>>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 all going? Making it super multi-Cloud is around the corner and public cloud is winning at the private cloud on premise and edge. Got a great guest here, Vascar go, D 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 thing about what you guys are doing a 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 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 asbs 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, have been pumping on all cylinders as an economic input. And if you look at the tech trends, GDPs down, but not tech. Nope. Because 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 >>E 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, Alan and I want to do with Super Cloud and calling at 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 the 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. >>I, 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? But they're not connected. So you can say okay, it's more than one cloud. So it's you know, multi-cloud. But Supercloud 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, 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 pane, 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, 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 skillset. 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. There 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 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. >>And what benefits are they seeing? Are they seeing speed? >>Speed, definitely. Okay. Definitely their 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 two part of one thing, but ops and great specifically need to catch up. Speedwise, 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 were just two sides to that coin. You've got the dev side and then >>And the infrastructure >>Side. Okay, >>Another customer, I 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 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 rack in the store or they can't move everything to the cloud because the store operations have 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 of 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 say little service, 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. Yeah, 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 plugin >>Play two plugin 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 >>Youcar, of course Detroit's >>Coming in, 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 and business logic using modern tools. Hopefully more open source and, and I think that's what Cloudnative is and we are seeing lot of our customers in that journey. Now everybody wants to be cloud native, 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 you use. Okay? So, so I think the complexities 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 Domino's 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, they were the pioneer in it. To me, those are the kinds of customer experiences that cloud native can provide. >>Being agility and having that flow through 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 will not, >>Thats got 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 in 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 of 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, 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, being at an asb, being 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 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, couple of parallels come to me. One is, think of it, which was forced to on us, 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 cloud native. 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 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 then made you an e-commerce or e retailer or 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. Spend 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.

>>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 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, as code comes in, we talked to Bacar, talking about Super Cloud. I met her about, you know, the new Arlon, our, our 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 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 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. 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 integrations, configuration. These are areas that we're starting to see computer science principles around automation, machine learning, assisting open source. Cuz you've 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's, >>I wrote a LinkedIn post today, it 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 tried to do with this new project. Arlon. >>Yeah. So, so we're gonna get into our line 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 community invite in two where 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 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 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 to 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, when I wrote another post today on LinkedIn called the Silicon Wars AMD stock is down arm has been on a 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 serviced 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 want to 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 we will be covering. So that brings up the whole what's next? You guys just announced Arlon at ar GoCon, 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 arlon, 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 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, 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 to 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 wanna 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 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 Arlon 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 like creates a mechanism. Exactly. Standardized, declarative kind of configurations. And it's like a playbook, deploy it. Now what there between say a script like I'm, I have scripts, I can 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 got 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 super set of infrastructures code because it's >>An evolution. >>You need edge re's code, but then you can configure the code by just saying do it. You basically declaring it's 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 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. Rogue got Coan coming up and obviously this'll be shipping this segment series out before. What do you expect to see at 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, 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 cub 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's 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. 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 a Rod 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? Open Stack was an example where 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, 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 Yeah. On this segment, what is at scale, how many clusters do you see that would be a, a watermark for an at scale conversation around an enterprise? Is it workloads we're looking at or, or clusters? How would you Yeah, I 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 Native at scale? >>The the hyper square? >>Yeah. Yeah. Abras, 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 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 you got software and you got middleware. And he kinda over, well he 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 Feer with the cube. Thanks for Washington. We'll stay tuned for another great segment coming right up.

(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.

>>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, Bacar, go deep 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 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 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, >>In fact, you know, as we were talking offline, I was in one of those asbs 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 their 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, 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. Because 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 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 at 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, more >>Real. I, 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 to, 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, 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, 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 OpenStack, 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 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 it, the beauty of it is yes, open source, but the biggest gating factor is the skill. 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. There are thousands of stores in a retailer. So we provide that and also for specific segments where data sovereignty and data residency, our key regulatory reasons. We also run 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 their tools, they're using whatever app development tools they use, but they use our platform. >>And 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 two part of one thing, 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, So you were just two sides of that coin. You've got the dev side and then and the infrastructure side. Okay, >>Another customer, like 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 rack in the store or they can't move everything to the cloud because the store operations have to be local. The menu changes based on it's classic edge if >>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 >>Say little shares, 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, 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 >>Tucan of course Detroit's >>Coming here, 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 cloudnative, okay? You can't port to cloud, not 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 lot of our customers in that journey. Now everybody wants to be cloud native, 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 you use. Okay? So, 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 Domino's 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, they were the pioneer in it. To me, those are the kinds of customer experiences that cloud native can provide. >>Being agility and having that flow through the application changes what the expectations of are for the customer. >>Customer, the customer's expectations change, right? Once you get used to a better customer experience, you will not >>Best part. 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 in 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 of 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, the big companies, you've seen the different waves. What's going on right now Put into context this moment in time. Sure. Around Super >>Cloud. Sure. I think as you said, a lot of battles. Cars being, being 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, couple of parallels come to me. One is, think of it, which was forced to on us 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 wasn't existence. Question. Yeah, I think we are at that pivotal moment now in companies trying to become digital and cloud native and 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 then made you an e-commerce or e retailer or 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 for 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 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 everyone. 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 Forer, host of the Cube. My 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 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 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 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. We even know what's around the corner. You got buildings, you got I O D OT and IT kind of coming together. But you also got this idea of regions, global infrastructure is 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 gotta 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 some 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 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 notes 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 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 we're seeing cloud data 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 about at scale Yep. Challenges here. Yeah, >>Absolutely. And I think, you know, I I like to call it, you know, 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. 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 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 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. 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 triage us, right? And so in the Kubernetes environment, this problem kind of multi folds, it goes, you know, escalate 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 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 the 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. 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 Arlan 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, 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? Lon. And if you look at the logo we've designed, it's this funny little robot, and it's because when we think of lon, 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 Alon 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 line, things are flowing. See 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 is 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 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 dev op, So the DevOps is the cloud needed developer, The kins 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, Kubernetes 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 Arlan addresses that problem at the heart of it, and it does that using existing open source, well known solutions. >>Medo, 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 there, 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 that 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 SAS model or onpro 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 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, behind a blog 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, 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 it is. Well, but that's, that's the benefit. Open source. This is why standards and open source growing so fast, you have that confluence of, you know, a way fors to try before they buy, but also actually kind of date the application, if you will. We, you know, Adrian Karo uses the dating 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 in, 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 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 sa 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 if 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 will 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 CD 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 spic would be delighted. The folks that we've spoken, 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 the ability. >>Yeah, I think people are scared. Not, I won't say scare, that's a 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 Cuban 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 sla I'm an enterprise, I got tight, you know, I love the open source kind of 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 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 capi, which is a 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 your 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 arlon at scale, because you've been, you know, playing with it in your dev tested 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 cdn, 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 lawn 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 SAS 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 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 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 Super Cloud, 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 is 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 are land 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 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 Super Cloud successful. And, you know, our long flows >>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 of 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 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, 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 fulls stack developer to the more specialized role. But this is a key point, and I have to ask you because if this, our low solution takes place, as you say, and the apps are gonna be stupid, they designed to do, the question is, what did, does the current pain look like? Are the apps breaking? What is 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 does 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. That 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 your, 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 the, 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 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 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, 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. 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 are saying, How is technology driving the top line revenue? That's the number one conversation. Yep. Do you see the same thing? >>Yeah, it's interesting. I think there's multiple pressures at the cx, OCI O level, right? One is that there needs to be that visibility and clarity and guarantee almost that, you know, the, 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 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 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 and the CIO doesn't exist. There's no seeso there 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 well 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 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 Basco talking about Super Cloud. I met her about, you know, the new Arlon, our R 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 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 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, >>Yeah. And that really means it developer just accessing resources. Okay, not clearing, 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 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've got a lot of code that's right in hearing software, supply chain issues. So infrastructure as code has to factor in these, 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. The 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 our GoCon, which was put on here in Silicon Valley at the computer by, in two, where they had their own little day over there at their headquarters. But before we get there, Bacar, your CEO came on and he talked about Super Cloud at our in aural 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 the 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 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'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, 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 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 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 Arlan 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, 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's 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 Arlon 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 >>It's essentially standard, like creates a mechanism. Exactly. Standardized, declarative kind of configurations. And it's like a playbook, deploy it. Now what's there is between say a script like I have scripts, I can 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 its super set of infrastructures code because it's an evolution. You need edge retro's code, but then you can configure the code by just saying do it. You basically declaring it saying Go, go do that. That's right. Okay, So, all right, so Cloudnative at scale, take me through your vision of what that means. Someone says, Hey, what does cloudnative 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 co con 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, we try to address with Arlan. 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 Coan 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 jocking 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 their 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 cub 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're 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. >>B, you've had a storied career VMware over decades with them, obviously with 12 years, with 14 years or something like that. Big number. Co-founder here, a platform. Now you guys 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 a Rod team at that time. We 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 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 where 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, 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. Yeah, I think 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, 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 tran 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 hyperscalers? >>Yeah. A's Azure, Google >>You mean from a business perspective, technical, 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, and platforms. >>Yeah. Not joking aside, Paul Morritz, when he was the CEO of VMware, when he took over once said, and 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 kind of 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. 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.

>>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.

foreign [Music] to the special presentation of cloud native at scale the cube and Platform 9 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 dick Lee who's the Chief Architect and co-founder of platform nine pick great to see you Cube alumni we we met at openstack event in about eight years ago or later earlier uh when openstack was going great to see you and great congratulations on the success of platform nine thank you very much yeah you guys been at this for a while and this is really 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 the success of containerization 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 as code comes in we talked to baskar talking about super cloud I met her about you know the new Arlo our our lawn um you guys just launched the infrastructure's code is going to another level and it's always been devops infrastructure is code that's been the ethos that's been like from day one developers just code I think 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 um I'm glad you mentioned it everybody or most people know about infrastructure as code but with kubernetes I think that project has evolved at the concept even further and these days it's um 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 uh 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 infrastructure as 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 them off turn them 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 integration it's configuration these are areas that we're starting to see computer science principles around automation machine learning assisting open source because you've got a lot of code that's what you're 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 um 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 um hundreds if not thousands of these yaml 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 this space I wrote a LinkedIn post today those comments about you know hey Enterprise is the new breed the trend of SAS companies moving uh our consumer 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 Dynam power dynamics for developers 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 as code people have done some things to simulate or or make infrastructures 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 proliferation of of methods tools Technologies so for example today it's a 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 also 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 methodology apologies and that's something that we try to do with this new project Arlon yeah so so we're going to get to our line in a second I want to get to the yr lawn you guys announced that at argocon which was put on here in Silicon Valley at the community meeting by Intuit they had their own little day over their headquarters but before we get there um Bhaskar 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 has become a thing what's your what would be your contribution to that definition or the narrative well it's it's uh 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 uh super cloud the way I interpret that is you know um clouds and infrastructure um 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 uh um better abstractions that are perhaps business or application specific to help companies and businesses solve real business problems yeah I remember it's a great great definition I remember not to date myself but back in the old days you know IBM had its proprietary Network operating system so the deck for the mini computer vintage deck net and sna respectively um but tcpip 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 if I never heard of it it's IBM's you know a connection for the internet at the layer two is Amazon the ethernet right so if TCP could be the kubernetes and containers 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 um this is um I think a saying that's been heard many times in this industry and I forgot who originated it but um 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 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 going to continue it's interesting just when I wrote another post today on LinkedIn called the Silicon Wars AMD stock is down arm has been on the rise we've been reporting for many years now that arm's going to be huge 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 atoms the pro you know this is where the Innovation they're going so deep and so strong at is the more that they get that gets gone they have more performance so if you're an app developer wouldn't you want the best performance and you'd want to 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 meshes a lot of action going on in the service mesh area in this community of kubecon which we'll be covering so that brings up the whole what's next you guys just announced our lawn at argocon which came out of Intuit we've had Mariana Tesla out our supercloud event she's a CTO you know they're all in the cloud so there contributed that project where did Arlon come from what was the origination what's the purpose why our lawn why this announcement yeah so um the the Inception of the project this was the result of um 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 um 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 um you know we built um Arlon and we built it um 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 get 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 um 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 and what's the benefit of that what does that give what does that give the developer the user in this case the developers the the platform engineer team members the devops engineers they uh get a ways to provision not just infrastructure and clusters but also applications and configurations they get away 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 abstraction or thin layer below is 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 talked to cxos and um I.T folks that are now devops Engineers they care about the workloads and they want the infrastructure's code to work they want to 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 as my workloads running if effectively so how do you guys look at the workload side because 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 um host for the application and it kind of comes bundled with it in many cases it's 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 extremely like an ec2 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 I explained so with our own 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 essentially standard that creates a mechanism it's standardized declarative kind of configurations and it's like a Playbook you just deploy it now what's this between say a script like I have scripts I can just automate Scripts or yes this is where that um declarative API and um 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 um 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 uh figures it out and there are these things called controllers which will in the background reconcile all the state to converge towards your desire to say 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 superset of infrastructures code because different Evolution you need Edge restaurant's code but then you can configure The Code by just saying do it you're basically declaring and saying go go do that that's right okay so all right so Cloud native at scale take me through your vision of what that means someone says hey what is cloud native at scale mean what's success look like how does it roll out in the future as you that 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 going to hear this year kubecon a lot of this what is 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 users there's that aspect to scale there's also um an equally important aspect of scale which is also something that we try to address with Arlon 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 uh 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 going to 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 where I've got kubecon coming up and obviously this will be shipping this seg series out before what do you expect to see at kubecon issue 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 on the overall systems map about the different areas get runtime and observability in all these different areas what's the where's the action where's the smoke where's the fire where's the piece where's the tension yeah so uh I think uh one thing that has been happening over the past couple of coupons and I expect to continue and and that is uh the the word on the street is kubernetes getting boring right which is good right or I mean simple well um well maybe yeah invisible no drama right so so the rate of change of the kubernetes features and and all that has slowed but in a positive way um but um there's still a general sentiment and feeling that there's just too much stuff if you look at a stack necessary for uh hosting applications based on kubernetes they're just still too many moving Parts too many uh components right too much complexity I go I keep going back to the complexity problem so I expect kubecon and all the vendors and the players and the startups and the people there to continue to focus on that complexity problem and introduce a further simplifications uh to to the stack yeah Vic you've had a storied career VMware over decades with them uh obviously 12 years for the 14 years or something like that big number co-founder here platform I think it's been around for a while at this game uh we man we'll talk about openstack that project you we interviewed at one of their events so openstack was the beginning of that this new Revolution I remember the early days 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 Colorado team at that time I mean it's a 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 because you guys are in a good position right now a lot of DNA in Cloud native um what are you most excited about and what is 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 within 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 uh next year or the year after that but platform will a Platform 9 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 uh very consumable to customers simpler faster cheaper always a good business model technically to make that happen yeah I think the reigning in the chaos is key you know now we have now visibility into the scale final question before we depart you know this segment um what is that scale how many clusters do you see that would be a high a watermark for an at scale conversation around an Enterprise um is it workloads we're looking at or or clusters how would you yeah how would you describe that and when people try to squint through and evaluate what's a scale what's the at scale kind of threshold yeah 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 um maybe a hundreds uh two thousands yeah and final final question what's the role of the hyperscalers you've got AWS continuing to do well but they got their core I asked they got a pass they're not too too much putting assess out there they have some SAS apps but mostly it's the ecosystem they have marketplaces doing over two billion dollars billions of transactions a year um and and it's just like just sitting there it has really they're now innovating on it but that's going to change ecosystems what's the role the cloud play and the cloud native at scale the the hyperscale yeah Abus Azure Google you mean from a business they have their own interests that you know that they're uh they will keep catering to they they will continue to find ways to lock their users into their ecosystem of uh services and and apis um so I don't think that's going to change right they're just going to keep well they got great uh performance I mean from a from a hardware standpoint yes that's going to be key right yes I think the uh the move from x86 being the dominant away and platform to run workloads is changing right 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 platforms yeah that joking aside Paul maritz when he was the CEO of VMware when he took over once said I remember our first year doing the cube the cloud is one big distributed computer it's it's hardware and you've got software and you got middleware and uh he kind of over these 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 Vic thank you for coming on the segment appreciate your time this is uh Cloud native at scale special presentation with platform nine really unpacking super cloud rlon open source and how to run large-scale applications uh on the cloud cloud native philadelph4 developers and John Furrier with the cube thanks for watching and we'll stay tuned for another great segment coming right up foreign [Music]

(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)

>> Hi, I'm Natalie Ehrlich and welcome to the AWS startup showcase presented by The Cube. We have an amazing lineup of great guests who will share their insights on the latest innovations and solutions and leveraging cloud scale data in AI, security and life sciences. And now we're joined by the co-founders and co-CEOs of The Cube, Dave Vellante and John Furrier. Thank you gentlemen for joining me. >> Hey Natalie. >> Hey Natalie. >> How are you doing. Hey John. >> Well, I'd love to get your insights here, let's kick it off and what are you looking forward to. >> Dave, I think one of the things that we've been doing on the cube for 11 years is looking at the signal in the marketplace. I wanted to focus on this because AI is cutting across all industries. So we're seeing that with cybersecurity and life sciences, it's the first time we've had a life sciences track in the showcase, which is amazing because it shows that growth of the cloud scale. So I'm super excited by that. And I think that's going to showcase some new business models and of course the keynotes Ali Ghodsi, who's the CEO Data bricks pushing a billion dollars in revenue, clear validation that startups can go from zero to a billion dollars in revenues. So that should be really interesting. And of course the top venture capitalists coming in to talk about what the enterprise dynamics are all about. And what about you, Dave? >> You know, I thought it was an interesting mix and choice of startups. When you think about, you know, AI security and healthcare, and I've been thinking about that. Healthcare is the perfect industry, it is ripe for disruption. If you think about healthcare, you know, we all complain how expensive it is not transparent. There's a lot of discussion about, you know, can everybody have equal access that certainly with COVID the staff is burned out. There's a real divergence and diversity of the quality of healthcare and you know, it all results in patients not being happy, and I mean, if you had to do an NPS score on the patients and healthcare will be pretty low, John, you know. So when I think about, you know, AI and security in the context of healthcare in cloud, I ask questions like when are machines going to be able to better meet or make better diagnoses than doctors? And that's starting. I mean, it's really in assistance putting into play today. But I think when you think about cheaper and more accurate image analysis, when you think about the overall patient experience and trust and personalized medicine, self-service, you know, remote medicine that we've seen during the COVID pandemic, disease tracking, language translation, I mean, there are so many things where the cloud and data, and then it can help. And then at the end of it, it's all about, okay, how do I authenticate? How do I deal with privacy and personal information and tamper resistance? And that's where the security play comes in. So it's a very interesting mix of startups. I think that I'm really looking forward to hearing from... >> You know Natalie one of the things we talked about, some of these companies, Dave, we've talked a lot of these companies and to me the business model innovations that are coming out of two factors, the pandemic is kind of coming to an end so that accelerated and really showed who had the right stuff in my opinion. So you were either on the wrong side or right side of history when it comes to the pandemic and as we look back, as we come out of it with clear growth in certain companies and certain companies that adopted let's say cloud. And the other one is cloud scale. So the focus of these startup showcases is really to focus on how startups can align with the enterprise buyers and create the new kind of refactoring business models to go from, you know, a re-pivot or refactoring to more value. And the other thing that's interesting is that the business model isn't just for the good guys. If you look at say ransomware, for instance, the business model of hackers is gone completely amazing too. They're kicking it but in terms of revenue, they have their own they're well-funded machines on how to extort cash from companies. So there's a lot of security issues around the business model as well. So to me, the business model innovation with cloud-scale tech, with the pandemic forcing function, you've seen a lot of new kinds of decision-making in enterprises. You seeing how enterprise buyers are changing their decision criteria, and frankly their existing suppliers. So if you're an old guard supplier, you're going to be potentially out because if you didn't deliver during the pandemic, this is the issue that everyone's talking about. And it's kind of not publicized in the press very much, but this is actually happening. >> Well thank you both very much for joining me to kick off our AWS startup showcase. Now we're going to go to our very special guest Ali Ghodsi and John Furrier will seat with him for a fireside chat and Dave and I will see you on the other side. >> Okay, Ali great to see you. Thanks for coming on our AWS startup showcase, our second edition, second batch, season two, whatever we want to call it it's our second version of this new series where we feature, you know, the hottest startups coming out of the AWS ecosystem. And you're one of them, I've been there, but you're not a startup anymore, you're here pushing serious success on the revenue side and company. Congratulations and great to see you. >> Likewise. Thank you so much, good to see you again. >> You know I remember the first time we chatted on The Cube, you weren't really doing much software revenue, you were really talking about the new revolution in data. And you were all in on cloud. And I will say that from day one, you were always adamant that it was cloud cloud scale before anyone was really talking about it. And at that time it was on premises with Hadoop and those kinds of things. You saw that early. I remember that conversation, boy, that bet paid out great. So congratulations. >> Thank you so much. >> So I've got to ask you to jump right in. Enterprises are making decisions differently now and you are an example of that company that has gone from literally zero software sales to pushing a billion dollars as it's being reported. Certainly the success of Data bricks has been written about, but what's not written about is the success of how you guys align with the changing criteria for the enterprise customer. Take us through that and these companies here are aligning the same thing and enterprises want to change. They want to be in the right side of history. What's the success formula? >> Yeah. I mean, basically what we always did was look a few years out, the how can we help these enterprises, future proof, what they're trying to achieve, right? They have, you know, 30 years of legacy software and, you know baggage, and they have compliance and regulations, how do we help them move to the future? So we try to identify those kinds of secular trends that we think are going to maybe you see them a little bit right now, cloud was one of them, but it gets more and more and more. So we identified those and there were sort of three or four of those that we kind of latched onto. And then every year the passes, we're a little bit more right. Cause it's a secular trend in the market. And then eventually, it becomes a force that you can't kind of fight anymore. >> Yeah. And I just want to put a plug for your clubhouse talks with Andreessen Horowitz. You're always on clubhouse talking about, you know, I won't say the killer instinct, but being a CEO in a time where there's so much change going on, you're constantly under pressure. It's a lonely job at the top, I know that, but you've made some good calls. What was some of the key moments that you can point to, where you were like, okay, the wave is coming in now, we'd better get on it. What were some of those key decisions? Cause a lot of these startups want to be in your position, and a lot of buyers want to take advantage of the technology that's coming. They got to figure it out. What was some of those key inflection points for you? >> So if you're just listening to what everybody's saying, you're going to miss those trends. So then you're just going with the stream. So, Juan you mentioned that cloud. Cloud was a thing at the time, we thought it's going to be the thing that takes over everything. Today it's actually multi-cloud. So multi-cloud is a thing, it's more and more people are thinking, wow, I'm paying a lot's to the cloud vendors, do I want to buy more from them or do I want to have some optionality? So that's one. Two, open. They're worried about lock-in, you know, lock-in has happened for many, many decades. So they want open architectures, open source, open standards. So that's the second one that we bet on. The third one, which you know, initially wasn't sort of super obvious was AI and machine learning. Now it's super obvious, everybody's talking about it. But when we started, it was kind of called artificial intelligence referred to robotics, and machine learning wasn't a term that people really knew about. Today, it's sort of, everybody's doing machine learning and AI. So betting on those future trends, those secular trends as we call them super critical. >> And one of the things that I want to get your thoughts on is this idea of re-platforming versus refactoring. You see a lot being talked about in some of these, what does that even mean? It's people trying to figure that out. Re-platforming I get the cloud scale. But as you look at the cloud benefits, what do you say to customers out there and enterprises that are trying to use the benefits of the cloud? Say data for instance, in the middle of how could they be thinking about refactoring? And how can they make a better selection on suppliers? I mean, how do you know it used to be RFP, you deliver these speeds and feeds and you get selected. Now I think there's a little bit different science and methodology behind it. What's your thoughts on this refactoring as a buyer? What do I got to do? >> Well, I mean let's start with you said RFP and so on. Times have changed. Back in the day, you had to kind of sign up for something and then much later you're going to get it. So then you have to go through this arduous process. In the cloud, would pay us to go model elasticity and so on. You can kind of try your way to it. You can try before you buy. And you can use more and more. You can gradually, you don't need to go in all in and you know, say we commit to 50,000,000 and six months later to find out that wow, this stuff has got shelf where it doesn't work. So that's one thing that has changed it's beneficial. But the second thing is, don't just mimic what you had on prem in the cloud. So that's what this refactoring is about. If you had, you know, Hadoop data lake, now you're just going to have an S3 data lake. If you had an on-prem data warehouse now you just going to have a cloud data warehouse. You're just repeating what you did on prem in the cloud, architected for the future. And you know, for us, the most important thing that we say is that this lake house paradigm is a cloud native way of organizing your data. That's different from how you would do things on premises. So think through what's the right way of doing it in the cloud. Don't just try to copy paste what you had on premises in the cloud. >> It's interesting one of the things that we're observing and I'd love to get your reaction to this. Dave a lot** and I have been reporting on it is, two personas in the enterprise are changing their organization. One is I call IT ops or there's an SRE role developing. And the data teams are being dismantled and being kind of sprinkled through into other teams is this notion of data, pipelining being part of workflows, not just the department. Are you seeing organizational shifts in how people are organizing their resources, their human resources to take advantage of say that the data problems that are need to being solved with machine learning and whatnot and cloud-scale? >> Yeah, absolutely. So you're right. SRE became a thing, lots of DevOps people. It was because when the cloud vendors launched their infrastructure as a service to stitch all these things together and get it all working you needed a lot of devOps people. But now things are maturing. So, you know, with vendors like Data bricks and other multi-cloud vendors, you can actually get much higher level services where you don't need to necessarily have lots of lots of DevOps people that are themselves trying to stitch together lots of services to make this work. So that's one trend. But secondly, you're seeing more data teams being sort of completely ubiquitous in these organizations. Before it used to be you have one data team and then we'll have data and AI and we'll be done. ' It's a one and done. But that's not how it works. That's not how Google, Facebook, Twitter did it, they had data throughout the organization. Every BU was empowered. It's sales, it's marketing, it's finance, it's engineering. So how do you embed all those data teams and make them actually run fast? And you know, there's this concept of a data mesh which is super important where you can actually decentralize and enable all these teams to focus on their domains and run super fast. And that's really enabled by this Lake house paradigm in the cloud that we're talking about. Where you're open, you're basing it on open standards. You have flexibility in the data types and how they're going to store their data. So you kind of provide a lot of that flexibility, but at the same time, you have sort of centralized governance for it. So absolutely things are changing in the market. >> Well, you're just the professor, the masterclass right here is amazing. Thanks for sharing that insight. You're always got to go out of date and that's why we have you on here. You're amazing, great resource for the community. Ransomware is a huge problem, it's now the government's focus. We're being attacked and we don't know where it's coming from. This business models around cyber that's expanding rapidly. There's real revenue behind it. There's a data problem. It's not just a security problem. So one of the themes in all of these startup showcases is data is ubiquitous in the value propositions. One of them is ransomware. What's your thoughts on ransomware? Is it a data problem? Does cloud help? Some are saying that cloud's got better security with ransomware, then say on premise. What's your vision of how you see this ransomware problem being addressed besides the government taking over? >> Yeah, that's a great question. Let me start by saying, you know, we're a data company, right? And if you say you're a data company, you might as well just said, we're a privacy company, right? It's like some people say, well, what do you think about privacy? Do you guys even do privacy? We're a data company. So yeah, we're a privacy company as well. Like you can't talk about data without talking about privacy. With every customer, with every enterprise. So that's obviously top of mind for us. I do think that in the cloud, security is much better because, you know, vendors like us, we're investing so much resources into security and making sure that we harden the infrastructure and, you know, by actually having all of this infrastructure, we can monitor it, detect if something is, you know, an attack is happening, and we can immediately sort of stop it. So that's different from when it's on prem, you have kind of like the separated duties where the software vendor, which would have been us, doesn't really see what's happening in the data center. So, you know, there's an IT team that didn't develop the software is responsible for the security. So I think things are much better now. I think we're much better set up, but of course, things like cryptocurrencies and so on are making it easier for people to sort of hide. There decentralized networks. So, you know, the attackers are getting more and more sophisticated as well. So that's definitely something that's super important. It's super top of mind. We're all investing heavily into security and privacy because, you know, that's going to be super critical going forward. >> Yeah, we got to move that red line, and figure that out and get more intelligence. Decentralized trends not going away it's going to be more of that, less of the centralized. But centralized does come into play with data. It's a mix, it's not mutually exclusive. And I'll get your thoughts on this. Architectural question with, you know, 5G and the edge coming. Amazon's got that outpost stringent, the wavelength, you're seeing mobile world Congress coming up in this month. The focus on processing data at the edge is a huge issue. And enterprises are now going to be commercial part of that. So architecture decisions are being made in enterprises right now. And this is a big issue. So you mentioned multi-cloud, so tools versus platforms. Now I'm an enterprise buyer and there's no more RFPs. I got all this new choices for startups and growing companies to choose from that are cloud native. I got all kinds of new challenges and opportunities. How do I build my architecture so I don't foreclose a future opportunity. >> Yeah, as I said, look, you're actually right. Cloud is becoming even more and more something that everybody's adopting, but at the same time, there is this thing that the edge is also more and more important. And the connectivity between those two and making sure that you can really do that efficiently. My ask from enterprises, and I think this is top of mind for all the enterprise architects is, choose open because that way you can avoid locking yourself in. So that's one thing that's really, really important. In the past, you know, all these vendors that locked you in, and then you try to move off of them, they were highly innovative back in the day. In the 80's and the 90's, there were the best companies. You gave them all your data and it was fantastic. But then because you were locked in, they didn't need to innovate anymore. And you know, they focused on margins instead. And then over time, the innovation stopped and now you were kind of locked in. So I think openness is really important. I think preserving optionality with multi-cloud because we see the different clouds have different strengths and weaknesses and it changes over time. All right. Early on AWS was the only game that either showed up with much better security, active directory, and so on. Now Google with AI capabilities, which one's going to win, which one's going to be better. Actually, probably all three are going to be around. So having that optionality that you can pick between the three and then artificial intelligence. I think that's going to be the key to the future. You know, you asked about security earlier. That's how people detect zero day attacks, right? You ask about the edge, same thing there, that's where the predictions are going to happen. So make sure that you invest in AI and artificial intelligence very early on because it's not something you can just bolt on later on and have a little data team somewhere that then now you have AI and it's one and done. >> All right. Great insight. I've got to ask you, the folks may or may not know, but you're a professor at Berkeley as well, done a lot of great work. That's where you kind of came out of when Data bricks was formed. And the Berkeley basically was it invented distributed computing back in the 80's. I remember I was breaking in when Unix was proprietary, when software wasn't open you actually had the deal that under the table to get code. Now it's all open. Isn't the internet now with distributed computing and how interconnects are happening. I mean, the internet didn't break during the pandemic, which proves the benefit of the internet. And that's a positive. But as you start seeing edge, it's essentially distributed computing. So I got to ask you from a computer science standpoint. What do you see as the key learnings or connect the dots for how this distributed model will work? I see hybrids clearly, hybrid cloud is clearly the operating model but if you take it to the next level of distributed computing, what are some of the key things that you look for in the next five years as this starts to be completely interoperable, obviously software is going to drive a lot of it. What's your vision on that? >> Yeah, I mean, you know, so Berkeley, you're right for the gigs, you know, there was a now project 20, 30 years ago that basically is how we do things. There was a project on how you search in the very early on with Inktomi that became how Google and everybody else to search today. So workday was super, super early, sometimes way too early. And that was actually the mistake. Was that they were so early that people said that that stuff doesn't work. And then 20 years later you were invented. So I think 2009, Berkeley published just above the clouds saying the cloud is the future. At that time, most industry leaders said, that's just, you know, that doesn't work. Today, recently they published a research paper called, Sky Computing. So sky computing is what you get above the clouds, right? So we have the cloud as the future, the next level after that is the sky. That's one on top of them. That's what multi-cloud is. So that's a lot of the research at Berkeley, you know, into distributed systems labs is about this. And we're excited about that. Then we're one of the sky computing vendors out there. So I think you're going to see much more innovation happening at the sky level than at the compute level where you needed all those DevOps and SRE people to like, you know, build everything manually themselves. I can just see the memes now coming Ali, sky net, star track. You've got space too, by the way, space is another frontier that is seeing a lot of action going on because now the surface area of data with satellites is huge. So again, I know you guys are doing a lot of business with folks in that vertical where you starting to see real time data acquisition coming from these satellites. What's your take on the whole space as the, not the final frontier, but certainly as a new congested and contested space for, for data? >> Well, I mean, as a data vendor, we see a lot of, you know, alternative data sources coming in and people aren't using machine learning< AI to eat out signal out of the, you know, massive amounts of imagery that's coming out of these satellites. So that's actually a pretty common in FinTech, which is a vertical for us. And also sort of in the public sector, lots of, lots of, lots of satellites, imagery data that's coming. And these are massive volumes. I mean, it's like huge data sets and it's a super, super exciting what they can do. Like, you know, extracting signal from the satellite imagery is, and you know, being able to handle that amount of data, it's a challenge for all the companies that we work with. So we're excited about that too. I mean, definitely that's a trend that's going to continue. >> All right. I'm super excited for you. And thanks for coming on The Cube here for our keynote. I got to ask you a final question. As you think about the future, I see your company has achieved great success in a very short time, and again, you guys done the work, I've been following your company as you know. We've been been breaking that Data bricks story for a long time. I've been excited by it, but now what's changed. You got to start thinking about the next 20 miles stair when you look at, you know, the sky computing, you're thinking about these new architectures. As the CEO, your job is to one, not run out of money which you don't have to worry about that anymore, so hiring. And then, you got to figure out that next 20 miles stair as a company. What's that going on in your mind? Take us through your mindset of what's next. And what do you see out in that landscape? >> Yeah, so what I mentioned around Sky company optionality around multi-cloud, you're going to see a lot of capabilities around that. Like how do you get multi-cloud disaster recovery? How do you leverage the best of all the clouds while at the same time not having to just pick one? So there's a lot of innovation there that, you know, we haven't announced yet, but you're going to see a lot of it over the next many years. Things that you can do when you have the optionality across the different parts. And the second thing that's really exciting for us is bringing AI to the masses. Democratizing data and AI. So how can you actually apply machine learning to machine learning? How can you automate machine learning? Today machine learning is still quite complicated and it's pretty advanced. It's not going to be that way 10 years from now. It's going to be very simple. Everybody's going to have it at their fingertips. So how do we apply machine learning to machine learning? It's called auto ML, automatic, you know, machine learning. So that's an area, and that's not something that can be done with, right? But the goal is to eventually be able to automate a way the whole machine learning engineer and the machine learning data scientist altogether. >> You know it's really fun and talking with you is that, you know, for years we've been talking about this inside the ropes, inside the industry, around the future. Now people starting to get some visibility, the pandemics forced that. You seeing the bad projects being exposed. It's like the tide pulled out and you see all the scabs and bad projects that were justified old guard technologies. If you get it right you're on a good wave. And this is clearly what we're seeing. And you guys example of that. So as enterprises realize this, that they're going to have to look double down on the right projects and probably trash the bad projects, new criteria, how should people be thinking about buying? Because again, we talked about the RFP before. I want to kind of circle back because this is something that people are trying to figure out. You seeing, you know, organic, you come in freemium models as cloud scale becomes the advantage in the lock-in frankly seems to be the value proposition. The more value you provide, the more lock-in you get. Which sounds like that's the way it should be versus proprietary, you know, protocols. The protocol is value. How should enterprises organize their teams? Is it end to end workflows? Is it, and how should they evaluate the criteria for these technologies that they want to buy? >> Yeah, that's a great question. So I, you know, it's very simple, try to future proof your decision-making. Make sure that whatever you're doing is not blocking your in. So whatever decision you're making, what if the world changes in five years, make sure that if you making a mistake now, that's not going to bite you in about five years later. So how do you do that? Well, open source is great. If you're leveraging open-source, you can try it out already. You don't even need to talk to any vendor. Your teams can already download it and try it out and get some value out of it. If you're in the cloud, this pay as you go models, you don't have to do a big RFP and commit big. You can try it, pay the vendor, pay as you go, $10, $15. It doesn't need to be a million dollar contract and slowly grow as you're providing value. And then make sure that you're not just locking yourself in to one cloud or, you know, one particular vendor. As much as possible preserve your optionality because then that's not a one-way door. If it turns out later you want to do something else, you can, you know, pick other things as well. You're not locked in. So that's what I would say. Keep that top of mind that you're not locking yourself into a particular decision that you made today, that you might regret in five years. >> I really appreciate you coming on and sharing your with our community and The Cube. And as always great to see you. I really enjoy your clubhouse talks, and I really appreciate how you give back to the community. And I want to thank you for coming on and taking the time with us today. >> Thanks John, always appreciate talking to you. >> Okay Ali Ghodsi, CEO of Data bricks, a success story that proves the validation of cloud scale, open and create value, values the new lock-in. So Natalie, back to you for continuing coverage. >> That was a terrific interview John, but I'd love to get Dave's insights first. What were your takeaways, Dave? >> Well, if we have more time I'll tell you how Data bricks got to where they are today, but I'll say this, the most important thing to me that Allie said was he conveyed a very clear understanding of what data companies are outright and are getting ready. Talked about four things. There's not one data team, there's many data teams. And he talked about data is decentralized, and data has to have context and that context lives in the business. He said, look, think about it. The way that the data companies would get it right, they get data in teams and sales and marketing and finance and engineering. They all have their own data and data teams. And he referred to that as a data mesh. That's a term that is your mock, the Gany coined and the warehouse of the data lake it's merely a node in that global message. It meshes discoverable, he talked about federated governance, and Data bricks, they're breaking the model of shoving everything into a single repository and trying to make that the so-called single version of the truth. Rather what they're doing, which is right on is putting data in the hands of the business owners. And that's how true data companies do. And the last thing you talked about with sky computing, which I loved, it's that future layer, we talked about multi-cloud a lot that abstracts the underlying complexity of the technical details of the cloud and creates additional value on top. I always say that the cloud players like Amazon have given the gift to the world of 100 billion dollars a year they spend in CapEx. Thank you. Now we're going to innovate on top of it. Yeah. And I think the refactoring... >> Hope by John. >> That was great insight and I totally agree. The refactoring piece too was key, he brought that home. But to me, I think Data bricks that Ali shared there and why he's been open and sharing a lot of his insights and the community. But what he's not saying, cause he's humble and polite is they cracked the code on the enterprise, Dave. And to Dave's points exactly reason why they did it, they saw an opportunity to make it easier, at that time had dupe was the rage, and they just made it easier. They was smart, they made good bets, they had a good formula and they cracked the code with the enterprise. They brought it in and they brought value. And see that's the key to the cloud as Dave pointed out. You get replatform with the cloud, then you refactor. And I think he pointed out the multi-cloud and that really kind of teases out the whole future and landscape, which is essentially distributed computing. And I think, you know, companies are starting to figure that out with hybrid and this on premises and now super edge I call it, with 5G coming. So it's just pretty incredible. >> Yeah. Data bricks, IPO is coming and people should know. I mean, what everybody, they created spark as you know John and everybody thought they were going to do is mimic red hat and sell subscriptions and support. They didn't, they developed a managed service and they embedded AI tools to simplify data science. So to your point, enterprises could buy instead of build, we know this. Enterprises will spend money to make things simpler. They don't have the resources, and so this was what they got right was really embedding that, making a building a managed service, not mimicking the kind of the red hat model, but actually creating a new value layer there. And that's big part of their success. >> If I could just add one thing Natalie to that Dave saying is really right on. And as an enterprise buyer, if we go the other side of the equation, it used to be that you had to be a known company, get PR, you fill out RFPs, you had to meet all the speeds. It's like going to the airport and get a swab test, and get a COVID test and all kinds of mechanisms to like block you and filter you. Most of the biggest success stories that have created the most value for enterprises have been the companies that nobody's understood. And Andy Jazz's famous quote of, you know, being misunderstood is actually a good thing. Data bricks was very misunderstood at the beginning and no one kind of knew who they were but they did it right. And so the enterprise buyers out there, don't be afraid to test the startups because you know the next Data bricks is out there. And I think that's where I see the psychology changing from the old IT buyers, Dave. It's like, okay, let's let's test this company. And there's plenty of ways to do that. He illuminated those premium, small pilots, you don't need to go on these big things. So I think that is going to be a shift in how companies going to evaluate startups. >> Yeah. Think about it this way. Why should the large banks and insurance companies and big manufacturers and pharma companies, governments, why should they burn resources managing containers and figuring out data science tools if they can just tap into solutions like Data bricks which is an AI platform in the cloud and let the experts manage all that stuff. Think about how much money in time that saves enterprises. >> Yeah, I mean, we've got 15 companies here we're showcasing this batch and this season if you call it. That episode we are going to call it? They're awesome. Right? And the next 15 will be the same. And these companies could be the next billion dollar revenue generator because the cloud enables that day. I think that's the exciting part. >> Well thank you both so much for these insights. Really appreciate it. AWS startup showcase highlights the innovation that helps startups succeed. And no one knows that better than our very next guest, Jeff Barr. Welcome to the show and I will send this interview now to Dave and John and see you just in the bit. >> Okay, hey Jeff, great to see you. Thanks for coming on again. >> Great to be back. >> So this is a regular community segment with Jeff Barr who's a legend in the industry. Everyone knows your name. Everyone knows that. Congratulations on your recent blog posts we have reading. Tons of news, I want to get your update because 5G has been all over the news, mobile world congress is right around the corner. I know Bill Vass was a keynote out there, virtual keynote. There's a lot of Amazon discussion around the edge with wavelength. Specifically, this is the outpost piece. And I know there is news I want to get to, but the top of mind is there's massive Amazon expansion and the cloud is going to the edge, it's here. What's up with wavelength. Take us through the, I call it the power edge, the super edge. >> Well, I'm really excited about this mostly because it gives a lot more choice and flexibility and options to our customers. This idea that with wavelength we announced quite some time ago, at least quite some time ago if we think in cloud years. We announced that we would be working with 5G providers all over the world to basically put AWS in the telecom providers data centers or telecom centers, so that as their customers build apps, that those apps would take advantage of the low latency, the high bandwidth, the reliability of 5G, be able to get to some compute and storage services that are incredibly close geographically and latency wise to the compute and storage that is just going to give customers this new power and say, well, what are the cool things we can build? >> Do you see any correlation between wavelength and some of the early Amazon services? Because to me, my gut feels like there's so much headroom there. I mean, I was just riffing on the notion of low latency packets. I mean, just think about the applications, gaming and VR, and metaverse kind of cool stuff like that where having the edge be that how much power there. It just feels like a new, it feels like a new AWS. I mean, what's your take? You've seen the evolutions and the growth of a lot of the key services. Like EC2 and SA3. >> So welcome to my life. And so to me, the way I always think about this is it's like when I go to a home improvement store and I wander through the aisles and I often wonder through with no particular thing that I actually need, but I just go there and say, wow, they've got this and they've got this, they've got this other interesting thing. And I just let my creativity run wild. And instead of trying to solve a problem, I'm saying, well, if I had these different parts, well, what could I actually build with them? And I really think that this breadth of different services and locations and options and communication technologies. I suspect a lot of our customers and customers to be and are in this the same mode where they're saying, I've got all this awesomeness at my fingertips, what might I be able to do with it? >> He reminds me when Fry's was around in Palo Alto, that store is no longer here but it used to be back in the day when it was good. It was you go in and just kind of spend hours and then next thing you know, you built a compute. Like what, I didn't come in here, whether it gets some cables. Now I got a motherboard. >> I clearly remember Fry's and before that there was the weird stuff warehouse was another really cool place to hang out if you remember that. >> Yeah I do. >> I wonder if I could jump in and you guys talking about the edge and Jeff I wanted to ask you about something that is, I think people are starting to really understand and appreciate what you did with the entrepreneur acquisition, what you do with nitro and graviton, and really driving costs down, driving performance up. I mean, there's like a compute Renaissance. And I wonder if you could talk about the importance of that at the edge, because it's got to be low power, it has to be low cost. You got to be doing processing at the edge. What's your take on how that's evolving? >> Certainly so you're totally right that we started working with and then ultimately acquired Annapurna labs in Israel a couple of years ago. I've worked directly with those folks and it's really awesome to see what they've been able to do. Just really saying, let's look at all of these different aspects of building the cloud that were once effectively kind of somewhat software intensive and say, where does it make sense to actually design build fabricate, deploy custom Silicon? So from putting up the system to doing all kinds of additional kinds of security checks, to running local IO devices, running the NBME as fast as possible to support the EBS. Each of those things has been a contributing factor to not just the power of the hardware itself, but what I'm seeing and have seen for the last probably two or three years at this point is the pace of innovation on instance types just continues to get faster and faster. And it's not just cranking out new instance types because we can, it's because our awesomely diverse base of customers keeps coming to us and saying, well, we're happy with what we have so far, but here's this really interesting new use case. And we needed a different ratio of memory to CPU, or we need more cores based on the amount of memory, or we needed a lot of IO bandwidth. And having that nitro as the base lets us really, I don't want to say plug and play, cause I haven't actually built this myself, but it seems like they can actually put the different elements together, very very quickly and then come up with new instance types that just our customers say, yeah, that's exactly what I asked for and be able to just do this entire range of from like micro and nano sized all the way up to incredibly large with incredible just to me like, when we talk about terabytes of memory that are just like actually just RAM memory. It's like, that's just an inconceivably large number by the standards of where I started out in my career. So it's all putting this power in customer hands. >> You used the term plug and play, but it does give you that nitro gives you that optionality. And then other thing that to me is really exciting is the way in which ISVs are writing to whatever's underneath. So you're making that, you know, transparent to the users so I can choose as a customer, the best price performance for my workload and that that's just going to grow that ISV portfolio. >> I think it's really important to be accurate and detailed and as thorough as possible as we launch each one of these new instance types with like what kind of processor is in there and what clock speed does it run at? What kind of, you know, how much memory do we have? What are the, just the ins and outs, and is it Intel or arm or AMD based? It's such an interesting to me contrast. I can still remember back in the very very early days of back, you know, going back almost 15 years at this point and effectively everybody said, well, not everybody. A few people looked and said, yeah, we kind of get the value here. Some people said, this just sounds like a bunch of generic hardware, just kind of generic hardware in Iraq. And even back then it was something that we were very careful with to design and optimize for use cases. But this idea that is generic is so, so, so incredibly inaccurate that I think people are now getting this. And it's okay. It's fine too, not just for the cloud, but for very specific kinds of workloads and use cases. >> And you guys have announced obviously the performance improvements on a lamb** does getting faster, you got the per billing, second billings on windows and SQL server on ECE too**. So I mean, obviously everyone kind of gets that, that's been your DNA, keep making it faster, cheaper, better, easier to use. But the other area I want to get your thoughts on because this is also more on the footprint side, is that the regions and local regions. So you've got more region news, take us through the update on the expansion on the footprint of AWS because you know, a startup can come in and these 15 companies that are here, they're global with AWS, right? So this is a major benefit for customers around the world. And you know, Ali from Data bricks mentioned privacy. Everyone's a privacy company now. So the huge issue, take us through the news on the region. >> Sure, so the two most recent regions that we announced are in the UAE and in Israel. And we generally like to pre-announce these anywhere from six months to two years at a time because we do know that the customers want to start making longer term plans to where they can start thinking about where they can do their computing, where they can store their data. I think at this point we now have seven regions under construction. And, again it's all about customer trice. Sometimes it's because they have very specific reasons where for based on local laws, based on national laws, that they must compute and restore within a particular geographic area. Other times I say, well, a lot of our customers are in this part of the world. Why don't we pick a region that is as close to that part of the world as possible. And one really important thing that I always like to remind our customers of in my audience is, anything that you choose to put in a region, stays in that region unless you very explicitly take an action that says I'd like to replicate it somewhere else. So if someone says, I want to store data in the US, or I want to store it in Frankfurt, or I want to store it in Sao Paulo, or I want to store it in Tokyo or Osaka. They get to make that very specific choice. We give them a lot of tools to help copy and replicate and do cross region operations of various sorts. But at the heart, the customer gets to choose those locations. And that in the early days I think there was this weird sense that you would, you'd put things in the cloud that would just mysteriously just kind of propagate all over the world. That's never been true, and we're very very clear on that. And I just always like to reinforce that point. >> That's great stuff, Jeff. Great to have you on again as a regular update here, just for the folks watching and don't know Jeff he'd been blogging and sharing. He'd been the one man media band for Amazon it's early days. Now he's got departments, he's got peoples on doing videos. It's an immediate franchise in and of itself, but without your rough days we wouldn't have gotten all the great news we subscribe to. We watch all the blog posts. It's essentially the flow coming out of AWS which is just a tsunami of a new announcements. Always great to read, must read. Jeff, thanks for coming on, really appreciate it. That's great. >> Thank you John, great to catch up as always. >> Jeff Barr with AWS again, and follow his stuff. He's got a great audience and community. They talk back, they collaborate and they're highly engaged. So check out Jeff's blog and his social presence. All right, Natalie, back to you for more coverage. >> Terrific. Well, did you guys know that Jeff took a three week AWS road trip across 15 cities in America to meet with cloud computing enthusiasts? 5,500 miles he drove, really incredible I didn't realize that. Let's unpack that interview though. What stood out to you John? >> I think Jeff, Barr's an example of what I call direct to audience a business model. He's been doing it from the beginning and I've been following his career. I remember back in the day when Amazon was started, he was always building stuff. He's a builder, he's classic. And he's been there from the beginning. At the beginning he was just the blog and it became a huge audience. It's now morphed into, he was power blogging so hard. He has now support and he still does it now. It's basically the conduit for information coming out of Amazon. I think Jeff has single-handedly made Amazon so successful at the community developer level, and that's the startup action happened and that got them going. And I think he deserves a lot of the success for AWS. >> And Dave, how about you? What is your reaction? >> Well I think you know, and everybody knows about the cloud and back stop X** and agility, and you know, eliminating the undifferentiated, heavy lifting and all that stuff. And one of the things that's often overlooked which is why I'm excited to be part of this program is the innovation. And the innovation comes from startups, and startups start in the cloud. And so I think that that's part of the flywheel effect. You just don't see a lot of startups these days saying, okay, I'm going to do something that's outside of the cloud. There are some, but for the most part, you know, if you saw in software, you're starting in the cloud, it's so capital efficient. I think that's one thing, I've throughout my career. I've been obsessed with every part of the stack from whether it's, you know, close to the business process with the applications. And right now I'm really obsessed with the plumbing, which is why I was excited to talk about, you know, the Annapurna acquisition. Amazon bought and a part of the $350 million, it's reported, you know, maybe a little bit more, but that isn't an amazing acquisition. And the reason why that's so important is because Amazon is continuing to drive costs down, drive performance up. And in my opinion, leaving a lot of the traditional players in their dust, especially when it comes to the power and cooling. You have often overlooked things. And the other piece of the interview was that Amazon is actually getting ISVs to write to these new platforms so that you don't have to worry about there's the software run on this chip or that chip, or x86 or arm or whatever it is. It runs. And so I can choose the best price performance. And that's where people don't, they misunderstand, you always say it John, just said that people are misunderstood. I think they misunderstand, they confused, you know, the price of the cloud with the cost of the cloud. They ignore all the labor costs that are associated with that. And so, you know, there's a lot of discussion now about the cloud tax. I just think the pace is accelerating. The gap is not closing, it's widening. >> If you look at the one question I asked them about wavelength and I had a follow up there when I said, you know, we riff on it and you see, he lit up like he beam was beaming because he said something interesting. It's not that there's a problem to solve at this opportunity. And he conveyed it to like I said, walking through Fry's. But like, you go into a store and he's a builder. So he sees opportunity. And this comes back down to the Martine Casada paradox posts he wrote about do you optimize for CapEx or future revenue? And I think the tell sign is at the wavelength edge piece is going to be so creative and that's going to open up massive opportunities. I think that's the place to watch. That's the place I'm watching. And I think startups going to come out of the woodwork because that's where the action will be. And that's just Amazon at the edge, I mean, that's just cloud at the edge. I think that is going to be very effective. And his that's a little TeleSign, he kind of revealed a little bit there, a lot there with that comment. >> Well that's a to be continued conversation. >> Indeed, I would love to introduce our next guest. We actually have Soma on the line. He's the managing director at Madrona venture group. Thank you Soma very much for coming for our keynote program. >> Thank you Natalie and I'm great to be here and will have the opportunity to spend some time with you all. >> Well, you have a long to nerd history in the enterprise. How would you define the modern enterprise also known as cloud scale? >> Yeah, so I would say I have, first of all, like, you know, we've all heard this now for the last, you know, say 10 years or so. Like, software is eating the world. Okay. Put it another way, we think about like, hey, every enterprise is a software company first and foremost. Okay. And companies that truly internalize that, that truly think about that, and truly act that way are going to start up, continue running well and things that don't internalize that, and don't do that are going to be left behind sooner than later. Right. And the last few years you start off thing and not take it to the next level and talk about like, not every enterprise is not going through a digital transformation. Okay. So when you sort of think about the world from that lens. Okay. Modern enterprise has to think about like, and I am first and foremost, a technology company. I may be in the business of making a car art, you know, manufacturing paper, or like you know, manufacturing some healthcare products or what have you got out there. But technology and software is what is going to give me a unique, differentiated advantage that's going to let me do what I need to do for my customers in the best possible way [Indistinct]. So that sort of level of focus, level of execution, has to be there in a modern enterprise. The other thing is like not every modern enterprise needs to think about regular. I'm competing for talent, not anymore with my peers in my industry. I'm competing for technology talent and software talent with the top five technology companies in the world. Whether it is Amazon or Facebook or Microsoft or Google, or what have you cannot think, right? So you really have to have that mindset, and then everything flows from that. >> So I got to ask you on the enterprise side again, you've seen many ways of innovation. You've got, you know, been in the industry for many, many years. The old way was enterprises want the best proven product and the startups want that lucrative contract. Right? Yeah. And get that beach in. And it used to be, and we addressed this in our earlier keynote with Ali and how it's changing, the buyers are changing because the cloud has enabled this new kind of execution. I call it agile, call it what you want. Developers are driving modern applications, so enterprises are still, there's no, the playbooks evolving. Right? So we see that with the pandemic, people had needs, urgent needs, and they tried new stuff and it worked. The parachute opened as they say. So how do you look at this as you look at stars, you're investing in and you're coaching them. What's the playbook? What's the secret sauce of how to crack the enterprise code today. And if you're an enterprise buyer, what do I need to do? I want to be more agile. Is there a clear path? Is there's a TSA to let stuff go through faster? I mean, what is the modern playbook for buying and being a supplier? >> That's a fantastic question, John, because I think that sort of playbook is changing, even as we speak here currently. A couple of key things to understand first of all is like, you know, decision-making inside an enterprise is getting more and more de-centralized. Particularly decisions around what technology to use and what solutions to use to be able to do what people need to do. That decision making is no longer sort of, you know, all done like the CEO's office or the CTO's office kind of thing. Developers are more and more like you rightly said, like sort of the central of the workflow and the decision making process. So it'll be who both the enterprises, as well as the startups to really understand that. So what does it mean now from a startup perspective, from a startup perspective, it means like, right. In addition to thinking about like hey, not do I go create an enterprise sales post, do I sell to the enterprise like what I might have done in the past? Is that the best way of moving forward, or should I be thinking about a product led growth go to market initiative? You know, build a product that is easy to use, that made self serve really works, you know, get the developers to start using to see the value to fall in love with the product and then you think about like hey, how do I go translate that into a contract with enterprise. Right? And more and more what I call particularly, you know, startups and technology companies that are focused on the developer audience are thinking about like, you know, how do I have a bottom up go to market motion? And sometime I may sort of, you know, overlap that with the top down enterprise sales motion that we know that has been going on for many, many years or decades kind of thing. But really this product led growth bottom up a go to market motion is something that we are seeing on the rise. I would say they're going to have more than half the startup that we come across today, have that in some way shape or form. And so the enterprise also needs to understand this, the CIO or the CTO needs to know that like hey, I'm not decision-making is getting de-centralized. I need to empower my engineers and my engineering managers and my engineering leaders to be able to make the right decision and trust them. I'm going to give them some guard rails so that I don't find myself in a soup, you know, sometime down the road. But once I give them the guard rails, I'm going to enable people to make the decisions. People who are closer to the problem, to make the right decision. >> Well Soma, what are some of the ways that startups can accelerate their enterprise penetration? >> I think that's another good question. First of all, you need to think about like, Hey, what are enterprises wanting to rec? Okay. If you start off take like two steps back and think about what the enterprise is really think about it going. I'm a software company, but I'm really manufacturing paper. What do I do? Right? The core thing that most enterprises care about is like, hey, how do I better engage with my customers? How do I better serve my customers? And how do I do it in the most optimal way? At the end of the day that's what like most enterprises really care about. So startups need to understand, what are the problems that the enterprise is trying to solve? What kind of tools and platform technologies and infrastructure support, and, you know, everything else that they need to be able to do what they need to do and what only they can do in the most optimal way. Right? So to the extent you are providing either a tool or platform or some technology that is going to enable your enterprise to make progress on what they want to do, you're going to get more traction within the enterprise. In other words, stop thinking about technology, and start thinking about the customer problem that they want to solve. And the more you anchor your company, and more you anchor your conversation with the customer around that, the more the enterprise is going to get excited about wanting to work with you. >> So I got to ask you on the enterprise and developer equation because CSOs and CXOs, depending who you talk to have that same answer. Oh yeah. In the 90's and 2000's, we kind of didn't, we throttled down, we were using the legacy developer tools and cloud came and then we had to rebuild and we didn't really know what to do. So you seeing a shift, and this is kind of been going on for at least the past five to eight years, a lot more developers being hired yet. I mean, at FinTech is clearly a vertical, they always had developers and everyone had developers, but there's a fast ramp up of developers now and the role of open source has changed. Just looking at the participation. They're not just consuming open source, open source is part of the business model for mainstream enterprises. How is this, first of all, do you agree? And if so, how has this changed the course of an enterprise human resource selection? How they're organized? What's your vision on that? >> Yeah. So as I mentioned earlier, John, in my mind the first thing is, and this sort of, you know, like you said financial services has always been sort of hiring people [Indistinct]. And this is like five-year old story. So bear with me I'll tell you the firewall story and then come to I was trying to, the cloud CIO or the Goldman Sachs. Okay. And this is five years ago when people were still like, hey, is this cloud thing real and now is cloud going to take over the world? You know, am I really ready to put my data in the cloud? So there are a lot of questions and conversations can affect. The CIO of Goldman Sachs told me two things that I remember to this day. One is, hey, we've got a internal edict. That we made a decision that in the next five years, everything in Goldman Sachs is going to be on the public law. And I literally jumped out of the chair and I said like now are you going to get there? And then he laughed and said like now it really doesn't matter whether we get there or not. We want to set the tone, set the direction for the organization that hey, public cloud is here. Public cloud is there. And we need to like, you know, move as fast as we realistically can and think about all the financial regulations and security and privacy. And all these things that we care about deeply. But given all of that, the world is going towards public load and we better be on the leading edge as opposed to the lagging edge. And the second thing he said, like we're talking about like hey, how are you hiring, you know, engineers at Goldman Sachs Canada? And he said like in hey, I sort of, my team goes out to the top 20 schools in the US. And the people we really compete with are, and he was saying this, Hey, we don't compete with JP Morgan or Morgan Stanley, or pick any of your favorite financial institutions. We really think about like, hey, we want to get the best talent into Goldman Sachs out of these schools. And we really compete head to head with Google. We compete head to head with Microsoft. We compete head to head with Facebook. And we know that the caliber of people that we want to get is no different than what these companies want. If you want to continue being a successful, leading it, you know, financial services player. That sort of tells you what's going on. You also talked a little bit about like hey, open source is here to stay. What does that really mean kind of thing. In my mind like now, you can tell me that I can have from given my pedigree at Microsoft, I can tell you that we were the first embraces of open source in this world. So I'll say that right off the bat. But having said that we did in our turn around and said like, hey, this open source is real, this open source is going to be great. How can we embrace and how can we participate? And you fast forward to today, like in a Microsoft is probably as good as open source as probably any other large company I would say. Right? Including like the work that the company has done in terms of acquiring GitHub and letting it stay true to its original promise of open source and community can I think, right? I think Microsoft has come a long way kind of thing. But the thing that like in all these enterprises need to think about is you want your developers to have access to the latest and greatest tools. To the latest and greatest that the software can provide. And you really don't want your engineers to be reinventing the wheel all the time. So there is something available in the open source world. Go ahead, please set up, think about whether that makes sense for you to use it. And likewise, if you think that is something you can contribute to the open source work, go ahead and do that. So it's really a two way somebody Arctic relationship that enterprises need to have, and they need to enable their developers to want to have that symbiotic relationship. >> Soma, fantastic insights. Thank you so much for joining our keynote program. >> Thank you Natalie and thank you John. It was always fun to chat with you guys. Thank you. >> Thank you. >> John we would love to get your quick insight on that. >> Well I think first of all, he's a prolific investor the great from Madrona venture partners, which is well known in the tech circles. They're in Seattle, which is in the hub of I call cloud city. You've got Amazon and Microsoft there. He'd been at Microsoft and he knows the developer ecosystem. And reason why I like his perspective is that he understands the value of having developers as a core competency in Microsoft. That's their DNA. You look at Microsoft, their number one thing from day one besides software was developers. That was their army, the thousand centurions that one won everything for them. That has shifted. And he brought up open source, and .net and how they've embraced Linux, but something that tele before he became CEO, we interviewed him in the cube at an Xcel partners event at Stanford. He was open before he was CEO. He was talking about opening up. They opened up a lot of their open source infrastructure projects to the open compute foundation early. So they had already had that going and at that price, since that time, the stock price of Microsoft has skyrocketed because as Ali said, open always wins. And I think that is what you see here, and as an investor now he's picking in startups and investing in them. He's got to read the tea leaves. He's got to be in the right side of history. So he brings a great perspective because he sees the old way and he understands the new way. That is the key for success we've seen in the enterprise and with the startups. The people who get the future, and can create the value are going to win. >> Yeah, really excellent point. And just really quickly. What do you think were some of our greatest hits on this hour of programming? >> Well first of all I'm really impressed that Ali took the time to come join us because I know he's super busy. I think they're at a $28 billion valuation now they're pushing a billion dollars in revenue, gap revenue. And again, just a few short years ago, they had zero software revenue. So of these 15 companies we're showcasing today, you know, there's a next Data bricks in there. They're all going to be successful. They already are successful. And they're all on this rocket ship trajectory. Ali is smart, he's also got the advantage of being part of that Berkeley community which they're early on a lot of things now. Being early means you're wrong a lot, but you're also right, and you're right big. So Berkeley and Stanford obviously big areas here in the bay area as research. He is smart, He's got a great team and he's really open. So having him share his best practices, I thought that was a great highlight. Of course, Jeff Barr highlighting some of the insights that he brings and honestly having a perspective of a VC. And we're going to have Peter Wagner from wing VC who's a classic enterprise investors, super smart. So he'll add some insight. Of course, one of the community session, whenever our influencers coming on, it's our beat coming on at the end, as well as Katie Drucker. Another Madrona person is going to talk about growth hacking, growth strategies, but yeah, sights Raleigh coming on. >> Terrific, well thank you so much for those insights and thank you to everyone who is watching the first hour of our live coverage of the AWS startup showcase for myself, Natalie Ehrlich, John, for your and Dave Vellante we want to thank you very much for watching and do stay tuned for more amazing content, as well as a special live segment that John Furrier is going to be hosting. It takes place at 12:30 PM Pacific time, and it's called cracking the code, lessons learned on how enterprise buyers evaluate new startups. Don't go anywhere.

>> Sue: Hi, everybody. Thank you for joining us today, for the Virtual Vertica BDC 2020. This breakout session is entitled "Vertica @ Uber Scale" My name is Sue LeClaire, Director of Marketing at Vertica. And I'll be your host for this webinar. Joining me is Girish Baliga, Director I'm sorry, user, Uber Engineering Manager of Big Data at Uber. Before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment in the question box below the slides and click Submit. There will be a Q and A session, at the end of the presentation. We'll answer as many questions as we're able to during that time. Any questions that we don't address, we'll do our best to answer offline. Alternately, you can also Vertica forums to post your questions there after the session. Our engineering team is planning to join the forums to keep the conversation going. And as a reminder, you can maximize your screen by clicking the double arrow button, in the lower right corner of the slides. And yet, this virtual session is being recorded, and you'll be able to view on demand this week. We'll send you a notification as soon as it's ready. So let's get started. Girish over to you. >> Girish: Thanks a lot Sue. Good afternoon, everyone. Thanks a lot for joining this session. My name is Girish Baliga. And as Sue mentioned, I manage interactive and real time analytics teams at Uber. Vertica is one of the main platforms that we support, and Vertica powers a lot of core business use cases. In today's talk, I wanted to cover two main things. First, how Vertica is powering critical business use cases, across a variety of orgs in the company. And second, how we are able to do this at scale and with reliability, using some of the additional functionalities and systems that we have built into the Vertica ecosystem at Uber. And towards the end, I also have a little extra bonus for all of you. I will be sharing an easy way for you to take advantage of, many of the ideas and solutions that I'm going to present today, that you can apply to your own Vertica deployments in your companies. So stick around and put on your seat belts, and let's go start on the ride. At Uber, our mission is to ignite opportunity by setting the world in motion. So we are focused on solving mobility problems, and enabling people all over the world to solve their local problems, their local needs, their local issues, in a manner that's efficient, fast and reliable. As our CEO Dara has said, we want to become the mobile operating system of local cities and communities throughout the world. As of today, Uber is operational in over 10,000 cities around the world. So, across our various business lines, we have over 110 million monthly users, who use our rides, services, or eat services, and a whole bunch of other services that we provide to Uber. And just to give you a scale of our daily operations, we in the ride business, have over 20 million trips per day. And that each business is also catching up, particularly during the recent times that we've been having. And so, I hope these numbers give you a scale of the amount of data, that we process each and every day. And support our users in their analytical and business reporting needs. So who are these users at Uber? Let's take a quick look. So, Uber to describe it very briefly, is a lot like Amazon. We are largely an operation and logistics company. And employee work based reflects that. So over 70% of our employees work in teams, which come under the umbrella of Community Operations and Centers of Excellence. So these are all folks working in various cities and towns that we operate around the world, and run the Uber businesses, as somewhat local businesses responding to local needs, local market conditions, local regulation and so forth. And Vertica is one of the most important tools, that these folks use in their day to day business activities. So they use Vertica to get insights into how their businesses are going, to deeply into any issues that they want to triage , to generate reports, to plan for the future, a whole lot of use cases. The second big class of users, are in our marketplace team. So marketplace is the engineering team, that backs our ride shared business. And as part of this, running this business, a key problem that they have to solve, is how to determine what prices to set, for particular rides, so that we have a good match between supply and demand. So obviously the real time pricing decisions they're made by serving systems, with very detailed and well crafted machine learning models. However, the training data that goes into this models, the historical trends, the insights that go into building these models, a lot of these things are powered by the data that we store, and serve out of Vertica. Similarly, in each business, we have use cases spanning all the way from engineering and back-end systems, to support operations, incentives, growth, and a whole bunch of other domains. So the big class of applications that we support across a lot of these business lines, is dashboards and reporting. So we have a lot of dashboards, which are built by core data analysts teams and shared with a whole bunch of our operations and other teams. So these are dashboards and reports that run, periodically say once a week or once a day even, depending on the frequency of data that they need. And many of these are powered by the data, and the analytics support that we provide on our Vertica platform. Another big category of use cases is for growth marketing. So this is to understand historical trends, figure out what are various business lines, various customer segments, various geographical areas, doing in terms of growth, where it is necessary for us to reinvest or provide some additional incentives, or marketing support, and so forth. So the analysis that backs a lot of these decisions, is powered by queries running on Vertica. And finally, the heart and soul of Uber is data science. So data science is, how we provide best in class algorithms, pricing, and matching. And a lot of the analysis that goes into, figuring out how to build these systems, how to build the models, how to build the various coefficients and parameters that go into making real time decisions, are based on analysis that data scientists run on Vertica systems. So as you can see, Vertica usage spans a whole bunch of organizations and users, all across the different Uber teams and ecosystems. Just to give you some quick numbers, we have over 5000 weekly active, people who run queries at least once a week, to do some critical business role or problem to solve, that they have in their day to day operations. So next, let's see how Vertica fits into the Uber data ecosystem. So when users open up their apps, and request for a ride or order food delivery on each platform, the apps are talking to our serving systems. And the serving systems use online storage systems, to store the data as the trips and eat orders are getting processed in real time. So for this, we primarily use an in house built, key value storage system called Schemaless, and an open source system called Cassandra. We also have other systems like MySQL and Redis, which we use for storing various bits of data to support serving systems. So all of this operations generates a lot of data, that we then want to process and analyze, and use for our operational improvements. So, we have ingestion systems that periodically pull in data from our serving systems and land them in our data lake. So at Uber a data lake is powered by Hadoop, with files stored on HDFS clusters. So once the raw data lines on the data lake, we then have ETL jobs that process these raw datasets, and generate, modeled and customize datasets which we then use for further analysis. So once these model datasets are available, we load them into our data warehouse, which is entirely powered by Vertica. So then we have a business intelligence layer. So with internal tools, like QueryBuilder, which is a UI interface to write queries, and look at results. And it read over the front-end sites, and Dashbuilder, which is a dash, board building tool, and report management tool. So these are all various tools that we have built within Uber. And these can talk to Vertica and run SQL queries to power, whatever, dashboards and reports that they are supporting. So this is what the data ecosystem looks like at Uber. So why Vertica and what does it really do for us? So it powers insights, that we show on dashboards as folks use, and it also powers reports that we run periodically. But more importantly, we have some core, properties and core feature sets that Vertica provides, which allows us to support many of these use cases, very well and at scale. So let me take a brief tour of what these are. So as I mentioned, Vertica powers Uber's data warehouse. So what this means is that we load our core fact and dimension tables onto Vertica. The core fact tables are all the trips, all the each orders and all these other line items for various businesses from Uber, stored as partitioned tables. So think of having one partition per day, as well as dimension tables like cities, users, riders, career partners and so forth. So we have both these two kinds of datasets, which will load into Vertica. And we have full historical data, all the way since we launched these businesses to today. So that folks can do deeper longitudinal analysis, so they can look at patterns, like how the business has grown from month to month, year to year, the same month, over a year, over multiple years, and so forth. And, the really powerful thing about Vertica, is that most of these queries, you run the deep longitudinal queries, run very, very fast. And that's really why we love Vertica. Because we see query latency P90s. That is 90 percentile of all queries that we run on our platform, typically finish in under a minute. So that's very important for us because Vertica is used, primarily for interactive analytics use cases. And providing SQL query execution times under a minute, is critical for our users and business owners to get the most out of analytics and Big Data platforms. Vertica also provides a few advanced features that we use very heavily. So as you might imagine, at Uber, one of the most important set of use cases we have is around geospatial analytics. In particular, we have some critical internal dashboards, that rely very heavily on being able to restrict datasets by geographic areas, cities, source destination pairs, heat maps, and so forth. And Vertica has a rich array of functions that we use very heavily. We also have, support for custom projections in Vertica. And this really helps us, have very good performance for critical datasets. So for instance, in some of our core fact tables, we have done a lot of query and analysis to figure out, how users run their queries, what kind of columns they use, what combination of columns they use, and what joints they do for typical queries. And then we have laid out our custom projections to maximize performance on these particular dimensions. And the ability to do that through Vertica, is very valuable for us. So we've also had some very successful collaborations, with the Vertica engineering team. About a year and a half back, we had open-sourced a Python Client, that we had built in house to talk to Vertica. We were using this Python Client in our business intelligence layer that I'd shown on the previous slide. And we had open-sourced it after working closely with Eng team. And now Vertica formally supports the Python Client as an open-source project, which you can download to and integrate into your systems. Another more recent example of collaboration is the Vertica Eon mode on GCP. So as most of or at least some of you know, Vertica Eon mode is formally supported on AWS. And at Uber, we were also looking to see if we could run our data infrastructure on GCP. So Vertica team hustled on this, and provided us early preview version, which we've been testing out to see how performance, is impacted by running on the Cloud, and on GCP. And so far, I think things are going pretty well, but we should have some numbers about this very soon. So here I have a visualization of an internal dashboard, that is powered solely by data and queries running on Vertica. So this GIF has sequence have different visualizations supported by this tool. So for instance, here you see a heat map, downgrading heat map of source of traffic demand for ride shares. And then you will see a bunch of arrows here about source destination pairs and the trip lines. And then you can see how demand moves around. So, as the cycles through the various animations, you can basically see all the different kinds of insights, and query shapes that we send to Vertica, which powers this critical business dashboard for our operations teams. All right, so now how do we do all of this at scale? So, we started off with a single Vertica cluster, a few years back. So we had our data lake, the data would land into Vertica. So these are the core fact and dimension tables that I just spoke about. And then Vertica powers queries at our business intelligence layer, right? So this is a very simple, and effective architecture for most use cases. But at Uber scale, we ran into a few problems. So the first issue that we have is that, Uber is a pretty big company at this point, with a lot of users sending almost millions of queries every week. And at that scale, what we began to see was that a single cluster was not able to handle all the query traffic. So for those of you who have done an introductory course, on queueing theory, you will realize that basically, even though you could have all the query is processed through a single serving system. You will tend to see larger and larger queue wait times, as the number of queries pile up. And what this means in practice for end users, is that they are basically just seeing longer and longer query latencies. But even though the actual query execution time on Vertica itself, is probably less than a minute, their query sitting in the queue for a bunch of minutes, and that's the end user perceived latency. So this was a huge problem for us. The second problem we had was that the cluster becomes a single point of failure. Now Vertica can handle single node failures very gracefully, and it can probably also handle like two or three node failures depending on your cluster size and your application. But very soon, you will see that, when you basically have beyond a certain number of failures or nodes in maintenance, then your cluster will probably need to be restarted or you will start seeing some down times due to other issues. So another example of why you would have to have a downtime, is when you're upgrading software in your clusters. So, essentially we're a global company, and we have users all around the world, we really cannot afford to have downtime, even for one hour slot. So that turned out to be a big problem for us. And as I mentioned, we could have hardware issues. So we we might need to upgrade our machines, or we might need to replace storage or memory due to issues with the hardware in there, due to normal wear and tear, or due to abnormal issues. And so because of all of these things, having a single point of failure, having a single cluster was not really practical for us. So the next thing we did, was we set up multiple clusters, right? So we had a bunch of identities clusters, all of which have the same datasets. So then we would basically load data using ingestion pipelines from our data lake, onto each of these clusters. And then the business intelligence layer would be able to query any of these clusters. So this actually solved most of the issues that I pointed out in the previous slide. So we no longer had a single point of failure. Anytime we had to do version upgrades, we would just take off one cluster offline, upgrade the software on it. If we had node failures, we would probably just take out one cluster, if we had to, or we would just have some spare nodes, which would rotate into our production clusters and so forth. However, having multiple clusters, led to a new set of issues. So the first problem was that since we have multiple clusters, you would end up with inconsistent schema. So one of the things to understand about our platform, is that we are an infrastructure team. So we don't actually own or manage any of the data that is served on Vertica clusters. So we have dataset owners and publishers, who manage their own datasets. Now exposing multiple clusters to these dataset owners. Turns out, it's not a great idea, right? Because they are not really aware of, the importance of having consistency of schemas and datasets across different clusters. So over time, what we saw was that the schema for the same tables would basically get out of order, because they were all the updates are not consistently applied on all clusters. Or maybe they were just experimenting some new columns or some new tables in one cluster, but they forgot to delete it, whatever the case might be. We basically ended up in a situation where, we saw a lot of inconsistent schemas, even across some of our core tables in our different clusters. A second issue was, since we had ingestion pipelines that were ingesting data independently into all these clusters, these pipelines could fail independently as well. So what this meant is that if, for instance, the ingestion pipeline into cluster B failed, then the data there would be older than clusters A and C. So, when a query comes in from the BI layer, and if it happens to hit B, you would probably see different results, than you would if you went to a or C. And this was obviously not an ideal situation for our end users, because they would end up seeing slightly inconsistent, slightly different counts. But then that would lead to a bad situation for them where they would not able to fully trust the data that was, and the results and insights that were being returned by the SQL queries and Vertica systems. And then the third problem was, we had a lot of extra replication. So the 20/80 Rule, or maybe even the 90/10 Rule, applies to datasets on our clusters as well. So less than 10% of our datasets, for instance, in 90% of the queries, right? And so it doesn't really make sense for us to replicate all of our data on all the clusters. And so having this set up where we had to do that, was obviously very suboptimal for us. So then what we did, was we basically built some additional systems to solve these problems. So this brings us to our Vertica ecosystem that we have in production today. So on the ingestion side, we built a system called Vertica Data Manager, which basically manages all the ingestion into various clusters. So at this point, people who are managing datasets or dataset owners and publishers, they no longer have to be aware of individual clusters. They just set up their ingestion pipelines with an endpoint in Vertica Data Manager. And the Vertica Data Manager ensures that, all the schemas and data is consistent across all our clusters. And on the query side, we built a proxy layer. So what this ensures is that, when queries come in from the BI layer, the query was forwarded, smartly and with knowledge and data about which cluster up, which clusters are down, which clusters are available, which clusters are loaded, and so forth. So with these two layers of abstraction between our ingestion and our query, we were able to have a very consistent, almost single system view of our entire Vertica deployment. And the third bit, we had put in place, was the data manifest, which were the communication mechanism between ingestion and proxy. So the data manifest basically is a listing of, which tables are available on which clusters, which clusters are up to date, and so forth. So with this ecosystem in place, we were also able to solve the extra replication problem. So now we basically have some big clusters, where all the core tables, and all the tables, in fact, are served. So any query that hits 90%, less so tables, goes to the big clusters. And most of the queries which hit 10% heavily queried important tables, can also be served by many other small clusters, so much more efficient use of resources. So this basically is the view that we have today, of Vertica within Uber, so external to our team, folks, just have an endpoint, where they basically set up their ingestion jobs, and another endpoint where they can forward their Vertica SQL queries. And they are so to a proxy layer. So let's get a little more into details, about each of these layers. So, on the data management side, as I mentioned, we have two kinds of tables. So we have dimension tables. So these tables are updated every cycle, so the list of cities list of drivers, the list of users and so forth. So these change not so frequently, maybe once a day or so. And so we are able to, and since these datasets are not very big, we basically swap them out on every single cycle. Whereas the fact tables, so these are tables which have information about our trips or each orders and so forth. So these are partition. So we have one partition roughly per day, for the last couple of years, and then we have more of a hierarchical partitions set up for older data. So what we do is we load the partitions for the last three days on every cycle. The reason we do that, is because not all our data comes in at the same time. So we have updates for trips, going over the past two or three days, for instance, where people add ratings to their trips, or provide feedback for drivers and so forth. So we want to capture them all in the row corresponding to that particular trip. And so we upload partitions for the last few days to make sure we capture all those updates. And we also update older partitions, if for instance, records were deleted for retention purposes, or GDPR purposes, for instance, or other regulatory reasons. So we do this less frequently, but these are also updated if necessary. So there are endpoints which allow dataset owners to specify what partitions they want to update. And as I mentioned, data is typically managed using a hierarchical partitioning scheme. So in this way, we are able to make sure that, we take advantage of the data being clustered by day, so that we don't have to update all the data at once. So when we are recovering from an cluster event, like a version upgrade or software upgrade, or hardware fix or failure handling, or even when we are adding a new cluster to the system, the data manager takes care of updating the tables, and copying all the new partitions, making sure the schemas are all right. And then we update the data and schema consistency and make sure everything is up to date before we, add this cluster to our serving pool, and the proxy starts sending traffic to it. The second thing that the data manager provides is consistency. So the main thing we do here, is we do atomic updates of our tables and partitions for fact tables using a two-phase commit scheme. So what we do is we load all the new data in temp tables, in all the clusters in phase one. And then when all the clusters give us access signals, then we basically promote them to primary and set them as the main serving tables for incoming queries. We also optimize the load, using Vertica Data Copy. So what this means is earlier, in a parallel pipelines scheme, we had to ingest data individually from HDFS clusters into each of the Vertica clusters. That took a lot of HDFS bandwidth. But using this nice feature that Vertica provides called Vertica Data Copy, we just load it data into one cluster and then much more efficiently copy it, to the other clusters. So this has significantly reduced our ingestion overheads, and speed it up our load process. And as I mentioned as the second phase of the commit, all data is promoted at the same time. Finally, we make sure that all the data is up to date, by doing some checks around the number of rows and various other key signals for freshness and correctness, which we compare with the data in the data lake. So in terms of schema changes, VDM automatically applies these consistently across all the clusters. So first, what we do is we stage these changes to make sure that these are correct. So this catches errors that are trying to do, an incompatible update, like changing a column type or something like that. So we make sure that schema changes are validated. And then we apply them to all clusters atomically again for consistency. And provide a overall consistent view of our data to all our users. So on the proxy side, we have transparent support for, replicated clusters to all our users. So the way we handle that is, as I mentioned, the cluster to table mapping is maintained in the manifest database. And when we have an incoming query, the proxy is able to see which cluster has all the tables in that query, and route the query to the appropriate cluster based on the manifest information. Also the proxy is aware of the health of individual clusters. So if for some reason a cluster is down for maintenance or upgrades, the proxy is aware of this information. And it does the monitoring based on query response and execution times as well. And it uses this information to route queries to healthy clusters, and do some load balancing to ensure that we award hotspots on various clusters. So the key takeaways that I have from the stock, are primarily these. So we started off with single cluster mode on Vertica, and we ran into a bunch of issues around scaling and availability due to cluster downtime. We had then set up a bunch of replicated clusters to handle the scaling and availability issues. Then we run into issues around schema consistency, data staleness, and data replication. So we built an entire ecosystem around Vertica, with abstraction layers around data management and ingestion, and proxy. And with this setup, we were able to enforce consistency and improve storage utilization. So, hopefully this gives you all a brief idea of how we have been able to scale Vertica usage at Uber, and power some of our most business critical and important use cases. So as I mentioned at the beginning, I have a interesting and simple extra update for you. So an easy way in which you all can take advantage of many of the features that we have built into our ecosystem, is to use the Vertica Eon mode. So the Vertica Eon mode, allows you to set up multiple clusters with consistent data updates, and set them up at various different sizes to handle different query loads. And it automatically handles many of these issues that I mentioned in our ecosystem. So do check it out. We've also been, trying it out on DCP, and initial results look very, very promising. So thank you all for joining me on this talk today. I hope you guys learned something new. And hopefully you took away something that you can also apply to your systems. We have a few more time for some questions. So I'll pause for now and take any questions.

>>Well, hello everybody. John Walls here and continuing our coverage here at AWS Reinvent 22 on the queue. We continue our segments here in the Global Startup program, which of course is sponsored by AWS Startup Showcase, and with us to talk about any scale as the co-founder and CEO of the company, Robert and n, you are Robert. Good to see you. Thanks for joining us. >>Yeah, great. And thank you. >>You bet. Yeah. Glad to have you aboard here. So let's talk about Annie Scale, first off, for those at home and might not be familiar with what you do. Yeah. Because you've only been around for a short period of time, you're telling me >>Company's about >>Three years now. Three >>Years old, >>Yeah. Yeah. So tell us all about it. Yeah, >>Absolutely. So one of the biggest things happening in computing right now is the proliferation of ai. AI is just spreading throughout every industry has the potential to transform every industry. But the thing about doing AI is that it's incredibly computationally intensive. So if you wanna do do ai, you're not, you're probably not just doing it on your laptop, you're doing it across many machines, many gpu, many compute resources, and that's incredibly hard to do. It requires a lot of software engineering expertise, a lot of infrastructure expertise, a lot of cloud computing expertise to build the software infrastructure and distributed systems to really scale AI across all of the, across the cloud. And to do it in a way where you're really getting value out of ai. And so that is the, the problem statement that AI has tremendous potential. It's incredibly hard to do because of the, the scale required. >>And what we are building at any scale is really trying to make that easy. So trying to get to the point where, as a developer, if you know how to program on your laptop, then if you know how to program saying Python on your laptop, then that's enough, right? Then you can do ai, you can get value out of it, you can scale it, you can build the kinds of, you know, incredibly powerful applica AI applications that companies like Google and, and Facebook and others can build. But you don't have to learn about all of the distributed systems and infrastructure. It just, you know, we'll handle that for you. So that's, if we're successful, you know, that's what we're trying to achieve here. >>Yeah. What, what makes AI so hard to work with? I mean, you talk about the complexity. Yeah. A lot of moving parts. I mean, literally moving parts, but, but what is it in, in your mind that, that gets people's eyes spinning a little bit when they, they look at great potential. Yeah. But also they look at the downside of maybe having to work your way through Pike mere of sorts. >>So, so the potential is definitely there, but it's important to remember that a lot of AI initiatives fail. Like a lot of initiative AI initiatives, something like 80 or 90% don't make it out of, you know, the research or prototyping phase and inter production. Hmm. So, some of the things that are hard about AI and the reasons that AI initiatives can fail, one is the scale required, you know, moving. It's one thing to develop something on your laptop, it's another thing to run it across thousands of machines. So that's scale, right? Another is the transition from development and prototyping to production. Those are very different, have very different requirements. Absolutely. A lot of times it's different teams within a company. They have different tech stacks, different software they're using. You know, we hear companies say that when they move from develop, you know, once they prototype and develop a model, it could take six to 12 weeks to get that model in production. >>And that often involves rewriting a lot of code and handing it off to another team. So the transition from development to production is, is a big challenge. So the scale, the development to production handoff. And then lastly, a big challenge is around flexibility. So AI's a fast moving field, you see new developments, new algorithms, new models coming out all the time. And a lot of teams we work with, you know, they've, they've built infrastructure. They're using products out there to do ai, but they've found that it's sort of locking them into rigid workflows or specific tools, and they don't have the flexibility to adopt new algorithms or new strategies or approaches as they're being developed as they come out. And so they, but their developers want the flexibility to use the latest tools, the latest strategies. And so those are some of the main problems we see. It's really like, how do you scale scalability? How do you move easily from development and production and back? And how do you remain flexible? How do you adapt and, and use the best tools that are coming out? And so those are, yeah, just those are and often reasons that people start to use Ray, which is our open source project in any scale, which is our, our product. So tell >>Me about Ray, right? Yeah. Opensource project. I think you said you worked on it >>At Berkeley. That's right. Yeah. So before this company, I did a PhD in machine learning at Berkeley. And one of the challenges that we were running into ourselves, we were trying to do machine learning. We actually weren't infrastructure or distributed systems people, but we found ourselves in order to do machine learning, we found ourselves building all sorts of tools, ad hoc tools and systems to scale the machine learning, to be able to run it in a reasonable amount of time and to be able to leverage the compute that we needed. And it wasn't just us people all across, you know, machine learning researchers, machine learning practitioners were building their own tooling and infrastructure. And that was one of the things that we felt was really holding back progress. And so that's how we slowly and kind of gradually got into saying, Hey, we could build better tools here. >>We could build, we could try to make this easier to do so that all of these people don't have to build their own infrastructure. They can focus on the actual machine learning applications that they're trying to build. And so we started, Ray started this open source project for basically scaling Python applications and scaling machine learning applications. And, well, initially we were running around Berkeley trying to get all of our friends to try it out and, and adopt it and, you know, and give us feedback. And if it didn't work, we would debug it right away. And that slow, you know, that gradually turned into more companies starting to adopt it, bigger teams starting to adopt it, external contributors starting to, to contribute back to the open source project and make it better. And, you know, before you know it, we were hosting meetups, giving to talks, running tutorials, and the project was just taking off. And so that's a big part of what we continue to develop today at any scale, is like really fostering this open source community, growing the open source user base, making sure Ray is just the best way to scale Python applications and, and machine learning applications. >>So, so this was a graduate school project That's right. You say on, on your way to getting your doctorate and now you commercializing now, right? Yeah. I mean, so you're being able to offer it, first off, what a journey that was, right? I mean, who would've thought Absolutely. I guess you probably did think that at some point, but >>No, you know, when we started, when we were working on Ray, we actually didn't anticipate becoming a company, or we at least just weren't looking that far ahead. We were really excited about solving this problem of making distributed computing easy, you know, getting to the point where developers just don't have to learn about infrastructure and distributed systems, but get all the benefits. And of course, it wasn't until, you know, later on as we were graduating from Berkeley and we wanted to continue really taking this project further and, and really solving this problem that it, we realized it made sense to start a company. >>So help me out, like, like what, what, and I might have missed this, so I apologize if I did, but in terms of, of Ray's that building block and essential for your, your ML or AI work down the road, you know, what, what is it doing for me or what, what will it allow me to do in either one of those realms that I, I can't do now? >>Yeah. And so, so like why use Ray versus not using Ray? Yeah, I think the, the answer is that you, you know, if you're doing ai, you need to scale. It's becoming, if you don't find that to be the case today, you probably will tomorrow, you know, or the day after that. And so it's really increasingly, it's a requirement. It's not an option. And so if you're scaling, if you're trying to build these scalable applications you are building, you're either going to use Ray or, or something like Ray or you're going to build the infrastructure yourself and building the infrastructure yourself, that's a long journey. >>So why take that on, right? >>And many of the companies we work with don't want to be in the business of building and managing infrastructure. No. Because, you know, if they, they want their their best engineers to build their product, right? To, to get their product to market faster. >>I want, I want you to do that for me. >>Right? Exactly. And so, you know, we can really accelerate what these teams can do and, you know, and if we can make the infrastructure something they just don't have to think about, that's, that's why you would choose to use Ray. >>Okay. You know, between a and I and ml are, are they different animals in terms of what you're trying to get done or what Ray can do? >>Yeah, and actually I should say like, it's not just, you know, teams that are new teams that are starting out, that are using Ray, many companies that have built, already built their own infrastructure will then switch to using Ray. And to give you a few examples, like Uber runs all their deep learning on Ray, okay. And, you know, open ai, which is really at the frontier of training large models and, and you know, pushing the boundaries of, of ai, they train their largest models using Ray. You know, companies like Shopify rebuilt their entire machine learning platform using Ray, >>But they started somewhere else. >>They had, this is all, you know, like, it's not like the v1, you know, of their, of their machine learning infrastructure. This is like, they did it a different way before, this is like the second version or the third iteration of of, of how they're doing it. And they realize often it's because, you know, I mean in the case of, of Uber, just to give you one example, they built a system called hova for scaling deep learning on a bunch of GPUs. Right Now, as you scale deep learning on GPUs for them, the bottleneck shifted away from, you know, as you scale GPU's training, the bottleneck shifted away from training and to the data ingest and pre-processing. And they wanted to scale data ingest and pre-processing on CPUs. So now Hova, it's a deep learning framework. It doesn't do the data ingest and pre-processing on CPUs, but you can, if you run Hova on top of Ray, you can scale training on GPUs. >>And then Ray has another library called Ray Data you can, that lets you scale the ingest and pre-processing on CPUs. You can pipeline them together. And that allowed them to train larger models on more data before, just to take one example, ETA prediction, if you get in an Uber, it tells you what time you're supposed to arrive. Sure. That uses a deep learning model called d eta. And before they were able to train on about two weeks worth of data. Now, you know, using Ray and for scaling the data, ingestive pre-processing and training, they can train on much more data. You know, you can get more accurate ETA predictions. So that's just one example of the kind of benefit they were able to get. Right. Also, because it's running on top of, of Ray and Ray has this ecosystem of libraries, you know, they can also use Ray's hyper parameter tuning library to do hyper parameter tuning for their deep learning models. >>They can also use it for inference and you know, because these are all built on top of Ray, they inherit the like, elasticity and fault tolerance of running on top of Ray. So really it simplifies things on the infrastructure side cuz there's just, if you have Ray as common infrastructure for your machine learning workloads, there's just one system to, to kind of manage and operate. And if you are, it simplifies things for the end users like the developers because from their perspective, they're just writing a Python application. They don't have to learn how to use three different distributed systems and stitch them together and all of this. >>So aws, before I let you go, how do they come into play here for you? I mean, are you part of the showcase, a startup showcase? So obviously a major partner and major figure in the offering that you're presenting >>People? Yeah, well you can run. So any scale is a managed ray service. Like any scale is just the best way to run Ray and deploy Ray. And we run on top of aws. So many of our customers are, you know, using Ray through any scale on aws. And so we work very closely together and, and you know, we have, we have joint customers and basically, and you know, a lot of the value that any scale is adding on top of Ray is around the production story. So basically, you know, things like high availability, things like failure handling, retry alerting, persistence, reproducibility, these are a lot of the value, the values of, you know, the value that our platform adds on top of the open source project. A lot of stuff as well around collaboration, you know, imagine you are, you, something goes wrong with your application, your production job, you want to debug it, you can just share the URL with your, your coworker. They can click a button, reproduce the exact same thing, look at the same logs, you know, and, and, and figure out what's going on. And also a lot around, one thing that's, that's important for a lot of our customers is efficiency around cost. And so we >>Support every customer. >>Exactly. A lot of people are spending a lot of money on, on aws. Yeah. Right? And so any scale supports running out of the box on cheaper like spot instances, these preempt instances, which, you know, just reduce costs by quite a bit. And so things like that. >>Well, the company is any scale and you're on the show floor, right? So if you're having a chance to watch this during reinvent, go down and check 'em out. Robert Ashihara joining us here, the co-founder and ceo and Robert, thanks for being with us. Yeah. Here on the cube. Really enjoyed it. Me too. Thanks so much. Boy, three years graduate program and boom, here you are, you know, with off to the enterprise you go. Very nicely done. All right, we're gonna continue our coverage here on the Cube with more here from Las Vegas. We're the Venetian, we're AWS Reinvent 22 and you're watching the Cube, the leader in high tech coverage.

(upbeat music) >> Welcome back friends. It's theCUBE LIVE in Las Vegas at the Venetian Expo, covering the first full day of AWS re:Invent 2022. I'm Lisa Martin, and I have the privilege of working much of this week with Dave Vellante. >> Hey. Yeah, it's good to be with you Lisa. >> It's always good to be with you. Dave, this show is, I can't say enough about the energy. It just keeps multiplying as I've been out on the show floor for a few minutes here and there. We've been having great conversations about cloud migration, digital transformation, business transformation. You name it, we're talking about it. >> Yeah, and I got to say the soccer Christians are really happy. (Lisa laughing) >> Right? Because the USA made it through. So that's a lot of additional excitement. >> That's true. >> People were crowded around the TVs at lunchtime. >> They were, they were. >> So yeah, but back to data. >> Back to data. We have a couple of guests here. We're going to be talking a lot with customer challenges, how they're helping to overcome them. Please welcome Kevin Zawodzinski, VP of Sales Engineering at COMMVAULT. >> Thank you. >> And Paul Meighan, Director of Product Management at AWS. Guys, it's great to have you on the program. Thank you for joining us. >> Thanks for having us. >> Thanks for having us. >> Isn't it great to be back in person? >> Paul: It really is. >> Kevin: Hell, yeah. >> You cannot replicate this on virtual, you just can't. It's nice to see how excited people are to be back. There's been a ton of buzz on our program today about Adam's keynote this morning. Amazing. A lot of synergies with the direction, Paul, that AWS is going in and where we're seeing its ecosystem as well. Paul, first question for you. Talk about, you know, in the customer environment, we know AWS is very customer obsessed. Some of the main challenges customers are facing today is they really continue this business transformation, this digital transformation, and they move to cloud native apps. What are some of those challenges and how do you help them eradicate those? >> Well, I can tell you that the biggest contribution that we make is really by focusing on the fundamentals when it comes to running storage at scale, right? So Amazon S3 is unique, distributed architecture, you know, it really does deliver on those fundamentals of durability, availability, performance, security and it does it at virtually unlimited scale, right? I mean, you guys have talked to a lot of storage folks in the industry and anyone who's run an estate at scale knows that doing that and executing on those fundamentals day after day is just super hard, right? And so we come to work every day, we focus on the fundamentals, and that focus allows customers to spend their time thinking about innovation instead of on how to keep their data durably stored. >> Well, and you guys both came out of the storage world. >> Right. >> Yeah, yeah. >> It was a box world, (Kevin laughs) and it ain't no more. >> Kevin: That's right, absolutely. >> It's a service and a service of scale. >> Kevin: Yeah. So architecture matters, right? >> Yeah. >> Yeah. >> Paul, talk a little bit about, speaking of innovation, talk about the evolution of S3. It's been around for a while now. Everyone knows it, loves it, but how has AWS architected it to really help meet customers where they are? >> Paul: Right. >> Because we know, again, there's that customer first focus. You write the press release down the road, you then follow that. How is it evolving? >> Well, I can tell you that architecture matters a lot and the architecture of Amazon S3 is pretty unique, right? I think, you know, the most important thing to understand about the architecture of S3 is that it is truly a regional service. So we're laid out across a minimum of 3 Availability Zones, or AZs, which are physically separated and isolated and have a distance of miles between them to protect against local events like floods and fires and power interruption, stuff like that. And so when you give us an object, we distribute that data across that minimum of 3 Availability Zones and then within multiple devices within each AZ, right? And so what that means is that when you store data with us, your data is on storage that's able to tolerate the failure of multiple devices with no impact to the integrity of your data, which is super powerful. And then again, super hard to do when you're trying to roll your own. So that's sort of a, like an overview of the architecture. In terms of how we think about our roadmap, you know, 90% of our roadmap comes directly from what customers tell us matters, and that's a tenant of how we think about customer obsession at AWS and it really is how we drive a roadmap. >> Right, so speaking of customers Kevin, what are customers asking you guys- >> Yeah. >> for, how does it relate to what you're doing with S3? >> Yeah, it's a wonderful question and one that is actually really appropriate for us being at re:Invent, right? So we got, last three years we've had customers here with us on stage talking about it. First of all, 3 years ago we did a virtual session, unfortunately, but glad to be back as you mentioned, with Coca-Cola and theirs was about scale and scope and really about how can we protect hundreds of thousands of objects, petabyte to data, in a simple and secure way, right. Then last year we actually met with a ACT, Inc. as well and co-presented with them and really talked about how we could protect modern workloads and their modern workloads around whether it was Aurora or as well as EKS and how they continue to evolve as well. And, last but not least it's going to be, this year we're talking with Illinois State University as well about how they're going to continue to grow, adapt and really leverage AWS and ourselves to further their support of their teachers and their staff. So that is really helping us quite a bit to continue to move forward. And the things we're doing, again, with our customer base it's really around, focused on what's important to them, right? Customer obsession, how are we working with that? How are we making sure that we're listening to them? Again, working with AWS to understand how can we evolve together and really ultimately their journeys. As you heard, even with those 3 examples they're all very different, right? And that's the point, is that everybody's at a different point in the journey. They're at a different place from a modernization perspective. So we're helping them evolve, as they're helping us evolve as well, and transform with AWS. >> So very mature COMMVAULT stack, the S3 bucket and all the other capabilities. Paul, you just talked about coming together- >> Right. >> Dave: for your customers. >> Yeah, yeah, absolutely. And just, you know, we were talking the other day, Paul and I were talking the other day, it's been, you know, we've worked with AWS, with integration since 2009, right? So a long time, right? I mean, for some that may not seem like a long time ago, but it is, right? It's, you know, over a decade of time and we've really advanced that integration considerably as well. >> What are some of the things that, I don't know if you had a chance to see the keynote this morning? >> Yeah, a little bit. >> What are some of the things that there was, and in fact this is funny, funny data point for you on data. One of my previous guests told me that Adam Selipsky spent exactly 52 minutes talking about data this morning. 52 minutes. >> Okay. >> That there's a data point. But talk about some of the things that he talked about, the direction AWS is going in, obviously new era in the last year. Talk about what you heard and how you think that will evolve the COMMVAULT-AWS relationship. >> Yeah, I think part of that is about flexibility, as Paul mentioned too, architecture matters, right? So as we evolve and some of the things that we pride ourselves on is that we developed our systems and our software and everything else to not worry about what do I have to build to today but how do I continue to evolve with my customer base? And that's what AWS does, right? And continues to do. So that's really how we would see the data environment. It's really about that integration. As they grow, as they add more features we're going to add more features as well. And we're right there with them, right? So there's a lot of things that we also talk about, Paul and I talk about, around, you know, how do we, like Graviton3 was brought up today around some of the innovations around that. We're supporting that with Auto Scale right now, right? So we're right there releasing, right when AWS releasing, co-developing things when necessary as well. >> So let's talk about security a little bit. First of all, what is COMMVAULT, right? You're not a security company but you're an adjacency to security. It's sort of, we're rethinking security. >> Kevin: Yep. >> including data protection, not a bolt-on anymore. You guys both have a background in that world and I'm sure that resonates. >> Yeah. >> So what is the security play here? What role does COMMVAULT play? I think we know pretty well what role AWS plays, but love to hear, Paul, your thoughts as well on security. >> Yeah, I'll start I guess. >> Go on Paul. >> Okay. Yeah, so on the security side of things, there's a quite a few things. So again, on the development side of things, we do things like file anomaly detection, so seeing patterns in data. We talked a lot about analytics as well in the keynote this morning. We look at what is happening in the customer environment, if there's something odd or out of place that's happening, we can detect that and we'll notify people. And we've seen that, we have case studies about that. Other things we do are simple, simple but elegant. Is with our security dashboard. So we'll use our security dashboard to show best practices. Are they using Multi-Factor Authentication? Are you viewing password complexity? You know, things like that. And allows people to understand from a security landscape perspective, how do we layer in protection with their other systems around security. We don't profess to be the security company, or a security company, but we help, you know, obviously add in those additional layers. >> And obviously you're securing, you know, the S3 piece of it. >> Mmmhmm. >> You know, from your standpoint because building it in. >> That's right. And we can tell you that for us, security is job zero. And anyone at AWS will tell you that, and not only that but it will always be our top priority. Right from the infrastructure on down. We're very focused on our shared responsibility model where we handle security from the hypervisor, or host operating system level, down to the physical security of the facilities in which our services run and then it's our customer's responsibility to build secure applications, right. >> Yeah. And you talk about Graviton earlier, Nitro comes into play and how you're, sort of, fencing off, you know, the various components of the system from the operating system, the VMs, and then that is designed in and that's a new evolution that it comes as part of the package. >> Yeah, absolutely. >> Absolutely. >> Paul, talk a little bit about, you know, security, talking about that we had so many conversations this year alone about the threat landscape and how it's dramatically changing, it's top of mind for everybody. Huge rise in ransomware attacks. Ransomware is now, when are we going to get hit? How often? What's the damage going to be? Rather than, are we going to get hit? It's, unfortunately it's progressed in that direction. How does ensuring data security impact how you're planning the roadmap at AWS and how are partners involved in shaping that? >> Right, so like I said, you know, 90% of our roadmap comes from what customers tell us matters, right? And clearly this is an issue that matters very much to customers right now, right? And so, you know, we're certainly hearing that from customers, and COMMVAULT, and partners like COMMVAULT have a big role to play in helping customers to secure and protect their applications, right? And that's why it's so critical that we come together here at re:Invent and we have a bunch of time here at the show with the COMMVAULT technical folks to talk through what they're hearing from customers and what we're hearing. And we have a number of regular touch points throughout the year as well, right? And so what COMMVAULT gets from the relationship is, sort of, early access and feedback into our features and roadmap. And what we get out of it really is that feedback from that large number of customers who interface with Amazon S3 through COMMVAULT. Who are using S3 as a backup target behind COMMVAULT, right? And so, you know, that partnership really allows us to get close to those customers and understand what really matters to them. >> Are you doing joint engineering, or is it more just, hey here you go COMMVAULT, here's the tools available, go, go build. Can you address that? >> Yeah, no, absolutely. There's definitely joint engineering like even things around, you know, data migration and movement of data, we integrate really well and we talk a lot about, hey, what are you, like as Paul mentioned, what are you seeing out there? We actually, I just left a conversation about an hour ago where we're talking about, you know, where are we seeing placement of data and how does that matter to, do you put it on, you know, instant access, or do you put it on Glacier, you know, what should be the best practices? And we tell them, again, some of the telemetry data that we have around what do we see customers doing, what's the patterns of data? And then we feed that back in and we use that to create joint solutions as well. >> You know, I wonder if we could talk about cloud, you know, optimization of cloud costs for a minute. That's obviously a big discussion point in the hallways with customers. And on your earnings call you guys talked about specifically some customers and they specifically mentioned, for example, pushing storage to lower cost tiers. So you brought up Glacier just then. What are you seeing in the field in that regard? How are customers taking advantage of that? And where does COMMVAULT play in, sort of, helping make that decision? >> You want to take part one or you want me to take it? >> I can take part one. I can tell you that, you know, we're very focused on helping customers optimize costs, however necessary, right? And, you know, we introduced intelligent hearing here at the show in 2019 and since launch it's helped customers to reduce costs by over $750 million, right? So that's a real commitment to optimizing costs on behalf of customers. We also launched, you know, later in 2020, Glacier Deep Archive, which is the lowest cost storage in the cloud. So it's an important piece of the puzzle, is to provide those storage options that can allow customers to match the workloads that are, that need to be on folder storage to the appropriate store. >> Yeah, and so, you know, S3 is not this, you know, backup and recovery system, not an archiving system and, you know, in terms of, but you have that intelligence in your platform. 'Cause when I heard that from the earnings call I was like, okay, how do customers then go about deciding what they can, you know, when it's all good times, like yeah, who cares? You know, just go, go, go. But when you got to tighten the belt, how do you guys? >> Yeah, and that goes back to understanding the data pattern. So some of that is we have intelligence and artificial intelligence and everything else and machine learning within our, so we can detect those patterns, right? We understand the patterns, we learn from that and we help customers right size, right. So ultimately we do see a blend, right? As Paul mentioned, we see, you know, hey I'm not going to put everything on Glacier necessarily upfront. Maybe they are, it all depends on their workloads and patterns. So we use the data that we collect from the different customers that we have to share those best practices out and create, you know, the right templates, so to speak, in ways for people to apply it. >> Guys, great joint, you talked about the joint engineering, joint go to market, obviously a very strong synergistic partnership between the two. A lot of excitement. This is only day one, I can only imagine what's going to be coming the next couple of days. But I have one final question for you, but I have same question for both of you. You had the chance to create your own bumper sticker, so you get a shiny new car and for some reason you want to put a bumper sticker on it. About COMMVAULT, what would it say? >> Yeah, so for me I would say comprehensive, yet simple, right? So ultimately about giving you all the bells and whistles but if you want to be very simple we can help you in every shape and form. >> Paul, what's your bumper sticker say about AWS? >> I would say that AWS starts with the customer and works backwards from there. >> Great one. >> Excellent. Guys- >> Kevin: Well done. >> it's been a pleasure to have you on the program. Thank you- >> Kevin: Thank you. >> for sharing what's going on, the updates on the AWS-COMMVAULT partnership and what's in it for customers. We appreciate it. >> Dave: Thanks you guys. >> Thanks a lot. >> Thank you. >> All right. For our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

>>We're back at Supercomputing 22 in Dallas, winding down day four of this conference. I'm Paul Gillan, my co-host Dave Nicholson. We are talking, we've been talking super computing all week and you hear a lot about what's going on in the United States, what's going on in China, Japan. What we haven't talked a lot about is what's going on in Europe and did you know that two of the top five supercomputers in the world are actually from European countries? Well, our guest has a lot to do with that. Florian, bearish, I hope I pronounce that correctly. My German is, German is not. My strength is the operations director for price, ais, S B L. And let's start with that. What is price? >>So, hello and thank you for the invitation. I'm Flon and Price is a partnership for Advanced Computing in Europe. It's a non-profit association with the seat in Brussels in Belgium. And we have 24 members. These are representatives from different European countries dealing with high performance computing in at their place. And we, so far, we provided the resources for our European research communities. But this changed in the last year, this oral HPC joint undertaking who put a lot of funding in high performance computing and co-funded five PET scale and three preis scale systems. And two of the preis scale systems. You mentioned already, this is Lumi and Finland and Leonardo in Bologna in Italy were in the place for and three and four at the top 500 at least. >>So why is it important that Europe be in the top list of supercomputer makers? >>I think Europe needs to keep pace with the rest of the world. And simulation science is a key technology for the society. And we saw this very recently with a pandemic, with a covid. We were able to help the research communities to find very quickly vaccines and to understand how the virus spread around the world. And all this knowledge is important to serve the society. Or another example is climate change. Yeah. With these new systems, we will be able to predict more precise the changes in the future. So the more compute power you have, the better the smaller the grid and there is resolution you can choose and the lower the error will be for the future. So these are, I think with these systems, the big or challenges we face can be addressed. This is the climate change, energy, food supply, security. >>Who are your members? Do they come from businesses? Do they come from research, from government? All of the >>Above. Yeah. Our, our members are public organization, universities, research centers, compute sites as a data centers, but But public institutions. Yeah. And we provide this services for free via peer review process with excellence as the most important criteria to the research community for free. >>So 40 years ago when, when the idea of an eu, and maybe I'm getting the dates a little bit wrong, when it was just an idea and the idea of a common currency. Yes. Reducing friction between, between borders to create a trading zone. Yes. There was a lot of focus there. Fast forward to today, would you say that these efforts in supercomputing, would they be possible if there were not an EU super structure? >>No, I would say this would not be possible in this extent. I think when though, but though European initiatives are, are needed and the European Commission is supporting these initiatives very well. And before praise, for instance 2008, there were research centers and data centers operating high performance computing systems, but they were not talking to each other. So it was isolated praise created community of operation sites and it facilitated the exchange between them and also enabled to align investments and to, to get the most out of the available funding. And also at this time, and still today for one single country in Europe, it's very hard to provide all the different architectures needed for all the different kind of research communities and applications. If you want to, to offer always the latest technologies, though this is really hardly possible. So with this joint action and opening the resources for other research groups from other countries, you, we, we were able to, yeah, get access to the latest technology for different communities at any given time though. And >>So, so the fact that the two systems that you mentioned are physically located in Finland and in Italy, if you were to walk into one of those facilities and meet the people that are there, they're not just fins in Finland and Italians in Italy. Yeah. This is, this is very much a European effort. So this, this is true. So, so in this, in that sense, the geography is sort of abstracted. Yeah. And the issues of sovereignty that make might take place in in the private sector don't exist or are there, are there issues with, can any, what are the requirements for a researcher to have access to a system in Finland versus a system in Italy? If you've got a EU passport, Hmm. Are you good to go? >>I think you are good to go though. But EU passport, it's now it becomes complicated and political. It's, it's very much, if we talk about the recent systems, well first, let me start a praise. Praise was inclusive and there was no any constraints as even we had users from US, Australia, we wanted just to support excellence in science. And we did not look at the nationality of the organization, of the PI and and so on. There were quotas, but these quotas were very generously interpreted. So, and if so, now with our HPC joint undertaking, it's a question from what European funds, these systems were procured and if a country or being country are associated to this funding, the researchers also have access to these systems. And this addresses basically UK and and Switzerland, which are not in the European Union, but they were as created to the Horizon 2020 research framework. And though they could can access the systems now available, Lumi and Leono and the Petascale system as well. How this will develop in the future, I don't know. It depends to which research framework they will be associated or not. >>What are the outputs of your work at price? Are they reference designs? Is it actual semiconductor hardware? Is it the research? What do you produce? >>So the, the application we run or the simulation we run cover all different scientific domains. So it's, it's science, it's, but also we have industrial let projects with more application oriented targets. Aerodynamics for instance, for cars or planes or something like this. But also fundamental science like the physical elementary physics particles for instance or climate change, biology, drug design, protein costa, all these >>Things. Can businesses be involved in what you do? Can they purchase your, your research? Do they contribute to their, I'm sure, I'm sure there are many technology firms in Europe that would like to be involved. >>So this involving industry though our calls are open and is, if they want to do open r and d, they are invited to submit also proposals. They will be evaluated and if this is qualifying, they will get the access and they can do their jobs and simulations. It's a little bit more tricky if it's in production, if they use these resources for their business and do not publish the results. They are some, well, probably more sites who, who are able to deal with these requests. Some are more dominant than others, but this is on a smaller scale, definitely. Yeah. >>What does the future hold? Are you planning to, are there other countries who will be joining the effort, other institutions? Do you plan to expand your, your scope >>Well, or I think or HPC joint undertaking with 36 member states is quite, covers already even more than Europe. And yeah, clearly if, if there are other states interest interested to join that there is no limitation. Although the focus lies on European area and on union. >>When, when you interact with colleagues from North America, do you, do you feel that there is a sort of European flavor to supercomputing that is different or are we so globally entwined? No. >>So research is not national, it's not European, it's international. This is also clearly very clear and I can, so we have a longstanding collaboration with our US colleagues and also with Chap and South Africa and Canada. And when Covid hit the world, we were able within two weeks to establish regular seminars inviting US and European colleagues to talk to to other, to each other and exchange the results and find new collaboration and to boost the research activities. So, and I have other examples as well. So when we, we already did the joint calls US exceed and in Europe praise and it was a very interesting experience. So we received applications from different communities and we decided that we will review this on our side, on European, with European experts and US did it in US with their experts. And you can guess what the result was at the meeting when we compared our results, it was matching one by one. It was exactly the same. Recite >>That it, it's, it's refreshing to hear a story of global collaboration. Yeah. Where people are getting along and making meaningful progress. >>I have to mention you, I have to to point out, you did not mention China as a country you were collaborating with. Is that by, is that intentional? >>Well, with China, definitely we have less links and collaborations also. It's also existing. There, there was initiative to look at the development of the technologies and the group meet on a regular basis. And there, there also Chinese colleagues involved. It's on a lower level, >>Yes, but is is the con conversations are occurring. We're out of time. Florian be operations director of price, European Super Computing collaborative. Thank you so much for being with us. I'm always impressed when people come on the cube and submit to an interview in a language that is not their first language. Yeah, >>Absolutely. >>Brave to do that. Yeah. Thank you. You're welcome. Thank you. We'll be right back after this break from Supercomputing 22 in Dallas.