can work whether it's online or offline, it's one of the reasons that Arthrex selected us because the fact that it can, you know, often sometimes there's not connectivity in the operating room, I'd say deep inside of a hospital. So, these devices work regardless and then when they get connectivity, it sinks back to that centralized service. So, it's one of the main reasons that they selected us. >> That's outstanding. You know, one of the things that John Furrier, you know, John, well, you guys go way back. >> John: Way back. >> He had a sit down with Adam Selensky, oh, about 10 days or so ago. He gets an exclusive with the CEO of AWS every pre re:Invent. And one of the things that Adam said is that the role or the title, data analyst, is going to go away, in that every role will have responsibilities of analyzing data. And I always think of that in terms of operations, marketing, finance, sales, but you just brought up physicians as data analysts in their jobs, right? Probably not, we're thinking about it in that way. >> Yeah. >> But it's so interesting how data is really being democratized. >> John: Yeah. >> And how Couchbase is an enabler of that in an operating room. >> John: Yeah, yeah, yeah. >> That's amazing. >> It's a great story. There's many others and I think, you know, we have embedded operational analytics in Couchbase Capella, and, you know, in our offerings in general. So, what that does is allows us to do real-time, highly personalized applications based on that analytics that are coming in real-time from the data from the applications. And so that's something that's actually driving a highly interactive user experience, one that's very personalized and customized. And that's one of the things that our customers really like about what we do. >> It's fascinating. I never thought about it from a medical device perspective. >> Lisa: No, no. >> John: No. >> My gosh is if doctors don't have enough cognitive burden load. >> John: I know. >> You know, right? Like, they don't need to be a data analyst. I would much rather they were just good at the surgery part. That's a piece of the puzzle I need them to do. Yeah, for sure. That's a fascinating customer example. Can you share any other joint AWS examples with us? >> Joint AW- I mean, there's many in the gaming area where, because Couchbase is memory-first architecture, we deliver very, very interactive user experiences and we're used a lot for session management, user ID management in the gaming space, specifically with AWS. It's an area we've done some joint work already and had a lot of success, you know, with small and large gaming companies. >> Yeah. It looks like you also, according to my notes here, we've got things in travel and hospitality as well. >> Yes. Also Carnival Cruises is a great example. We enable their on-ship, on-board experience, highly customized, everybody wears a device called a medallion, and as they move around the ship, it knows where they are and it's able to provide customized services. You walk up to a bar, you have your favorite drink, it can be hit the bar when you land there. >> I'll take that. >> How about that? (laugh) >> That's outstanding. >> Isn't that great? >> Can we carry that onto the AWS show floor? >> Exactly. >> Or Starbucks order? >> Yeah, yeah. Yes, please. Yes, please. Well, another thing that's so interesting these days, is that every company has to be a data company. Say they have to be a software company. They have to be a data company. You just gave some great examples. Hospitality, gaming, healthcare, where that data democratization has to happen. >> John: Yeah. >> Businesses has to transform. But one of the things that Adam also told John is that CIOs, CEOs are coming to him not wanting to talk about technology but about transformation. >> Yeah. >> Huge topic. >> And that's a journey where every customer is at different levels. >> Yeah. >> How is Couchbase helping businesses transform and where are your customer conversations these days? >> Yeah, yeah, yeah. So, I mean, the transformation of the business is a major topic of conversation. So, we completely agree with that. How Couchbase helps is, you know, in our database, one of the things we have is the SQL engine. And so as people are looking to move and modernize their infrastructure, if they're moving off of, or from like a technology that's principally based on SQL but doesn't give all the flexibility of a JSON database or document database like we do, we actually enable them to get more easily onto our platform so that they can start that transformation. And then it's a, you know, it's a journey of how they want to transform their business and it's really focused on how do they better serve their customers and clients, whether it's internal or external? >> It really matters. I mean, and that ease of use as well as the transformation journey. It takes a long time for people to adapt. So, every piece of that puzzle, every Lego being quicker or easier, more intuitive, like you said, with the user experience, we can tell you're very thoughtful. How does this improve the total cost of ownership for your customers? >> That's one of the things that we announced along with that developer changes, was a new storage engine underneath Couchbase Capella. And it's 10 X more dense storage. And what that means is fewer servers. So, fewer servers is a much better cost of ownership story. That plus just the performance of the platform itself, we find, you know, against competition, we can do things on say six nodes that take 18 nodes for others. >> Lisa: Oh wow. >> And we have a great consolidation story as well because we have, it's a multi-modal database, meaning that it has SQL engine, document database, full tech search, eventing and analytics, all these pieces on one common data layer. So, you can actually consolidate off of other technologies onto one, onto Couchbase, and that actually saves you money. So, that's a great story for us. >> There's got to be a sustainability element to that as well? >> Yeah, I mean it's, obviously, if you're using less, using fewer servers, there's a kind of power consumption aspect of it as well. Absolutely. >> Are you finding that a lot of customers and companies we talk to these days have in their RFPs, they must only work with vendors who have an actual ESG program? Are you finding more customers coming to you saying, how can you help us dial down our carbon emissions? >> John: Yeah. >> Savannah: Great question. >> We've got a sustainability program that we've got to meet, we've got commitments to our customers. >> John: Yeah. >> Is that something that's really now kind of a hard and fast requirement? >> We're hearing it, we're definitely hearing it. I wouldn't say it's, you know, massively pervasive but I would say it's a growing component of, as you said, RFPs. And it's something that we feel like we have a great story for. And so, you know, it's something that helps when we get into those conversations, we can clearly articulate how we can provide that value and how we meet some of those needs that they have. >> Yeah, that's awesome. So, we have a bit of a challenge, new to the show at re:Invent. >> John: Mm-hm. >> Where we are prompting you to give us your 30 second Instagram Reel sizzle highlight. Don't worry, I'm not actually timing you, but your thought leadership hot-take on the most important theme or takeaway from this year's show. >> From the conference here. I would say that, and I think this was talked about a little bit by AWS as well, but the convergence of analytics and operational data, you know, through the applications is one that we're certainly seeing as well. It's the reason we have analytics in our database. But as I walk around and look at it, I see that very much as a common theme as well, in terms of what other vendors are saying and just the conversations we're having. So for me, that's one of the things I think would be a takeaway from this show. >> Yeah. Embedded analytics, real-time, everybody wants to know what's going on, in context. >> Yeah. That's right. >> Right now, not last week, not what we're processing from last month. >> Exactly. >> I mean, right? (cross-talking) >> So, I can react and take advantage or take an action if I have to. >> Exactly. And then deliver that personalized experience that we all expect these days. >> Oh, yes. >> I'll take that medallion- >> It's about the medallion. I was like, okay. >> You up with that, John? >> We'll get right on it. >> Lisa: All right. (laughs) >> About this. So, what's next for Couchbase? >> John: Well- >> I know you got the partnership, you've got all this exciting momentum. >> So, we're excited heading into next year. We're going to continue to innovate on Capella, right? Continue to deliver more value, lean into our developer community that we have. We're investing heavily, not just from a product standpoint but from a company standpoint in terms of, you know, our community meetups and some of those things. We have a big community-focused event coming up in March called Connect, Couchbase Connect. So, that's something that we'll, you know, continue to drive. That'll be a major theme for us next year. Cloud and developers and, you know, continuing to enable that ecosystem. >> Lisa: Excellent. >> I just had a Microsoft moment where I saw you saying, "Cloud developers," on stage. (Lisa and Savannah laughing) >> I'm not going Steve Ballmer on you. (all laughing) >> Pardon. I was trying to get someone to sing yesterday. I was hoping you were my Ballmer dance. Oh, man. Well, this has been a really great way to start the day. John, thank you so much for being on the show with us, seriously. And it's great that you keep coming back. I'm glad we haven't scared you off. (John laughing) >> Never. >> Savannah: We will have you anytime. >> Thank you. >> And thank you all for tuning in for yet another fantastic day of all day live coverage here from AWS re:Invent. We are in Sin City, having a fabulous time with Lisa Martin. I'm Savannah Peterson. This is theCUBE and we are the leader in high-tech technology coverage. (upbeat music) (upbeat music fades)

(upbeat music) >> We are back, approaching the finish line here at Supercomputing 22, our last interview of the day, our last interview of the show. And I have to say Dave Nicholson, my co-host, My name is Paul Gillin. I've been attending trade shows for 40 years Dave, I've never been to one like this. The type of people who are here, the type of problems they're solving, what they talk about, the trade shows are typically, they're so speeds and feeds. They're so financial, they're so ROI, they all sound the same after a while. This is truly a different event. Do you get that sense? >> A hundred percent. Now, I've been attending trade shows for 10 years since I was 19, in other words, so I don't have necessarily your depth. No, but seriously, Paul, totally, completely, completely different than any other conference. First of all, there's the absolute allure of looking at the latest and greatest, coolest stuff. I mean, when you have NASA lecturing on things when you have Lawrence Livermore Labs that we're going to be talking to here in a second it's a completely different story. You have all of the academics you have students who are in competition and also interviewing with organizations. It's phenomenal. I've had chills a lot this week. >> And I guess our last two guests sort of represent that cross section. Armando Acosta, director of HPC Solutions, High Performance Solutions at Dell. And Matt Leininger, who is the HPC Strategist at Lawrence Livermore National Laboratory. Now, there is perhaps, I don't know you can correct me on this, but perhaps no institution in the world that uses more computing cycles than Lawrence Livermore National Laboratory and is always on the leading edge of what's going on in Supercomputing. And so we want to talk to both of you about that. Thank you. Thank you for joining us today. >> Sure, glad to be here. >> For having us. >> Let's start with you, Armando. Well, let's talk about the juxtaposition of the two of you. I would not have thought of LLNL as being a Dell reference account in the past. Tell us about the background of your relationship and what you're providing to the laboratory. >> Yeah, so we're really excited to be working with Lawrence Livermore, working with Matt. But actually this process started about two years ago. So we started looking at essentially what was coming down the pipeline. You know, what were the customer requirements. What did we need in order to make Matt successful. And so the beauty of this project is that we've been talking about this for two years, and now it's finally coming to fruition. And now we're actually delivering systems and delivering racks of systems. But what I really appreciate is Matt coming to us, us working together for two years and really trying to understand what are the requirements, what's the schedule, what do we need to hit in order to make them successful >> At Lawrence Livermore, what drives your computing requirements I guess? You're working on some very, very big problems but a lot of very complex problems. How do you decide what you need to procure to address them? >> Well, that's a difficult challenge. I mean, our mission is a national security mission dealing with making sure that we do our part to provide the high performance computing capabilities to the US Department of Energy's National Nuclear Security Administration. We do that through the Advanced Simulation computing program. Its goal is to provide that computing power to make sure that the US nuclear rep of the stockpile is safe, secure, and effective. So how we go about doing that? There's a lot of work involved. We have multiple platform lines that we accomplish that goal with. One of them is the advanced technology systems. Those are the ones you've heard about a lot, they're pushing towards exit scale, the GPU technologies incorporated into those. We also have a second line, a platform line, called the Commodity Technology Systems. That's where right now we're partnering with Dell on the latest generation of those. Those systems are a little more conservative, they're right now CPU only driven but they're also intended to be the everyday work horses. So those are the first systems our users get on. It's very easy for them to get their applications up and running. They're the first things they use usually on a day to day basis. They run a lot of small to medium size jobs that you need to do to figure out how to most effectively use what workloads you need to move to the even larger systems to accomplish our mission goals. >> The workhorses. >> Yeah. >> What have you seen here these last few days of the show, what excites you? What are the most interesting things you've seen? >> There's all kinds of things that are interesting. Probably most interesting ones I can't talk about in public, unfortunately, 'cause of NDA agreements, of course. But it's always exciting to be here at Supercomputing. It's always exciting to see the products that we've been working with industry and co-designing with them on for, you know, several years before the public actually sees them. That's always an exciting part of the conference as well specifically with CTS-2, it's exciting. As was mentioned before, I've been working with Dell for nearly two years on this, but the systems first started being delivered this past August. And so we're just taking the initial deliveries of those. We've deployed, you know, roughly about 1600 nodes now but that'll ramp up to over 6,000 nodes over the next three or four months. >> So how does this work intersect with Sandia and Los Alamos? Explain to us the relationship there. >> Right, so those three laboratories are the laboratories under the National Nuclear Security Administration. We partner together on CTS. So the architectures, as you were asking, how do we define these things, it's the labs coming together. Those three laboratories we define what we need for that architecture. We have a joint procurement that is run out of Livermore but then the systems are deployed at all three laboratories. And then they serve the programs that I mentioned for each laboratory as well. >> I've worked in this space for a very long time you know I've worked with agencies where the closest I got to anything they were actually doing was the sort of guest suite outside the secure area. And sometimes there are challenges when you're communicating, it's like you have a partner like Dell who has all of these things to offer, all of these ideas. You have requirements, but maybe you can't share 100% of what you need to do. How do you navigate that? Who makes the decision about what can be revealed in these conversations? You talk about NDA in terms of what's been shared with you, you may be limited in terms of what you can share with vendors. Does that cause inefficiency? >> To some degree. I mean, we do a good job within the NSA of understanding what our applications need and then mapping that to technical requirements that we can talk about with vendors. We also have kind of in between that we've done this for many years. A recent example is of course with the exit scale computing program and some things it's doing creating proxy apps or mini apps that are smaller versions of some of the things that we are important to us. Some application areas are important to us, hydrodynamics, material science, things like that. And so we can collaborate with vendors on those proxy apps to co-design systems and tweak the architectures. In fact, we've done a little bit that with CTS-2, not as much in CTS as maybe in the ATS platforms but that kind of general idea of how we collaborate through these proxy applications is something we've used across platforms. >> Now is Dell one of your co-design partners? >> In CTS-2 absolutely, yep. >> And how, what aspects of CTS-2 are you working on with Dell? >> Well, the architecture itself was the first, you know thing we worked with them on, we had a procurement come out, you know they bid an architecture on that. We had worked with them, you know but previously on our requirements, understanding what our requirements are. But that architecture today is based on the fourth generation Intel Xeon that you've heard a lot about at the conference. We are one of the first customers to get those systems in. All the systems are interconnected together with the Cornell Network's Omni-Path Network that we've used before and are very excited about as well. And we build up from there. The systems get integrated in by the operations teams at the laboratory. They get integrated into our production computing environment. Dell is really responsible, you know for designing these systems and delivering to the laboratories. The laboratories then work with Dell. We have a software stack that we provide on top of that called TOSS, for Tri-Lab Operating System. It's based on Redhead Enterprise Linux. But the goal there is that it allows us, a common user environment, a common simulation environment across not only CTS-2, but maybe older systems we have and even the larger systems that we'll be deploying as well. So from a user perspective they see a common user interface, a common environment across all the different platforms that they use at Livermore and the other laboratories. >> And Armando, what does Dell get out of the co-design arrangement with the lab? >> Well, we get to make sure that they're successful. But the other big thing that we want to do, is typically when you think about Dell and HPC, a lot of people don't make that connection together. And so what we're trying to do is make sure that, you know they know that, hey, whether you're a work group customer at the smallest end or a super computer customer at the highest end, Dell wants to make sure that we have the right setup portfolio to match any needs across this. But what we were really excited about this, this is kind of our, you know big CTS-2 first thing we've done together. And so, you know, hopefully this has been successful. We've made Matt happy and we look forward to the future what we can do with bigger and bigger things. >> So will the labs be okay with Dell coming up with a marketing campaign that said something like, "We can't confirm that alien technology is being reverse engineered." >> Yeah, that would fly. >> I mean that would be right, right? And I have to ask you the question directly and the way you can answer it is by smiling like you're thinking, what a stupid question. Are you reverse engineering alien technology at the labs? >> Yeah, you'd have to suck the PR office. >> Okay, okay. (all laughing) >> Good answer. >> No, but it is fascinating because to a degree it's like you could say, yeah, we're working together but if you really want to dig into it, it's like, "Well I kind of can't tell you exactly how some of this stuff is." Do you consider anything that you do from a technology perspective, not what you're doing with it, but the actual stack, do you try to design proprietary things into the stack or do you say, "No, no, no, we're going to go with standards and then what we do with it is proprietary and secret."? >> Yeah, it's more the latter. >> Is the latter? Yeah, yeah, yeah. So you're not going to try to reverse engineer the industry? >> No, no. We want the solutions that we develop to enhance the industry to be able to apply to a broader market so that we can, you know, gain from the volume of that market, the lower cost that they would enable, right? If we go off and develop more and more customized solutions that can be extraordinarily expensive. And so we we're really looking to leverage the wider market, but do what we can to influence that, to develop key technologies that we and others need that can enable us in the high forms computing space. >> We were talking with Satish Iyer from Dell earlier about validated designs, Dell's reference designs for for pharma and for manufacturing, in HPC are you seeing that HPC, Armando, and is coming together traditionally and more of an academic research discipline beginning to come together with commercial applications? And are these two markets beginning to blend? >> Yeah, I mean so here's what's happening, is you have this convergence of HPC, AI and data analytics. And so when you have that combination of those three workloads they're applicable across many vertical markets, right? Whether it's financial services, whether it's life science, government and research. But what's interesting, and Matt won't brag about, but a lot of stuff that happens in the DoE labs trickles down to the enterprise space, trickles down to the commercial space because these guys know how to do it at scale, they know how to do it efficiently and they know how to hit the mark. And so a lot of customers say, "Hey we want what CTS-2 does," right? And so it's very interesting. The way I love it is their process the way they do the RFP process. Matt talked about the benchmarks and helping us understand, hey here's kind of the mark you have to hit. And then at the same time, you know if we make them successful then obviously it's better for all of us, right? You know, I want to secure nuclear stock pile so I hope everybody else does as well. >> The software stack you mentioned, I think Tia? >> TOSS. >> TOSS. >> Yeah. >> How did that come about? Why did you feel the need to develop your own software stack? >> It originated back, you know, even 20 years ago when we first started building Linux clusters when that was a crazy idea. Livermore and other laboratories were really the first to start doing that and then push them to larger and larger scales. And it was key to have Linux running on that at the time. And so we had the. >> So 20 years ago you knew you wanted to run on Linux? >> Was 20 years ago, yeah, yeah. And we started doing that but we needed a way to have a version of Linux that we could partner with someone on that would do, you know, the support, you know, just like you get from an EoS vendor, right? Security support and other things. But then layer on top of that, all the HPC stuff you need either to run the system, to set up the system, to support our user base. And that evolved into to TOSS which is the Tri-Lab Operating System. Now it's based on the latest version of Redhead Enterprise Linux, as I mentioned before, with all the other HPC magic, so to speak and all that HPC magic is open source things. It's not stuff, it may be things that we develop but it's nothing closed source. So all that's there we run it across all these different environments as I mentioned before. And it really originated back in the early days of, you know, Beowulf clusters, Linux clusters, as just needing something that we can use to run on multiple systems and start creating that common environment at Livermore and then eventually the other laboratories. >> How is a company like Dell, able to benefit from the open source work that's coming out of the labs? >> Well, when you look at the open source, I mean open source is good for everybody, right? Because if you make a open source tool available then people start essentially using that tool. And so if we can make that open source tool more robust and get more people using it, it gets more enterprise ready. And so with that, you know, we're all about open source we're all about standards and really about raising all boats 'cause that's what open source is all about. >> And with that, we are out of time. This is our 28th interview of SC22 and you're taking us out on a high note. Armando Acosta, director of HPC Solutions at Dell. Matt Leininger, HPC Strategist, Lawrence Livermore National Laboratories. Great discussion. Hopefully it was a good show for you. Fascinating show for us and thanks for being with us today. >> Thank you very much. >> Thank you for having us >> Dave it's been a pleasure. >> Absolutely. >> Hope we'll be back next year. >> Can't believe, went by fast. Absolutely at SC23. >> We hope you'll be back next year. This is Paul Gillin. That's a wrap, with Dave Nicholson for theCUBE. See here in next time. (soft upbear music)

(lively music) >> Good morning. Welcome back to theCUBE's coverage at Supercomputing Conference 2022, live here in Dallas. I'm Dave Nicholson with my co-host Paul Gillin. So far so good, Paul? It's been a fascinating morning Three days in, and a fascinating guest, Ian from AWS. Welcome. >> Thanks, Dave. >> What are we going to talk about? Batch computing, HPC. >> We've got a lot, let's get started. Let's dive right in. >> Yeah, we've got a lot to talk about. I mean, first thing is we recently announced our batch support for EKS. EKS is our Kubernetes, managed Kubernetes offering at AWS. And so batch computing is still a large portion of HPC workloads. While the interactive component is growing, the vast majority of systems are just kind of fire and forget, and we want to run thousands and thousands of nodes in parallel. We want to scale out those workloads. And what's unique about our AWS batch offering, is that we can dynamically scale, based upon the queue depth. And so customers can go from seemingly nothing up to thousands of nodes, and while they're executing their work they're only paying for the instances while they're working. And then as the queue depth starts to drop and the number of jobs waiting in the queue starts to drop, then we start to dynamically scale down those resources. And so it's extremely powerful. We see lots of distributed machine learning, autonomous vehicle simulation, and traditional HPC workloads taking advantage of AWS Batch. >> So when you have a Kubernetes cluster does it have to be located in the same region as the HPC cluster that's going to be doing the batch processing, or does the nature of batch processing mean, in theory, you can move something from here to somewhere relatively far away to do the batch processing? How does that work? 'Cause look, we're walking around here and people are talking about lengths of cables in order to improve performance. So what does that look like when you peel back the cover and you look at it physically, not just logically, AWS is everywhere, but physically, what does that look like? >> Oh, physically, for us, it depends on what the customer's looking for. We have workflows that are all entirely within a single region. And so where they could have a portion of say the traditional HPC workflow, is within that region as well as the batch, and they're saving off the results, say to a shared storage file system like our Amazon FSx for Lustre, or maybe aging that back to an S3 object storage for a little lower cost storage solution. Or you can have customers that have a kind of a multi-region orchestration layer to where they say, "You know what? "I've got a portion of my workflow that occurs "over on the other side of the country "and I replicate my data between the East Coast "and the West Coast just based upon business needs. "And I want to have that available to customers over there. "And so I'll do a portion of it in the East Coast "a portion of it in the West Coast." Or you can think of that even globally. It really depends upon the customer's architecture. >> So is the intersection of Kubernetes with HPC, is this relatively new? I know you're saying you're, you're announcing it. >> It really is. I think we've seen a growing perspective. I mean, Kubernetes has been a long time kind of eating everything, right, in the enterprise space? And now a lot of CIOs in the industrial space are saying, "Why am I using one orchestration layer "to manage my HPC infrastructure and another one "to manage my enterprise infrastructure?" And so there's a growing appreciation that, you know what, why don't we just consolidate on one? And so that's where we've seen a growth of Kubernetes infrastructure and our own managed Kubernetes EKS on AWS. >> Last month you announced a general availability of Trainium, of a chip that's optimized for AI training. Talk about what's special about that chip or what is is customized to the training workloads. >> Yeah, what's unique about the Trainium, is you'll you'll see 40% price performance over any other GPU available in the AWS cloud. And so we've really geared it to be that most price performance of options for our customers. And that's what we like about the silicon team, that we're part of that Annaperna acquisition, is because it really has enabled us to have this differentiation and to not just be innovating at the software level but the entire stack. That Annaperna Labs team develops our network cards, they develop our ARM cards, they developed this Trainium chip. And so that silicon innovation has become a core part of our differentiator from other vendors. And what Trainium allows you to do is perform similar workloads, just at a lower price performance. >> And you also have a chip several years older, called Inferentia- >> Um-hmm. >> Which is for inferencing. What is the difference between, I mean, when would a customer use one versus the other? How would you move the workload? >> What we've seen is customers traditionally have looked for a certain class of machine, more of a compute type that is not as accelerated or as heavy as you would need for Trainium for their inference portion of their workload. So when they do that training they want the really beefy machines that can grind through a lot of data. But when you're doing the inference, it's a little lighter weight. And so it's a different class of machine. And so that's why we've got those two different product lines with the Inferentia being there to support those inference portions of their workflow and the Trainium to be that kind of heavy duty training work. >> And then you advise them on how to migrate their workloads from one to the other? And once the model is trained would they switch to an Inferentia-based instance? >> Definitely, definitely. We help them work through what does that design of that workflow look like? And some customers are very comfortable doing self-service and just kind of building it on their own. Other customers look for a more professional services engagement to say like, "Hey, can you come in and help me work "through how I might modify my workflow to "take full advantage of these resources?" >> The HPC world has been somewhat slower than commercial computing to migrate to the cloud because- >> You're very polite. (panelists all laughing) >> Latency issues, they want to control the workload, they want to, I mean there are even issues with moving large amounts of data back and forth. What do you say to them? I mean what's the argument for ditching the on-prem supercomputer and going all-in on AWS? >> Well, I mean, to be fair, I started at AWS five years ago. And I can tell you when I showed up at Supercomputing, even though I'd been part of this community for many years, they said, "What is AWS doing at Supercomputing?" I know you care, wait, it's Amazon Web Services. You care about the web, can you actually handle supercomputing workloads? Now the thing that very few people appreciated is that yes, we could. Even at that time in 2017, we had customers that were performing HPC workloads. Now that being said, there were some real limitations on what we could perform. And over those past five years, as we've grown as a company, we've started to really eliminate those frictions for customers to migrate their HPC workloads to the AWS cloud. When I started in 2017, we didn't have our elastic fabric adapter, our low-latency interconnect. So customers were stuck with standard TCP/IP. So for their highly demanding open MPI workloads, we just didn't have the latencies to support them. So the jobs didn't run as efficiently as they could. We didn't have Amazon FSx for Lustre, our managed lustre offering for high performant, POSIX-compliant file system, which is kind of the key to a large portion of HPC workloads is you have to have a high-performance file system. We didn't even, I mean, we had about 25 gigs of networking when I started. Now you look at, with our accelerated instances, we've got 400 gigs of networking. So we've really continued to grow across that spectrum and to eliminate a lot of those really, frictions to adoption. I mean, one of the key ones, we had a open source toolkit that was jointly developed by Intel and AWS called CFN Cluster that customers were using to even instantiate their clusters. So, and now we've migrated that all the way to a fully functional supported service at AWS called AWS Parallel Cluster. And so you've seen over those past five years we have had to develop, we've had to grow, we've had to earn the trust of these customers and say come run your workloads on us and we will demonstrate that we can meet your demanding requirements. And at the same time, there's been, I'd say, more of a cultural acceptance. People have gone away from the, again, five years ago, to what are you doing walking around the show, to say, "Okay, I'm not sure I get it. "I need to look at it. "I, okay, I, now, oh, it needs to be a part "of my architecture but the standard questions, "is it secure? "Is it price performant? "How does it compare to my on-prem?" And really culturally, a lot of it is, just getting IT administrators used to, we're not eliminating a whole field, right? We're just upskilling the people that used to rack and stack actual hardware, to now you're learning AWS services and how to operate within that environment. And it's still key to have those people that are really supporting these infrastructures. And so I'd say it's a little bit of a combination of cultural shift over the past five years, to see that cloud is a super important part of HPC workloads, and part of it's been us meeting the the market segment of where we needed to with innovating both at the hardware level and at the software level, which we're going to continue to do. >> You do have an on-prem story though. I mean, you have outposts. We don't hear a lot of talk about outposts lately, but these innovations, like Inferentia, like Trainium, like the networking innovation you're talking about, are these going to make their way into outposts as well? Will that essentially become this supercomputing solution for customers who want to stay on-prem? >> Well, we'll see what the future lies, but we believe that we've got the, as you noted, we've got the hardware, we've got the network, we've got the storage. All those put together gives you a a high-performance computer, right? And whether you want it to be redundant in your local data center or you want it to be accessible via APIs from the AWS cloud, we want to provide that service to you. >> So to be clear, that's not that's not available now, but that is something that could be made available? >> Outposts are available right now, that have this the services that you need. >> All these capabilities? >> Often a move to cloud, an impetus behind it comes from the highest levels in an organization. They're looking at the difference between OpEx versus CapEx. CapEx for a large HPC environment, can be very, very, very high. Are these HPC clusters consumed as an operational expense? Are you essentially renting time, and then a fundamental question, are these multi-tenant environments? Or when you're referring to batches being run in HPC, are these dedicated HPC environments for customers who are running batches against them? When you think about batches, you think of, there are times when batches are being run and there are times when they're not being run. So that would sort of conjure, in the imagination, multi-tenancy, what does that look like? >> Definitely, and that's been, let me start with your second part first is- >> Yeah. That's been a a core area within AWS is we do not see as, okay we're going to, we're going to carve out this super computer and then we're going to allocate that to you. We are going to dynamically allocate multi-tenant resources to you to perform the workloads you need. And especially with the batch environment, we're going to spin up containers on those, and then as the workloads complete we're going to turn those resources over to where they can be utilized by other customers. And so that's where the batch computing component really is powerful, because as you say, you're releasing resources from workloads that you're done with. I can use those for another portion of the workflow for other work. >> Okay, so it makes a huge difference, yeah. >> You mentioned, that five years ago, people couldn't quite believe that AWS was at this conference. Now you've got a booth right out in the center of the action. What kind of questions are you getting? What are people telling you? >> Well, I love being on the show floor. This is like my favorite part is talking to customers and hearing one, what do they love, what do they want more of? Two, what do they wish we were doing that we're not currently doing? And three, what are the friction points that are still exist that, like, how can I make their lives easier? And what we're hearing is, "Can you help me migrate my workloads to the cloud? "Can you give me the information that I need, "both from a price for performance, "for an operational support model, "and really help me be an internal advocate "within my environment to explain "how my resources can be operated proficiently "within the AWS cloud." And a lot of times it's, let's just take your application a subset of your applications and let's benchmark 'em. And really that, AWS, one of the key things is we are a data-driven environment. And so when you take that data and you can help a customer say like, "Let's just not look at hypothetical, "at synthetic benchmarks, let's take "actually the LS-DYNA code that you're running, perhaps. "Let's take the OpenFOAM code that you're running, "that you're running currently "in your on-premises workloads, "and let's run it on AWS cloud "and let's see how it performs." And then we can take that back to your to the decision makers and say, okay, here's the price for performance on AWS, here's what we're currently doing on-premises, how do we think about that? And then that also ties into your earlier question about CapEx versus OpEx. We have models where actual, you can capitalize a longer-term purchase at AWS. So it doesn't have to be, I mean, depending upon the accounting models you want to use, we do have a majority of customers that will stay with that OpEx model, and they like that flexibility of saying, "Okay, spend as you go." We need to have true ups, and make sure that they have insight into what they're doing. I think one of the boogeyman is that, oh, I'm going to spend all my money and I'm not going to know what's available. And so we want to provide the, the cost visibility, the cost controls, to where you feel like, as an HPC administrator you have insight into what your customers are doing and that you have control over that. And so once you kind of take away some of those fears and and give them the information that they need, what you start to see too is, you know what, we really didn't have a lot of those cost visibility and controls with our on-premises hardware. And we've had some customers tell us we had one portion of the workload where this work center was spending thousands of dollars a day. And we went back to them and said, "Hey, we started to show this, "what you were spending on-premises." They went, "Oh, I didn't realize that." And so I think that's part of a cultural thing that, at an HPC, the question was, well on-premises is free. How do you compete with free? And so we need to really change that culturally, to where people see there is no free lunch. You're paying for the resources whether it's on-premises or in the cloud. >> Data scientists don't worry about budgets. >> Wait, on-premises is free? Paul mentioned something that reminded me, you said you were here in 2017, people said AWS, web, what are you even doing here? Now in 2022, you're talking in terms of migrating to cloud. Paul mentioned outposts, let's say that a customer says, "Hey, I'd like you to put "in a thousand-node cluster in this data center "that I happen to own, but from my perspective, "I want to interact with it just like it's "in your data center." In other words, the location doesn't matter. My experience is identical to interacting with AWS in an AWS data center, in a CoLo that works with AWS, but instead it's my physical data center. When we're tracking the percentage of IT that's that is on-prem versus off-prem. What is that? Is that, what I just described, is that cloud? And in five years are you no longer going to be talking about migrating to cloud because people go, "What do you mean migrating to cloud? "What do you even talking about? "What difference does it make?" It's either something that AWS is offering or it's something that someone else is offering. Do you think we'll be at that point in five years, where in this world of virtualization and abstraction, you talked about Kubernetes, we should be there already, thinking in terms of it doesn't matter as long as it meets latency and sovereignty requirements. So that, your prediction, we're all about insights and supercomputing- >> My prediction- >> In five years, will you still be talking about migrating to cloud or will that be something from the past? >> In five years, I still think there will be a component. I think the majority of the assumption will be that things are cloud-native and you start in the cloud and that there are perhaps, an aspect of that, that will be interacting with some sort of an edge device or some sort of an on-premises device. And we hear more and more customers that are saying, "Okay, I can see the future, "I can see that I'm shrinking my footprint." And, you can see them still saying, "I'm not sure how small that beachhead will be, "but right now I want to at least say "that I'm going to operate in that hybrid environment." And so I'd say, again, the pace of this community, I'd say five years we're still going to be talking about migrations, but I'd say the vast majority will be a cloud-native, cloud-first environment. And how do you classify that? That outpost sitting in someone's data center? I'd say we'd still, at least I'll leave that up to the analysts, but I think it would probably come down as cloud spend. >> Great place to end. Ian, you and I now officially have a bet. In five years we're going to come back. My contention is, no we're not going to be talking about it anymore. >> Okay. >> And kids in college are going to be like, "What do you mean cloud, it's all IT, it's all IT." And they won't remember this whole phase of moving to cloud and back and forth. With that, join us in five years to see the result of this mega-bet between Ian and Dave. I'm Dave Nicholson with theCUBE, here at Supercomputing Conference 2022, day three of our coverage with my co-host Paul Gillin. Thanks again for joining us. Stay tuned, after this short break, we'll be back with more action. (lively music)

(uplifting music) >> Hello and welcome to The Cube, here in Palo Alto, California for a special program on cloud-native at scale, enabling next generation cloud or SuperCloud for modern application cloud-native developers. I'm John Furrier, host of The Cube. My pleasure to have here Madhura Maskasky, co-founder and VP of Product at Platform9. Thanks for coming in today for this cloud-native at scale conversation. >> Thank you for having me. >> So, cloud-native at scale, something that we're talking about because we're seeing the next level of mainstream success of containers, Kubernetes and cloud-native developers, basically DevOps in the CICD pipeline. It's changing the landscape of infrastructure as code, it's accelerating the value proposition and the SuperCloud 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 SuperCloud as it fits to cloud-native as scales up? >> Yeah, you know, I think what's interesting, and I think the reason why SuperCloud is a really good and 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 distributors 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 micro sites. These micro sites 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 that direction. And so you got to refer that with a terminology that indicates the scale and complexity of it. And so I think SuperCloud is an appropriate term for that. >> So, you brought a couple 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. What 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 infrastructure is a 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 got to have edge. So, hybrid cloud is a winning formula. Everybody knows that it's a steady state. >> Madhura: 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 going to happen. It's only going to get accelerated with the edge and beyond globally. So I have to ask you, what is the technical challenges in doing this? Because it's something business consequences as well, but there are technical challenges. Can you share your view on what the technical challenges are for the SuperCloud or across multiple edges and regions? >> Yeah, absolutely. So, I think, you know, in the context of this, this term of SuperCloud, I think, it's sometimes easier to visualize things in terms of two axes, right? I think on one end you can think of the scale in terms of just pure number of nodes that you have, deploy number of clusters in the Kubernetes space. And then, on the other access 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 axes 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 your 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're seeing cloud-native becomes successful. There's a lot to configure, there's a lot to install. Can you scope the scale of the problem? Because about at scale, >> Madhura: Yeah. >> Challenges here. >> Yeah. Absolutely. And I think, you know, I like to call it, you know, the problem that the scale creates, you know, there's various problems, but I think one problem, one way to think about it is you know, it works on my cluster problem, right? So, 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 change, everything was fantastic, it worked flawlessly on my machine, on production, it's not working. And the exact same problem now happens in 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 add 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 ballgame of issues come in the context of security, right? Because when you are deploying applications at scale in a distributed manner, you got to 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 configure 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 got to scale it. Then you're seeing security breaches, you're seeing configuration errors. This seems to be where the hotspot is. And 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 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 P ones from support teams, et cetera. And those issues can be really difficult to triage. 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 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 like (indistinct) hard, right? It's just one of the examples of the problem. Another whole bucket of issues is security, which is you have these distributed clusters at scale, you got to ensure someone's job is on the line to make sure that the security policies are configured properly. >> So, this is a huge problem. I love that comment. That's not happening on my system. It's the classic, you know, debugging mentality. >> Madhura: 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. I'm 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 a complete end-to-end management of not just your clusters, but your clusters, all of the infrastructure that goes within and along the sites of those clusters, security policies, your middleware plugins, and finally your applications. So, what Arlon lets 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 terms of the chaos you guys are reigning in, 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 assembly line brings, right? Arlon, and if you look at the logo we've designed, it's this funny little robot, and it's because when we think of Arlon, 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 where again, it gets, you know, processed in a standardized way. And that's what Arlon really does. That's like deliver the 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 line, things are flowing, CICD, pipelining. >> Madhura: 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 middle layer 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 secured properly, that they are logging, logs are being collected properly, monitoring and observability is integrated. And so, it solves problems for both those teams. >> Yeah, it's DevOps. So, the DevOps is the cloud-needed developer. 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, you know, Kubernetes really introduced or elevated this declarative management, right? Because you know, Kubernetes clusters are, or your, yeah, you know, specifications of components that go in Kubernetes are defined in 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, I want 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 current state of the product? You run the product group over there, Platform9, 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? >> Madhura: 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 you know, maybe want to buy the commercial. So, I'm assuming you have that thought through, can you share? >> Madhura: Yeah. >> 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 Fission, 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 open source 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 a block box. >> Well, and that's what the developers want too. I mean, what we're seeing in reporting with SuperCloud is the new model of consumption is I want to look at the code and see what's in there. >> Madhura: 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 want to 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 is, well, but that's the benefit of open source. This is why standards and open source growing so fast, you have that confluence of, you know, a way for us to try before they buy, but also actually kind of date the application, if you will. We, you know, Adrian (indistinct) uses the dating metaphor, you know, hey, you know, I want to check it out first before I get married. >> Madhura: 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 in, you know, two things, I think one is just, you know, this cloud-native space is so vast that if you're building a close flow solution, sometimes there's also a risk that it may not apply to every single enterprise's 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 Saas-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 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, why should I be enthused about Arlon? What's in it for me? You know. 'Cause if I'm not enthused about it, I'm not going to be confident and it's going to be hard for me to get behind this. Can you share your enthusiastic view of, you know, why I should be enthused about Arlon? 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 cloud-native Kubernetes, and then, we have our CICD 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, your CICD 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 start by being done hand grown. And then, as you scale, what typically enterprises would do today is they will have their homegrown DIY solutions for this. I mean, a number of folks that I talk to that have built Terraform 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, (indistinct) 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 EKS Amazon, and we want to 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. 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 I think, this is going to come up at (indistinct) this year where enterprises are going to say, okay, I need to see SLAs. I want to see track record, I want to see other companies that have used it. >> Madhura: Yeah. >> How would you answer that question to, or challenge, you know, hey, I love this, but is there any guarantees? Is there any, what's the SLA, 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, two parts to that, right? One is Arlon leverages existing open-source components, products that are extremely popular. Two specifically. One is Arlon uses ArgoCD, which is probably one of the highest rated and used CD open-source tools that's out there, right? It'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 Arlon also makes use of cluster API (indistinct), which is a Kubernetes' sub-component, right? For life cycle management of clusters. So, there is enough of, you know, community users, et cetera, around these two products, right? Or open-source projects that will find Arlon to be right up in their alley because they're already comfortable, familiar with ArgoCD. Now, Arlon just extends the scope of what ArgoCD can do. And so, that's one. And then, the second part is going back to your point of the comfort. And that's where, you know, Platform9 has a role to play, which is when you are ready to deploy Arlon at scale, because you've been, you know, playing with it in your (indistinct) test environments, you're happy with what you get with it, then Platform9 will stand behind it and provide that SLA. >> And what's been the reaction from customers you've talked to Platform9 customers with, that are familiar with Argo and then Arlon? What's been some of the feedback? >> Yeah, I think, the feedback's been fantastic. I mean, I can give examples of customers where, you know, initially, you know, when you are telling them about your entire portfolio of solutions, it might not strike a card right away. But then we start talking about Arlon, and we talk about the fact that it uses ArgoCD 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 Arlon 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 at, I have, I'm so busy, my team's overworked. I got a skills gap, I don't need another project that's so I'm so tied up right now, and I'm just chasing my tail. How does Platform9 help me? >> Yeah, absolutely. So I think, you know, one of the core tenants of Platform9 has always 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 customer's 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 then, >> So, customers have clusters that are growing, that's a sign, >> Correct. >> Call you guys. >> Absolutely. Either they have massive large clusters. Right. That they want to 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 >> Madhura: Yeah. >> 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. >> Profile and policy-based, declarative configuration and life cycle 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 SuperCloud, right? If you think about a SuperCloud environment, there is at least few key ingredients 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 Arlon solves, then you need to compliment that with the right kind of observability and monitoring tools at scale, right? Because ultimately issues are going to happen and you're going to 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 at on the public cloud, you need some cost management tools. In my mind, these three things are like the most necessary ingredients to make SuperCloud successful. And you know, Arlon flows in one, >> Okay, so now, the next level is, okay, that makes sense. It's under the covers kind of speak under the hood. >> Madhura: Yeah. >> How does that impact the app developers of the cloud-native modern application workflows? Because the impact to me seems the apps are going to be impacted. Are they going to 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 today 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 Arlon solution takes place, as you say, and the apps are going to be (indistinct), they're designed to do, the question is, what does the current pain look like? Are the apps breaking? What is the signals to the customer, >> Madhura: Yeah. >> That they should be calling you guys up into implementing Arlon, Argo, and on all the other goodness to automate, what does some of the signals, is it downtime? Is it failed apps, is it latency? What are some of the things that, >> Madhura: Yeah, absolutely. >> Would be indications of things are F'ed up a little bit. >> Yeah. More frequent down times, down times that are, that take longer to triage. And so your, you know, your mean times on resolution, et cetera, are escalating or growing larger, right? Like we have environments of customers where 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 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 scripts. So, these are the kinds of challenges, and those are the pain points, which is, you know, if you're looking to reduce your mean time 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're 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 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 (indistinct) terminals and some PCs and handhelds. Now, if technology's running, the business is the business. >> Yeah. >> Company is the application. >> Yeah. >> So, it can't be down. So, there's a lot of pressure on CSOs and CIOs now and boards is saying, how is technology driving the top-line revenue? That's the number one conversation. >> Yeah. >> Do you see the 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, the technology that's, you know, that's going to drive your top line is going to 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 cloud-native at scale look like to you? If all the things happen the way we want them 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 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 themselves. >> John: And the CIO doesn't exist and there's no CISO, there at the beach. >> (laughs) Yeah. >> 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 Furrier here, for special program presentation, special programming cloud-native at scale, enabling SuperCloud modern applications with Platform9. Thanks for watching. (gentle music)

>>Everyone, 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 Furry, your host of The Cube. We've got a great lineup of three interviews we're streaming today. Mattor Makki, 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 cloud native at scale. So enjoy the program, see you soon. Hello and welcome to the cube here in Palo Alto, California for a special program on cloud native at scale, enabling next generation cloud or super cloud for modern application cloud native developers. I'm John Forry, host of the Cube. Pleasure to have here me Makowski, 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 and 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 distributors 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 micro sites. These micro sites 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 super cloud is a, is an appropriate term for >>That. So you brought a couple things I want to dig into. You mentioned Edge Notes. 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, o ot, and it kind of coming together, but you also got this idea of regions, global infrastructures, big part of it. I just saw some news around cloud flare 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 it's something business consequences as well, but there are technical challenge. Can you share your view on what the technical challenges are for the super cloud 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 number of clusters in the Kubernetes space. And then on the other access 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 skill 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, 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 change, 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's 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 at 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 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 configure 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 hotpot is. And 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, the two factors of scale is 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 try us, right? And so in the Kubernetes environment, this problem kind of multi folds, it goes, you know, escalates to a higher degree because yeah, 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 you radio sell 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 it, 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 like ishly 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 our lawn is, this new product, What is it all about? Talk about this new introduction. >>Yeah, absolutely. I'm 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 arwan 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 sites of those clusters, security policies, your middleware plugins, and finally your applications. So what alarm lets 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 this solves 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 online. 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, 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 components, 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 I 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. C C I CD 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, the developers are responsible for one picture of that layer, which is my apps, and then maybe that middleware of application 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 native developer. The OP 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, Kubernetes really in introduced or elevated this declarative management, right? Because, you know, c communities clusters are Yeah. Or your, yeah, you know, specifications of components that go in Kubernetes are defined in 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 online addresses that problem at the heart of it, and it does that using existing open source well known solutions. >>Ed, do I wanna 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 on from 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 fi, 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 opensource 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. I mean, 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, 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, Well, 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 helpers to try before they buy, but also actually kind of date the application, if you will. We, you know, Adrian Karo uses the dating me 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, why should I be enthused about Arlo? 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 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 es, and then we have our C I CD 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 CD pipelines can deploy the apps. Somebody needs to do all of their 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 Spico would be delighted. The folks that we've talked, 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 s 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 >>Stability. 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 Lon uses Argo cd, which is probably one of the highest rated and used CD open source tools that's out there, right? It's created by folks that are as part of Intuit team now, you know, really brilliant team. And it's used at scale across enterprises. That's one. Second is arlon also makes use of cluster api capi, which is a ES 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 algo cd. Now Arlan just extends the scope of what Algo CD 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 nine has a role to play, which is when you are ready to deploy Alon 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 Arlo? What's been some of the feedback? >>Yeah, I, 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 CD and 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 line 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 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 customer's 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, >>So 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 Yeah. 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 >>Profile and policy based declarative configuration and life cycle management for clusters. >>If I asked you how this enables Super club, 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 alon 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, alarm flows >>In one. Okay, so now the next level is, Okay, that makes sense. There's 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 Arlo solution takes place, as you say, and the apps are gonna be stupid, there's 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, and on 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 in 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, 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 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, 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, 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 IIA terminals and some PCs and handhelds. Now if technology's running, the business is the business. Yeah. The 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 see, and boards is saying, how is technology driving the top line revenue? That's the number one conversation. Yeah. 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 with things. So just >>Taking care of, and the CIO doesn't exist. There's no CSO there at the beach. >>Yeah. >>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 at Bickley, who's the chief architect and co-founder of Platform nine b. Great to see you Cube alumni. We, we met at an OpenStack event in about eight years ago, or well later, earlier when opens Stack 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 was realized, and you've seen what Docker's doing with the new docker, the open source Docker now just a 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 infrastructure's code comes in, we talked to Bacar talking about Super Cloud, I met her about, you know, the new Arlon, our R lawn you guys just launched, the infrastructure's code is going to another level. And then it's always been DevOps infrastructure is 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 infrastructures 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 as 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 specify. So I think it's, it's a even better version of infrastructures code. >>Yeah, yeah. And, and that really means it's developer just accessing resources. Okay. Not declaring, 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. It's 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, 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, >>That's, I wrote a LinkedIn post today was comments about, you know, hey, enterprise is the 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 our GoCon, which was put on here in Silicon Valley at the, at the by intu. 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 inaugural 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 application 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 to 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 too 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 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 really wrote another post today on LinkedIn called the Silicon Wars AMD Stock is down arm has been on rise, we've 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 co con, which will be a covering. So that brings up the whole what's next? You guys just announced our lawn at ar GoCon, which came out of Intuit. We've had Maria Teel at our super cloud event, She's a 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 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 AR 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 the infrastructure. We talked a lot about what's going on below that. Yeah. Above our workloads at the end of the day, and I talk to CXOs and IT folks that, that are now DevOps engineers. They care about the workloads and they want the infrastructure's 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 here's 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 coming like an EC two instance, spin up a cluster. We've heard 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 AR loan 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 the profile. And then you can stamp out your app, your applications and your clusters and manage them in a very, So >>It's essentially standard, like creates a mechanism. Exactly. Standardized, declarative kind of configurations. And it's like a playbook, just deploy it. Now what there is between say a script like I'm, I have scripts, I can just automate scripts >>Or yes, this is where that declarative API and infrastructure as 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 are 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 as configuration is built kind of on, it's a 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 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. Kubernetes has value. We're gonna hear this year at CubeCon 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 rogue, that CubeCon coming up and now this'll be shipping this segment series out before. What do you expect to see at this year? It'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 jockeying 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 coupon 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 within 12 years with 14 years or something like that. Big number co-founder here a platform. I you's been around for a while at this game, man. We talked about OpenStack, that project we interviewed at one of their events. So OpenStack was the beginning of that, this new revolution. 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 Aati team at that time. We would 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 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? Opens Stack was an example and then 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, 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 need of its scale? >>The, the hyper squares? >>Yeah, yeah. A's Azure Google, >>You mean from a business perspective, 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 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 hyper skaters really want to be in the game in terms of, you know, the, the new risk and arm ecosystems, 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 you got software and you got middleware and he kinda 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 in the same game. 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 develop 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 is 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 there. Panel was really to discuss the role of Kubernetes. Now great conversation operations is impacted. What's just 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, 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 to run the infrastructure. The biggest blocking factor now is having a unified platform. And that's what where we come into >>Patrick, we were talking before we came on stage here about your background and we were kind of talking about the glory days in 2000, 2001 when the first ASPs application service providers came out. Kind of a SaaS vibe, but that was kind of all kind of cloud-like >>It wasn't, >>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, >>In fact, 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 inflations sea year. It's interesting. This is the first downturn, 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. Cause 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 infrastructure is not just some, you know, 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 Alon 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 unreal. >>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 pan or 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 as a pilot 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, >>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 in the journey is going on. And you know, most companies are, 70 plus percent of them have 1, 2, 3 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 it, 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 about 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 on-prem 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 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 heard it all here, ops and security teams. Cause they're kind of part of one thing, but option 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. So >>You working two sides to that coin. You've got the dev side and then >>And then infrastructure >>Side. >>Okay. Another customer that I give 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 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 box, like a small little box, >>Right? 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 locations. >>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, very well. >>Tucan, of course Detroit's >>Coming so, so it's already there, right? 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, not you have to rewrite and redevelop your application in 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 complexity is there, but the business benefits of agility and uniformity and customer experience are truly being done. >>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 >>Are >>For the customer. Customer, >>The customer's expectations change, right? Once you get used to a better customer experience, you learn. >>That's 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 let's 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. CARSs being been in an asb, being in a real time 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 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. You know, that is what I see >>Happening there. I think that that e-commerce is 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 gain, it's just changing how you operate, >>How you think and how you operate. See, if you think about the early days of e-commerce, just putting up a shopping cart that made you an e-commerce or e retailer or an e 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. >>Nascar, 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 Fur with Super Cloud 22 in the Cube. Thanks for watching. >>Thank you. Thank you. >>Hello and 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 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.

>>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.

(upbeat music) >> Hello, and welcome to theCUBE here in Palo Alto, California, for a special program on Cloud Native at Scale, Enabling Next Generation Cloud or Supercloud for Modern Application Cloud Native Developers. I'm John Furrier, host of theCUBE. My pleasure to have here, me Madhura Maskasky, Co-founder and VP of Product at Platform9. Thanks for coming in today for this cloud native at scale conversation. >> Thank you for having me. >> So cloud native at scale, something that we're talking about because we're seeing the next level of mainstream success of containers, Kubernetes and cloud native develop, basically DevOps in the CI/CD pipeline. It's changing the landscape of infrastructure as code. It's accelerating the value proposition. And the Supercloud 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 Supercloud as it fits to cloud native, it scales up. >> Yeah, you know, I think what's interesting. And I think the reason why Supercloud is a really good and 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 multicloud 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's kind of flipped around, right? Where you have a large number of micro-sites. These micro-sites could be your public cloud deployment, your private OnPrem infrastructure deployment, or it could be your Edge environment, right? And every single enterprise, every single industry is moving in that direction. And so you got to refer that with a terminology that indicates the scale and complexity of it. And so I think Supercloud is an appropriate term for that. >> So you brought a couple 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 infrastructure is a 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 got to have Edge. So hybrid cloud is a winning formula. Everybody knows that, it's a steady state. 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 going to happen. It's only going to 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 Supercloud across multiple edges and regions? >> Yeah, absolutely. So I think, you know, in the context of this term of Supercloud, I think it's sometimes easier to visualize things in terms of two axis, right? I think on one end you can think of the scale in terms of just pure number of nodes that you have deployed, 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 flare of this, there is enough complexity, but potentially manageable. But when you are expanding on both these axis, 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 your 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 is also getting better. We're seeing cloud native become successful. There's a lot to configure. There's lot to install. Can you scope the scale of the problem because we're about at scale challenges here. >> Yeah absolutely, and I think I like to call it, you know, the problem that the scale creates, there's various problems. But I think one problem, one way to think about it is it works on my cluster problem, right? So, you know, I come from engineering background and 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 change, everything was fantastic. It worked flawlessly on my machine. On production, it's not working. The exact same problem now happens in these distributed environments, but at massive scale, right. Which is that, you know, developers test their applications, et cetera within these 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 add 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 ballgame of issues come in the context of security, right? Because when you are deploying applications at scale, in a distributed manner, you got to 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 experimentation, they set up a cluster, say it's containers and Kubernetes. And then you say, okay, we got this. We configure 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 got to 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 the two factors of scale, as we talked about, start expanding. I think one of them is what I like to call the, 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 0s and POS from support teams, et cetera. And those issues can be really difficult to try 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 radial cell power site, or you hand it over to a customer to run it on their cluster, they might not have configured that cluster exactly how you did, or they might not have configured some of the infrastructure plugins. And so 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, as you have these distributed clusters at scale. You got to ensure someone's job is on the line to make sure that the security policies are configured properly. >> So this is a huge problem. I love that comment. That's not happening on my system. It's the classic, you know, debugging mentality. 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, I'm 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 OnPrem or in the cloud or at Edge environments. And what Arlon is, it's an open source project, and it is a tool, 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 sites of those clusters, security policies, your middleware plugins, and finally your applications. So what Arlon lets 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 this solves in terms of the chaos you guys are reigning in, what's the bumper sticker. What did 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 an auto manufacturing factory, or et cetera, and the level of efficiency at scale that that assembly line brings, right. Arlon, and if you look at the logo we've designed, it's this funny little robot. And it's because when we think of Arlon, 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 where again, it gets processed in a standardized way. And that's what Arlon really does. That's like the elevator 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 problems. >> So keeping it smooth, the assembly line, things are flowing, see CI/CD pipe-lining. 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, 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 secured properly, that they are logging, logs are being collected properly. Monitoring and observability is integrated. And so it solves problems for both those teams. >> Yeah, it's DevOps. So the DevOps is the cloud native developer. The OPS team have to kind of set policies. Is that where the declarative piece comes in? Is that why that's important? >> Absolutely, yeah. And you know, Kubernetes really introduced or elevated this declarative management, right. Because you know, Kubernetes clusters are you know your specifications of components that go in Kubernetes are defined in 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 current state of the product? You run the product group over there at Platform9. 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? And what is the consumption? I mean open source is great. People want opensource, they can download and look up the code, but maybe want to 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 opensource? I think it's, you know, we, as a company, we have one of the things that's absolutely critical to us is that we take mainstream open source technologies, components, and then we make them available to our customers at scale through either a SaaS model or OnPrem 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 are 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 Fission, which was a serverless product that we had built, to various other examples that I can give. But that's one of the main reasons why open source. And also open source because we want the community to really first-hand engage with us on this problem, which is very difficult to achieve if your product is behind a wall, you know, behind a black box. >> Well, and that's what the developers want too. What we're seeing in reporting with Supercloud is the new model of consumption is I want to 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 want to 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 it is, but that's the benefit of 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 Kakroff uses the dating metaphor, you know, hey, you know, I want to check it out first before I get married. And that's what open source is. 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 two things, I think one is just, you know, this cloud native space is so vast that if you're building a cluster 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's backed by us, a SaaS-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 a partner to collaborate with who can 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. Why should I be enthused about Arlon? What's in it for me? You know, 'cause if I'm not enthused about it, I'm not going to be confident, and it's going to be hard for me to get behind this. Can you share your enthusiastic view of, you know, why I should be enthused about Arlon, if I'm a customer. >> Yeah, absolutely. And so, and there's multiple, you know, enterprises that we talk to, many of them, are customers where this is a very kind of typical story that you will hear, which is we have a Kubernetes distribution. It could be On-Premise. It could be public cloud native Kubernetes. And then we have our CI/CD 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, your CI/CD pipelines can deploy the apps, somebody needs to do all of their groundwork of, you know, defining those clusters, and yeah properly configuring them. And as these things start by being done hand-grown. And then as you scale, what typically enterprises would do today is they will have their homegrown DIY solutions for this. I mean, the number of folks that I talk to that have built Terraform 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 their problem. And so that's that pitch. I think OPS people would be delighted. The folks that we've talked, you know, spoken with have been absolutely excited and have shared that this is a major challenge we have today, because we have few hundreds of clusters on EKS, Amazon, and we want to scale them to few thousands, but we don't think we are ready to do that. And this will give us the ability to do that. >> Yeah, I think people are scared. I won't say scared, 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 I think this is going to come up at KubeCon this year where enterprises are going to say, okay, I need to see SLAs. I want to see track record. I want to see other companies that have used it. How would you answer that question to, 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 two parts to that, right? One is Arlon leverages, existing opensource components, products that are extremely popular. Two specifically, one is Arlon uses Argo CD, which is probably one of the highest rated and used CD opensource tools that's out there, right. Created by folks that are as part of Intuit team now, you know, really brilliant team, and it's used at scale across enterprises. That's one. Second is Arlon also makes use of cluster API, CAPI, 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 or open source projects that will find Arlon to be right up in their alley, because they're already comfortable, familiar with Argo CD. Now Arlon just extends the scope of what Argo CD can do. And so that's one. And then the second part is going back to your point of the comfort. And that's where, you know, Platform9 has a role to play, which is when you are ready to deploy Arlon at scale, because you've been, you know playing with it in your DEV test environments, you're happy with what you get with it. Then Platform9 will stand behind it and provide that SLA. >> And what's been the reaction from customers you've talked to, Platform9 customers that are familiar with Argo, and then Arlo? What's been some of the feedback? >> Yeah, I think the feedback's been fantastic. I mean, I can give you examples of customers where you know, initially, when you're telling them about your entire portfolio of solutions, it might not strike a chord right away. But then we start talking about Arlon, and we talk about the fact that it uses Argo CD. 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 Arlon, 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, I'm so busy. My team's overworked, I got a skills gap. I don't need another project. I'm so tied up right now, and I'm just chasing my tail. How does Platform9 help me? >> Yeah, absolutely. So I think, you know, one of the core tenants of Platform9 has always been, that we try to bring that public cloud like simplicity by hosting, you know, this and 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 customer's 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 give you an inventory. >> So customers have clusters that are growing. That's a sign, call you guys. >> Absolutely, either they have massive, large clusters, right, that they want to 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 move things around, and reconfigure, 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. >> Profile and policy based declarative configuration and life cycle 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 Supercloud, right? If you think about a Supercloud environment, there is at least few key ingredients 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, Arlon solves. Then you need to compliment that with the right kind of observability and monitoring tools at scale, right? Because ultimately issues are going to happen, and you're going to 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 Supercloud successful. And you know, Arlon is one of them. >> Okay so now the next level is, okay, that makes sense is under the covers, kind of speak under the hood. How does that impact the app developers of the cloud native modern application workflows? Because the impact to me seems, the apps are going to be impacted. Are they going to 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 today 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 these stack end to end. I have to rely on my OPS counterpart to do their part, right. And so this really gives them the right tooling for that. >> This is actually a great kind of relevant point. You know, as cloud becomes more scalable, you're starting to see this fragmentation, gone are 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 Arlo solution takes place, as you say, and the apps are going to do what they're designed to do, the question is what does the current pain look like? Are the apps breaking? What is the signals to the customer that they should be calling you guys up and implementing Arlo, Argo, and all the other goodness to automate, what are some of the signals? Is it downtime? Is it failed apps? Is it latency? What are some of the things that would be indications of things are effed up a little bit. >> Yeah, more frequent down times, down times that take longer to triage. And so your, you know, your mean times on resolution, et cetera, are escalating or growing larger, right? Like we have environments of customers where they have a number of folks 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 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. So 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're 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 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 immediate terminals and some PCs and handhelds. Now, if technology's running the business, is the business, company's the application. So it can't be down. So there's a lot of pressure on CSOs and CIOs now, and boards are saying, how is technology driving the top line revenue? That's the number one conversation. Do you see the same thing? >> Yeah, it's interesting. I think there's multiple pressures at the CSO, CIO level, right? One, is that there needs to be that visibility and clarity and guarantee almost that, you know, the technology that's going to drive your top line is going to 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 providing those goods to their end customers. So I think 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 cloud native 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 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 themselves. >> And the CIO doesn't exist. There's no CISO, they're at the beach. >> (laughing) Yeah. >> Madhura, thank you for coming on, sharing the cloud native at scale here on theCUBE. Thank you for your time. >> Fantastic, thanks for having me. >> Okay, I'm John Furrier here for special program presentation, special programming Cloud Native at Scale, Enabling Supercloud Modern Applications with Platform9. Thanks for watching. (upbeat music)

(upbeat music) >> Hello everyone. This is Dave Vellante. We're diving into the deep end with AMD and Oracle on the topic of mySQL HeatWave performance. And we want to explore the important issues around machine learning. As applications become more data intensive and machine intelligence continues to evolve, workloads increasingly are seeing a major shift where data and AI are being infused into applications. And having a database that simplifies the convergence of transaction and analytics data without the need to context, switch and move data out of and into different data stores. And eliminating the need to perform extensive ETL operations is becoming an industry trend that customers are demanding. At the same time, workloads are becoming more automated and intelligent. And to explore these issues further, we're happy to have back in theCUBE Nipun Agarwal, who's the Senior Vice President of mySQL HeatWave and Kumaran Siva, who's the Corporate Vice President Strategic Business Development at AMD. Gents, hello again. Welcome back. >> Hello. Hi Dave. >> Thank you, Dave. >> Okay. Nipun, obviously machine learning has become a must have for analytics offerings. It's integrated into mySQL HeatWave. Why did you take this approach and not the specialized database approach as many competitors do right tool for the right job? >> Right? So, there are a lot of customers of mySQL who have the need to run machine learning on the data which is store in mySQL database. So in the past, customers would need to extract the data out of mySQL and they would take it to a specialized service for running machine learning. Now, the reason we decided to incorporate machine learning inside the database, there are multiple reasons. One, customers don't need to move the data. And if they don't need to move the data, it is more secure because it's protected by the same access controlled mechanisms as rest of the data There is no need for customers to manage multiple services. But in addition to that, when we run the machine learning inside the database customers are able to leverage the same service the same hardware, which has been provisioned for OTP analytics and use machine learning capabilities at no additional charge. So from a customer's perspective, they get the benefits that it is a single database. They don't need to manage multiple services. And it is offered at no additional charge. And then as another aspect, which is kind of hard to learn which is based on the IP, the work we have done it is also significantly faster than what customers would get by having a separate service. >> Just to follow up on that. How are you seeing customers use HeatWaves machine learning capabilities today? How is that evolving? >> Right. So one of the things which, you know customers very often want to do is to train their models based on the data. Now, one of the things is that data in a database or in a transaction database changes quite rapidly. So we have introduced support for auto machine learning as a part of HeatWave ML. And what it does is that it fully automates the process of training. And this is something which is very important to database users, very important to mySQL users that they don't really want to hire or data scientists or specialists for doing training. So that's the first part that training in HeatWave ML is fully automated. Doesn't require the user to provide any like specific parameters, just the source data and the task which they want to train. The second aspect is the training is really fast. So the training is really fast. The benefit is that customers can retrain quite often. They can make sure that the model is up to date with any changes which have been made to their transaction database. And as a result of the models being up to date, the accuracy of the prediction is high. Right? So that's the first aspect, which is training. The second aspect is inference, which customers run once they have the models trained. And the third thing, which is perhaps been the most sought after request from the mySQL customers is the ability to provide explanations. So, HeatWave ML provides explanations for any model which has been generated or trained by HeatWave ML. So these are the three capabilities- training, inference and explanations. And this whole process is completely automated, doesn't require a specialist or a data scientist. >> Yeah, that's nice. I mean, training obviously very popular today. I've said inference I think is going to explode in the coming decade. And then of course, AI explainable AI is a very important issue. Kumaran, what are the relevant capabilities of the AMD chips that are used in OCI to support HeatWave ML? Are they different from say the specs for HeatWave in general? >> So, actually they aren't. And this is one of the key features of this architecture or this implementation that is really exciting. Um, there with HeatWave ML, you're using the same CPU. And by the way, it's not a GPU, it's a CPU for both for all three of the functions that Nipun just talked about- inference, training and explanation all done on CPU. You know, bigger picture with the capabilities we bring here we're really providing a balance, you know between the CPU cores, memory and the networking. And what that allows you to do here is be able to feed the CPU cores appropriately. And within the cores, we have these AVX instruc... extensions in with the Zen 2 and Zen 3 cores. We had AVX 2, and then with the Zen 4 core coming out we're going to have AVX 512. But we were able to with that balance of being able to bring in the data and utilize the high memory bandwidth and then use the computation to its maximum we're able to provide, you know, build pride enough AI processing that we are able to get the job done. And then we're built to build a fit into that larger pipeline that that we build out here with the HeatWave. >> Got it. Nipun you know, you and I every time we have a conversation we've got to talk benchmarks. So you've done machine learning benchmarks with HeatWave. You might even be the first in the industry to publish you know, transparent, open ML benchmarks on GitHub. I mean, I, I wouldn't know for sure but I've not seen that as common. Can you describe the benchmarks and the data sets that you used here? >> Sure. So what we did was we took a bunch of open data sets for two categories of tasks- classification and regression. So we took about a dozen data sets for classification and about six for regression. So to give an example, the kind of data sets we used for classifications like the airlines data set, hex sensors bank, right? So these are open data sets. And what we did was for on these data sets we did a comparison of what would it take to train using HeatWave ML? And then the other service we compared with is that RedShift ML. So, there were two observations. One is that with HeatWave ML, the user does not need to provide any tuning parameters, right? The HeatWave ML using RML fully generates a train model, figures out what are the right algorithms? What are the right features? What are the right hyper parameters and sets, right? So no need for any manual intervention not so the case with Redshift ML. The second thing is the performance, right? So the performance of HeatWave ML aggregate on these 12 data sets for classification and the six data sets on regression. On an average, it is 25 times faster than Redshift ML. And note that Redshift ML in turn involves SageMaker, right? So on an average, HeatWave ML provides 25 times better performance for training. And the other point to note is that there is no need for any human intervention. That's fully automated. But in the case of Redshift ML, many of these data sets did not even complete in the set duration. If you look at price performance, one of the things again I want to highlight is because of the fact that AMD does pretty well in all kinds of workloads. We are able to use the same cluster users and use the same cluster for analytics, for OTP or for machine learning. So there is no additional cost for customers to run HeatWave ML if they have provision HeatWave. But assuming a user is provisioning a HeatWave cluster only to run HeatWave ML, right? That's the case, even in that case the price performance advantage of HeatWave ML over Redshift ML is 97 times, right? So 25 times faster at 1% of the cost compared to Redshift ML And all these scripts and all this information is available on GitHub for customers to try to modify and like, see, like what are the advantages they would get on their workloads? >> Every time I hear these numbers, I shake my head. I mean, they're just so overwhelming. Um, and so we'll see how the competition responds when, and if they respond. So, but thank you for sharing those results. Kumaran, can you elaborate on how the specs that you talked about earlier contribute to HeatWave ML's you know, benchmark results. I'm particularly interested in scalability, you know Typically things degrade as you push the system harder. What are you seeing? >> No, I think, I think it's good. Look, yeah. That's by those numbers, just blow me, blow my head too. That's crazy good performance. So look from, from an AMD perspective, we have really built an architecture. Like if you think about the chiplet architecture to begin with, it is fundamentally, you know, it's kind of scaling by design, right? And, and one of the things that we've done here is been able to work with, with the HeatWave team and heat well ML team, and then been able to, to within within the CPU package itself, be able to scale up to take very efficient use of all of the course. And then of course, work with them on how you go between nodes. So you can have these very large systems that can run ML very, very efficiently. So it's really, you know, building on the building blocks of the chiplet architecture and how scaling happens there. >> Yeah. So it's you're saying it's near linear scaling or essentially. >> So, let Nipun comment on that. >> Yeah. >> Is it... So, how about as cluster sizes grow, Nipun? >> Right. >> What happens there? >> So one of the design points for HeatWave is scale out architecture, right? So as you said, that as we add more data set or increase the size of the data, or we add the number of nodes to the cluster, we want the performance to scale. So we show that we have near linear scale factor, or nearly near scale scalability for SQL workloads in the case of HeatWave ML, as well. As users add more nodes to the cluster so the size of the cluster the performance of HeatWave ML improves. So I was giving you this example that HeatWave ML is 25 times faster compared to Redshift ML. Well, that was on a cluster size of two. If you increase the cluster size of HeatWave ML to a larger number. But I think the number is 16. The performance advantage over Redshift ML increases from 25 times faster to 45 times faster. So what that means is that on a cluster size of 16 nodes HeatWave ML is 45 times faster for training these again, dozen data sets. So this shows that HeatWave ML skills better than the computation. >> So you're saying adding nodes offsets any management complexity that you would think of as getting in the way. Is that right? >> Right. So one is the management complexity and which is why by features like last customers can scale up or scale down, you know, very easily. The second aspect is, okay What gives us this advantage, right, of scalability? Or how are we able to scale? Now, the techniques which we use for HeatWave ML scalability are a bit different from what we use for SQL processing. So in the case of HeatWave ML, they really like, you know, three, two trade offs which we have to be careful about. One is the accuracy. Because we want to provide better performance for machine learning without compromising on the accuracy. So accuracy would require like more synchronization if you have multiple threads. But if you have too much of synchronization that can slow down the degree of patterns that we get. Right? So we have to strike a fine balance. So what we do is that in HeatWave ML, there are different phases of training, like algorithm selection, feature selection, hyper probability training. Each of these phases is analyzed. And for instance, one of the ways techniques we use is that if you're trying to figure out what's the optimal hyper parameter to be used? We start up with the search space. And then each of the VMs gets a part of the search space. And then we synchronize only when needed, right? So these are some of the techniques which we have developed over the years. And there are actually paper's filed, research publications filed on this. And this is what we do to achieve good scalability. And what that results to the customer is that if they have some amount of training time and they want to make it better they can just provision a larger cluster and they will get better performance. >> Got it. Thank you. Kumaran, when I think of machine learning, machine intelligence, AI, I think GPU but you're not using GPU. So how are you able to get this type of performance or price performance without using GPU's? >> Yeah, definitely. So yeah, that's a good point. And you think about what is going on here and you consider the whole pipeline that Nipun has just described in terms of how you get you know, your training, your algorithms And using the mySQL pieces of it to get to the point where the AI can be effective. In that process what happens is you have to have a lot of memory to transactions. A lot of memory bandwidth comes into play. And then bringing all that data together, feeding the actual complex that does the AI calculations that in itself could be the bottleneck, right? And you can have multiple bottlenecks along the way. And I think what you see in the AMD architecture for epic for this use case is the balance. And the fact that you are able to do the pre-processing, the AI, and then the post-processing all kind of seamlessly together, that has a huge value. And that goes back to what Nipun was saying about using the same infrastructure, gets you the better TCO but it also gets you gets you better performance. And that's because of the fact that you're bringing the data to the computation. So the computation in this case is not strictly the bottleneck. It's really about how you pull together what you need and to do the AI computation. And that is, that's probably a more, you know, it's a common case. And so, you know, you're going to start I think the least start to see this especially for inference applications. But in this case we're doing both inference explanation and training. All using the the CPU in the same OCI infrastructure. >> Interesting. Now Nipun, is the secret sauce for HeatWave ML performance different than what we've discussed before you and I with with HeatWave generally? Is there some, you know, additive engine additive that you're putting in? >> Right? Yes. The secret sauce is indeed different, right? Just the way I was saying that for SQL processing. The reason we get very good performance and price performance is because we have come up with new algorithms which help the SQL process can scale out. Similarly for HeatWave ML, we have come up with new IP, new like algorithms. One example is that we use meta-learn proxy models, right? That's the technique we use for automating the training process, right? So think of this meta-learn proxy models to be like, you know using machine learning for machine learning training. And this is an IP which we developed. And again, we have published the results and the techniques. But having such kind of like techniques is what gives us a better performance. Similarly, another thing which we use is adaptive sampling that you can have a large data set. But we intelligently sample to figure out that how can we train on a small subset without compromising on the accuracy? So, yes, there are many techniques that you have developed specifically for machine learning which is what gives us the better performance, better price performance, and also better scalability. >> What about mySQL autopilot? Is there anything that differs from HeatWave ML that is relevant? >> Okay. Interesting you should ask. So mySQL Autopilot is think of it to be an application using machine learning. So mySQL Autopilot uses machine learning to automate various aspects of the database service. So for instance, if you want to figure out that what's the right partitioning scheme to partition the data in memory? We use machine learning techniques to figure out that what's the right, the best column based on the user's workload to partition the data in memory Or given a workload, if you want to figure out what is the right cluster size to provision? That's something we use mySQL autopilot for. And I want to highlight that we don't aware of any other database service which provides this level of machine learning based automation which customers get with mySQL Autopilot. >> Hmm. Interesting. Okay. Last question for both of you. What are you guys working on next? What can customers expect from this collaboration specifically in this space? Maybe Nipun, you can start and then Kamaran can bring us home. >> Sure. So there are two things we are working on. One is based on the feedback we have gotten from customers, we are going to keep making the machine learning capabilities richer in HeatWave ML. That's one dimension. And the second thing is which Kamaran was alluding to earlier, We are looking at the next generation of like processes coming from AMD. And we will be seeing as to how we can more benefit from these processes whether it's the size of the L3 cache, the memory bandwidth, the network bandwidth, and such or the newer effects. And make sure that we leverage the all the greatness which the new generation of processes will offer. >> It's like an engineering playground. Kumaran, let's give you the final word. >> No, that's great. Now look with the Zen 4 CPU cores, we're also bringing in AVX 512 instruction capability. Now our implementation is a little different. It was in, in Rome and Milan, too where we use a double pump implementation. What that means is, you know, we take two cycles to do these instructions. But the key thing there is we don't lower our speed of the CPU. So there's no noisy neighbor effects. And it's something that OCI and the HeatWave has taken full advantage of. And so like, as we go out in time and we see the Zen 4 core, we can... we see up to 96 CPUs that that's going to work really well. So we're collaborating closely with, with OCI and with the HeatWave team here to make sure that we can take advantage of that. And we're also going to upgrade the memory subsystem to get to 12 channels of DDR 5. So it should be, you know there should be a fairly significant boost in absolute performance. But more important or just as importantly in TCO value for the customers, the end customers who are going to adopt this great service. >> I love their relentless innovation guys. Thanks so much for your time. We're going to have to leave it there. Appreciate it. >> Thank you, David. >> Thank you, David. >> Okay. Thank you for watching this special presentation on theCUBE. Your leader in enterprise and emerging tech coverage.

(upbeat music) >> For those of you who've been following the cloud database space, you know that MySQL HeatWave has been on a technology tear over the last 24 months with Oracle claiming record breaking benchmarks relative to other database platforms. So far, those benchmarks remain industry leading as competitors have chosen not to respond, perhaps because they don't feel the need to, or maybe they don't feel that doing so would serve their interest. Regardless, the HeatWave team at Oracle has been very aggressive about its performance claims, making lots of noise, challenging the competition to respond, publishing their scripts to GitHub. But so far, there are no takers, but customers seem to be picking up on these moves by Oracle and it's likely the performance numbers resonate with them. Now, the other area we want to explore, which we haven't thus far, is the engine behind HeatWave and that is AMD. AMD's epic processors have been the powerhouse on OCI, running MySQL HeatWave since day one. And today we're going to explore how these two technology companies are working together to deliver these performance gains and some compelling TCO metrics. In fact, a recent Wikibon analysis from senior analyst Marc Staimer made some TCO comparisons in OLAP workloads relative to AWS, Snowflake, GCP, and Azure databases, you can find that research on wikibon.com. And with that, let me introduce today's guest, Nipun Agarwal senior vice president of MySQL HeatWave and Kumaran Siva, who's the corporate vice president for strategic business development at AMD. Welcome to theCUBE gentlemen. >> Welcome. Thank you. >> Thank you, Dave. >> Hey Nipun, you and I have talked a lot about this. You've been on theCUBE a number of times talking about MySQL HeatWave. But for viewers who may not have seen those episodes maybe you could give us an overview of HeatWave and how it's different from competitive cloud database offerings. >> Sure. So MySQL HeatWave is a fully managed MySQL database service offering from Oracle. It's a single database, which can be used to run transactional processing, analytics and machine learning workloads. So, in the past, MySQL has been designed and optimized for transaction processing. So customers of MySQL when they had to run, analytics machine learning, would need to extract the data out of MySQL, into some other database or service, to run analytics or machine learning. MySQL HeatWave offers a single database for running all kinds of workloads so customers don't need to extract data into some of the database. In addition to having a single database, MySQL HeatWave is also very performant compared to one up databases and also it is very price competitive. So the advantages are; single database, very performant, and very good price performance. >> Yes. And you've published some pretty impressive price performance numbers against competitors. Maybe you could describe those benchmarks and highlight some of the results, please. >> Sure. So one thing to notice that the performance of any database is going to like vary, the performance advantage is going to vary based on, the size of the data and the specific workloads, so the mileage varies, that's the first thing to know. So what we have done is, we have published multiple benchmarks. So we have benchmarks on PPCH or PPCDS and we have benchmarks on different data sizes because based on the customer's workload, the mileage is going to vary, so we want to give customers a broad range of comparisons so that they can decide for themselves. So in a specific case, where we are running on a 30 terabyte PPCH workload, HeatWave is about 18 times better price performance compared to Redshift. 18 times better compared to Redshift, about 33 times better price performance, compared to Snowflake, and 42 times better price performance compared to Google BigQuery. So, this is on 30 Terabyte PPCH. Now, if the data size is different, or the workload is different, the characteristics may vary slightly but this is just to give a flavor of the kind of performance advantage MySQL HeatWave offers. >> And then my last question before we bring in Kumaran. We've talked about the secret sauce being the tight integration between hardware and software, but would you add anything to that? What is that secret sauce in HeatWave that enables you to achieve these performance results and what does it mean for customers? >> So there are three parts to this. One is HeatWave has been designed with a scale out architecture in mind. So we have invented and implemented new algorithms for skill out query processing for analytics. The second aspect is that HeatWave has been really optimized for cloud, commodity cloud, and that's where AMD comes in. So for instance, many of the partitioning schemes we have for processing HeatWave, we optimize them for the L3 cache of the AMD processor. The thing which is very important to our customers is not just the sheer performance but the price performance, and that's where we have had a very good partnership with AMD because not only does AMD help us provide very good performance, but the price performance, right? And that all these numbers which I was showing, big part of it is because we are running on AMD which provides very good price performance. So that's the second aspect. And the third aspect is, MySQL autopilot, which provides machine learning based automation. So it's really these three things, a combination of new algorithms, design for scale out query processing, optimized for commodity cloud hardware, specifically AMD processors, and third, MySQL auto pilot which gives us this performance advantage. >> Great, thank you. So that's a good segue for AMD and Kumaran. So Kumaran, what is AMD bringing to the table? What are the, like, for instance, relevance specs of the chips that are used in Oracle cloud infrastructure and what makes them unique? >> Yeah, thanks Dave. That's a good question. So, OCI is a great customer of ours. They use what we call the top of stack devices meaning that they have the highest core count and they also are very, very fast cores. So these are currently Zen 3 cores. I think the HeatWave product is right now deployed on Zen 2 but will shortly be also on the Zen 3 core as well. But we provide in the case of OCI 64 cores. So that's the largest devices that we build. What actually happens is, because these large number of CPUs in a single package and therefore increasing the density of the node, you end up with this fantastic TCO equation and the cost per performance, the cost per for deployed services like HeatWave actually ends up being extraordinarily competitive and that's a big part of the contribution that we're bringing in here. >> So Zen 3 is the AMD micro architecture which you introduced, I think in 2017, and it's the basis for EPIC, which is sort of the enterprise grade that you really attacked the enterprise with. Maybe you could elaborate a little bit, double click on how your chips contribute specifically to HeatWave's, price performance results. >> Yeah, absolutely. So in the case of HeatWave, so as Nipun alluded to, we have very large L3 caches, right? So in our very, very top end parts just like the Milan X devices, we can go all the way up to like 768 megabytes of L3 cache. And that gives you just enormous performance and performance gains. And that's part of what we're seeing with HeatWave today and that not that they're currently on the second generation ROM based product, 'cause it's a 7,002 based product line running with the 64 cores. But as time goes on, they'll be adopting the next generation Milan as well. And the other part of it too is, as our chip led architecture has evolved, we know, so from the first generation Naples way back in 2017, we went from having multiple memory domains and a sort of NUMA architecture at the time, today we've really optimized that architecture. We use a common I/O Die that has all of the memory channels attached to it. And what that means is that, these scale out applications like HeatWave, are able to really scale very efficiently as they go from a small domain of CPUs to, for example the entire chip, all 64 cores that scaling, is been a key focus for AMD and being able to design and build architectures that can take advantage of that and then have applications like HeatWave that scale so well on it, has been, a key aim of ours. >> And Gen 3 moving up the Italian countryside. Nipun, you've taken the somewhat unusual step of posting the benchmark parameters, making them public on GitHub. Now, HeatWave is relatively new. So people felt that when Oracle gained ownership of MySQL it would let it wilt on the vine in favor of Oracle database, so you lost some ground and now, you're getting very aggressive with HeatWave. What's the reason for publishing those benchmark parameters on GitHub? >> So, the main reason for us to publish price performance numbers for HeatWave is to communicate to our customers a sense of what are the benefits they're going to get when they use HeatWave. But we want to be very transparent because as I said the performance advantages for the customers may vary, based on the data size, based on the specific workloads. So one of the reasons for us to publish, all these scripts on GitHub is for transparency. So we want customers to take a look at the scripts, know what we have done, and be confident that we stand by the numbers which we are publishing, and they're very welcome, to try these numbers themselves. In fact, we have had customers who have downloaded the scripts from GitHub and run them on our service to kind of validate. The second aspect is in some cases, they may be some deviations from what we are publishing versus what the customer would like to run in the production deployments so it provides an easy way, for customers to take the scripts, modify them in some ways which may suit their real world scenario and run to see what the performance advantages are. So that's the main reason, first, is transparency, so the customers can see what we are doing, because of the comparison, and B, if they want to modify it to suit their needs, and then see what is the performance of HeatWave, they're very welcome to do so. >> So have customers done that? Have they taken the benchmarks? And I mean, if I were a competitor, honestly, I wouldn't get into that food fight because of the impressive performance, but unless I had to, I mean, have customers picked up on that, Nipun? >> Absolutely. In fact, we have had many customers who have benchmarked the performance of MySQL HeatWave, with other services. And the fact that the scripts are available, gives them a very good starting point, and then they've also tweaked those queries in some cases, to see what the Delta would be. And in some cases, customers got back to us saying, hey the performance advantage of HeatWave is actually slightly higher than what was published and what is the reason. And the reason was, when the customers were trying, they were trying on the latest version of the service, and our benchmark results were posted let's say, two months back. So the service had improved in those two to three months and customers actually saw better performance. So yes, absolutely. We have seen customers download the scripts, try them and also modify them to some extent and then do the comparison of HeatWave with other services. >> Interesting. Maybe a question for both of you how is the competition responding to this? They haven't said, "Hey, we're going to come up "with our own benchmarks." Which is very common, you oftentimes see that. Although, for instance, Snowflake hasn't responded to data bricks, so that's not their game, but if the customers are actually, putting a lot of faith in the benchmarks and actually using that for buying decisions, then it's inevitable. But how have you seen the competition respond to the MySQL HeatWave and AMD combo? >> So maybe I can take the first track from the database service standpoint. When customers have more choice, it is invariably advantages for the customer because then the competition is going to react, right? So the way we have seen the reaction is that we do believe, that the other database services are going to take a closer eye to the price performance, right? Because if you're offering such good price performance, the vendors are already looking at it. And, you know, instances where they have offered let's say discount to the customers, to kind of at least like close the gap to some extent. And the second thing would be in terms of the capability. So like one of the things which I should have mentioned even early on, is that not only does MySQL HeatWave on AMD, provide very good price performance, say on like a small cluster, but it's all the way up to a cluster size of 64 nodes, which has about 1000 cores. So the point is, that HeatWave performs very well, both on a small system, as well as a huge scale out. And this is again, one of those things which is a differentiation compared to other services so we expect that even other database services will have to improve their offerings to provide the same good scale factor, which customers are now starting to expectancy, with MySQL HeatWave. >> Kumaran, anything you'd add to that? I mean, you guys are an arms dealer, you love all your OEMs, but at the same time, you've got chip competitors, Silicon competitors. How do you see the competitive-- >> I'd say the broader answer and the big picture for AMD, we're very maniacally focused on our customers, right? And OCI and Oracle are huge and important customers for us, and this particular use cases is extremely interesting both in that it takes advantage, very well of our architecture and it pulls out some of the value that AMD bring. I think from a big picture standpoint, our aim is to execute, to build to bring out generations of CPUs, kind of, you know, do what we say and say, sorry, say what we do and do what we say. And from that point of view, we're hitting, the schedules that we say, and being able to bring out the latest technology and bring it in a TCO value proposition that generationally keeps OCI and HeatWave ahead. That's the crux of our partnership here. >> Yeah, the execution's been obvious for the last several years. Kumaran, staying with you, how would you characterize the collaboration between, the AMD engineers and the HeatWave engineering team? How do you guys work together? >> No, I'd say we're in a very, very deep collaboration. So, there's a few aspects where, we've actually been working together very closely on the code and being able to optimize for both the large L3 cache that AMD has, and so to be able to take advantage of that. And then also, to be able to take advantage of the scaling. So going between, you know, our architecture is chip like based, so we have these, the CPU cores on, we call 'em CCDs and the inter CCD communication, there's opportunities to optimize an application level and that's something we've been engaged with. In the broader engagement, we are going back now for multiple generations with OCI, and there's a lot of input that now, kind of resonates in the product line itself. And so we value this very close collaboration with HeatWave and OCI. >> Yeah, and the cadence, Nip, and you and I have talked about this quite a bit. The cadence has been quite rapid. It's like this constant cycle every couple of months I turn around, is something new on HeatWave. But for question again, for both of you, what new things do you think that organizations, customers, are going to be able to do with MySQL HeatWave if you could look out next 12 to 18 months, is there anything you can share at this time about future collaborations? >> Right, look, 12 to 18 months is a long time. There's going to be a lot of innovation, a lot of new capabilities coming out on in MySQL HeatWave. But even based on what we are currently offering, and the trend we are seeing is that customers are bringing, more classes of workloads. So we started off with OLTP for MySQL, then it went to analytics. Then we increased it to mixed workloads, and now we offer like machine learning as alike. So one is we are seeing, more and more classes of workloads come to MySQL HeatWave. And the second is a scale, that kind of data volumes people are using HeatWave for, to process these mixed workloads, analytics machine learning OLTP, that's increasing. Now, along the way we are making it simpler to use, we are making it more cost effective use. So for instance, last time, when we talked about, we had introduced this real time elasticity and that's something which is a very, very popular feature because customers want the ability to be able to scale out, or scale down very efficiently. That's something we provided. We provided support for compression. So all of these capabilities are making it more efficient for customers to run a larger part of their workloads on MySQL HeatWave, and we will continue to make it richer in the next 12 to 18 months. >> Thank you. Kumaran, anything you'd add to that, we'll give you the last word as we got to wrap it. >> No, absolutely. So, you know, next 12 to 18 months we will have our Zen 4 CPUs out. So this could potentially go into the next generation of the OCI infrastructure. This would be with the Genoa and then Bergamo CPUs taking us to 96 and 128 cores with 12 channels at DDR five. This capability, you know, when applied to an application like HeatWave, you can see that it'll open up another order of magnitude potentially of use cases, right? And we're excited to see what customers can do do with that. It certainly will make, kind of the, this service, and the cloud in general, that this cloud migration, I think even more attractive. So we're pretty excited to see how things evolve in this period of time. >> Yeah, the innovations are coming together. Guys, thanks so much, we got to leave it there really appreciate your time. >> Thank you. >> All right, and thank you for watching this special Cube conversation, this is Dave Vellante, and we'll see you next time. (soft calm music)

>>Hey, everyone. Welcome back to San Francisco. Lisa Martin and John furrier live on the floor at VMware Explorer, 2022. This is our third day of wall to wall coverage on the cube. But you know that cuz you've been here the whole time. We're pleased to welcome up. First timer to the cubes we saw is here. The CEO and founder of ZDA. Saed welcome to the program. >>Thank you for having me >>Talk to me a little bit about what ZDA does in edge. >>Sure. So ZDA is a company purely focused in edge computing. I started a company about five years ago, go after edge. So what we do is we help customers with orchestrating their edge, helping them to deploy secure monitor application services and devices at the edge. >>What's the business model for you guys. We get that out there. So the targeting the edge, which is everything from telco to whatever. Yeah. What's the business model. Yeah. >>Maybe before we go there, let's talk about edge itself. Cuz edge is complex. There's a lot of companies. I call 'em lens company nowadays, if you're not a cloud company, you're probably an edge company at this point. So we are focusing something called the distributed edge. So distributed edge. When you start putting tiny servers in environments like factory floors, solar farms, wind farms, even inside machines or well sites, et cetera. And a question that people always ask me, like why, why would you want to put, you know, servers there on servers supposed to be in a data center in the cloud? And the answer to the question actually is data gravity. So traditionally wherever the data gets created is where your applications live. But as we're connecting more and more devices to the edge of the network, we basically customers now are required to push the applications to the edge cause they can't go all the data to the cloud. So basically that's where we focus on people call it the far edge as well. You know, that's the term we've heard in the past as well. And what we do in our business model is provide customers a, a software as a service solution where they can basically deploy and monitor these applications at these highly distributed environments. >>Data, gravity comes up a lot and I want you to take a minute to explain the definition as it is today. And people have used that term, you know, with big data, going back to 2010 leads when we covering the Hadoop wave, which ended up becoming, you know, data, data, bricks, and snowflake now, but, but a lots changed, but what does it mean to be data gravity? It means that staying local, it's just what specifically describe and, and define what data gravity is. >>Yeah. So for me, data gravity is where you need to process the data, right? It's where the data usually gets created. So if you think about a web app, where does the data get created? Where people click on buttons, they, they interface with it. They, they upload content to it, et cetera. So that's where the data gravity therefore is therefore that's where you do your analytics. That's where you do your visualization processing, machine learning and all of those pieces. So it's really where that data gets created is where the data gravity in my view says, >>What are some of the challenges that data and opportunities that data gravity presents to customers? >>Well, obviously I think every enterprise in this day is trying to take data and make it a competitive advantage, right? Like faster decisions, better decisions, outcompete your competition by, you know, being first with a product or being first with a product with the future, et cetera. So, so I think, you know, if you're not a data driven enterprise by now, then I think the future may be a little bit bleak. >>Okay. So you're targeting the market distributed edge business model, SAS technology, secret sauce. What's that piece. >>Yeah. So that's, that's what the interesting part comes in. I think, you know, if you kind of look at the data center in the cloud, we've had these virtualization and orchestration stacks create, I mean, we're here in VMware Explorer. And as an example, what we basically, what we saw is that the edge is so unique and so different than what we've seen in the data center, in the cloud that we needed to build a complete brand new purpose-built illustration and virtualization solution. So that's really what we, we set off to do. So there's two components that we do. One end is we built a purpose-built edge operating system for the edge and we actually open sourced it. And the reason we opensource it, we said, Hey, you know, edge is so diverse. You know, depending on the environment you're running in a machine or in a vehicle or in a well site, you have different hardware, different networks, different applications you need to enable. >>And we will never be able to support all of them ourselves. As a matter of fact, we actually think there's a need for standardization at the edge. We need to kind of cut through all these silos that have been created traditionally from the embedded way of thinking. So we created basically an open source project in the Linux foundation in LFS, which is a sister organization through the CNCF it's called project Eve. And the idea is to create the Android of the edge, basically what Android became for mobile computing, an a common operating system. So you build one app. You can run in any phone in the world that runs Android, build an architecture. You build one app. You can run in any Eve powered node in the world, >>So distributed edge and you get the tech here, get the secret sauce. We'll get more into that in a second, but I wanna just tie one kick quick point and get your clarification on edge is becoming much more about the physical side too. I mean, absolutely. So when you talk about Android, you're making the reference of a phone. I get that's metaphor to what you're doing at the edge, wind farms, factories, alarms, light bulbs, buildings. I mean, that's what you're talking about, right? Yes. We're getting down to that very, >>Very physical, dark distributed locations. >>We're gonna come back to the CISO CSO. We're gonna come back to the CISO versus CSO question because is the CISO or CIO or who runs that anyway? So that's true. What's the important thing that's happening because that sounds like old OT world, like yes. Operating technology, not it information technology, is it a complete reset of those worlds or is it a collision? >>It's a great question. So what we're seeing is first of all, there is already compute in these environments, industrial PCs of existed well beyond, you know, an industrial automation has been done for many, many decades. The point is that that stuff has been done. Collect data has been collected, but never connected, right? So with edge computing, we're connecting now this data from an industrial machine and industrial process to the cloud, right? And one of the problems is it's data that comes of that industrial process too much to upload to the cloud. So I gotta analyze, analyze it locally. So one of the, the things we saw early on in edge is there's a lot of brownfield. Most of our customers today actually have applications running on windows and they would love to make in Linux and containers and Kubernetes, but it took them 20, 30 years to build those apps. And they basically are the money makers of the enterprise. So they are in a, in a transitionary phase and they need something that can take them from the brown to the Greenfield. So to your point, you gotta support all of these types of unique brownfield applications. >>So you're, you're saying I don't really care if this is a customer, how you get the data, you wanna start new start fresh. That's cool. But if you wanna take your old data, you'll >>Take that. Yeah. You don't wanna rebuild the whole machine. You're >>Just, they can life cycle it out on their own timetable. Yeah. >>So we had to learn, first of all, how do we take and lift and shift windows based industrial application and make it run at the edge on, on our architecture. Right? And then the second step is how do we then Sen off that data that this application is generating and do we fuse it with cloud native capability? Like, >>So your cloud, so your staff is your open source that you're giving to the Linux foundation as part of that Eve project that's available to everybody. So they can, they can look at the code, which is great by the way. Yeah. So people wanna do that. Yeah. Your self source, I'm assuming, is your hardened version with support? >>Well, we took what we took, what the open source companies did, opensource companies traditionally have sold, you know, basically a support model around the open source. We actually saw another problem. Customers has like, okay, now I have this node running and I can, you know, do this data analytics, but what if I have 15 or 20,000 of these node? And they're all around the world in remote locations on satellite links or wireless connectivity, how do I orchestrate them? So we actually build an orchestration service for these nodes running this open source >>Software. So that's a key secret sauce right there. >>That is the business model that taking open store and a lot. >>And you're taking your own code that you have. Okay. Got it. Cool. And then the customer's customer piece is, is key. So that's the final piece, I guess who's using it. >>Yeah. Well, and, >>And, and one of the business outcomes that they're achieving. Oh >>Yeah. Well, so maybe start with that first. I mean, we are deployed in customers in all and gas, for instance, helping them with the transition to renewable energy, right? So basically we, we have customers for instance, that deploy us in the, how they drill Wells is one use case and doing that better, faster, and cheaper and, and less environmental impacting. But we also have customers that use us in wind farms. We have, and solar farms, like we, one of the leading solar energy companies in the world is using us to bring down the cost of power by predicting failures ahead of time, for >>Instance. And when you're working with customers to create the optimal solution at the distributed edge, who are you working with in, within an organization? Yeah. >>It's usually a mix of OT and it people. Okay. So the OT people typically they're >>Arm wrestling, well, or they're getting along, actually, >>I think they're getting along very well. Okay, good. But they also agree that they have to have swim lanes. The it folks, obviously their job is to make sure, you know, everything is secure. Everything is according to the compliance it's, it's, you know, the, the best TCO on the infrastructure, those type of things, the OT guy, they, they, or girl, they care about the application. They care about the services. They care about the support new business. So how can you create a model that too can coexist? And if you do that, they get along really well. >>You know, we had an event called Supercloud and@theurlsupercloud.world, if you're watching check it out, it's our version of what we think multicloud will merge into including edge cuz edge is just another node in the, in the, in the network. As far as we're concerned, hybrid is the steady state. That's distributed computing on premise, private cloud, public cloud. We know what that looks like. People love that things are happening. Edge is like a whole nother new area. That's blossoming and with disruption, yeah. There's a lot of existing market and incumbents that need to be disrupted. And there's also a new capabilities that are coming that we don't yet see. So we're seeing it with the super cloud idea that these new kinds of clouds are emerging. Like there could be an edge cloud. Yeah. Why isn't there a security cloud, whereas the financial services cloud, whereas the insurance cloud, whereas the, so these become super clouds where the CapEx could be done by the Amazon, whatnot you've been following them is edge cloud. Can you make that a cloud? Is that what you guys are trying to do? And if so, what does that look like? Cause we we're adding a new track to our super cloud site. I mentioned on edge specifically, we're trying to figure out you and if you share your opinion, it'd be great. Can the E can edge clouds exist and be run by companies? Yeah. Or is that what you guys are trying to do? >>I, I, I mean, I think first of all, there is no edge without cloud, right? So when I meet any customer who says, Hey, we're gonna do edge without cloud. Then I'm like, you're probably not gonna do edge computing. Right. And, and the way we built the company and the way we think about it, it's about extending the cloud experience all the way into these embedded distributed environments. That's really, I think what customers are looking for, cuz customers love the simplicity of the cloud. They love the ease of use agility, all of that greatness. And they're like, Hey, I want that. But not in a, you know, in an Amazon or Azure data center. I want that in my factories. I want that in my wealth sites, in my vehicles. And that's really what I think the future >>Is gonna. And how long have you guys been around? What's the, what's the history of the company because you might actually be that cloud. Yeah. And are you on AWS or Azure? You're building your own. What's the, >>Yeah. Yeah. So >>Take it through the, the architecture because yeah, yeah, sure. You're a modern startup. I mean you gotta, and the edges you're going after you gotta be geared up. Yeah. To win that. Yeah. >>So, so the company's about five years old. So we, when we started focusing on edge, people didn't necessarily talk as much about edge. We kind of identified the it's like, you know, how do you find a black hole in, in the universe? Cuz you can't see it, but you sort of look around that's why you in it. And so we were like looking at it, like there's something gonna happen here at the edge of the network, because everybody's saying we're connecting these vice upload the data to the cloud's never gonna work. My background is networking. I worked at companies like Juniper and Ericsson ran several products there. So I know how the internet networks have built. And it was very Evan to me. It's not gonna be possible. My co-founders come from open source companies like pivotal and Cloudera. My auto co-founder was a, an engineer at sun Microsystems built the first network stack in the solar is operating system. So a lot of experience that kind of came together to build this. >>Yeah. Cloudera is a big day. That's where the cube started by the way. Yeah. >>Yeah. So, so we, we, we have, I think a good view on the stack, the cloud stack and therefore a good view of what the ed stack needs to look like. And then I think, you know, to answer your other question, our orchestration service runs in the cloud. We have, we actually are multi-cloud company. So we offer customers choice where they want to orchestrate the node from the nodes themself, never sit in a data center. They always highly embedded. We have customers are putting machines or inside these factory lines, et cetera. Are >>You running your SAS on Amazon web services or which >>Cloud we're running it on several clouds, including Amazon, all of, pretty much the cloud. So some customers say, Hey, I'd prefer to be on the Amazon set. And others customers say, I wanna be on Azure set. >>And you leverage their CapEx on that side. Yes. On behalf of yeah. >>Yeah. We, yes. Yes. But the majority of the customer data and, and all the data that the nodes process, the customer send it to their clouds. They don't send it to us. We don't get a copy of the camera feed analytics or the machine data. We actually decouple those though. So basically the, the team production data go straight to the customer's cloud and that's why they love us. >>And they choose that they can control their own desktop. >>Yeah. So we separate the management plane from the data plane at the edge. Yeah. >>That's a good call >>Actually. Yeah. That was another very important part of the architecture early on. Cause customers don't want us to see their, you know, highly confidential production data and we don't wanna have it either. So >>We had a great chat with Chris Wolf who works with kit culvert about control plane, data, plane. So that seems to be the trend data, plane customers want full yeah. Management of that. Yeah. Control plane. Maybe give multiple >>Versions. Yeah. Yeah. So our cloud consumption what the data we stories about the apps, their behavior, the networking, the security, all of that. That's what we store in our cloud. And then customers can access that and monitor. But the actual machine that I go somewhere else >>Here we are at VMware. Explore. Talk a little bit about the VMware relationship. You just had some big news the other day. >>Yeah. So two days ago we actually made a big announcement with VMware. So we signed an OEM agreement with VMware. So we're part now of VMware's edge compute stack. So VMware customers, as they start using the recently announced edge compute stack 2.0, that was announced here. Basically it's powered by Edda technology. So it's a really exciting partnership as part of this, we actually building integrations with the VMware organization products. So that's basically now extending to more, you know, other groups inside VMware. >>So what's the value in it for VMware customers. >>Yeah. So I think the, the, the benefit of, of VMware customers, I think cus VMware customers want that multi-cloud multi edge orchestration experience. So they wanna be able to deploy workloads in the cloud. They wanna deploy the workloads in the data center. And of course also at the edge. So by us integrating in that vision customers now can have that unified experience from cloud to edge and anywhere in between. >>What's the big vision that you see happening at the edge. I mean, a lot of the VMware customers here, they're classic it that have evolved into ops now, dev ops. Now you've got second data ops coming. The edge is gonna right around the corner for them. They're dealing with it now, probably just kicking the tires, towing the water kind of thing. Where do you see the vision going? Cuz now, no matter what happens with VMware, the Broadcom, this wave is still here. You got AWS, got Azure, got Google cloud, you got Oracle, Alibaba internationally. And the cloud native surges here. How do you see that disrupting the existing edge? Because let's face it the O some of those OT players, a little bit old and antiquated, a little bit outdated. I mean, I was talking to a telco person. They, they puked the word open source. I mean, these people are so dogmatic on, on their architecture. Yeah. They're gonna get disrupted. It's a matter of time. Yeah. Where's the new guard come in. How do you see the configuration changing in the landscape? Because some people will cross over to the right side of the street here. Yeah. Some won't yeah. Open circle. Dominate cloud native will be key. Yeah. >>Well, I mean, I think, again, let's, let's take an example of a vertical that's heavily disrupted now as the automotive market, right? The, so look at Tesla and look at all these companies, they built, they built software first cars, right? Software, first delivery of capabilities and everything else. And the, and the incumbents. They have only two options, right? Either they try to respond by adopting open source cloud, native technologies. Like the, these new entrants have done and really, you know, compete with them at that level, or they can become commodity. Right. So, and I think that's the customers we're seeing the smart customers go like, we need to compete with these guys. We need to figure out how to take this technology in. And they need partners like us and partners like VMware for them. >>Do you see customers becoming cloud super cloud players? If they continue to keep leveraging the CapEx of the clouds and focus all their operational capital on top line revenue, generating activities. >>Yeah. I, so I think the CapEx model of the cloud is a great benefit of the cloud, but I think that is not, what's the longer term future of the cloud. I think the op the cloud operating model is the future. Like the agility, the ability imagine embedded software that, you know, you do an over the year update to fix a bug, but it's very hard to make a, an embedded device smarter over time. And then imagine if you can run cloud native software, you can roll out every two weeks new features and make that thing smarter, intelligent, and continue to help you in your business. That I think is what cloud did ultimately. And I think that is what really these customers are gonna need at their edge. >>Well, we talked about the value within it for customers with the VMware partnership, but what are some of your expectations? Obviously, this is a pretty powerful partnership for you guys. Yeah. What are some of the things that you're expecting that this is gonna drive? Yeah, >>So we, we, we have always operated at the more OT layer, distributed organizations in retail, energy, industrial automotive. Those are the verticals we, so we've developed. I think a lot of experience there, what, what we're seeing as we talk to those customers is they obviously have it organizations and the it organizations, Hey, that's great. You're looking at its computing, but how do we tie this into the existing investments we made with VMware? And how do we kind of take that also to this new environment? And I think that's the expectation I have is that I think we will be able to, to talk to the it folks and say, Hey, you can actually talk to the OT person. And both of you will speak the same language. You probably will both standardize on the same architecture and you'll be together deploying and enabling this new agility at the edge. >>What are some of the next things coming up for ZDA and the team? >>Well, so we've had a really amazing few quarters. We just close a series B round. So we've raised the companies raised over 55 million so far, we're growing very rapidly. We opened up no new international offices. I would say the, the early customers that we started deploying, wait a while back, they're now going into mass scale deployment. So we have now deployments underway in, you know, the 10 to hundred thousands of nodes at certain customers and in amazing environments. And so, so for us, it's continuing to prove the product in more and more verticals. Our, our product is really built for the largest of the largest. So, you know, for the size of the company, we are, we have a high concentration of fortune 500 global 500 customers, and some of them even invested in our rounds recently. So we we've been really, you know, honored with that support. Well, congratulations. Good stuff, edges popping. All right. Thank you. >>Thank you so much for joining us, talking about what you're doing in distributed edge. What's in it for customers, the VMware partnership, and by the way, congratulations on >>That too. Thank you. Thank you so much. Nice to meet you. Thank >>You. All right. Nice to meet you as well for our guest and John furrier. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 22, John and I will be right back with our next guest.