(upbeat music) >> Hi everyone. Welcome back to this Cube conversation here in Palo Alto, California. I'm John Furrier, host of "theCUBE." Got a great conversation around Cloud Native, Cloud Native Journey, how enterprises are looking at Cloud Native and putting it all together. And it comes down to operations, developer productivity, and security. It's the hottest topic in technology. We got Chris Jones here in the studio, director of Product Management for Platform9. Chris, thanks for coming in. >> Hey, thanks. >> So when we always chat about, when we're at KubeCon. KubeConEU is coming up and in a few, in a few months, the number one conversation is developer productivity. And the developers are driving all the standards. It's interesting to see how they just throw everything out there and whatever gets adopted ends up becoming the standard, not the old school way of kind of getting stuff done. So that's cool. Security Kubernetes and Containers are all kind of now that next level. So you're starting to see the early adopters moving to the mainstream. Enterprises, a variety of different approaches. You guys are at the center of this. We've had a couple conversations with your CEO and your tech team over there. What are you seeing? You're building the products. What's the core product focus right now for Platform9? What are you guys aiming for? >> The core is that blend of enabling your infrastructure and PlatformOps or DevOps teams to be able to go fast and run in a stable environment, but at the same time enable developers. We don't want people going back to what I've been calling Shadow IT 2.0. It's, hey, I've been told to do something. I kicked off this Container initiative. I need to run my software somewhere. I'm just going to go figure it out. We want to keep those people productive. At the same time we want to enable velocity for our operations teams, be it PlatformOps or DevOps. >> Take us through in your mind and how you see the industry rolling out this Cloud Native journey. Where do you see customers out there? Because DevOps have been around, DevSecOps is rocking, you're seeing AI, hot trend now. Developers are still in charge. Is there a change to the infrastructure of how developers get their coding done and the infrastructure, setting up the DevOps is key, but when you add the Cloud Native journey for an enterprise, what changes? What is the, what is the, I guess what is the Cloud Native journey for an enterprise these days? >> The Cloud Native journey or the change? When- >> Let's start with the, let's start with what they want to do. What's the goal and then how does that happen? >> I think the goal is that promise land. Increased resiliency, better scalability, and overall reduced costs. I've gone from physical to virtual that gave me a higher level of density, packing of resources. I'm moving to Containers. I'm removing that OS layer again. I'm getting a better density again, but all of a sudden I'm running Kubernetes. What does that, what does that fundamentally do to my operations? Does it magically give me scalability and resiliency? Or do I need to change what I'm running and how it's running so it fits that infrastructure? And that's the reality, is you can't just take a Container and drop it into Kubernetes and say, hey, I'm now Cloud Native. I've got reduced cost, or I've got better resiliency. There's things that your engineering teams need to do to make sure that application is a Cloud Native. And then there's what I think is one of the largest shifts of virtual machines to containers. When I was in the world of application performance monitoring, we would see customers saying, well, my engineering team have this Java app, and they said it needs a VM with 12 gig of RAM and eight cores, and that's what we gave it. But it's running slow. I'm working with the application team and you can see it's running slow. And they're like, well, it's got all of its resources. One of those nice features of virtualization is over provisioning. So the infrastructure team would say, well, we gave it, we gave it all a RAM it needed. And what's wrong with that being over provisioned? It's like, well, Java expects that RAM to be there. Now all of a sudden, when you move to the world of containers, what we've got is that's not a set resource limit, really is like it used to be in a VM, right? When you set it for a container, your application teams really need to be paying attention to your resource limits and constraints within the world of Kubernetes. So instead of just being able to say, hey, I'm throwing over the fence and now it's just going to run on a VM, and that VMs got everything it needs. It's now really running on more, much more of a shared infrastructure where limits and constraints are going to impact the neighbors. They are going to impact who's making that decision around resourcing. Because that Kubernetes concept of over provisioning and the virtualization concept of over provisioning are not the same. So when I look at this problem, it's like, well, what changed? Well, I'll do my scale tests as an application developer and tester, and I'd see what resources it needs. I asked for that in the VM, that sets the high watermark, job's done. Well, Kubernetes, it's no longer a VM, it's a Kubernetes manifest. And well, who owns that? Who's writing it? Who's setting those limits? To me, that should be the application team. But then when it goes into operations world, they're like, well, that's now us. Can we change those? So it's that amalgamation of the two that is saying, I'm a developer. I used to pay attention, but now I need to pay attention. And an infrastructure person saying, I used to just give 'em what they wanted, but now I really need to know what they've wanted, because it's going to potentially have a catastrophic impact on what I'm running. >> So what's the impact for the developer? Because, infrastructure's code is what everybody wants. The developer just wants to get the code going and they got to pay attention to all these things, or don't they? Is that where you guys come in? How do you guys see the problem? Actually scope the problem that you guys solve? 'Cause I think you're getting at I think the core issue here, which is, I've got Kubernetes, I've got containers, I've got developer productivity that I want to focus on. What's the problem that you guys solve? >> Platform operation teams that are adopting Cloud Native in their environment, they've got that steep learning curve of Kubernetes plus this fundamental change of how an app runs. What we're doing is taking away the burden of needing to operate and run Kubernetes and giving them the choice of the flexibility of infrastructure and location. Be that an air gap environment like a, let's say a telco provider that needs to run a containerized network function and containerized workloads for 5G. That's one thing that we can deploy and achieve in a completely inaccessible environment all the way through to Platform9 running traditionally as SaaS, as we were born, that's remotely managing and controlling your Kubernetes environments on-premise AWS. That hybrid cloud experience that could be also Bare Metal, but it's our platform running your environments with our support there, 24 by seven, that's proactively reaching out. So it's removing a lot of that burden and the complications that come along with operating the environment and standing it up, which means all of a sudden your DevOps and platform operations teams can go and work with your engineers and application developers and say, hey, let's get, let's focus on the stuff that, that we need to be focused on, which is running our business and providing a service to our customers. Not figuring out how to upgrade a Kubernetes cluster, add new nodes, and configure all of the low level. >> I mean there are, that's operations that just needs to work. And sounds like as they get into the Cloud Native kind of ops, there's a lot of stuff that kind of goes wrong. Or you go, oops, what do we buy into? Because the CIOs, let's go, let's go Cloud Native. We want to, we got to get set up for the future. We're going to be Cloud Native, not just lift and shift and we're going to actually build it out right. Okay, that sounds good. And when we have to actually get done. >> Chris: Yeah. >> You got to spin things up and stand up the infrastructure. What specifically use case do you guys see that emerges for Platform9 when people call you up and you go talk to customers and prospects? What's the one thing or use case or cases that you guys see that you guys solve the best? >> So I think one of the, one of the, I guess new use cases that are coming up now, everyone's talking about economic pressures. I think the, the tap blows open, just get it done. CIO is saying let's modernize, let's use the cloud. Now all of a sudden they're recognizing, well wait, we're spending a lot of money now. We've opened that tap all the way, what do we do? So now they're looking at ways to control that spend. So we're seeing that as a big emerging trend. What we're also sort of seeing is people looking at their data centers and saying, well, I've got this huge legacy environment that's running a hypervisor. It's running VMs. Can we still actually do what we need to do? Can we modernize? Can we start this Cloud Native journey without leaving our data centers, our co-locations? Or if I do want to reduce costs, is that that thing that says maybe I'm repatriating or doing a reverse migration? Do I have to go back to my data center or are there other alternatives? And we're seeing that trend a lot. And our roadmap and what we have in the product today was specifically built to handle those, those occurrences. So we brought in KubeVirt in terms of virtualization. We have a long legacy doing OpenStack and private clouds. And we've worked with a lot of those users and customers that we have and asked the questions, what's important? And today, when we look at the world of Cloud Native, you can run virtualization within Kubernetes. So you can, instead of running two separate platforms, you can have one. So all of a sudden, if you're looking to modernize, you can start on that new infrastructure stack that can run anywhere, Kubernetes, and you can start bringing VMs over there as you are containerizing at the same time. So now you can keep your application operations in one environment. And this also helps if you're trying to reduce costs. If you really are saying, we put that Dev environment in AWS, we've got a huge amount of velocity out of it now, can we do that elsewhere? Is there a co-location we can go to? Is there a provider that we can go to where we can run that infrastructure or run the Kubernetes, but not have to run the infrastructure? >> It's going to be interesting too, when you see the Edge come online, you start, we've got Mobile World Congress coming up, KubeCon events we're going to be at, the conversation is not just about public cloud. And you guys obviously solve a lot of do-it-yourself implementation hassles that emerge when people try to kind of stand up their own environment. And we hear from developers consistency between code, managing new updates, making sure everything is all solid so they can go fast. That's the goal. And that, and then people can get standardized on that. But as you get public cloud and do it yourself, kind of brings up like, okay, there's some gaps there as the architecture changes to be more distributed computing, Edge, on-premises cloud, it's cloud operations. So that's cool for DevOps and Cloud Native. How do you guys differentiate from say, some the public cloud opportunities and the folks who are doing it themselves? How do you guys fit in that world and what's the pitch or what's the story? >> The fit that we look at is that third alternative. Let's get your team focused on what's high value to your business and let us deliver that public cloud experience on your infrastructure or in the public cloud, which gives you that ability to still be flexible if you want to make choices to run consistently for your developers in two different locations. So as I touched on earlier, instead of saying go figure out Kubernetes, how do you upgrade a hundred worker nodes in place upgrade. We've solved that problem. That's what we do every single day of the week. Don't go and try to figure out how to upgrade a cluster and then upgrade all of the, what I call Kubernetes friends, your core DNSs, your metrics server, your Kubernetes dashboard. These are all things that we package, we test, we version. So when you click upgrade, we've already handled that entire process. So it's saying don't have your team focused on that lower level piece of work. Get them focused on what is important, which is your business services. >> Yeah, the infrastructure and getting that stood up. I mean, I think the thing that's interesting, if you look at the market right now, you mentioned cost savings and recovery, obviously kind of a recession. I mean, people are tightening their belts for sure. I don't think the digital transformation and Cloud Native spend is going to plummet. It's going to probably be on hold and be squeezed a little bit. But to your point, people are refactoring looking at how to get the best out of what they got. It's not just open the tap of spend the cash like it used to be. Yeah, a couple months, even a couple years ago. So okay, I get that. But then you look at the what's coming, AI. You're seeing all the new data infrastructure that's coming. The containers, Kubernetes stuff, got to get stood up pretty quickly and it's got to be reliable. So to your point, the teams need to get done with this and move on to the next thing. >> Chris: Yeah, yeah, yeah. >> 'Cause there's more coming. I mean, there's a lot coming for the apps that are building in Data Native, AI-Native, Cloud Native. So it seems that this Kubernetes thing needs to get solved. Is that kind of what you guys are focused on right now? >> So, I mean to use a customer, we have a customer that's in AI/ML and they run their platform at customer sites and that's hardware bound. You can't run AI machine learning on anything anywhere. Well, with Platform9 they can. So we're enabling them to deliver services into their customers that's running their AI/ML platform in their customer's data centers anywhere in the world on hardware that is purpose-built for running that workload. They're not Kubernetes experts. That's what we are. We're bringing them that ability to focus on what's important and just delivering their business services whilst they're enabling our team. And our 24 by seven proactive management are always on assurance to keep that up and running for them. So when something goes bump at the night at 2:00am, our guys get woken up. They're the ones that are reaching out to the customer saying, your environments have a problem, we're taking these actions to fix it. Obviously sometimes, especially if it is running on Bare Metal, there's things you can't do remotely. So you might need someone to go and do that. But even when that happens, you're not by yourself. You're not sitting there like I did when I worked for a bank in one of my first jobs, three o'clock in the morning saying, wow, our end of day processing is stuck. Who else am I waking up? Right? >> Exactly, yeah. Got to get that cash going. But this is a great use case. I want to get to the customer. What do some of the successful customers say to you for the folks watching that aren't yet a customer of Platform9, what are some of the accolades and comments or anecdotes that you guys hear from customers that you have? >> It just works, which I think is probably one of the best ones you can get. Customers coming back and being able to show to their business that they've delivered growth, like business growth and productivity growth and keeping their organization size the same. So we started on our containerization journey. We went to Kubernetes. We've deployed all these new workloads and our operations team is still six people. We're doing way more with growth less, and I think that's also talking to the strength that we're bringing, 'cause we're, we're augmenting that team. They're spending less time on the really low level stuff and automating a lot of the growth activity that's involved. So when it comes to being able to grow their business, they can just focus on that, not- >> Well you guys do the heavy lifting, keep on top of the Kubernetes, make sure that all the versions are all done. Everything's stable and consistent so they can go on and do the build out and provide their services. That seems to be what you guys are best at. >> Correct, correct. >> And so what's on the roadmap? You have the product, direct product management, you get the keys to the kingdom. What is, what is the focus? What's your focus right now? Obviously Kubernetes is growing up, Containers. We've been hearing a lot at the last KubeCon about the security containers is getting better. You've seen verification, a lot more standards around some things. What are you focused on right now for at a product over there? >> Edge is a really big focus for us. And I think in Edge you can look at it in two ways. The mantra that I drive is Edge must be remote. If you can't do something remotely at the Edge, you are using a human being, that's not Edge. Our Edge management capabilities and being in the market for over two years are a hundred percent remote. You want to stand up a store, you just ship the server in there, it gets racked, the rest of it's remote. Imagine a store manager in, I don't know, KFC, just plugging in the server, putting in the ethernet cable, pressing the power button. The rest of all that provisioning for that Cloud Native stack, Kubernetes, KubeVirt for virtualization is done remotely. So we're continuing to focus on that. The next piece that is related to that is allowing people to run Platform9 SaaS in their data centers. So we do ag app today and we've had a really strong focus on telecommunications and the containerized network functions that come along with that. So this next piece is saying, we're bringing what we run as SaaS into your data center, so then you can run it. 'Cause there are many people out there that are saying, we want these capabilities and we want everything that the Platform9 control plane brings and simplifies. But unfortunately, regulatory compliance reasons means that we can't leverage SaaS. So they might be using a cloud, but they're saying that's still our infrastructure. We're still closed that network down, or they're still on-prem. So they're two big priorities for us this year. And that on-premise experiences is paramount, even to the point that we will be delivering a way that when you run an on-premise, you can still say, wait a second, well I can send outbound alerts to Platform9. So their support team can still be proactively helping me as much as they could, even though I'm running Platform9s control plane. So it's sort of giving that blend of two experiences. They're big, they're big priorities. And the third pillar is all around virtualization. It's saying if you have economic pressures, then I think it's important to look at what you're spending today and realistically say, can that be reduced? And I think hypervisors and virtualization is something that should be looked at, because if you can actually reduce that spend, you can bring in some modernization at the same time. Let's take some of those nos that exist that are two years into their five year hardware life cycle. Let's turn that into a Cloud Native environment, which is enabling your modernization in place. It's giving your engineers and application developers the new toys, the new experiences, and then you can start running some of those virtualized workloads with KubeVirt, there. So you're reducing cost and you're modernizing at the same time with your existing infrastructure. >> You know Chris, the topic of this content series that we're doing with you guys is finding the right path, trusting the right path to Cloud Native. What does that mean? I mean, if you had to kind of summarize that phrase, trusting the right path to Cloud Native, what does that mean? It mean in terms of architecture, is it deployment? Is it operations? What's the underlying main theme of that quote? What's the, what's? How would you talk to a customer and say, what does that mean if someone said, "Hey, what does that right path mean?" >> I think the right path means focusing on what you should be focusing on. I know I've said it a hundred times, but if your entire operations team is trying to figure out the nuts and bolts of Kubernetes and getting three months into a journey and discovering, ah, I need Metrics Server to make something function. I want to use Horizontal Pod Autoscaler or Vertical Pod Autoscaler and I need this other thing, now I need to manage that. That's not the right path. That's literally learning what other people have been learning for the last five, seven years that have been focused on Kubernetes solely. So the why- >> There's been a lot of grind. People have been grinding it out. I mean, that's what you're talking about here. They've been standing up the, when Kubernetes started, it was all the promise. >> Chris: Yep. >> And essentially manually kind of getting in in the weeds and configuring it. Now it's matured up. They want stability. >> Chris: Yeah. >> Not everyone can get down and dirty with Kubernetes. It's not something that people want to generally do unless you're totally into it, right? Like I mean, I mean ops teams, I mean, yeah. You know what I mean? It's not like it's heavy lifting. Yeah, it's important. Just got to get it going. >> Yeah, I mean if you're deploying with Platform9, your Ops teams can tinker to their hearts content. We're completely compliant upstream Kubernetes. You can go and change an API server flag, let's go and mess with the scheduler, because we want to. You can still do that, but don't, don't have your team investing in all this time to figure it out. It's been figured out. >> John: Got it. >> Get them focused on enabling velocity for your business. >> So it's not build, but run. >> Chris: Correct? >> Or run Kubernetes, not necessarily figure out how to kind of get it all, consume it out. >> You know we've talked to a lot of customers out there that are saying, "I want to be able to deliver a service to my users." Our response is, "Cool, let us run it. You consume it, therefore deliver it." And we're solving that in one hit versus figuring out how to first run it, then operate it, then turn that into a consumable service. >> So the alternative Platform9 is what? They got to do it themselves or use the Cloud or what's the, what's the alternative for the customer for not using Platform9? Hiring more people to kind of work on it? What's the? >> People, building that kind of PaaS experience? Something that I've been very passionate about for the past year is looking at that world of sort of GitOps and what that means. And if you go out there and you sort of start asking the question what's happening? Just generally with Kubernetes as well and GitOps in that scope, then you'll hear some people saying, well, I'm making it PaaS, because Kubernetes is too complicated for my developers and we need to give them something. There's some great material out there from the likes of Intuit and Adobe where for two big contributors to Argo and the Argo projects, they almost have, well they do have, different experiences. One is saying, we went down the PaaS route and it failed. The other one is saying, well we've built a really stable PaaS and it's working. What are they trying to do? They're trying to deliver an outcome to make it easy to use and consume Kubernetes. So you could go out there and say, hey, I'm going to build a Kubernetes cluster. Sounds like Argo CD is a great way to expose that to my developers so they can use Kubernetes without having to use Kubernetes and start automating things. That is an approach, but you're going to be going completely open source and you're going to have to bring in all the individual components, or you could just lay that, lay it down, and consume it as a service and not have to- >> And mentioned to it. They were the ones who kind of brought that into the open. >> They did. Inuit is the primary contributor to the Argo set of products. >> How has that been received in the market? I mean, they had the event at the Computer History Museum last fall. What's the momentum there? What's the big takeaway from that project? >> Growth. To me, growth. I mean go and track the stars on that one. It's just, it's growth. It's unlocking machine learning. Argo workflows can do more than just make things happen. Argo CD I think the approach they're taking is, hey let's make this simple to use, which I think can be lost. And I think credit where credit's due, they're really pushing to bring in a lot of capabilities to make it easier to work with applications and microservices on Kubernetes. It's not just that, hey, here's a GitOps tool. It can take something from a Git repo and deploy it and maybe prioritize it and help you scale your operations from that perspective. It's taking a step back and saying, well how did we get to production in the first place? And what can be done down there to help as well? I think it's growth expansion of features. They had a huge release just come out in, I think it was 2.6, that brought in things that as a product manager that I don't often look at like really deep technical things and say wow, that's powerful. But they have, they've got some great features in that release that really do solve real problems. >> And as the product, as the product person, who's the target buyer for you? Who's the customer? Who's making that? And you got decision maker, influencer, and recommender. Take us through the customer persona for you guys. >> So that Platform Ops, DevOps space, right, the people that need to be delivering Containers as a service out to their organization. But then it's also important to say, well who else are our primary users? And that's developers, engineers, right? They shouldn't have to say, oh well I have access to a Kubernetes cluster. Do I have to use kubectl or do I need to go find some other tool? No, they can just log to Platform9. It's integrated with your enterprise id. >> They're the end customer at the end of the day, they're the user. >> Yeah, yeah. They can log in. And they can see the clusters you've given them access to as a Platform Ops Administrator. >> So job well done for you guys. And your mind is the developers are moving 'em fast, coding and happy. >> Chris: Yeah, yeah. >> And and from a customer standpoint, you reduce the maintenance cost, because you keep the Ops smoother, so you got efficiency and maintenance costs kind of reduced or is that kind of the benefits? >> Yeah, yep, yeah. And at two o'clock in the morning when things go inevitably wrong, they're not there by themselves, and we're proactively working with them. >> And that's the uptime issue. >> That is the uptime issue. And Cloud doesn't solve that, right? Everyone experienced that Clouds can go down, entire regions can go offline. That's happened to all Cloud providers. And what do you do then? Kubernetes isn't your recovery plan. It's part of it, right, but it's that piece. >> You know Chris, to wrap up this interview, I will say that "theCUBE" is 12 years old now. We've been to OpenStack early days. We had you guys on when we were covering OpenStack and now Cloud has just been booming. You got AI around the corner, AI Ops, now you got all this new data infrastructure, it's just amazing Cloud growth, Cloud Native, Security Native, Cloud Native, Data Native, AI Native. It's going to be all, this is the new app environment, but there's also existing infrastructure. So going back to OpenStack, rolling our own cloud, building your own cloud, building infrastructure cloud, in a cloud way, is what the pioneers have done. I mean this is what we're at. Now we're at this scale next level, abstracted away and make it operational. It seems to be the key focus. We look at CNCF at KubeCon and what they're doing with the cloud SecurityCon, it's all about operations. >> Chris: Yep, right. >> Ops and you know, that's going to sound counterintuitive 'cause it's a developer open source environment, but you're starting to see that Ops focus in a good way. >> Chris: Yeah, yeah, yeah. >> Infrastructure as code way. >> Chris: Yep. >> What's your reaction to that? How would you summarize where we are in the industry relative to, am I getting, am I getting it right there? Is that the right view? What am I missing? What's the current state of the next level, NextGen infrastructure? >> It's a good question. When I think back to sort of late 2019, I sort of had this aha moment as I saw what really truly is delivering infrastructure as code happening at Platform9. There's an open source project Ironic, which is now also available within Kubernetes that is Metal Kubed that automates Bare Metal as code, which means you can go from an empty server, lay down your operating system, lay down Kubernetes, and you've just done everything delivered to your customer as code with a Cloud Native platform. That to me was sort of the biggest realization that I had as I was moving into this industry was, wait, it's there. This can be done. And the evolution of tooling and operations is getting to the point where that can be achieved and it's focused on by a number of different open source projects. Not just Ironic and and Metal Kubed, but that's a huge win. That is truly getting your infrastructure. >> John: That's an inflection point, really. >> Yeah. >> If you think about it, 'cause that's one of the problems. We had with the Bare Metal piece was the automation and also making it Cloud Ops, cloud operations. >> Right, yeah. I mean, one of the things that I think Ironic did really well was saying let's just treat that piece of Bare Metal like a Cloud VM or an instance. If you got a problem with it, just give the person using it or whatever's using it, a new one and reimage it. Just tell it to reimage itself and it'll just (snaps fingers) go. You can do self-service with it. In Platform9, if you log in to our SaaS Ironic, you can go and say, I want that physical server to myself, because I've got a giant workload, or let's turn it into a Kubernetes cluster. That whole thing is automated. To me that's infrastructure as code. I think one of the other important things that's happening at the same time is we're seeing GitOps, we're seeing things like Terraform. I think it's important for organizations to look at what they have and ask, am I using tools that are fit for tomorrow or am I using tools that are yesterday's tools to solve tomorrow's problems? And when especially it comes to modernizing infrastructure as code, I think that's a big piece to look at. >> Do you see Terraform as old or new? >> I see Terraform as old. It's a fantastic tool, capable of many great things and it can work with basically every single provider out there on the planet. It is able to do things. Is it best fit to run in a GitOps methodology? I don't think it is quite at that point. In fact, if you went and looked at Flux, Flux has ways that make Terraform GitOps compliant, which is absolutely fantastic. It's using two tools, the best of breeds, which is solving that tomorrow problem with tomorrow solutions. >> Is the new solutions old versus new. I like this old way, new way. I mean, Terraform is not that old and it's been around for about eight years or so, whatever. But HashiCorp is doing a great job with that. I mean, so okay with Terraform, what's the new address? Is it more complex environments? Because Terraform made sense when you had basic DevOps, but now it sounds like there's a whole another level of complexity. >> I got to say. >> New tools. >> That kind of amalgamation of that application into infrastructure. Now my app team is paying way more attention to that manifest file, which is what GitOps is trying to solve. Let's templatize things. Let's version control our manifest, be it helm, customize, or just a straight up Kubernetes manifest file, plain and boring. Let's get that version controlled. Let's make sure that we know what is there, why it was changed. Let's get some auditability and things like that. And then let's get that deployment all automated. So that's predicated on the cluster existing. Well why can't we do the same thing with the cluster, the inception problem. So even if you're in public cloud, the question is like, well what's calling that API to call that thing to happen? Where is that file living? How well can I manage that in a large team? Oh my God, something just changed. Who changed it? Where is that file? And I think that's one of big, the big pieces to be sold. >> Yeah, and you talk about Edge too and on-premises. I think one of the things I'm observing and certainly when DevOps was rocking and rolling and infrastructures code was like the real push, it was pretty much the public cloud, right? >> Chris: Yep. >> And you did Cloud Native and you had stuff on-premises. Yeah you did some lifting and shifting in the cloud, but the cool stuff was going in the public cloud and you ran DevOps. Okay, now you got on-premise cloud operation and Edge. Is that the new DevOps? I mean 'cause what you're kind of getting at with old new, old new Terraform example is an interesting point, because you're pointing out potentially that that was good DevOps back in the day or it still is. >> Chris: It is, I was going to say. >> But depending on how you define what DevOps is. So if you say, I got the new DevOps with public on-premise and Edge, that's just not all public cloud, that's essentially distributed Cloud Native. >> Correct. Is that the new DevOps in your mind or is that? How would you, or is that oversimplifying it? >> Or is that that term where everyone's saying Platform Ops, right? Has it shifted? >> Well you bring up a good point about Terraform. I mean Terraform is well proven. People love it. It's got great use cases and now there seems to be new things happening. We call things like super cloud emerging, which is multicloud and abstraction layers. So you're starting to see stuff being abstracted away for the benefits of moving to the next level, so teams don't get stuck doing the same old thing. They can move on. Like what you guys are doing with Platform9 is providing a service so that teams don't have to do it. >> Correct, yeah. >> That makes a lot of sense, So you just, now it's running and then they move on to the next thing. >> Chris: Yeah, right. >> So what is that next thing? >> I think Edge is a big part of that next thing. The propensity for someone to put up with a delay, I think it's gone. For some reason, we've all become fairly short-tempered, Short fused. You know, I click the button, it should happen now, type people. And for better or worse, hopefully it gets better and we all become a bit more patient. But how do I get more effective and efficient at delivering that to that really demanding- >> I think you bring up a great point. I mean, it's not just people are getting short-tempered. I think it's more of applications are being deployed faster, security is more exposed if they don't see things quicker. You got data now infrastructure scaling up massively. So, there's a double-edged swords to scale. >> Chris: Yeah, yeah. I mean, maintenance, downtime, uptime, security. So yeah, I think there's a tension around, and one hand enthusiasm around pushing a lot of code and new apps. But is the confidence truly there? It's interesting one little, (snaps finger) supply chain software, look at Container Security for instance. >> Yeah, yeah. It's big. I mean it was codified. >> Do you agree that people, that's kind of an issue right now. >> Yeah, and it was, I mean even the supply chain has been codified by the US federal government saying there's things we need to improve. We don't want to see software being a point of vulnerability, and software includes that whole process of getting it to a running point. >> It's funny you mentioned remote and one of the thing things that you're passionate about, certainly Edge has to be remote. You don't want to roll a truck or labor at the Edge. But I was doing a conversation with, at Rebars last year about space. It's hard to do brake fix on space. It's hard to do a, to roll a someone to configure satellite, right? Right? >> Chris: Yeah. >> So Kubernetes is in space. We're seeing a lot of Cloud Native stuff in apps, in space, so just an example. This highlights the fact that it's got to be automated. Is there a machine learning AI angle with all this ChatGPT talk going on? You see all the AI going the next level. Some pretty cool stuff and it's only, I know it's the beginning, but I've heard people using some of the new machine learning, large language models, large foundational models in areas I've never heard of. Machine learning and data centers, machine learning and configuration management, a lot of different ways. How do you see as the product person, you incorporating the AI piece into the products for Platform9? >> I think that's a lot about looking at the telemetry and the information that we get back and to use one of those like old idle terms, that continuous improvement loop to feed it back in. And I think that's really where machine learning to start with comes into effect. As we run across all these customers, our system that helps at two o'clock in the morning has that telemetry, it's got that data. We can see what's changing and what's happening. So it's writing the right algorithms, creating the right machine learning to- >> So training will work for you guys. You have enough data and the telemetry to do get that training data. >> Yeah, obviously there's a lot of investment required to get there, but that is something that ultimately that could be achieved with what we see in operating people's environments. >> Great. Chris, great to have you here in the studio. Going wide ranging conversation on Kubernetes and Platform9. I guess my final question would be how do you look at the next five years out there? Because you got to run the product management, you got to have that 20 mile steer, you got to look at the customers, you got to look at what's going on in the engineering and you got to kind of have that arc. This is the right path kind of view. What's the five year arc look like for you guys? How do you see this playing out? 'Cause KubeCon is coming up and we're you seeing Kubernetes kind of break away with security? They had, they didn't call it KubeCon Security, they call it CloudNativeSecurityCon, they just had in Seattle inaugural events seemed to go well. So security is kind of breaking out and you got Kubernetes. It's getting bigger. Certainly not going away, but what's your five year arc of of how Platform9 and Kubernetes and Ops evolve? >> It's to stay on that theme, it's focusing on what is most important to our users and getting them to a point where they can just consume it, so they're not having to operate it. So it's finding those big items and bringing that into our platform. It's something that's consumable, that's just taken care of, that's tested with each release. So it's simplifying operations more and more. We've always said freedom in cloud computing. Well we started on, we started on OpenStack and made that simple. Stable, easy, you just have it, it works. We're doing that with Kubernetes. We're expanding out that user, right, we're saying bring your developers in, they can download their Kube conflict. They can see those Containers that are running there. They can access the events, the log files. They can log in and build a VM using KubeVirt. They're self servicing. So it's alleviating pressures off of the Ops team, removing the help desk systems that people still seem to rely on. So it's like what comes into that field that is the next biggest issue? Is it things like CI/CD? Is it simplifying GitOps? Is it bringing in security capabilities to talk to that? Or is that a piece that is a best of breed? Is there a reason that it's been spun out to its own conference? Is this something that deserves a focus that should be a specialized capability instead of tooling and vendors that we work with, that we partner with, that could be brought in as a service. I think it's looking at those trends and making sure that what we bring in has the biggest impact to our users. >> That's awesome. Thanks for coming in. I'll give you the last word. Put a plug in for Platform9 for the people who are watching. What should they know about Platform9 that they might not know about it or what should? When should they call you guys and when should they engage? Take a take a minute to give the plug. >> The plug. I think it's, if your operations team is focused on building Kubernetes, stop. That shouldn't be the cloud. That shouldn't be in the Edge, that shouldn't be at the data center. They should be consuming it. If your engineering teams are all trying different ways and doing different things to use and consume Cloud Native services and Kubernetes, they shouldn't be. You want consistency. That's how you get economies of scale. Provide them with a simple platform that's integrated with all of your enterprise identity where they can just start consuming instead of having to solve these problems themselves. It's those, it's those two personas, right? Where the problems manifest. What are my operations teams doing, and are they delivering to my company or are they building infrastructure again? And are my engineers sprinting or crawling? 'Cause if they're not sprinting, you should be asked the question, do I have the right Cloud Native tooling in my environment and how can I get them back? >> I think it's developer productivity, uptime, security are the tell signs. You get that done. That's the goal of what you guys are doing, your mission. >> Chris: Yep. >> Great to have you on, Chris. Thanks for coming on. Appreciate it. >> Chris: Thanks very much. 0 Okay, this is "theCUBE" here, finding the right path to Cloud Native. I'm John Furrier, host of "theCUBE." Thanks for watching. (upbeat music)

>>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.

>>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.

>>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]

>> Welcome back to theCUBE's covering of AWS Public Sector Summit. In-person here in Washington, DC. I'm John Furrier, your host, great to be back face to face. We've got a great, special guest Clint Crosier, who is the Director of AWS' Aerospace & Satellite. Major General of The Air Force/Space Force. Retired. Great to see you in person again. Thanks for coming on theCUBE. >> Thank you for having me. I appreciate that. >> First of all, props to you for doing a great job at Amazon, bringing all your knowledge from Space Force and Air Force into the cloud. >> Thank you. >> So that's great, historical context. >> It's been valuable and it's provided a whole lot of insight into what we're building with the AWS space team, for sure. >> So number one question I get a lot is: We want more space content. What's the coolest thing going on in space? Is there a really a satellite behind the moon there, hidden there somewhere? What's the coolest thing going on in space? >> Well, the coolest thing that's going on in space, I think is you're seeing the rapid growth of the space industry, I mean, to me. I've been in the space industry for 34 years now, and there have been periods where we projected lots of growth and activity and it just didn't really come about, especially in the 80's and the 90's. But what we're seeing today is that growth is taking place. Whether it's the numbers of satellites that are being launched around the globe every year, there's some 3,000 objects on orbit today. Estimates are that there'll be 30,000 objects at the end of the decade, or the number of new companies, or the number of global spinning. It is just happening right now, and it's really exciting. >> So, when people say or hear space, there's a lot of economic changes in terms of the cost structures of how to get things deployed into space. That brings up the question of: Is space an opportunity? Is it a threat vector? What about congestion and security? >> Yeah, well three great things, absolutely an opportunity. We're seeing the rapid growth of the space industry, and we're seeing more commercialization than ever before. In my whole career, The Air Force or, NASA, or the NRO would sort of, hold things and do them themselves Today, you're seeing commercial contracts going out from the National Reconnaissance Office, NASA, from The Air Force, from the Space Force. So lots of opportunity for commercial companies. Security. Absolutely, priority number one should be security is baked into everything we do at AWS. And our customers, our Government classified customers tell us the reason they came to AWS is our security is top notch and certified for all their workloads. And as you well know, we have from unclassified all the way up to top secret capabilities on the AWS cloud. So just powerful opportunities for our customers. >> Yeah. And a lot of competitors will throw foot on that. I know, I've reported on some of that and not a lot of people have that same credential. >> Sure. >> Compared to the competition. >> Sure. >> Now I have to ask you, now that you have the top secret, all these clouds that are very tailorable, flexible with space: How are you helping customers with this Aerospace Division? Is it is a commercial? In the public sector together? What's the... >> All of the above. >> Take us through the value proposition. >> Yeah, happy to do this. So what we recognized over the last two years or so we, at AWS, recognized all this rapid growth that we're talking about within the space industry. Every sector from launch to on-orbit activities, to space exploration, all of it. And so AWS saw that and we looked at ourselves and said: "Do we have the right organization and expertise in place really to help our customers lean into that?" And the answer was: we decided to build a team that had deep experience in space, and that was the team that we grew because our thesis was: If you have a deep experience in space, a deep experience in cloud, you bring those two together and it's a powerful contribution. And so we've assembled a team with more than 500 years of collective hands-on experience, flying satellites, launching rockets. And when we sit down with our customers to innovate on their behalf, we're able to come up with some incredible solutions and I'm happy to talk about those. >> I'd love to, but tell you what, first of all, there's a lot of space nerds out there. I love space. I love space geeking out on the technology, but take us through the year you had, you've had a pretty incredible year with some results. You have that brain trust there. I know you're hiring. I know that people want to work for you. I'm sure the resumes are flying in, a lot of action. >> There is. >> What are the highlights from this year? >> So the highlights I think is, we've built a team that the industry is telling us was needed. Again, there was no organization that really served the space cloud industry. And so we're kind of building this industry within the industry, the space cloud industry. And so number one, just establishing that team and leaning into that industry has been valuable. The other thing that we're real proud of is we built a global team, because space is a global enterprise. We have teams in Europe and in Asia and South America here in the U.S., so we built a global team. One of the things that we did right up front, we weren't even six months old, when we envisioned the idea of doing the AWS Space Accelerator. And some of the folks told me: "Clint, six months under your belt, maybe you ought to get your feet under you." And I said: "No, no. We move fast to support our customers." And so we made a call for any space startup that wanted to come on board with AWS and go through our four week Space Accelerator. We partnered with Sarah from Capital. And the idea was: if you're a small company that wants to grow and build and learn how you can use the cloud to gain competitive advantage, come with us. And so John, I would have been happy if we had 50 companies applied, we had 194 companies across 44 countries that applied to our accelerator. We had to down select a 10, but that was a tremendous accomplishment, two of those are speaking this afternoon, where they met each other at our accelerator and now have formed a partnership: Ursa Space and HawkEye 360 on how they build on the cloud together. Fascinating. >> Well, I love that story. First of all, I love the military mindset. No, we're not going to wait. >> Move it out. >> It's not take that hill, it's take that planet. >> Our customers won't wait, innovation, doesn't wait, the future doesn't wait. We have to move out. >> So, this brings up the entrepreneurship angle. We got there a little early, but I want to talk about it because it's super important. There's an entrepreneurial culture happening right now in the space community >> There is. At large, and it's getting bigger and wider. >> Bigger every day. >> What is that? What if someone says: "Hey, what's going on with entrepreneurship in this space? What are the key dynamics? What's the power dynamics?" It's not money, there's money out there, but like what's the structural thing happening? >> The key dynamic, I think, is we're seeing that we can unlock things that we could never do before. And one of our goals is: the more space data we can make more accessible to more people around the world. It unlocks things we couldn't do. We're working with space companies who are using space data to track endangered whales off the coast of California. We're working with companies that are using space data to measure thermal and greenhouse emissions for climate change and climate management. We're working with one company, Edgybees, who has a small satellite constellation, and they're using it to build satellite based, augmented reality, to provide it to first responders as they go into a disaster response area. And they get a 3D-view of what they're going into. None of those workloads were possible five years ago. And the cloud and cloud-based technologies are really what opens those kinds of workloads up. >> What kind of higher level services do you see emerging from space cloud? Because you know, obviously you have to have some infrastructure. >> Absolutely. Got to put some stuff into space. That's a supply chain, reliability, also threat. I mean, I can have a satellite attack, another satellite, or I'm just making that up, but I'm sure there's other scenarios that the generals are thinking about. >> So space security and cyberspace security is critical. And as I said, it's built into everything we do in all of our platforms, so you're absolutely right about that, but when we think about the entrepreneurship, you know, what we're seeing is, and I'll give you a good example of why the industry is growing so fast and why cloud. So one company we work with, LeoLabs. So Leo identified the growth in the LEO: Low Earth Orbit segment. 3,000 objects on orbit today, 30,000 tomorrow. Who's going to do the space traffic management for 30,000 objects in space that are all in the same orbital regime? And so LeoLabs built a process to do space traffic management, collision avoidance. They were running it on premises. It took them eight hours to do a single run for a single satellite conjunction. We got them to help understand how to use the cloud. They moved all that to AWS. Now that same run they do in 10 seconds. Eight hours to 10 seconds. Those are the kind of workloads as space proliferates in and we grow, that we just can't execute without cloud and cloud-based technologies. >> It's interesting, you know, the cloud has that same kind of line: move your workloads to the cloud and then refactor. >> Yeah. So space workloads are coming to the cloud. >> They are. >> Just changing the culture. So I have to ask you, I know there's a lot of young people out there looking for careers and interests. I mean, Cal poly is going into the high school now offering classes. >> Yeah So high school, there's so much interest in space and technology. What is the cultural mindset to be successful? Andy Jassy last year, reading and talk about the mindset of the builder and the enterprise CXO: "Get off your butt and start building" There's a space ethos going on. What is the mindset? Would you share your view on it? >> The mindset is innovation and moving fast, right? We, we lived, most of my career, in the time where we had an unlimited amount of money and unlimited amount of time. And so we were really slow and deliberate about how we built things. The future won't wait, whether it's commercial application, or military application, we have to move fast. And so the culture is: the faster we can move, The more we'll succeed, and there's no way to move faster than when you're building on the AWS cloud. Ground station is a good example. You know, the proposition of the cloud is: Don't invest your limited resources in your own infrastructure that doesn't differentiate your capability. And so we did that same thing with ground station. And we've said to companies: "Don't spend millions of dollars on developing your own ground station infrastructure, pay by the minute to use AWS's and focus your limited resources back in your product, which differentiate your space mission." and that's just been power. >> How is that going from customer perspective? >> Great. It's going great. We continue to grow. We added another location recently. And just in the last week we announced a licensed accelerator. One of the things our customers told us is it takes too long to work with global governments to get licensed, to operate around the world. And we know that's been the case. So we put together a team that leaned in to solve that problem, and we just announced the licensed accelerator, where we will work with companies to walk them through that process, and we can shave an 18 month process into a three or four month process. And that's been... we've gotten great response on that from our company. >> I've always said: >> I remember when you were hired and the whole space thing was happening. I remember saying to myself: "Man, if democratization can bring, come to space" >> And we're seeing that happening >> You guys started it and you guys, props to your team. >> Making space available to more and more people, and they'll dazzle us with the innovative ways we use space. 10 years ago, we couldn't have envisioned those things I told you about earlier. Now, we're opening up all sorts of workloads and John, real quick, one of the reasons is, in the past, you had to have a specific forte or expertise in working with space data, 'cause it was so unique and formatted and in pipeline systems. We're making that democratized. So it's just like any other data, like apps on your phone. If you can build apps for your phone and manage data, we want to make it that easy to operate with space data, and that's going to change the way the industry operates. >> And that's fundamentally, that's great innovation because you're enabling that. That's why I have to ask you on that note Of the innovation trends that you see or activities: What excites you the most? >> So a lot of things, but I'll give you two examples very quickly: One is high-performance compute. We're seeing more and more companies really lean in to understanding how fast they can go on AWS. I told you about LeoLabs, eight hours to 10 seconds. But that high-performance computes going to be a game changer. The other thing is: oh, and real quick, I want to tell you, Descartes Labs. So Descartes Labs came to us and said: "We want to compete in the Annual Global Top 500 supercomputer challenge" And so we worked with them for a couple of weeks. We built a workload on the AWS standard platform. We came in number 40 in the globe for the Top 500 super computer lists, just by building some workloads on our standard platform. That's powerful, high-performance compute. But the second example I wanted to give you is: digital modeling, digital simulation, digital engineering. Boom Aerospace is a company, Boom, that we work with. Boom decided to build their entire supersonic commercial, supersonic aircraft, digital engineering on the AWS cloud. In the last three years, John, they've executed 6,000 years of high-performance compute in the last three years. How do you do 6,000 years in compute in three years? You spin up thousands of AWS servers simultaneously, let them do your digital management, digital analysis, digital design, bring back a million different perturbations of a wing structure and then pick the one that's best and then come back tomorrow and run it again. That's powerful. >> And that was not even possible, years ago. >> Not at that speed, no, not at that speed. And that's what it's really opening up in terms of innovation. >> So now you've done it so much in your career, okay? Now you're here with Amazon. Looking back on this past year or so, What's the learnings for you? >> The learning is, truly how valuable cloud can be to the space industry, I'll admit to you most people in the space industry and especially in the government space industry. If you ask us a year ago, two years ago: "Hey, what do you think about cloud?" We would have said: "Well, you know, I hear people talk about the cloud. There's probably some value. We should probably look at that" And I was in the same boat, but now that I've dug deeply into the cloud and understand the value of artificial intelligence, machine learning, advanced data analytics, a ground station infrastructure, all those things, I'm more excited than ever before about what the space industry can benefit from cloud computing, and so bringing that, customer by customer is just a really fulfilling way to continue to be part of the space industry. Even though I retired from government service. >> Is there a... I'm just curious because you brought it up. Is there a lot of people coming in from the old, the space industry from public sector? Are they coming into commercial? >> Absolutely. >> Commercial rising up and there's, I mean, I know there's a lot of public/private partnerships, What's the current situation? >> Yeah, lots of partnerships, but we're seeing an interesting trend. You know, it used to be that NASA led the way in science and technology, or the military led the way in science and technology, and they still do in some areas. And then the commercial industry would follow along. We're seeing that's reversed. There's so much growth in the commercial industry. So much money, venture capital being poured in and so many innovative solutions being built, for instance, on the cloud that now the commercial industry is leading technology and building new technology trends that the military and the DOD and their government are trying to take advantage of. And that's why you're seeing all these commercial contracts being led from Air Force, Space Force, NASA, and NRO. To take advantage of that commercialization. >> You like your job. >> I love my job. (laughing) -I can tell, >> I love my job. >> I mean, it is a cool job. I kind of want to work for you. >> So John, space is cool. That's our tagline: space is cool. >> Space is cool. Space equals ratings in the digital TV realm, it is really, super exciting a lot of young people are interested, I mean, robotics clubs in high schools are now varsity sports, eSports, all blend together. >> Space, robotics, artificial intelligence, machine learning, advanced analytics. It's all becoming a singular sector today and it's open to more people than ever before, for the reasons we talked about. >> Big wave and you guys are building the surf boards, everyone a ride it, congratulations. Great to see you in person. >> Thank you. Again, thanks for coming on theCUBE, appreciate that. >> Thanks for having us. >> Clint Crosier is the Director of AWS Aerospace & Satellite. Legend in the industry. Now at AWS. I'm John Furrier with theCUBE. Thanks for watching.

>> Announcer: From around the globe, it's theCUBE! With digital coverage of AnsibleFest 2020, brought to you by Red Hat. >> Hello, everyone, welcome back to theCUBE's coverage of AnsibleFest 2020. We're not face-to-face this year, we're in virtual remote mode. This is theCUBE virtual and obviously it's AnsibleFest 2020 virtual. We've got a great panel of experts and leaders at Red Hat and Ansible. I want to introduce them. Dave Lindquist, general manager and vice president of engineering of hybrid cloud management at Red Hat. Joe Fernandes, vice president and general manager of the Core Cloud platforms at Red Hat. And Tom Anderson, vice President at Red Hat, Ansible Automation Platform, the big news and feature of this event. Tom, great to see you, Joe and David, thanks for coming on. >> Great to be here. >> Every year I love talking about Red Hat because I remember going back a few years ago, Arvind from IBM was on at Red Hat Summit in San Francisco, and you can see the twinkle in his eye. This was three, four years ago. Cloud native was really gearing up and now it's kind of mainstream. Last year at AnsibleFest, all the buzz was collaboration, collections, and you can start to see that integration piece kicking in, and this year at the event, the big story is the same. More collections, more integrations, a lot of collaboration around code. Content equals code. So it really points to the trend with Kubernetes of multi-cloud, multi-cluster. So the first question for you guys is, why would anyone want to deploy multiple clusters simultaneously and why is multi-cluster such a big deal? Tom, we'll start with you. >> Great, okay, yeah. So why is multi-cluster such a big deal? Basically, Kubernetes and our OpenShift container platform have now become a strategic part of our customers' environments, of their infrastructure for building and deploying cloud native applications on. And as becoming a strategic part of that, when you're deploying production applications you're going to need all kinds of things like scale out, redundancy, cloud location for access to different cloud provider locations for application requirements and whatnot. So there are a bunch of requirements for why customers would deploy OpenShift in a multi-cluster way. And maybe I'll turn it over to Joe Fernandes a little bit 'cause he's got a lot of background on the OpenShift side of this. >> Joe, what's your thoughts? >> Yeah, thanks, Tom. Yeah, so I mean, as Tom mentions, a number of reasons why customers may deploy or need to deploy more than one Kubernetes cluster. So within a cluster, you can certainly have multiple applications, multiple developers, multiple teams work, but as you start to scale your usage you may want additional clusters. It could be because you want to separate your production environments from your dev and test environments. It could be for capacity, right? You have more development teams or more production environments than you want to sort of tie to a single cluster. Then you start expanding out into locations, right? Maybe you started in the data center, then you started doing deployments to one public cloud, then to other public clouds, and then that's only going to grow. We see more and more customers deploying multi-cloud strategies. And then the new thing right now that everybody wants to talk to us about is edge, and as you get into edge deployments, now those, the number of clusters could really explode into the hundreds or thousands. And so it all points back to you need a sane way to manage across all these clusters regardless of where they run and regardless of how many you have, and that's really what we've been working on with the Advanced Cluster Management for Kubernetes. >> What's the big draw? What's drawing the customers in with multi-cluster and multi-cloud? Obviously, the multi-cloud makes a lot of sense, you have multiple clouds. Sounds easier just saying it than doing it. But what is it about multi-cluster and multi-cloud that's drawing customers and people into this concept? >> Yes, I can start. I think what's drawing customers in is the need, the desire to have sort of a common abstraction for the applications that's consistent regardless of where they happen to run, right? So making sure that the developers don't have to worry about what infrastructure the applications are landing on, and they have that consistent experience that it's, abstracts their applications away from that infrastructure. So that gives the developers more flexibility, but it's also about flexibility and agility for those infrastructure owners, right, because they too want to make decisions on where stuff runs. Not because they're particularly tied to an infrastructure, but based on things like cost or security or other concerns. And so these are all drivers for multi-cluster and multi-cloud strategies and I think our hybrid cloud strategy at Red Hat really hits the mark to address those needs. >> Well, you guys had great performance. We've been following the past few years just the OpenShift and beyond, kind of the whole Red Hat, and Ansible specifically too, is doing real well in the marketplace so congratulations. David, I want to ask you about the management piece. This comes up over and over again. It's all good having the abstraction layer, you got all kinds of new sets of services, but multi-cluster management is not, (laughs) is not trivial. There's challenges for ops and automation teams. Could you share your perspective on how you guys are looking at the multi-cluster management? >> Sure, sure. The first thing we saw, and this kind of follows on the points that Joe and Tom are making, is that as customers start embracing the development with containers and leveraging Kubernetes, you start finding that they're putting up clusters across their data centers, across cloud, to support different parts of the life cycle of development, or supporting their own production environment or distributed workloads across clouds, across the data centers. And so the challenges that operations and management run into, and security in particular, is how do you start managing the clusters, their life cycle. It's easy to put 'em up, to provision 'em quickly, but how do you update and upgrade those? How do you make sure they're compliant with your various regulatory compliance like PCI, HIPAA, or the various federal standards? How do you make sure that compliance is adhered to across, and security across those clusters, as well as the applications themselves? How do you manage the applications through their life cycle? How do you have deployment policies? So the challenges for ops and automation and security are to have a consistent policy-driven way to take care of the clusters across these hybrid environments, and making sure they adhere to the compliance and security of the enterprise. >> Tom, multi-cluster deployments is a big part of this integration. We heard a little bit, obviously, compliance and governance is huge. IT's been living this world of policies and governance, but when we start moving fast into these new cutting edge services that are providing a lot of value, integration into existing IT infrastructure is important with clusters. How do you view that because this is where I think maybe collections are other things are, is this an indicator of what's happening? Can you give your thoughts on the customers out there who want to do multiple clusters for all the benefits, but then go, "Oh, I got to integrate it into existing IT infrastructure"? >> Yeah, absolutely. So that's what's happening right now. As Kubernetes and as OpenShift has become a strategic platform for our customers, the idea of, I'm going to say, kind of normalizing the operations of that platform as part of a greater IT ecosystem has become a challenge for them. And for the most part, they've already automated security, network, provisioning, app deployment, application updates, using the Ansible Automation Platform, and so it only makes sense that as Kubernetes and as OpenShift becomes a strategic platform for them, they want to use that same language, that same tool set, that same automation fabric, if you will, to integrate the applications that are running on OpenShift with the rest of the environment. So, for example, when I add a new node to a cluster or more capacity to a cluster or to clusters, I probably want to update my systems of record, right? My CMDBs or my ITSM systems. When I deploy a new app or make an update to an app on a cluster or across clusters, I'm probably going to want to update my load balancer to be able to direct traffic correctly to that, and that load balancer probably isn't running, my enterprise load balancer is kind of platform independent, so I'd need to be able to update that load balancer to properly direct traffic. Well, IT has already automated that function using Ansible. So by creating the collections that we have created for OpenShift and for Kubernetes, it makes it much easier for our customers to be able to just plug that in and adapt that to their existing automation infrastructure. So now it just becomes part of their overall IT environment. >> So just a follow-up real quick, if you don't mind. What are some of the challenges you're hearing from your customers around containerization and that growing space? I just talked to the IDC research analyst earlier at another virtual CUBE session where she says, roughly their estimate is 5 to 10% of enterprises are containerized, which is huge growth opportunities. The headroom in containers is massive, so what are some of the challenges? Is it easy to get started? This seems to be a nice opportunity for you guys. What's your take on that? >> Yeah, I think that the way of looking at it with all that growth space, it's also the speed at which Kubernetes adoption and containerized application adoption is happening. And so, IT organizations are having to respond faster than they ever have before as this environment grows, and it is a multi-cloud environment. They have Kubernetes, OpenShift running on-prem, in the cloud, multiple data centers, as both Joe and Dave have said, and it becomes critical that they automate that correctly and accurately to ensure security, consistency, performance, availability. All of the other things that drive the requirement for automation standardization, all of those things that drive the requirements for automation are applicable to Kubernetes environments and containerized environments as well except they're moving and expanding faster, so teams have to respond quicker to the need. >> Joe, what's your take on this? I mean, to me, I'm the glass half full. I think I've seen containers be great and that maybe I'm looking at the early adopters, but those numbers seem a little bit low to me. What does that mean to you? More people are now getting up to speed. Is it a tipping point? It just seems a little bit low, and David, if you want to comment too, I think this an important number there. Joe, what's your take? >> Yeah, I mean, I think the rate represents an opportunity, but I see the growth as having been tremendous even in just the first few years. But to get to that broader market we did continue making it easier for customers to bring their applications to this new environment, to ride on existing infrastructure, and ultimately for our customers that means an evolution, right? An evolution of how they are going to manage those applications, how they're going to build and deploy them. And so with the integration of OpenShift and our advanced container management platforms with Ansible we can bring that automation to the mix to sort of tie those together, right? So to tie in the existing compute infrastructure, to tie in storage and networking and configure those as needed. And then as Tom mentioned, all those other systems, whether it's an IT service management system, something like a ServiceNow or other ticketing systems or other enterprise systems that exist that you just can't ignore. Because the more you try to go against the grain and do something different, the even harder it'll be. So we need to help customers evolve to take advantage of cloud and cloud native approaches, and the solutions that we're bringing to market are all about enterprise Kubernetes, enterprise container platforms. The combination of those technologies with something like Ansible really helps pave the path for the next phase of growth that we're expecting. >> So, ready for prime time right now. >> Right. >> David, your thoughts real quick on this. Containerization upside. >> Yeah, real quick, the development organizations, development teams, have picked up on containers very rapidly. Everybody is leveraging containers when they develop new applications or modernize the existing applications. So what we found is that a lot of the folks that pushed out very quickly, some greenfield apps, that's the 5, 10, 15, 20% that you're seeing occur. What started getting complex is how you really scale this to your enterprise. How do you really run this at scale from management operations and security perspective? OpenShift is critical, that gives a consistent platform across the hybrid cloud environments. What we're doing with ACM and the Advanced Cluster Management brings in the security and compliance. And what you'll see through AnsibleFest, what we're doing with Ansible is then, how do we then hook these environments right into all the existing IT environments? That's, to me, what's critical to really bring this to scale to the enterprise. >> Yeah, I think this, to me, the number points to exactly what you guys said. Ready for prime time, scale's there, and the demand's there. And I think, Tom and Joe, I want to ask you specifically the relationship between OpenShift and Ansible, but before that, I remember, forget what year it was, we were doing a CUBE event at, I think it might've been OpenStack, going back to the day, but I remember OpenShift and it was a moment where OpenShift adopted containers and then next year Kubernetes. And I remember talking to the team, them saying, "This is going to be a big bet for OpenShift." Looks like it was a good bet. (laughs) It paid out real well, congratulations. And it was good, you guys stayed the course. But you made it easier, and one of the things was is that the complaint at the time was they didn't want Kubernetes to be the next Hadoop. Easy to use but gets out of control. Not that I meant they're comparable, but Hadoop had that problem of it was easy open source but then it was hard to manage. So OpenShift really took advantage of that. You guys, I think, did a good job on that. But now you got Ansible winning the game on developers, on easy to deploy, so as that scales up, automation's there. So I'd like to hear you guys talk about the connection between OpenShift and Ansible and how that expands the scope of what both products can do for customers. >> Yeah, maybe I'll give it a shot first and then let Joe go after me, which is, look, here's what we have, is we have lots and lots and lots of customers, Red Hat customers that are OpenShift users and that are Ansible users, right? So we have this two large pools. They also represent two very large and vibrant open source community projects. The Ansible project and the Kubernetes project are two hugely popular, vibrant communities, and so it just made sense to kind of be a catalyst in those communities, to bring those two things together, to work together, to the benefit of our customers and to kind of capture the innovation that's going on upstream in the communities. So we decided to get really kind of serious about the integration of these two platforms and integrated Ansible in a native way on Kubernetes so that OpenShift and Kubernetes operators, as well as application developers, could take advantage of that integration without having to learn something new or foreign in order to be able to do it. So it was a native integration using operators, which is the right way to integrate with the Kubernetes platform, with OpenShift in particular. And so that's the way we kind of brought it together to the benefit of our customers. Our customers are, like I said, normalizing the operations of OpenShift as a strategic part of their infrastructure, deploying production applications, and want to be able to tie that into their other systems and other parts of their infrastructure, both from an app deployment process as well as from an infrastructure deployment and management process. So it only made sense that it actually, our customers have been asking us for this and talking to us about this, so it only kind of made perfect sense to kind of get out there and do that, get the communities together innovating, and then take that innovation out for our customer. >> Joe. >> Yeah, the only thing I'd add to that, there's really two specific personas at play here, right? When you think of, there's the IT operations and infrastructure teams. They own those clusters, the provisioning, the configuration, the management of those clusters. And with ACM, with Advanced Cluster Management for Kubernetes, we have now an interface that they can use to see and manage the life cycle of all their clusters. So through that we can integrate Ansible as another automation tool in their portfolio to do things that need to happen when those clusters first get configured or when those clusters get updated and so forth. So if they need to update an ITSM system or configure a network or do whatever it needs to, you have Ansible automation scripts that can be plugged in at the appropriate time in that cluster's life cycle to do that. On the other side, you have the developer and DevOps teams that are consumers of these platforms, right? And what they care about is the applications that they're building, but there's a lot that goes into building it, right? There's the source code management systems, there's the CI systems, the CD systems, there's the test environments and stage and prod. And so there's a lot of moving parts, and again, and then there's the services themselves that they're configuring so you have, or building, not configuring, you have Ansible again ready to sort of take on some of those tasks, automation tasks that go beyond what Kubernetes is focused on or what you're trying to do with OpenShift. And again, doing it at the appropriate time in the life cycle, all tied in through Advanced Cluster Management which can actually see out to all those clusters and be in that sort of application deployment workflow across those clusters. So those are sort of some of the specific areas and how they pertain to those specific personas that are driving the activity. >> What's interesting, this automation piece really is key across multiple environments, and we've heard that from some of your customers. 'Cause you got now private clouds out there, you got large scale. But, Dave, I want to ask you, what makes Advanced Cluster Management a natural fit with OpenShift and Ansible? What's your take? >> Yeah, good question, John. First, ACM is purpose-built for the Kubernetes environment. It's a cloud native management system, and as we said earlier, we really focused on managing the cluster life cycles, managing the security compliance, and managing applications deployed into these environments. So it was a very natural extension of OpenShift, to be able to manage OpenShift, multiple clusters of OpenShift in hybrid environments. Within your data center, across data centers, across clouds, and the combination. So, very natural fit with OpenShift. As we've been all talking about, as we looked at how did we then bring OpenShift and these resources closer through automation to many of the other parts of your IT environment, that made it natural from ACM to call out into the playbooks of Ansible. So just a simple example, and I think we circled around this a few times. You're deploying a cluster or you're deploying, say, an application to that cluster. You need to configure that into a firewall. Maybe configure it into a load balancer. Maybe register it with a service management system. That, all those calls, they come out through policy from ACM over into Ansible to take advantage of the wealth of playbooks that are available in Ansible to perform those operations. Whether it's security, network, service management, storage, et cetera. >> Real quick follow-up for you is, how has bringing your ACM team and product into Red Hat changed the scope and approach of what you're trying to do? >> Yeah, well, let me say first of all it's been a great experience bringing the team into Red Hat. The environment, the open culture, it's really been invigorating for the whole team. Also, getting much, much closer into the open communities and open sourcing ACM and doing development in the open has really brought us closer really to users, the ecosystem, the communities, accelerating our delivery quality, as well as really getting much more closer insights, getting insights into what's happening in the community, what's happening with the users. So it's really, it's been a great experience all the way around. >> Joe and Tom, quick comment, what do you think people should pay attention to this year at AnsibleFest 2020? What's the big story? Obviously we're in a pandemic. We're going to come out of the pandemic. People want to have a growth strategy that has the right projects on the right rails. They want to either maybe downplay some of the projects that maybe not be a fit, that were exposed during the pandemic. Best practices that are emerging, shifting left for security is one. You're seeing remote workers. People have kind of had a wake-up call on cloud native being relevant for the modern app. Now they're running as fast as they can to build the infrastructure, and guess what? People are not actually in the workplaces. The workforce, the workplace has all changed. Can you guys share your expertise over the years on what is the best practice and approach to take? Because the clock's ticking. >> Yeah, from my perspective and from an Ansible perspective here, we had always been about kind of automate everything, right? Automate every task that is automatable, right? A repeatable task, automate it. Repeatable task, automate it. And over the past couple of years we've really been focused on automation across teams by using Ansible content, the actual automation code, if you will, itself to bring teams together and to cross teams and cross functions. So not just focused on what a network operations person or a network engineer needs to do in their day-to-day job, but connect that to what a security operations person is doing day-to-day in their job in terms of threat detection and intrusion response, or intrusion detection and threat response, and connecting those two teams together via automation to make both of them more responsive and more effective. So we've been on this bandwagon for the past couple of years around Ansible content, and now Ansible collections and Automation Hub, to try and accelerate the way these teams can collaborate together. The pandemic and the pressures that put on the system with remote users and having to do things in a different way only exacerbated, it only kind of enhanced the requirement for that collaboration, that automation across teams. So in a lot of ways, the past six, seven months, both for our Ansible business as well as for the way our customers have been using the technology, has really been an accelerator for that kind of cross-team collaboration, our subscription business, and our Ansible consumption. >> Yeah, well, I said it last year in-person when we were in Atlanta for AnsibleFest 2019, a platform approach is a great way to go. You start out as a tool, you become a platform. You guys are doin' the work over there. I really appreciate it and I want to call that out 'cause I think it's worth calling out. Joe, cloud platforms. Cloud is certainly an enabler. Red Hat and OpenShift has been a great success and can, only has got more work to do. People still got to build out these platforms, and you're seeing private cloud not going away. I mean, we just had a conversation at OpenStack and you guys got customers with a lot of private cloud everywhere. (laughs) So you got private, you got hybrid, you got multi, and you got public. It's pretty crazy. What's your thoughts on what people should take away from AnsibleFest and then going forward post-pandemic? >> Yeah, so, first Tom hit on a number of key points there, right? COVID-19 and everything going on in the world has really just accelerated a lot of these transformations that were already in the works at many of our enterprise customer accounts, right? And now when we're all working remotely, we're all meeting virtually, we're educating our children remotely, it just exacerbates the need to scale our networks, to extend security out to remote workforces, and to do all of these things at much larger scales than we ever envisioned before, and you can't do that without automation. And I would argue, without taking advantage of some of these modern cloud native platforms and cloud native development approaches. And we always say Red Hat's been a big proponent of hybrid cloud, of our open hybrid cloud strategy. We've been talking about that for years, and what we always say is even if that's a strategy that you aren't specifically looking for, it's something that, everybody ends up there, right? Because nobody's running everything in the data center anymore, but as they move out to public cloud they're not completely shutting those data centers off either. As they expand their consumption of public cloud, they tend to start exploring multi-cloud strategies, and now that hybrid cloud is extending out to the edge. So the hybrid cloud is sort of where everybody is, right? And the ability to sort of manage consistently, to run consistently across all those environments, to be able to secure all those environments and scale those environments, and that's what we're all about here at Red Hat and that's sort of the key to our open hybrid cloud strategy and what we're really trying to do with our entire portfolio. >> Awesome, David, final word. We're in a systems world now. The cloud is one big distributed computer. We got the edge, we heard that. Developers just want to code, they want infrastructure as code, you guys got to help 'em get there. What's your take on the importance of AnsibleFest and this systems world we live in? >> Well, there's probably not a more critical time. We've all been saying this and seeing this the last 10 months now. The transformation digitally that's been going on for years, the development transformations, it's all hit a fever pitch. It's been accelerated through COVID. In particular, how quickly can I adjust to a digital transformation? How quickly can I adjust my business processes? How quickly can I really become a very agile DevOps SRE organization? That is so critical. So at AnsibleFest what we're doing is bringing together platforms with automation with the ability to manage it at scale with security. That's what's going on from Red Hat in a open environment, open world, with communities and huge ecosystems. That, to me, is the critical rallying points, and really necessary to drive this accelerated transformation. >> Yeah, and again, open source continues to power it. One thing I'm impressed with is this concept of content, not content as in a video, but content as code. It's collaboration. It's what people are sharing their playbooks and they're sharing their, are opening things up. I think there's going to be a whole 'nother level of developer collaboration that's going to emerge and you guys are on the front end of all of this. I think it's going to be pretty powerful. I don't think yet clearly understood yet by most folks, but when you start seeing the automation benefits, Tom, I'm sure your team will be like, "Yeah, see, automation platform." Thank you so much for coming on, appreciate it. >> Thank you. >> Thanks a lot. >> Thanks. >> I'm John Furrier with theCUBE, hosting theCUBE virtual for AnsibleFest 2020 virtual. Thanks for watching. (relaxing music)

>> (upbeat music) [Narrator] From around the globe, it's theCUBE with digital coverage of AnsibleFest 2020, brought to you by Red Hat. >> Hello welcome back to theCUBE virtual coverage of AnsibleFest 2020 Virtual. We're not face to face this year. I'm John Furrier, your host. We're bringing it together remotely. We're in the Palo Alto Studios with theCUBE and we're going remote for our guests this year. And I hope you can come together online enjoy the content. Of course, go check out the events site on Demand Live. And certainly I have a lot of great content. I've got a great guest Skyla Loomis Vice president, for the Z Application Platform at IBM. Also known as IBM Z talking Mainframe. Skyla, thanks for coming on theCUBE Appreciate it. >> Thank you for having me. So, you know, I've talked many conversations about the Mainframe of being relevant and valuable in context to cloud and cloud native because if it's got a workload you've got containers and all this good stuff, you can still run anything on anything these days. By integrating it in with all this great glue layer, lack of a better word or oversimplifying it, you know, things going on. So it's really kind of cool. Plus Walter Bentley in my previous interview was talking about the success of Ansible, and IBM working together on a really killer implementation. So I want to get into that, but before that let's get into IBM Z. How did you start working with IBM Z? What's your role there? >> Yeah, so I actually just got started with Z about four years ago. I spent most of my career actually on the distributed platform, largely with data and analytics, the analytics area databases and both On-premise and Public Cloud. But I always considered myself a friend to Z. So in many of the areas that I'd worked on, we'd, I had offerings where we'd enabled it to work with COS or Linux on Z. And then I had this opportunity come up where I was able to take on the role of leading some of our really core runtimes and databases on the Z platform, IMS and z/TPF. And then recently just expanded my scope to take on CICS and a number of our other offerings related to those kind of in this whole application platform space. And I was really excited because just of how important these runtimes and this platform is to the world,really. You know, our power is two thirds of our fortune 100 clients across banking and insurance. And it's you know, some of the most powerful transaction platforms in the world. You know doing hundreds of billions of transactions a day. And you know, just something that's really exciting to be a part of and everything that it does for us. >> It's funny how distributed systems and distributed computing really enable more longevity of everything. And now with cloud, you've got new capabilities. So it's super excited. We're seeing that a big theme at AnsibleFest this idea of connecting, making things easier you know, talk about distributed computing. The cloud is one big distribute computer. So everything's kind of playing together. You have a panel discussion at AnsibleFest Virtual. Could you talk about what your topic is and share, what was some of the content in there? Content being, content as in your presentation? Not content. (laughs) >> Absolutely. Yeah, so I had the opportunity to co-host a panel with a couple of our clients. So we had Phil Allison from Black Knight and Pat Lane from Allstate and they were really joining us and talking about their experience now starting to use Ansible to manage to z/OS. So we just actually launched some content collections and helping to enable and accelerate, client's use of using Ansible to manage to z/OS back in March of this year. And we've just seen tremendous client uptake in this. And these are a couple of clients who've been working with us and, you know, getting started on the journey of now using Ansible with Z they're both you know, have it in the enterprise already working with Ansible on other platforms. And, you know, we got to talk with them about how they're bringing it into Z. What use cases they're looking at, the type of culture change, that it drives for their teams as they embark on this journey and you know where they see it going for them in the future. >> You know, this is one of the hot items this year. I know that events virtual so has a lot of content flowing around and sessions, but collections is the top story. A lot of people talking collections, collections collections, you know, integration and partnering. It hits so many things but specifically, I like this use case because you're talking about real business value. And I want to ask you specifically when you were in that use case with Ansible and Z. People are excited, it seems like it's working well. Can you talk about what problems that it solves? I mean, what was some of the drivers behind it? What were some of the results? Could you give some insight into, you know, was it a pain point? Was it an enabler? Can you just share why that was getting people are getting excited about this? >> Yeah well, certainly automation on Z, is not new, you know there's decades worth of, of automation on the platform but it's all often proprietary, you know, or bundled up like individual teams or individual people on teams have specific assets, right. That they've built and it's not shared. And it's certainly not consistent with the rest of the enterprise. And, you know, more and more, you're kind of talking about hybrid cloud. You know, we're seeing that, you know an application is not isolated to a single platform anymore right. It really expands. And so being able to leverage this common open platform to be able to manage Z in the same way that you manage the entire rest of your enterprise, whether that's Linux or Windows or network or storage or anything right. You know you can now actually bring this all together into a common automation plane in control plane to be able to manage to all of this. It's also really great from a skills perspective. So, it enables us to really be able to leverage. You know Python on the platform and that's whole ecosystem of Ansible skills that are out there and be able to now use that to work with Z. >> So it's essentially a modern abstraction layer of agility and people to work on it. (laughs) >> Yeah >> You know it's not the joke, Hey, where's that COBOL programmer. I mean, this is a serious skill gap issues though. This is what we're talking about here. You don't have to replace the, kill the old to bring in the new, this is an example of integration where it's classic abstraction layer and evolution. Is that, am I getting that right? >> Absolutely. I mean I think that Ansible's power as an orchestrator is part of why, you know, it's been so successful here because it's not trying to rip and replace and tell you that you have to rewrite anything that you already have. You know, it is that glue sort of like you used that term earlier right? It's that glue that can span you know, whether you've got rec whether you've got ACL, whether you're using z/OSMF you know, or any other kind of custom automation on the platform, you know, it works with everything and it can start to provide that transparency into it as well, and move to that, like infrastructure as code type of culture. So you can bring it into source control. You can have visibility to it as part of the Ansible automation platform and tower and those capabilities. And so you, it really becomes a part of the whole enterprise and enables you to codify a lot of that knowledge. That, you know, exists again in pockets or in individuals and make it much more accessible to anybody new who's coming to the platform. >> That's a great point, great insight.& It's worth calling out. I'm going to make a note of that and make a highlight from that insight. That was awesome. I got to ask about this notion of client uptake. You know, when you have z/OS and Ansible kind of come in together, what are the clients area? When do they get excited? When do they know that they've got to do? And what are some of the client reactions? Are they're like, wake up one day and say, "Hey, yeah I actually put Ansible and z/OS together". You know peanut butter and chocolate is (mumbles) >> Honestly >> You know, it was just one of those things where it's not obvious, right? Or is it? >> Actually I have been surprised myself at how like resoundingly positive and immediate the reactions have been, you know we have something, one of our general managers runs a general managers advisory council and at some of our top clients on the platform and you know we meet with them regularly to talk about, you know, the future direction that we're going. And we first brought this idea of Ansible managing to Z there. And literally unanimously everybody was like yes, give it to us now. (laughs) It was pretty incredible, you know? And so it's you know, we've really just seen amazing uptake. We've had over 5,000 downloads of our core collection on galaxy. And again that's just since mid to late March when we first launched. So we're really seeing tremendous excitement with it. >> You know, I want to want to talk about some of the new announcements, but you brought that up. I wanted to kind of tie into it. It is addictive when you think modernization, people success is addictive. This is another theme coming out of AnsibleFest this year is that when the sharing, the new content you know, coders content is the theme. I got to ask you because you mentioned earlier about the business value and how the clients are kind of gravitating towards it. They want it.It is addictive, contagious. In the ivory towers in the big, you know, front office, the business. It's like, we've got to make everything as a service. Right. You know, you hear that right. You know, and say, okay, okay, boss You know, Skyla, just go do it. Okay. Okay. It's so easy. You can just do it tomorrow, but to make everything as a service, you got to have the automation, right. So, you know, to bridge that gap has everything is a service whether it's mainframe. I mean okay. Mainframe is no problem. If you want to talk about observability and microservices and DevOps, eventually everything's going to be a service. You got to have the automation. Could you share your, commentary on how you view that? Because again, it's a business objective everything is a service, then you got to make it technical then you got to make it work and so on. So what's your thoughts on that? >> Absolutely. I mean, agility is a huge theme that we've been focusing on. We've been delivering a lot of capabilities around a cloud native development experience for folks working on COBOL, right. Because absolutely you know, there's a lot of languages coming to the platform. Java is incredibly powerful and it actually runs better on Z than it runs on any other platform out there. And so, you know, we're seeing a lot of clients you know, starting to, modernize and continue to evolve their applications because the platform itself is incredibly modern, right? I mean we come out with new releases, we're leading the industry in a number of areas around resiliency, in our security and all of our, you know, the face of encryption and number of things that come out with, but, you know the applications themselves are what you know, has not always kept pace with the rate of change in the industry. And so, you know, we're really trying to help enable our clients to make that leap and continue to evolve their applications in an important way, and the automation and the tools that go around it become very important. So, you know, one of the things that we're enabling is the self service, provisioning experience, right. So clients can, you know, from Open + Shift, be able to you know, say, "Hey, give me an IMS and z/OS connect stack or a kicks into DB2 stack." And that is all under the covers is going to be powered by Ansible automation. So that really, you know, you can get your system programmers and your talent out of having to do these manual tasks, right. Enable the development community. So they can use things like VS Code and Jenkins and GET Lab, and you'll have this automated CICB pipeline. And again, Ansible under the covers can be there helping to provision those test environments. You know, move the data, you know, along with the application, changes through the pipeline and really just help to support that so that, our clients can do what they need to do. >> You guys got the collections in the hub there, so automation hub, I got to ask you where do you see the future of the automating within z/OS going forward? >> Yeah, so I think, you know one of the areas that we'd like to see go is head more towards this declarative state so that you can you know, have this declarative configuration defined for your Z environment and then have Ansible really with the data and potency right. Be able to, go out and ensure that the environment is always there, and meeting those requirements. You know that's partly a culture change as well which goes along with it, but that's a key area. And then also just, you know, along with that becoming more proactive overall part of, you know, AI ops right. That's happening. And I think Ansible on the automation that we support can become you know, an integral piece of supporting that more intelligent and proactive operational direction that, you know, we're all going. >> Awesome Skyla. Great to talk to you. And so insightful, appreciate it. One final question. I want to ask you a personal question because I've been doing a lot of interviews around skill gaps and cybersecurity, and there's a lot of jobs, more job openings and there are a lot of people. And people are with COVID working at home. People are looking to get new skilled up positions, new opportunities. Again cybersecurity and spaces and event we did and want to, and for us its huge, huge openings. But for people watching who are, you know, resetting getting through this COVID want to come out on the other side there's a lot of online learning tools out there. What skill sets do you think? Cause you brought up this point about modernization and bringing new people and people as a big part of this event and the role of the people in community. What areas do you think people could really double down on? If I wanted to learn a skill. Or an area of coding and business policy or integration services, solution architects, there's a lot of different personas, but what skills can I learn? What's your advice to people out there? >> Yeah sure. I mean on the Z platform overall and skills related to Z, COBOL, right. There's, you know, like two billion lines of COBOL out there in the world. And it's certainly not going away and there's a huge need for skills. And you know, if you've got experience from other platforms, I think bringing that in, right. And really being able to kind of then bridge the two things together right. For the folks that you're working for and the enterprise we're working with you know, we actually have a bunch of education out there. You got to master the mainframe program and even a competition that goes on that's happening now, for folks who are interested in getting started at any stage, whether you're a student or later in your career, but you know learning, you know, learn a lot of those platforms you're going to be able to then have a career for life. >> Yeah. And the scale on the data, this is so much going on. It's super exciting. Thanks for sharing that. Appreciate it. Want to get that plug in there. And of course, IBM, if you learn COBOL you'll have a job forever. I mean, the mainframe's not going away. >> Absolutely. >> Skyla, thank you so much for coming on theCUBE Vice President, for the Z Application Platform and IBM, thanks for coming. Appreciate it. >> Thanks for having me. >> I'm John Furrier your host of theCUBE here for AnsibleFest 2020 Virtual. Thanks for watching. (upbeat music)

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante snowflake's eye-popping ipo this week has the industry buzzing we have had dozens and dozens of inbound pr from firms trying to hook us offering perspectives on the snowflake ipo so they can pitch us on their latest and greatest product people are pumped and why not an event like this doesn't happen very often hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll give you our take on the snowflake ipo and address the many questions that we've been getting on the topic i'm also going to discuss at the end of this segment an angle for getting in on the ground floor and investments which is not for the faint of heart but it's something that i believe is worth talking about now let's first talk about the hottest ipo in software industry history first i want to say congratulations to the many people at snowflake you know the big hitters yeah they're all the news slootman mooglia spicer buffett benioff even scarpelli interestingly you know you don't hear much about the founders they're quite humble and we're going to talk about that in some future episodes but they created snowflake they had the vision and the smarts to bring in operators that could get the company to this point so awesome for them but you know i'm especially happy for the rank and file and the many snowflake people where an event like this it really can be life-changing versus the billionaires on the leaderboard so fantastic for you okay but let's get into the madness as you know by now snowflake ipod at a price of 120. now unless you knew a guy he paid around 245 at the open that's if you got in otherwise you bought at a higher price so you kind of just held your nose and made the trade i guess you know but snowflakes value it went from 33 billion to more than 80 billion in a matter of minutes now there's a lot of finger pointing going on this is this issue that people are claiming that it was underpriced and snowflake left four billion dollars on the table please stop that's just crazy to me snowflakes balance sheet is in great shape thanks to this offering and you know i'm not sure jamming later stage investors even more would have been the right thing to do this was a small float i think it was around 10 percent of the company so you would expect a sharp uptick on day one i had predicted a doubling to a 66 billion dollar valuation and it ended up around 70. now the big question that we now get is is this a fair valuation and can snowflake grow into its value we'll address this in more detail but the short answer is snowflake is overvalued in my opinion right now but it can grow into its valuation and of course as always they're going to be challenges now the other comment we get is yeah but the company is losing tons of money and i say no kidding that's why they're so valuable we've been saying for years that the street right now is rewarding growth because they understand that to compete in software you need to have massive scale so i'm not worried in the least about snowflakes bottom line not yet eventually i'm going to pay much closer attention to operating cash flow but right now i want to see growth i want to see them grow into their valuation now the other common question we get is should i buy when should i buy what are the risks and can snowflake compete with the biggest cloud vendors i'll say this before we get into it and i've said before look it's it's very rare that you're not going to get better buying opportunities than day one of an ipo and i think in this case you will i remember back in 2015 it was i think it was the first calendar for quarter and servicenow missed its earnings and the stock got hit and we had the opportunity to interview frank slootman then ceo of servicenow right after that and i think it's instructive to hear what he said let's listen roll the clip well yeah i think that a lot of the high-flying cloud companies and obviously we're one of them you know we're we're priced to perfection right um and that's that's not an easy place to be for uh for for anybody and you know we're not really focused on that it's it's this is a marathon you know every quarter is one mile marker you can't get too excited about you know one versus the other we're really pacing ourselves we're building you know an enterprise that's going to be here for for a long time you know and after that we saw the stock drop as low as 50 today servicenow is a 450 stock so my point is that snowflake like servicenow is going to be priced to perfection and there will be bumps in the road possibly macro factors or other and if you're a believer you'll have opportunities to get in so be patient now finally i'm going to make some comments later but i'll give you the bumper sticker right now i mean i calculated the weighted average price that the insiders paid on the the s1 that they paid for snowflake and it came out to around six dollars a share and i heard somebody say on tv it was five dollars but my weighted average math got me to six dollars regardless on day one of the ipo the insiders made a 50x return on their investment if you bought on day one you're probably losing some money or maybe about even and there are some ground floor opportunities that exist that are complicated and may be risky but if you're young and motivated or older and have some time to research i think you'll be interested in what i have to say later on all right let's compare snowflake to some other companies on a valuation basis this ought to be interesting so this chart shows some high flyers as compared to snowflake we show the company the trailing 12-month revenue the market cap at the close of the 16th which is the day that snowflake ipod and then we calculate and sort the data on the revenue multiple of the trailing 12 months and the last column is the year-on-year growth rate of the last quarter and i used trailing 12 months because it's simple and it's easy to understand and it makes the revenue multiple bigger so it's more dramatic and many prefer to use a forward revenue uh but that's why i put the growth rate there you can pick your own projected revenue growth and and do the math yourself so let's start with snowflake 400 million dollars in revenue and that's based on a newish pricing model of consumption not a sas subscription that locks you in for a year or two years or three years i love this model because it's true cloud and i've talked about it a while so for a while so i'm not going to dwell on it today but you can see the trailing 12-month revenue multiple is massive and the growth rate is 120 which is very very impressive for a company this size zoom we put zoom in the chart just because why not and and the growth grade is sick so so who knows how that correlates to the revenue multiple but as you can see snowflake actually tops the zoom frothiness on that metric now maybe zoom is undervalued i should take that back let's see i think crowdstrike is really interesting here and as a company that we've been following and talking about quite a bit in my last security breaking analysis they were at a 65 x trailing 12-month revenue multiple and you see how that's jumped since they reported and they beat expectations but they're similar in size to snowflake with a slower growth rate in a lower revenue multiple so there's some correlation between that growth rate and the revenue multiple sort of now snowflake pulled back on day two it was down early uh this morning as you would expect with both the market being off and maybe some profit taking you know if you got in an allocation at 120 why not take some profits and play with house money so snowflake's value is hovering today it actually bounced back is hovering today you're just under 70 billion and that that brings the revenue multiple down a bit but it's still very elevated now if you project 2x growth let's say 100 for next year and the stock stays in some kind of range which i think it likely will you could see snowflake coming down to crowdstrike revenue multiples in 12 months it'll depend of course on snowflakes earnings reports which i'm sure are going to beat estimates for the next several quarters and if if it's growing faster than these others at that time it should command a premium you know wherever the market prices market's going to go up it's going to go down but we'll look at all these companies i think on a relative basis snowflakes still should command a premium at higher growth rates so you can see also in this chart you've got shopify awesome mongodb twilio servicenow and their respective growth rates shopify incredibly impressive [ __ ] and twilio as well servicenow is like the old dog in this mix so that's kind of interesting now the other big question we get is can snowflake grow in to its valuation this is a chart we shared with you a bit ago and it talks to snowflake's total available market and its expansion opportunity there tam expansion this is something we saw slootman execute at servicenow when everybody underestimated that company's value and i'll briefly explain here look snowflake is disrupting the traditional data warehouse and data lake markets data lake spending is relatively small it's under 2 billion but data lakes they're inexpensive and that's what made them attractive the edw market however the enterprise data warehouse market is it's much much larger now traditional edws they're they're big they're slow they're cumbersome they're expensive and they're complicated but they've been operationalized and are critical for companies reporting and basic analytics but they've failed to live up to their promise of the 360 degree view of the customer and real-time analytics you know i had a customer tell me a while ago that my data warehouse it's like a snake swallowing a basketball he gave me example where a change in a regulation this was a financial company it would occur and it would force a change in the data model in their data warehouse and they'd have to ingest all this new data and the data warehouse choked and every time intel came out with a new processor they'd rush out they'd throw more compute at the problem he called this chasing the chips now what snowflake did was to envision a cloud native world where you could bring compute to massive data volumes on an elastic basis and only pay for what you use sounds so simple but technically snowflakes founders and those innovations of that innovation of separating compute from storage to leverage the flexibility of the cloud it really was profound and clearly based on this week's performance was the right call now i'll come back to this in a bit now where we think snowflake is going is to build a data cloud and and you can see this in the chart where your data can be ingested and accessed to perform near real-time analytics with machine learning and ai and snowflake's advantage as we've discussed in the past is that it runs on any cloud and it can ingest data from a variety of sources now there are some challenges here we're not saying that snowflake is going to participate in all these use cases that we show however with its resources now we expect snowflake to create new capabilities organically and then do tuck-in acquisitions that will allow it to attack many more more use cases in adjacent markets and so you look at this chart and the third layer if that's 60 billion it means snowflake needs to extend into the fourth layer because its valuation is already over 60 billion it's not going to get 100 market share so we call this next layer automated decision making this is where real time analytics and systems are making decisions for humans and acting in real time now clearly data is going to be a pretty critical part of this equation now at this point it's unclear that snowflake has the capability to go after this space as much of the data in this area is probably going to live at the edge but snowflake is betting on becoming a data data layer across clouds and presumably at the edge and as you can see this market is enormous so there's no lack of tam in our view for snowflakes that brings us to the other big question around competition everybody's talking about this look a lot of the investment thesis behind snowflakes snowflake is that slootman and his army including cfo mike scarpelli and what they did at servicenow will be repeated scarpelli is this operational guru he keeps the engine running you know with very very tight controls and you know what it's a pretty good bet snoopman and scarpelli and their team i'm not denying that but i will tell you that snowflake's competition is much more capable than what servicenow faced in its early days now here's a picture of some of the key competitors this is one of our favorites the xy graph and on the vertical axis is net score or spending momentum that is etr's version of velocity based on their quarterly surveys now i'm showing july survey october is in the works it's in the field as i speak on the horizontal axis is market share or pervasiveness in the data set so it's a proxy for market share it's it's based on mentions not dollars and and that's why microsoft is so far to the right because they're huge and they're everywhere and they get a lot of mentions the more relevant data to us is the position of snowflake it remains one of the highest net scores in the entire etr survey based not just the database sector aw aws is its biggest competitor because most of snowflake's business runs on aws but google bigquery you can see there is is technically the most capable relative to snowflake because it's a true cloud native database built from the ground up whereas aws took a database that was built for on-prem par excel and brilliantly really made it work in the cloud by re-architecting many of the pieces but it still has legacy parts to it now here's oracle oracle's huge it's slow growth overall but it's making investments in r d we've talked about that a lot and that's going to allow it to hold on to its customers huge base and you can see teradata and cloud era cloudera is a proxy for data lakes which are low cost as i said and cloudera which acquired hortonworks is credited with the commercialization of that whole big datum and hadoop movement and then teradata is in there as well which of course they've been around forever now there are a zillion other database players we've heard a lot of them from a lot of them this week is on that inbound pr that i talked about but these are the ones that we wanted to focus on today the bottom line is we expect snowflakes vertical axis spending momentum to remain elevated and we think it will continue to steadily move to the right now let's drill into this data a bit more here we break down the components of etr's net score and this is specifically for snowflake over time now remember lime green is new adoptions the forest green is spending more relative to last year than more five percent more uh than last year or or greater gray is flat spending the pink is less spending and the bright red is we're leaving the platform the line up top that's netscore which subtracts the red from the green is an indicator of spending velocity the yellow line at the bottom is market market share or pervasiveness in the survey based on mentions now note the the blue text there that's etr's number one takeaway on snowflake two h-20 spending intentions on snowflake continue to trend robustly mostly characterized by high customer acquisition and expansion rates new adoptions market share among all customers is simultaneously growing impressive let's now look at snowflake against the competition in fortune 500 customers now here we show net score or again spending momentum over time for some of the key competitors and you can see snowflakes net score has actually increased since the april survey again this is the july survey this was taken the april survey was taken at the height of the us lockdown so snowflake's net score is actually higher in the fortune 500 than it was overall which is a good proxy for spend because fortune 500 spends more google mongodb and microsoft also also show meaningful momentum growth since the april survey you know notably aws has come off its elevated levels from last october and april it's still strong but that's something that we're going to continue to watch finally let's look at snowflakes market share or pervasiveness within the big three cloud vendors again this is a cut on the fortune 500 and you can see there are 125 respondents within the big three cloud and the fortune 500 and 21 snowflake respondents within that base of 125 and you can see the steady and consistent growth of share not huge ends but enough to give some confidence in the data now again note the etr callout but this trend is occurring despite the fact that each of the big three cloud vendors has its own competitive offering okay but i want to stress this is not a layup for snowflake as i've said this is not servicenow part two it's a different situation so let's talk about that look the competition here is not bmc which was servicenow's target as much as i love the folks at bmc we're talking here about aws microsoft and google amazon with redshift is dialed into this i've said often that they have copycatted snowflake in many cases and last fall at re invent we heard andy jassy make a big deal about separating compute from storage and he took a kind of a swipe at snowflake without mentioning them by name but let's listen to what andy jassy had had to say and then we'll come back and talk about it play the clip then what we did is because we have nitro like i was talking about earlier we built unique instances that have very fast bandwidth so that if you actually need some of those data from s3 for a query it moves much faster than if you just had to leave it there with without that high speed bandwidth instance and so with ra3s you get to separate your storage from your compute if it turns out by the way on your local ssds that you're not using all the ssd on that local ssd you only pay for what you use so a pretty significant enhancement for customers using redshift at the same time if you think about the prevailing way that people are thinking about separating storage from compute letting people scale separately that way as well as how you're going to do this large-scale compute where you move the storage to the a bunch of awaiting compute nodes there are some issues with this that you got to think about the first is think about how much data you're going to have at the scale that we're at but then just fast forward a few years think about how much data you're going to actually have to move over the network to get to the compute and we so look first of all jassy is awesome he stands up at these events for like reinvent for two hours and it connects trends and business to technology he's got a very deep understanding of the tech he's amazing however what aws has done in separating compute and storage is good but it's not as elegant architecturally as snowflake aws essentially has tiered the storage off the cluster to lower the overall costs but you really you can't turn off the compute completely with snowflake they've truly separated compute and storage and the reason is that redshift is great but it's built on an on-prem architecture that was originally an on-prem architecture that they had to redo so when jassy talks about moving the data to compute what he's really saying is our architecture is such that we had to do this workaround which is actually quite clever but this whole narrative about the prevailing ways to separate compute from storage that's snowflake and moving the data's use the word data's plural to the compute it really doesn't apply to snowflake because they'll just move the compute to the data thank you hadoop for that profound concept now does this mean snowflake is going to cakewalk over redshift not at all aws is going to continue to innovate so snowflake had better keep moving fast multi-cloud new workloads adjacent markets tam expansion etc etc etc microsoft they're huge but as usual there's not a lot to say you know about them they're everywhere they put out 1.0 products they eventually get them right because with their heft they get mulligans that they turn into pars or birdies but i think snowflake is going to bring some innovations to azure and that they're going to get good traction there in my opinion now google bigquery is interesting by all accounts it gets very high technical marks google's playing the long game and i would expect that snowflake is going to have a harder time competing in google cloud than it does within aws and what i'm predicting for azure but we'll see the last point here is that many are talking about the convergence of analytic and operational and transaction databases and the thinking is this doesn't necessarily bode well for specialists like snowflake and i would say a couple of things here first is that while it's definitely true you're not seeing snowflake positioning today as responding at the point of transaction to say for instance influence and order in real time and this may have implications at the edge it's going to have a lot of real-time inferencing but we've learned there are a lot of ways to skin a cat and we see integration layers and innovative approaches emerging in the cloud that could address this gap and present opportunities for snowflake now the other thing i'd say is you know maybe that thinking misses something altogether with the idea of snowflake in that third data layer that we showed you in our tam chart that data as a service layer or data cloud which is maybe a giant opportunity that they are uniquely positioned to address because they're cloud agnostic they've got the vision and they've got the architecture to allow them to very simply ingest data and then serve it up to businesses nonetheless we're going to see this battle continue between what i've often talked about these integrated suites and converged databases in the case of oracle converged pipelines in the case of the cloud guys versus the best of breed players like snowflake we talk about this all the time and there really isn't one single answer it's really horses for courses and customer preferences okay well you know i know you've been waiting for for me to tell you about the angles on ground floor investing and you probably think this is going to be crazy but bear with me and i got to caution you this is a bit tongue-in-cheek and it's one big buyer beware but as i said the insiders on snowflake had a 50x return on day one you probably didn't so i want to talk about the confluence of software engineering crypto cryptography and game theory powered by the underlying value of blockchain and we're talking here about innovations around a new internet in a distributed web or d-web where many distributed computers come together to form one computer that guarantees trust between two or more users for a variety of use cases not just financial store like bitcoin but that too and the motivation behind this is the fact that a small number of companies say five or six today control the internet and have essentially co-opted the major protocols like tcp http smtp pop3 etc etc and these people that we're showing here on this chart they're working on these new innovations there are many of them but i just name a few here olaf carlson we he started poly chain capital to invest in core infrastructure around these new computing paradigms this gentleman mark nadal is someone who's working on new d apps tim berners-lee who invented the internet he's got a project called solid at mit and it emphasizes data ownership and privacy and of course satoshi got it all started when she invented bitcoin and created the notion of fractional shares and by the way the folks at andreessen horowitz are actively making bets in this space so you know maybe this is not so crazy but here's the premise if you're a little guy and you wanted to invest in snowflake you couldn't until late in the game if you wanted to invest in the lamp stack directly in the late 90s there was no way to do that you had to wait for red hat to go public or to get a piece of the linux action but in this world that we're talking about here there are opportunities that are not mainstream and often they're based yes on cryptocurrencies again it's dangerous there are scams and and losers but if you do your homework there are actually vehicles for you to get in on the ground floor and you know some of these innovations are going to take off you could get a 50x or 100 bagger but you have to do your research and there's no guarantee that these innovations are going to be able to take on the big internet giants but there are people really smart technologists and software engineers that are young they're mission driven and they're forming a collective voice against a dystopian future because they want to level the playing field on the internet and this may be the disruptive force that challenges today's giants and if your game i would take a look at the space and see if it's worth throwing a few dollars at okay a little tangent from snowflake but i wanted to put that out there snowflake wow closes its first trading week as a company worth 66 billion dollars roughly the same as goldman sachs worth more than vmware and the list goes on i mean what's what's more is there to say other than remember these episodes are all available as podcasts so please subscribe i publish weekly on wikibon.com and siliconangle.com so please check that out and please comment on my linkedin post or feel free to email me at david.velante at siliconangle.com this is dave vellante for the cube insights powered by etr thanks for watching everyone we'll see you next time you

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey, welcome back, you're ready. Jeff Frick here with theCUBE. We are still getting through the year of 2020. It's still the year of COVID and there's no end in sight I think until we get to a vaccine. That said, we're really excited to have one of our favorite guests. We haven't had him on for a while. I haven't talked to him for a long time. He used to I think have the record for the most CUBE appearances of probably any CUBE alumni. We're excited to have him joining us from his house in Palo Alto. Bill Schmarzo, you know him as the Dean of Big Data, he's got more titles. He's the chief innovation officer at Hitachi Vantara. He's also, we used to call him the Dean of Big Data, kind of for fun. Well, Bill goes out and writes a bunch of books. And now he teaches at the University of San Francisco, School of Management as an executive fellow. He's an honorary professor at NUI Galway. I think he's just, he likes to go that side of the pond and a many time author now, go check him out. His author profile on Amazon, the "Big Data MBA," "The Art of Thinking Like A Data Scientist" and another Big Data, kind of a workbook. Bill, great to see you. >> Thanks, Jeff, you know, I miss my time on theCUBE. These conversations have always been great. We've always kind of poked around the edges of things. A lot of our conversations have always been I thought, very leading edge and the title Dean of Big Data is courtesy of theCUBE. You guys were the first ones to give me that name out of one of the very first Strata Conferences where you dubbed me the Dean of Big Data, because I taught a class there called the Big Data MBA and look what's happened since then. >> I love it. >> It's all on you guys. >> I love it, and we've outlasted Strata, Strata doesn't exist as a conference anymore. So, you know, part of that I think is because Big Data is now everywhere, right? It's not the standalone thing. But there's a topic, and I'm holding in my hands a paper that you worked on with a colleague, Dr. Sidaoui, talking about what is the value of data? What is the economic value of data? And this is a topic that's been thrown around quite a bit. I think you list a total of 28 reference sources in this document. So it's a well researched piece of material, but it's a really challenging problem. So before we kind of get into the details, you know, from your position, having done this for a long time, and I don't know what you're doing today, you used to travel every single week to go out and visit customers and actually do implementations and really help people think these through. When you think about the value, the economic value, how did you start to kind of frame that to make sense and make it kind of a manageable problem to attack? >> So, Jeff, the research project was eyeopening for me. And one of the advantages of being a professor is, you have access to all these very smart, very motivated, very free research sources. And one of the problems that I've wrestled with as long as I've been in this industry is, how do you figure out what is data worth? And so what I did is I took these research students and I stick them on this problem. I said, "I want you to do some research. Let me understand what is the value of data?" I've seen all these different papers and analysts and consulting firms talk about it, but nobody's really got this thing clicked. And so we launched this research project at USF, professor Mouwafac Sidaoui and I together, and we were bumping along the same old path that everyone else got, which was inched on, how do we get data on our balance sheet? That was always the motivation, because as a company we're worth so much more because our data is so valuable, and how do I get it on the balance sheet? So we're headed down that path and trying to figure out how do you get it on the balance sheet? And then one of my research students, she comes up to me and she says, "Professor Schmarzo," she goes, "Data is kind of an unusual asset." I said, "Well, what do you mean?" She goes, "Well, you think about data as an asset. It never depletes, it never wears out. And the same dataset can be used across an unlimited number of use cases at a marginal cost equal to zero." And when she said that, it's like, "Holy crap." The light bulb went off. It's like, "Wait a second. I've been thinking about this entirely wrong for the last 30 some years of my life in this space. I've had the wrong frame. I keep thinking about this as an act, as an accounting conversation. An accounting determines valuation based on what somebody is willing to pay for." So if you go back to Adam Smith, 1776, "Wealth of Nations," he talks about valuation techniques. And one of the valuation techniques he talks about is valuation and exchange. That is the value of an asset is what someone's willing to pay you for it. So the value of this bottle of water is what someone's willing to pay you for it. So everybody fixates on this asset, valuation in exchange methodology. That's how you put it on balance sheet. That's how you run depreciation schedules, that dictates everything. But Adam Smith also talked about in that book, another valuation methodology, which is valuation in use, which is an economics conversation, not an accounting conversation. And when I realized that my frame was wrong, yeah, I had the right book. I had Adam Smith, I had "Wealth of Nations." I had all that good stuff, but I hadn't read the whole book. I had missed this whole concept about the economic value, where value is determined by not how much someone's willing to pay you for it, but the value you can drive by using it. So, Jeff, when that person made that comment, the entire research project, and I got to tell you, my entire life did a total 180, right? Just total of 180 degree change of how I was thinking about data as an asset. >> Right, well, Bill, it's funny though, that's kind of captured, I always think of kind of finance versus accounting, right? And then you're right on accounting. And we learn a lot of things in accounting. Basically we learn more that we don't know, but it's really hard to put it in an accounting framework, because as you said, it's not like a regular asset. You can use it a lot of times, you can use it across lots of use cases, it doesn't degradate over time. In fact, it used to be a liability. 'cause you had to buy all this hardware and software to maintain it. But if you look at the finance side, if you look at the pure play internet companies like Google, like Facebook, like Amazon, and you look at their valuation, right? We used to have this thing, we still have this thing called Goodwill, which was kind of this capture between what the market established the value of the company to be. But wasn't reflected when you summed up all the assets on the balance sheet and you had this leftover thing, you could just plug in goodwill. And I would hypothesize that for these big giant tech companies, the market has baked in the value of the data, has kind of put in that present value on that for a long period of time over multiple projects. And we see it captured probably in goodwill, versus being kind of called out as an individual balance sheet item. >> So I don't think it's, I don't know accounting. I'm not an accountant, thank God, right? And I know that goodwill is one of those things if I remember from my MBA program is something that when you buy a company and you look at the value you paid versus what it was worth, it stuck into this category called goodwill, because no one knew how to figure it out. So the company at book value was a billion dollars, but you paid five billion for it. Well, you're not an idiot, so that four billion extra you paid must be in goodwill and they'd stick it in goodwill. And I think there's actually a way that goodwill gets depreciated as well. So it could be that, but I'm totally away from the accounting framework. I think that's distracting, trying to work within the gap rules is more of an inhibitor. And we talk about the Googles of the world and the Facebooks of the world and the Netflix of the world and the Amazons and companies that are great at monetizing data. Well, they're great at monetizing it because they're not selling it, they're using it. Google is using their data to dominate search, right? Netflix is using it to be the leader in on-demand videos. And it's how they use all the data, how they use the insights about their customers, their products, and their operations to really drive new sources of value. So to me, it's this, when you start thinking about from an economics perspective, for example, why is the same car that I buy and an Uber driver buys, why is that car more valuable to an Uber driver than it is to me? Well, the bottom line is, Uber drivers are going to use that car to generate value, right? That $40,000, that car they bought is worth a lot more, because they're going to use that to generate value. For me it sits in the driveway and the birds poop on it. So, right, so it's this value in use concept. And when organizations can make that, by the way, most organizations really struggle with this. They struggle with this value in use concept. They want to, when you talk to them about data monetization and say, "Well, I'm thinking about the chief data officer, try not to trying to sell data, knocking on doors, shaking their tin cup, saying, 'Buy my data.'" No, no one wants your data. Your data is more valuable for how you use it to drive your operations then it's a sell to somebody else. >> Right, right. Well, on of the other things that's really important from an economics concept is scarcity, right? And a whole lot of economics is driven around scarcity. And how do you price for scarcity so that the market evens out and the price matches up to the supply? What's interesting about the data concept is, there is no scarcity anymore. And you know, you've outlined and everyone has giant numbers going up into the right, in terms of the quantity of the data and how much data there is and is going to be. But what you point out very eloquently in this paper is the scarcity is around the resources to actually do the work on the data to get the value out of the data. And I think there's just this interesting step function between just raw data, which has really no value in and of itself, right? Until you start to apply some concepts to it, you start to analyze it. And most importantly, that you have some context by which you're doing all this analysis to then drive that value. And I thought it was really an interesting part of this paper, which is get beyond the arguing that we're kind of discussing here and get into some specifics where you can measure value around a specific business objective. And not only that, but then now the investment of the resources on top of the data to be able to extract the value to then drive your business process for it. So it's a really different way to think about scarcity, not on the data per se, but on the ability to do something with it. >> You're spot on, Jeff, because organizations don't fail because of a lack of use cases. They fail because they have too many. So how do you prioritize? Now that scarcity is not an issue on the data side, but it is this issue on the people resources side, you don't have unlimited data scientists, right? So how do you prioritize and focus on those opportunities that are most important? I'll tell you, that's not a data science conversation, that's a business conversation, right? And figuring out how you align organizations to identify and focus on those use cases that are most important. Like in the paper we go through several different use cases using Chipotle as an example. The reason why I picked Chipotle is because, well, I like Chipotle. So I could go there and I could write it off as research. But there's a, think about the number of use cases where a company like Chipotle or any other company can leverage your data to drive their key business initiatives and their key operational use cases. It's almost unbounded, which by the way, is a huge challenge. In fact, I think part of the problem we see with a lot of organizations is because they do such a poor job of prioritizing and focusing, they try to solve the entire problem with one big fell swoop, right? It's slightly the old ERP big bang projects. Well, I'm just going to spend $20 million to buy this analytic capability from company X and I'm going to install it and then magic is going to happen. And then magic is going to happen, right? And then magic is going to happen, right? And magic never happens. We get crickets instead, because the biggest challenge isn't around how do I leverage the data, it's about where do I start? What problems do I go after? And how do I make sure the organization is bought in to basically use case by use case, build out your data and analytics architecture and capabilities. >> Yeah, and you start backwards from really specific business objectives in the use cases that you outline here, right? I want to increase my average ticket by X. I want to increase my frequency of visits by X. I want to increase the amount of items per order from X to 1.2 X, or 1.3 X. So from there you get a nice kind of big revenue hit that you can plan around and then work backwards into the amount of effort that it takes and then you can come up, "Is this a good investment or not?" So it's a really different way to get back to the value of the data. And more importantly, the analytics and the work to actually call out the information. >> The technologies, the data and analytic technologies available to us. The very composable nature of these allow us to take this use case by use case approach. I can build out my data lake one use case at a time. I don't need to stuff 25 data sources into my data lake and hope there's someone more valuable. I can use the first use case to say, "Oh, I need these three data sources to solve that use case. I'm going to put those three data sources in the data lake. I'm going to go through the entire curation process of making sure the data has been transformed and cleansed and aligned and enriched and met of, all the other governance, all that kind of stuff this goes on. But I'm going to do that use case by use case, 'cause a use case can tell me which data sources are most important for that given situation. And I can build up my data lake and I can build up my analytics then one use case at a time. And there is a huge impact then, huge impact when I build out use case by use case. That does not happen. Let me throw something that's not really covered in the paper, but it is very much covered in my new book that I'm working on, which is, in knowledge-based industries, the economies of learning are more powerful than the economies of scale. Now think about that for a second. >> Say that again, say that again. >> Yeah, the economies of learning are more powerful than the economies of scale. And what that means is what I learned on the first use case that I build out, I can apply that learning to the second use case, to the third use case, to the fourth use case. So when I put my data into my data lake for my first use case, and the paper covers this, well, once it's in my data lake, the cost of reusing that data in a second, third and fourth use cases is basically, you know marginal cost is zero. So I get this ability to learn about what data sets are most important and to reapply that across the organization. So this learning concept, I learn use case by use case, I don't have to do a big economies of scale approach and start with 25 datasets of which only three or four might be useful. But I'm incurring the overhead for all those other non-important data sets because I didn't take the time to go through and figure out what are my most important use cases and what data do I need to support those use cases. >> I mean, should people even think of the data per se or should they really readjust their thinking around the application of the data? Because the data in and of itself means nothing, right? 55, is that fast or slow? Is that old or young? Well, it depends on a whole lot of things. Am I walking or am I in a brand new Corvette? So it just, it's funny to me that the data in and of itself really doesn't have any value and doesn't really provide any direction into a decision or a higher order, predictive analytics until you start to manipulate the data. So is it even the wrong discussion? Is data the right discussion? Or should we really be talking about the capabilities to do stuff within and really get people focused on that? >> So Jeff, there's so many points to hit on there. So the application of data is what's the value, and the queue of you guys used to be famous for saying, "Separating noise from the signal." >> Signal from the noise. Signal from a noise, right. Well, how do you know in your dataset what's signal and what's noise? Well, the use case will tell you. If you don't know the use case and you have no way of figuring out what's important. One of the things I use, I still rail against, and it happens still. Somebody will walk up my data science team and say, "Here's some data, tell me what's interesting in it." Well, how do you separate signal from noise if I don't know the use case? So I think you're spot on, Jeff. The way to think about this is, don't become data-driven, become value-driven and value is driven from the use case or the application or the use of the data to solve that particular use case. So organizations that get fixated on being data-driven, I hate the term data-driven. It's like as if there's some sort of frigging magic from having data. No, data has no value. It's how you use it to derive customer product and operational insights that drive value,. >> Right, so there's an interesting step function, and we talk about it all the time. You're out in the weeds, working with Chipotle lately, and increase their average ticket by 1.2 X. We talk more here, kind of conceptually. And one of the great kind of conceptual holy grails within a data-driven economy is kind of working up this step function. And you've talked about it here. It's from descriptive, to diagnostic, to predictive. And then the Holy grail prescriptive, we're way ahead of the curve. This comes into tons of stuff around unscheduled maintenance. And you know, there's a lot of specific applications, but do you think we spend too much time kind of shooting for the fourth order of greatness impact, instead of kind of focusing on the small wins? >> Well, you certainly have to build your way there. I don't think you can get to prescriptive without doing predictive, and you can't do predictive without doing descriptive and such. But let me throw a really one at you, Jeff, I think there's even one beyond prescriptive. One we're talking more and more about, autonomous, a ton of analytics, right? And one of the things that paper talked about that didn't click with me at the time was this idea of orphaned analytics. You and I kind of talked about this before the call here. And one thing we noticed in the research was that a lot of these very mature organizations who had advanced from the retrospective analytics of BI to the descriptive, to the predicted, to the prescriptive, they were building one off analytics to solve a problem and getting value from it, but never reusing this analytics over and over again. They were done one off and then they were thrown away and these organizations were so good at data science and analytics, that it was easier for them to just build from scratch than to try to dig around and try to find something that was never actually ever built to be reused. And so I have this whole idea of orphaned analytics, right? It didn't really occur to me. It didn't make any sense into me until I read this quote from Elon Musk, and Elon Musk made this statement. He says, " I believe that when you buy a Tesla, you're buying an asset that appreciates in value, not depreciates through usage." I was thinking, "Wait a second, what does that mean?" He didn't actually say it, "Through usage." He said, "He believes you're buying an asset that appreciates not depreciates in value." And of course the first response I had was, "Oh, it's like a 1964 and a half Mustang. It's rare, so everybody is going to want these things. So buy one, stick it in your garage. And 20 years later, you're bringing it out and it's worth more money." No, no, there's 600,000 of these things roaming around the streets, they're not rare. What he meant is that he is building an autonomous asset. That the more that it's used, the more valuable it's getting, the more reliable, the more efficient, the more predictive, the more safe this asset's getting. So there is this level beyond prescriptive where we can think about, "How do we leverage artificial intelligence, reinforcement, learning, deep learning, to build these assets that the more that they are used, the smarter they get." That's beyond prescriptive. That's an environment where these things are learning. In many cases, they're learning with minimal or no human intervention. That's the real aha moment. That's what I miss with orphaned analytics and why it's important to build analytics that can be reused over and over again. Because every time you use these analytics in a different use case, they get smarter, they get more valuable, they get more predictive. To me that's the aha moment that blew my mind. I realized I had missed that in the paper entirely. And it took me basically two years later to realize, dough, I missed the most important part of the paper. >> Right, well, it's an interesting take really on why the valuation I would argue is reflected in Tesla, which is a function of the data. And there's a phenomenal video if you've never seen it, where they have autonomous vehicle day, it might be a year or so old. And he's got his number one engineer from, I think the Microprocessor Group, The Computer Vision Group, as well as the autonomous driving group. And there's a couple of really great concepts I want to follow up on what you said. One is that they have this thing called The Fleet. To your point, there's hundreds of thousands of these things, if they haven't hit a million, that are calling home reporting home every day as to exactly how everyone took the Northbound 101 on-ramp off of University Avenue. How fast did they go? What line did they take? What G-forces did they take? And every one of those cars feeds into the system, so that when they do the autonomous update, not only are they using all their regular things that they would use to map out that 101 Northbound entry, but they've got all the data from all the cars that have been doing it. And you know, when that other car, the autonomous car couple years ago hit the pedestrian, I think in Phoenix, which is not good, sad, killed a person, dark tough situation. But you know, we are doing an autonomous vehicle show and the guy who made a really interesting point, right? That when something like that happens, typically if I was in a car wreck or you're in a car wreck, hopefully not, I learned the person that we hit learns and maybe a couple of witnesses learn, maybe the inspector. >> But nobody else learns. >> But nobody else learns. But now with the autonomy, every single person can learn from every single experience with every vehicle contributing data within that fleet. To your point, it's just an order of magnitude, different way to think about things. >> Think about a 1% improvement compounded 365 times, equals I think 38 X improvement. The power of 1% improvements over these 600,000 plus cars that are learning. By the way, even when the autonomous FSD, the full self-driving mode module isn't turned on, even when it's not turned on, it runs in shadow mode. So it's learning from the human drivers, the human overlords, it's constantly learning. And by the way, not only they're collecting all this data, I did a little research, I pulled out some of their job search ads and they've built a giant simulator, right? And they're there basically every night, simulating billions and billions of more driven miles because of the simulator. They are building, he's going to have a simulator, not only for driving, but think about all the data he's capturing as these cars are riding down the road. By the way, they don't use Lidar, they use video, right? So he's driving by malls. He knows how many cars are in the mall. He's driving down roads, he knows how old the cars are and which ones should be replaced. I mean, he has this, he's sitting on this incredible wealth of data. If anybody could simulate what's going on in the world and figure out how to get out of this COVID problem, it's probably Elon Musk and the data he's captured, be courtesy of all those cars. >> Yeah, yeah, it's really interesting, and we're seeing it now. There's a new autonomous drone out, the Skydio, and they just announced their commercial product. And again, it completely changes the way you think about how you use that tool, because you've just eliminated the complexity of driving. I don't want to drive that, I want to tell it what to do. And so you're saying, this whole application of air force and companies around things like measuring piles of coal and measuring these huge assets that are volume metric measured, that these things can go and map out and farming, et cetera, et cetera. So the autonomy piece, that's really insightful. I want to shift gears a little bit, Bill, and talk about, you had some theories in here about thinking of data as an asset, data as a currency, data as monetization. I mean, how should people think of it? 'Cause I don't think currency is very good. It's really not kind of an exchange of value that we're doing this kind of classic asset. I think the data as oil is horrible, right? To your point, it doesn't get burned up once and can't be used again. It can be used over and over and over. It's basically like feedstock for all kinds of stuff, but the feedstock never goes away. So again, or is it that even the right way to think about, do we really need to shift our conversation and get past the idea of data and get much more into the idea of information and actionable information and useful information that, oh, by the way, happens to be powered by data under the covers? >> Yeah, good question, Jeff. Data is an asset in the same way that a human is an asset. But just having humans in your company doesn't drive value, it's how you use those humans. And so it's really again the application of the data around the use cases. So I still think data is an asset, but I don't want to, I'm not fixated on, put it on my balance sheet. That nice talk about put it on a balance sheet, I immediately put the blinders on. It inhibits what I can do. I want to think about this as an asset that I can use to drive value, value to my customers. So I'm trying to learn more about my customer's tendencies and propensities and interests and passions, and try to learn the same thing about my car's behaviors and tendencies and my operations have tendencies. And so I do think data is an asset, but it's a latent asset in the sense that it has potential value, but it actually has no value per se, inputting it into a balance sheet. So I think it's an asset. I worry about the accounting concept medially hijacking what we can do with it. To me the value of data becomes and how it interacts with, maybe with other assets. So maybe data itself is not so much an asset as it's fuel for driving the value of assets. So, you know, it fuels my use cases. It fuels my ability to retain and get more out of my customers. It fuels ability to predict what my products are going to break down and even have products who self-monitor, self-diagnosis and self-heal. So, data is an asset, but it's only a latent asset in the sense that it sits there and it doesn't have any value until you actually put something to it and shock it into action. >> So let's shift gears a little bit and start talking about the data and talk about the human factors. 'Cause you said, one of the challenges is people trying to bite off more than they can chew. And we have the role of chief data officer now. And to your point, maybe that mucks things up more than it helps. But in all the customer cases that you've worked on, is there a consistent kind of pattern of behavior, personality, types of projects that enables some people to grab those resources to apply to their data to have successful projects, because to your point there's too much data and there's too many projects and you talk a lot about prioritization. But there's a lot of assumptions in the prioritization model that you can, that you know a whole lot of things, especially if you're comparing project A over in group A with project B, with group B and the two may not really know the economics across that. But from an individual person who sees the potential, what advice do you give them? What kind of characteristics do you see, either in the type of the project, the type of the boss, the type of the individual that really lends itself to a higher probability of a successful outcome? >> So first off you need to find somebody who has a vision for how they want to use the data, and not just collect it. But how they're going to try to change the fortunes of the organization. So it always takes a visionary, may not be the CEO, might be somebody who's a head of marketing or the head of logistics, or it could be a CIO, it could be a chief data officer as well. But you've got to find somebody who says, "We have this latent asset we could be doing more with, and we have a series of organizational problem challenges against which I could apply this asset. And I need to be the matchmaker that brings these together." Now the tool that I think is the most powerful tool in marrying the latent capabilities of data with all the revenue generating opportunities in the application side, because there's a countless number, the most important tool that I found doing that is design thinking. Now, the reason why I think design thinking is so important, because one of the things that design thinking does a great job is it gives everybody a voice in the process of identifying, validating, valuing, and prioritizing use cases you're going to go after. Let me say that again. The challenge organizations have is identifying, validating, valuing, and prioritizing the use cases they want to go after. Design thinking is a marvelous tool for driving organizational alignment around where we're going to start and what's going to be next and why we're going to start there and how we're going to bring everybody together. Big data and data science projects don't die because of technology failure. Most of them die because of passive aggressive behaviors in the organization that you didn't bring everybody into the process. Everybody's voice didn't get a chance to be heard. And that one person who's voice didn't get a chance to get heard, they're going to get you. They may own a certain piece of data. They may own something, but they're just waiting and lay, they're just laying there waiting for their chance to come up and snag it. So what you got to do is you got to proactively bring these people together. We call this, this is part of our value engineering process. We have a value engineering process around envisioning where we bring all these people together. We help them to understand how data in itself is a latent asset, but how it can be used from an economics perspective, drive all those value. We get them all fired up on how these can solve any one of these use cases. But you got to start with one, and you've got to embrace this idea that I can build out my data and analytic capabilities, one use case at a time. And the first use case I go after and solve, makes my second one easier, makes my third one easier, right? It has this ability that when you start going use case by use case two really magical things happen. Number one, your marginal cost flatten. That is because you're building out your data lake one use case at a time, and you're bringing all the important data lake, that data lake one use case at a time. At some point in time, you've got most of the important data you need, and the ability that you don't need to add another data source. You got what you need, so your marginal costs start to flatten. And by the way, if you build your analytics as composable, reusable, continuous learning analytic assets, not as orphaned analytics, pretty soon you have all the analytics you need as well. So your marginal cost flatten, but effect number two is that you've, because you've have the data and the analytics, I can accelerate time to value, and I can de-risked projects as I go use case by use case. And so then the biggest challenge becomes not in the data and the analytics, it's getting the all the business stakeholders to agree on, here's a roadmap we're going to go after. This one's first, and this one is going first because it helps to drive the value of the second and third one. And then this one drives this, and you create a whole roadmap of rippling through of how the data and analytics are driving this value to across all these use cases at a marginal cost approaching zero. >> So should we have chief design thinking officers instead of chief data officers that really actually move the data process along? I mean, I first heard about design thinking years ago, actually interviewing Dan Gordon from Gordon Biersch, and they were, he had just hired a couple of Stanford grads, I think is where they pioneered it, and they were doing some work about introducing, I think it was a a new apple-based alcoholic beverage, apple cider, and they talked a lot about it. And it's pretty interesting, but I mean, are you seeing design thinking proliferate into the organizations that you work with? Either formally as design thinking or as some derivation of it that pulls some of those attributes that you highlighted that are so key to success? >> So I think we're seeing the birth of this new role that's marrying capabilities of design thinking with the capabilities of data and analytics. And they're calling this dude or dudette the chief innovation officer. Surprise. >> Title for someone we know. >> And I got to tell a little story. So I have a very experienced design thinker on my team. All of our data science projects have a design thinker on them. Every one of our data science projects has a design thinker, because the nature of how you build and successfully execute a data science project, models almost exactly how design thinking works. I've written several papers on it, and it's a marvelous way. Design thinking and data science are different sides of the same coin. But my respect for data science or for design thinking took a major shot in the arm, major boost when my design thinking person on my team, whose name is John Morley introduced me to a senior data scientist at Google. And I was bottom coffee. I said, "No," this is back in, before I even joined Hitachi Vantara, and I said, "So tell me the secret to Google's data science success? You guys are marvelous, you're doing things that no one else was even contemplating, and what's your key to success?" And he giggles and laughs and he goes, "Design thinking." I go, "What the hell is that? Design thinking, I've never even heard of the stupid thing before." He goes, "I'd make a deal with you, Friday afternoon let's pop over to Stanford's B school and I'll teach you about design thinking." So I went with him on a Friday to the d.school, Design School over at Stanford and I was blown away, not just in how design thinking was used to ideate and bring and to explore. But I was blown away about how powerful that concept is when you marry it with data science. What is data science in its simplest sense? Data science is about identifying the variables and metrics that might be better predictors of performance. It's that might phrase that's the real key. And who are the people who have the best insights into what values or metrics or KPIs you might want to test? It ain't the data scientists, it's the subject matter experts on the business side. And when you use design thinking to bring this subject matter experts with the data scientists together, all kinds of magic stuff happens. It's unbelievable how well it works. And all of our projects leverage design thinking. Our whole value engineering process is built around marrying design thinking with data science, around this prioritization, around these concepts of, all ideas are worthy of consideration and all voices need to be heard. And the idea how you embrace ambiguity and diversity of perspectives to drive innovation, it's marvelous. But I feel like I'm a lone voice out in the wilderness, crying out, "Yeah, Tesla gets it, Google gets it, Apple gets it, Facebook gets it." But you know, most other organizations in the world, they don't think like that. They think design thinking is this Wufoo thing. Oh yeah, you're going to bring people together and sing Kumbaya. It's like, "No, I'm not singing Kumbaya. I'm picking their brains because they're going to help make their data science team much more effective and knowing what problems we're going to go after and how I'm going to measure success and progress. >> Maybe that's the next Dean for the next 10 years, the Dean of design thinking instead of data science, and who knew they're one and the same? Well, Bill, that's a super insightful, I mean, it's so, is validated and supported by the trends that we see all over the place, just in terms of democratization, right? Democratization of the tools, more people having access to data, more opinions, more perspective, more people that have the ability to manipulate the data and basically experiment, does drive better business outcomes. And it's so consistent. >> If I could add one thing, Jeff, I think that what's really powerful about design thinking is when I think about what's happening with artificial intelligence or AI, there's all these conversations about, "Oh, AI is going to wipe out all these jobs. Is going to take all these jobs away." And what we're actually finding is that if we think about machine learning, driven by AI and human empowerment, driven by design thinking, we're seeing the opportunity to exploit these economies of learning at the front lines where every customer engagement, every operational execution is an opportunity to gather not only more data, but to gather more learnings, to empower the humans at the front lines of the organization to constantly be seeking, to try different things, to explore and to learn from each of these engagements. I think it's, AI to me is incredibly powerful. And I think about it as a source of driving more learning, a continuous learning and continuously adapting an organization where it's not just the machines that are doing this, but it's the humans who've been empowered to do that. And my chapter nine in my new book, Jeff, is all about team empowerment, because nothing you do with AI is going to matter of squat if you don't have empowered teams who know how to take and leverage that continuous learning opportunity at the front lines of customer and operational engagement. >> Bill, I couldn't set a better, I think we'll leave it there. That's a great close, when is the next book coming out? >> So today I do my second to last final review. Then it goes back to the editor and he does a review and we start looking at formatting. So I think we're probably four to six weeks out. >> Okay, well, thank you so much, congratulations on all the success. I just love how the Dean is really the Dean now, teaching all over the world, sharing the knowledge and attacking some of these big problems. And like all great economics problems, often the answer is not economics at all. It's completely really twist the lens and don't think of it in that, all that construct. >> Exactly. >> All right, Bill. Thanks again and have a great week. >> Thanks, Jeff. >> All right. He's Bill Schmarzo, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (gentle music)

(upbeat music) >> From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Hello everyone, welcome to theCUBE studio here in Palo Alto. I'm John Furrier your host of theCUBE. We're here introducing a new format for CUBE panel discussions, it's called Around theCUBE and we have a special segment here called Get Smart: Unpacking AI with some great with some great guests in the industry. Gene Santos, Professor of Engineering in College of Engineering Dartmouth College. Bob Friday, Vice President CTO at Mist at Juniper Company. And Ed Henry, Senior Scientist and Distinguished Member of the Technical Staff for Machine Learning at Dell EMC. Guys this is a format, we're going to keep score and we're going to throw out some interesting conversations around Unpacking AI. Thanks for joining us here, appreciate your time. >> Yeah, glad to be here. >> Okay, first question, as we all know AI is on the rise, we're seeing AI everywhere. You can't go to a show or see marketing literature from any company, whether it's consumer or tech company around, they all have AI, AI something. So AI is on the rise. The question is, is it real AI, is AI relevant from a reality standpoint, what really is going on with AI, Gene, is AI real? >> I think a good chunk of AI is real there. It depends on what you apply it to. If it's making some sort of decisions for you, that is AI that's blowing into play. But there's also a lot of AI left out there potentially is just simply a script. So, you know, one of the challenges that you'll always have is that, if it were scripted, is it scripted because, somebody's already developed the AI and now just pulled out all the answers and just using the answers straight? Or is it active learning and changing on its own? I would tend to say that anything that's learning and changing on its own, that's where you're having the evolving AI and that's where you get the most power from. >> Bob what's your take on this, AI real? >> Yeah, if you look at Google, What you see is AI really became real in 2014. That's when the AI and ML really became a thing in the industry and when you look why did it become a thing in 2014? It's really back when we actually saw TensorFlow, open source technology really become available. It's all that Amazon Compute story. You know, you look what we're doing here at Mist, I really don't have to worry about compute storage, except for the Amazon bill I get every month now. So I think you're really seeing AI become real, because of some key turning points in the industry. >> Ed, your take, AI real? >> Yeah, so it depends on what lens you want to kind of look at it through. The notion of intelligence is something that's kind of ill defined and depending how how you want to interpret that will kind of guide whether or not you think it's real. I tend to all things AI if it has a notion of agency. So if it can navigate its problem space without human intervention. So, really it depends on, again, what lens you kind of want to look at it through? It's a set of moving goalposts, right? If you take your smartphone back to Turing When he was coming up with the Turing test and asked them if this intelligent, or some value intelligent device was AI, would that be AI, to him probably back then. So really it depends on how you kind of want to look at it. >> Is AI the same as it was in 1988? Or has it changed, what's the change point with AI because some are saying, AI's been around for a while but there's more AI now than ever before, Ed we'll start with you, what's different with AI now versus say in the late 80s, early 90s? >> See what's funny is some of the methods that we're using aren't different, I think the big push that happened in the last decade or so has been the ability to store as much data as we can along with the ability to have as much compute readily disposable as we have today. Some of the methodologies I mean there was a great Wired article that was published and somebody referenced called, method called Eigenvector Decomposition they said it was from quantum mechanic, that came out in 1888 right? So it really a lot of the methodologies that we're using aren't much different, it's the amount of data that we have available to us that represents reality and the amount of compute that we have. >> Bob. >> Yeah so for me back in the 80s when I did my masters I actually did a masters on neural networks so yeah it's been around for a while but when I started Mist what really changed was a couple things. One is this modern cloud stack right so if you're going to have to build an AI solution really have to have all the pieces ingest tons of data and process it in real time so that is one big thing that's changed that we didn't have 20 years ago. The other big thing is we had access to all this open source TensorFlow stuff right now. People like Google and Facebook have made it so easy for the average person to actually do an AI project right? You know anyone here, anyone in the audience here could actually train a machine learning model over the weekend right now, you just have to go to Google, you have to find kind of the, you know they have the data sets you want to basically build a model to recognize letters and numbers, those data sets are on the internet right now and you personally yourself could go become a data scientist over the weekend. >> Gene, your take. >> Yeah I think also on top of that because of all that availability on the open software anybody can come in and start playing with AI, it's also building a really large experience base of what works and what doesn't work and because they have that now you can actually better define the problem you're shooting for and when you do that you increase you know what's going to work, what's not going to work and people can also tell you that on the part that's not going to work, how's it going to expand but I think overall though this comes back to the question of when people ask what is AI, and a lot of that is just being focused on machine learning and if it's just machine learning that's kind of a little limited use in terms of what you're classifying or not. Back in the early 80s AI back then is really what people are trying to call artificial general intelligence nowadays but it's that all encompassing piece. All the things that you know us humans can do, us humans can reason about, all the decision sequences that we make and so you know that's the part that we haven't quite gotten to but there is all the things that's why the applications that the AI with machine learning classification has gotten us this far. >> Okay machine learning is certainly relevant, it's been one of the most hottest, the hottest topic I think in computer science and with AI becoming much more democratized you guys mentioned TensorFlow, a variety of other open source initiatives been a great wave of innovation and again motivation, younger generations is easier to code now than ever before but machine learning seems to be at the heart of AI and there's really two schools of thought in the machine learning world, is it just math or is there more of a cognition learning machine kind of a thing going on? This has been a big debate in the industry, I want to get your guys' take on this, Gene is machine learning just math and running algorithms or is there more to it like cognition, where do you guys fall on this, what's real? >> If I look at the applications and look what people are using it for it's mostly just algorithms it's mostly that you know you've managed to do the pattern recognition, you've managed to compute out the things and find something interesting from it but then on the other side of it the folks working in say neurosciences, the first people working in cogno-sciences. You know I have the interest in that when we look at that, that machine learning does it correspond to what we're doing as human beings, now because the reason I fall more on the algorithm side is that a lot of those algorithms they don't match what we're often thinking so if they're not matching that it's like okay something else is coming up but then what do we do with it, you know you can get an answer and work from it but then if we want to build true human intelligence how does that all stack together to get to the human intelligence and I think that's the challenge at this point. >> Bob, machine learning, math, cognition is there more to do there, what's your take? >> Yeah I think right now you look at machine learning, machine learning are the algorithms we use, I mean I think the big thing that happened to machine learning is the neural network and deep learning, that was kind of a mild stepping stone where we got through and actually building kind of these AI behavior things. You know when you look what's really happening out there you look at the self driving car, what we don't realize is like it's kind of scary right now, you go to Vegas you can actually get on a driving bus now, you know so this AI machine learning stuff is starting to happen right before our eyes, you know when you go to the health care now and you get your diagnosis for cancer right, we're starting to see AI in image recognition really start to change how we get our diagnosis. And that's really starting to affect people's lives. So those are cases where we're starting to see this AI machine learning stuff is starting to make a difference. When we think about the AI singularity discussion right when are we finally going to build something that really has human behavior. I mean right now we're building AI that can actually play Jeopardy right, and that was kind of one of the inspirations for my company Mist was hey, if they can build something to play Jeopardy we should be able to build something answer questions on par with network domain experts. So I think we're seeing people build solutions now that do a lot of behaviors that mimic humans. I do think we're probably on the path to building something that is truly going to be on par with human thinking right, you know whether it's 50 years or a thousand years I think it's inevitable on how man is progressing right now if you look at the technologically exponential growth we're seeing in human evolution. >> Well we're going to get to that in the next question so you're jumping ahead, hold that thought. Ed, machine learning just math, pattern recognition or is there more cognition there to be had? Where do fall in this? >> Right now it's, I mean it's all math, so we collect something some data set about the world and then we use algorithms and some representation of mathematics to find some pattern, which is new and interesting, don't get me wrong, when you say cognition though we have to understand that we have a fundamentally flawed perspective on how maybe the one guiding light that we have on what intelligence could be would be ourselves right. Computers don't work like brains, brains are what we determine embody our intelligence right, computers, our brains don't have a clock, there's no state that's actually between different clock cycles that light up in the brain so when you start using words like cognition we end up trying to measure ourselves or use ourselves as a ruler and most of the methodologies that we have today don't necessarily head down that path. So yeah that's kind of how I view it. >> Yeah I mean stateless those are API kind of mindsets, you can't run Kubernetes in the brain. Maybe we will in the future, stateful applications are always harder than stateless as we all know but again when I'm sleeping, I'm still dreaming. So cognition in the question of human replacement. This has been a huge conversation. This is one, the singularity conversation you know the fear of most average people and then some technical people as well on the job front, will AI replace my job will it take over the world is there going to be a Skynet Terminator moment? This is a big conversation point because it just teases out what could be and tech for good tech for bad. Some say tech is neutral but it can be shaped. So the question is will AI replace humans and where does that line come from. We'll start with Ed on this one. What do you see this singularity discussion where humans are going to be replaced with AI? >> So replace is an interesting term, so there I mean we look at the last kind of Industrial Revolution that happened and people I think are most worried about the potential of job loss and when you look at what happened during the Industrial Revolution this concept of creative destruction kind of came about and the idea is that yes technology has taken some jobs out of the market in some way shape or form but more jobs were created because of that technology, that's kind of our one again lighthouse that we have with respect to measuring that singularity in and of itself. Again the ill defined definition, or the ill defined notion of intelligence that we have today, I mean when you go back and you read some of the early papers from psychologists from the early 1900s the experiment specifically who came up with this idea of intelligence he uses the term general intelligence as kind of the first time that all of civilization has tried to assign a definition to what is intelligent right? And it's only been roughly 100 years or so or maybe a little longer since we have had this understanding that's been normalized at least within western culture of what this notion of intelligence is so singularity this idea of the singularity is interesting because we just don't understand enough about the one measure ruler or yardstick that we have that we consider intelligence ourselves to be able to go and then embed that inside of a thing. >> Gene what's your thoughts on this, reasoning is a big part of your research you're doing a lot of research around intent and contextual, all these cool behavioral things you know this is where machines are there to augment or replace, this is the conversation, your view on this? >> I think one of the things with this is that that's where the downs still lie, if we have bad intentions, if we can actually start communicating then we can start getting the general intelligence yeah I mean sort of like what Ed was referring to how people have been trying to define this but I think one of the problems that comes up is that computers and stuff like that don't really capture that at this time, the intentions that they have are still at a low level, but if we start tying it to you know the question of the terminator moment to the singularity, one of the things is that autonomy, you know how much autonomy that we give to the algorithm, how much does the algorithm have access to? Now there could be you know just to be on an extreme there could be a disaster situation where you know we weren't very careful and we provided an API that gives full autonomy to whatever AI we have to run it and so you can start seeing elements of Skynet that can come from that but I also tend to come to analysis that hey even with APIs, while it's not AI, APIs a lot of that also we have the intentions of what you're going to give us to control. Then you have the AI itself where if you've defined the intentions of what it is supposed to do then you can avoid that terminator moment in terms of that's more of an act. So I'm seeing it at this point. And so overall singularity I still think we're a ways off and you know when people worry about job loss probably the closest thing that I think that can match that in recent history is the whole thing on automation, I grew up at the time in Ohio when the steel industry was collapsing and that was a trade off between automation and what the current jobs are and if you have something like that okay that's one thing that we go forward dealing with and I think that this is something that state governments, our national government something we should be considering. If you're going to have that job loss you know what better study, what better form can you do from that and I've heard different proposals from different people like, well if we need to retrain people where do you get the resources from it could be something even like AI job pack. And so there's a lot of things to discuss, we're not there yet but I do believe the lower, repetitive jobs out there, I should say the things where we can easily define, those can be replaceable but that's still close to the automation side. >> Yeah and there's a lot of opportunities there. Bob, you mentioned in the last segment the singularity, cognition learning machines, you mentioned deep learning, as the machines learn this needs more data, data informs. If it's biased data or real data how do you become cognitive, how do you become human if you don't have the data or the algorithms? The data's the-- >> I mean and I think that's one of the big ethical debates going on right now right you know are we basically going to basically take our human biases and train them into our next generation of AI devices right. But I think from my point of view I think it's inevitable that we will build something as complex as the brain eventually, don't know if it's 50 years or 500 years from now but if you look at kind of the evolution of man where we've been over the last hundred thousand years or so, you kind of see this exponential rise in technology right from, you know for thousands of years our technology was relatively flat. So in the last 200 years where we've seen this exponential growth in technology that's taking off and you know what's amazing is when you look at quantum computing what's scary is, I always thought of quantum computing as being a research lab thing but when you start to see VC's and investing in quantum computing startups you know we're going from university research discussions to I guess we're starting to commercialize quantum computing, you know when you look at the complexity of what a brain does it's inevitable that we will build something that has basic complexity of a neuron and I think you know if you look how people neural science looks at the brain, we really don't understand how it encodes, but it's clear that it does encode memories which is very similar to what we're doing right now with our AI machine right? We're building things that takes data and memories and encodes in some certain way. So yeah I'm convinced that we will start to see more AI cognizance and it starts to really happen as we start with the next hundred years going forward. >> Guys, this has been a great conversation, AI is real based upon this around theCUBE conversation. Look at I mean you've seen the evidence there you guys pointed it out and I think cloud computing has been a real accelerant with the combination of machine learning and open source so you guys have illustrated and so that brings up kind of the final question I'd love to get each of you's thought on this because Bob just brought up quantum computing which as the race to quantum supremacy goes on around the world this becomes maybe that next step function, kind of what cloud computing did for revitalizing or creating a renaissance in AI. What does quantum do? So that begs the question, five ten years out if machine learning is the beginning of it and it starts to solve some of these problems as quantum comes in, more compute, unlimited resource applied with software, where does that go, five ten years? We'll go start with Gene, Bob, then Ed. Let's wrap this up. >> Yeah I think if quantum becomes a reality that you know when you have the exponential growth this is going to be exponential and exponential. Quantum is going to address a lot of the harder AI problems that were from complexity you know when you talk about this regular search regular approaches of looking up stuff quantum is the one that allows you now to potentially take something that was exponential and make it quantum. And so that's going to be a big driver. That'll be a big enabler where you know a lot of the problems I look at trying to do intentions is that I have an exponential number of intentions that might be possible if I'm going to choose it as an explanation. But, quantum will allow me to narrow it down to one if that technology can work out and of course the real challenge if I can rephrase it into say a quantum program while doing it. But that's I think the advance is just beyond the step function. >> Beyond a step function you see. Okay Bob your take on this 'cause you brought it up, quantum step function revolution what's your view on this? >> I mean your quantum computing changes the whole paradigm right because it kind of goes from a paradigm of what we know, this binary if this then that type of computing. So I think quantum computing is more than just a step function, I think it's going to take a whole paradigm shift of you know and it's going to be another decade or two before we actually get all the tools we need to actually start leveraging quantum computing but I think that is going to be one of those step functions that basically takes our AI efforts into a whole different realm right? Let us solve another whole set of classic problems and that's why they're doing it right now because it starts to let you be able to crack all the encryption codes right? You know where you have millions of billions of choices and you have to basically find that one needle in the haystack so quantum computing's going to basically open that piece of the puzzle up and when you look at these AI solutions it's really a collection of different things going underneath the hood. It's not this one algorithm that you're doing and trying to mimic human behavior, so quantum computing's going to be yet one more tool in the AI toolbox that's going to move the whole industry forward. >> Ed, you're up, quantum. >> Cool, yeah so I think it'll, like Gene and Bob had alluded to fundamentally change the way we approach these problems and the reason is combinatorial problems that everybody's talking about so if I want to evaluate the state space of anything using modern binary based computers we have to kind of iteratively make that search over that search space where quantum computing allows you to kind of evaluate the entire search space at once. When you talk about games like AlphaGo, you talk about having more moves on a blank 19 by 19 AlphaGo board than you have if you put 1,000 universes on every proton of our universe. So the state space is absolutely massive so searching that is impossible. Using today's binary based computers but quantum computing allows you to evaluate kind of search spaces like that in one big chunk to really simplify the aspect but I think it will kind of change how we approach these problems to Bob and Gene's point with respect to how we approach, the technology once we crack that quantum nut I don't think will look anything like what we have today. >> Okay thank you guys, looks like we have a winner. Bob you're up by one point, we had a tie for second but Ed and Gene of course I'm the arbiter but I've decided Bob you nailed this one so since you're the winner, Gene you guys did a great job coming in second place, Ed good job, Bob you get the last word. Unpacking AI, what's the summary from your perspective as the winner of Around theCUBE. >> Yeah no I think you know from a societal point of view I think AI's going to be on par with kind of the internet. It's going to be one of these next big technology things. I think it'll start to impact our lives and people when you look around it it's kind of sneaking up on us, whether it's the self driving car the healthcare cancer, the self driving bus, so I think it's here, I think we're just at the beginnings of it. I think it's going to be one of these technologies that's going to basically impact our whole lives or our next one or two decades. Next 10, 20 years is just going to be exponentially growing everywhere in all our segments. >> Thanks so much for playing guys really appreciate it we have an inventor entrepreneur, Gene doing great research at Dartmouth check him out, Gene Santos at Dartmouth Computer Science. And Ed, technical genius at Dell, figuring out how to make those machines smarter and with the software abstractions growing you guys are doing some good work over there as well. Gentlemen thank you for joining us on this inaugural Around theCUBE unpacking AI Get Smart series, thanks for joining us. >> Thank you. >> Thank you. >> Okay, that's a wrap everyone this is theCUBE in Palo Alto, I'm John Furrier thanks for watching. (upbeat funk music)

>>from San Francisco. It's the Q covering pager duty. Summit 2019. Brought to you by pager Duty. >>Hey, welcome back. You're ready, Jeff? Rick here with the Cube. We're downtown San Francisco at the historic Western St Francis. A pager. Duty summit. It's the fourth year pager duty Summit, 30 year for the Q. Being here, I think they've about outgrown the venue. So he looked forward to seeing where we go next year. But we're excited to have somebody is at a very busy day. A lot of product announcements leading a lot of this effort. He's Jonathan. Randy, this s V P. Of product for pager duty. Jonathan, great to see you. Thanks for having me. So, congratulations. A lot of Ah lot of product announcements today. >>This is our biggest unveiling of the year. >>What s so I don't want you to pick your favorite baby, but what are some of the highlights? That goddess here today? >>Yes, a couple of big things today and tomorrow, not just today. >>Uh, >>first, we're really focused on applying. It is the buzzword of the sense of the new Millennium machine learning, but we're applying it across our entire portfolio, and we're doing it in a good way, not in a creepy way. We're doing in a good way to help organizations make sense of all the data they're getting. Tell him what's happening and, more importantly, what they could do to get better. And so that's something that we call our intelligence Dashboards is part of our analytics products. That's one big one, right? Right. And as you probably know, being here, pager duty is all about helping teams to be more effective in the moments that matter. And one of the other big announcements we have is intelligent triage. And so what is it way See with There's a lot of great companies here, partners that we're working with and whenever they're working, major issues within their companies were seconds, matter or even microseconds. They could lose millions of dollars that work in real time. They'll find out that there's multiple teams working on the same problems on Lee for one team to find out that somebody's undoing some of things that they're doing. So we focused in a huge way on building context, the visibility so that the teams in see what other issues air related That's what we call intelligent triage. So nobody needs to do double work, >>right? It's funny on the on the A I right in machine learning because they are the hot, hot, hot buzzword. But what I don't think are the hot buzzards, which is where all the excitement is happening, is it's the applied A I it's not Aye aye, for a eyes sake. Or were great. Aye aye company with an aye aye widget that we want to sell you. It's really leveraging a I within your core application space, your core domain expertise to make your abs do better things. And that's really what you guys have embraced. >>Absolutely. It's way have to be so empathetic to our users. Are users carry an unbelievable burden. They are on the front lines when things go down. They have, you know, minutes, seconds to make right decisions, and there's a lot of responsibility with that. So we're using a I in applied way to help them make sense of being overloaded with information, focus them in on the things that can make the biggest positive impact right, So it is applied a I in its purest form and >>the other part I found interesting is really anak knowledge mint that it's not just the people that have to fix the problem that needs to know about the problem, but there's a much larger kind of ecosystem that ecosystem around. That problem, whether it's sales reps executive for certain, is a whole bunch of people that should know, need to know, have value, to know beyond just the really smart person that I've now put on fixing the >>problem. You're bringing up a great point, which is a lot of people know page of duty because of how we help technical teams, developers and office people fix these incidents. When they happen right when a site goes down or when something search isn't working correctly but getting work done. We're taking that in its broadest context. It's beyond technical responders. First we have to service them. They're our core audience. They're why we're here today. But that unit of work getting work done goes beyond them as you're saying. It goes to what we call business responders who I could be working in a customer service team and while that incident is happening, I need that information so that I can ready my communication in case somebody calls up the sports desk and opens up a ticket. I need to know what to tell him right when it's gonna be fixed and how we're addressing their problems. Or I could be the CFO, a stakeholder and just want to know what's the real revenue impact of this outage of this time? So whether I'm taking action or I just need to know these air people outside of the sphere of the technical team and their business responders and stakeholders and we're automating the flow of information all of them so that they don't interrupt the poor responders team so they can focus on their work, >>right? Yeah. Another concept that kind of clarified today is all of your guys partnerships. You know, you've listened on your integration page on the Web site. It's clear. Well, data dog sales for Zenda Sumo AWS service now last CNN, IBM Blue mix. I mean, it's they can't go through the whole list. It's a huge list, but I think confusion in the market or maybe clarification is helpful is, you know, kind of where to those systems play versus your system when that Everyone wants to be a system of record, right? Everybody wants to be the database that has all the all the information. And yet you figured out a way to take your capabilities and augment all these other platforms and really puts you in a nice play across a really wide range of a problem. Sets. >>Yeah, it's it's so core to who we are way like to think of our pager duty platform. I always refer to it as it's a central nervous system, and what does that really mean? We always say it's a central nervous system and pager duty is about people. So all of those vendors, all of those companies, they're all valued partners. Many of them are customers of pager duty as well. They use us to keep their service is up on the monitoring world. But what pager duty is always focused on is ensuring that people two people collaboration to get real work done based on the information coming from those folks. So a lot of those vendors out there they play such an invaluable part of the ecosystem. They let us know they provide all the telemetry in the information in the data way, make sense of it and then engage people Finish that work. So in a way, you know that central nervous system is taking all these impulses just like a really central nervous system. And we're engaging the right people to help them effectively get the right right, and we couldn't do it without them. So the famous 350 plus way couldn't do what we do without them, and they're all here today. You >>didn't think I was going to read the whole hunt 350 >>Hope. That would be a long way >>Hades in desk on. And I know that was part of the new customer service and has been getting, you know, kind of your value kind of closer to the actual customer transaction. It's always in support of the customer transactions. The website's down transaction close, but this actually has taken it to the next level toe. Have a direct contact to the person who's actually engaged with the client to give them or inside is what's going on as being resolved in these type thing with a two way communication pattern. >>Yeah, it's something I'm personally really excited about. Where customer of zendesk as well. So we use end us and they use pager duty. So we get a lot of feedback on what's working, what's not working, which informed us and what we were doing. But there's two big problems in the industry that I've seen over, you know, two plus decades, which is customer service and support teams. They're dealing also on the front lines. Having them communicate and get information from development teams isn't always easy. And so both of us are really interested in kind of breaking down the walls between those organizations. But doing so in a way that's not interrupting those teams when they're doing their work that they have, right, so one, that's what we wanted to accomplish. How can we share information seamlessly automatically? So both teams are in sync, but they're not pestering each other and then to that work that's being done on the development side, when something does go wrong in a devil apps world, now, the customer support agents, the service agents they can get ahead of those cases that are being opened up, so they're not in the dark. They're not being flooded by tons of cases being opened up and they don't know what to say. They ready their communications and push it out because they're insane. >>It's really you think pager duty and notifications were surrounded by all these dashboards and computer stuff, but you made a really instant comment. It's all about the people you guys commissioned. A study called I'm gonna read an unplanned work, the human impact of an always on world and really going after unplanned work. Now it's funny, because everyone always talks about unplanned maintenance and on scheduled maintenance and the impacts on aircraft and the impacts on power generation and aircraft. This is the first time I've ever heard anyone couch it as as unplanned, which is completely disruptive fours on people and their lives, not to mention their service workers. And, according to the study, 2/3 of her pissed off and not too happy the way things are going at work anyway, with what kind of was zenith of that. And that's a really great way to reframe this problem into something much more human. >>The genesis of this all came from the concept that a CZ you'll read a lot we say we're always on. Let's keep it that way. Let's help help everyone. Keep it that way. It's a mantra with pager duty, and it comes from again when I say Genesis, it comes from even within our platform way. Don't have me Windows. We are on 24 7 360 days a year way have to be up when other service's aren't because of that. Whenever we work with organizations or vendors that that we pay for. And they say we have a maintenance window like a maintenance window my partner in crime runs engineering team are meant for. He always says maintenance Windows air for cars, not SAS software like there are no maintenance windows. And what that means as a first step is, if that's the case, there's no maintenance windows you're always on. Then you have to answer this question of how much time are you really spending unplanned work interruptions, right? So we really started taking not the heart. We really started trying to figure out what is the percentage everybody's trying to innovate more. That's planned war, right? Is it? 10% is a 20%. Is it 50%? The best organizations we see our 20 to 25% is unplanned work. We'll >>need 25% for the best organization. >>Yeah, so means not. So best organizations are very different, right? And so way feel that we uniquely can help organizations get way better at cutting down that time so that they can innovate more, Right? They're not firefighting. They're actually innovating and growing their business right. That's a big part of how we help people in these organizations do their job better. >>God, that's before you get in contact. Switching and pressure and disruption and >>way found some amazing statistics in my prior life. Iran Engineering. And it was at a sauce company. And what I found was whenever customers, whenever my top engineers would be put on Call Way, didn't have pager duty at the time, and they would be on call and interrupted on consecutive nights in the middle of the night. First, I would typically hear about when somebody was burned out is when I would see a resignation letter on my desk or somebody way no, after two or three or four successive interruptions in someone's personal life that goes on where they feel they're not being productive. One, they aren't productive at work either, to they're a huge retention risk. So way have that kind of data. We can look at it, and we can help management and organizations help them. And their teams take better care of their teams so that, you know, they're they're being more humane, humane knots, not human off pain, All right. And how you deal with those most expensive precious resource is in your company, which are your people is really important >>when they walk out the door every night, you know? So you gotta take care of him. So they come back the next day. It is? Yes. All right, Jonathan, last question is you as we wait, we're not quite done with some yet, but as we come to the closest on her arm really busy year. The AIPO. You guys have done amazing things, but you kind of flipped the calendar. Look forward. What are some of your kind of priorities as we as >>we move forward? Yeah. So it's been a crazy year. A lot of change and a couple things going forward. One were big partners with Amazon in a W S S O were attending reinvent. That's a big event for the company, but also at this event. As I mentioned before, it's probably our biggest unveiling of new innovations and products for our entire 12,000 plus customers. So for us, it may seem like it's an end. It's really just the beginning, because all of these products and intelligent triage business response, intelligent dashboards, these products that are apart, his capabilities that are part of our analytics and events intelligence on the pager duty, platform way have to keep evolving This we have to keep kind of moving forward because the world is always on and we've got to keep it that way. >>What? Andre just had a great line in his keynote about being scared is the generator of wisdom. But here it is, right here. Fear is the beginning of wisdom. Not necessarily fear, but fear getting caught. Keep moving that we have ahead of the pack. All right, Jonathan, Thanks for taking a few minutes and congratulations. I'm sure tough getting all those new babies out this week, but what a great what a great job. Thank you so much. All right. Pleasure. He's Jonathan. I'm Jeff. You're watching the cube. Where? Pager duty Summit in San Francisco. Thanks for watching. We'll see you next time.

>> From the SiliconANGLE Media Office in Boston, Massachusets, it's theCUBE! (techno music) Now, here's your host, Stu Miniman. >> Hi. I'm Stu Miniman and this is a Cube Conversation from our Boston area studio. Welcome back to the program. Bobby Patrick, who is the Chief Marketing Officer of UiPath. Bobby, good to see you. >> Great to be here Stu. >> Alright. Bobby, we're going to tackle head-on an interesting discussion that's been going on in the industry. Of course, Artificial Intelligence is this wave that is impacting a lot when you look at earnings reports, everyone's talking about it. Most companies are understanding how they're doing it. It is not a new term. I go back reading my history of technology, Ada Lovelace, 150 years ago when she was helping to define what a computer was. She made the Lovelace objective, I believe they said - >> Right. >> Which was later quoted by Turing and the like is that if we can describe it in code, it's probably not Artificial Intelligence cause their not building new things - >> Right. >> And being able to change on there, so there's hype around AI itself, but UiPath is one of the leaders in Robotic Process Automation and how that fits in with AI and Machine Learning, all of these other terms it can get a bit of an acronym soup and we all can't agree on what the terms are. So, let's start with some of the basics Bobby. Please give us RPA and AI and we'll get into it from there. >> Well, Robotic Process Automation, according to the analysts, like Forester are part of the overall AI broader kind of massive, massive market. AI itself has many different, different, routes. Deep learning right, and machine learning, natural language processing, right and so on. I think AI is a term that covers many different grounds. And RPA, AI applies two ways. It applies within RPA and that we have a technology called Computer Vision. It's how a robot looks at a screen like how a human does, which is very, very difficult actually. You look at a citrix terminal session, or a VDI session, different than an Excel sheet, different than as SASAB, and most processes across all of those, so a robot has to be able to look at all of, all of those screen elements, and understand them right. AI within Computer Vision around understanding documents, looking at unstructured data, looking at handwriting. Conversational understanding. Looking at text in an email determining context, helping with chatbots. But a number of those components, doesn't mean we have to build that all ourselves. What RPA does is we bring it all together. We make it easy to automate and build and create the data flow of a process. Then you can apply AI to that, right. So, I think, two years ago when I first joined UiPath, putting RPA and AI in the same sentence people laughed. Year ago we said, ya know what, RPA is really the path to AI in business operations. Now, ya know we say that we're the most highly valued AI company in the world and no one has ever disagreed. >> Yeah, so it's good to lay out some of the adopting cause one of the things to look at and say if I looked at this product two or three years ago, it's not the product that it is today. We know how fast software - >> Right. Is making changes along the line. Second thing, automation itself is something we've been talking about my entire career. >> Right. When I look at things we were doing 5, 10, 15 years ago, and calling automation, we kind of laugh at it. Because today, automation absolutely is making a lot of changes. RPA is taking that automation in a very strategic direction for many companies there. It's the conversation we had last year at your conference was, RPA is the gateway drug if you will. >> Right. >> Of that environment because automation has scared a lot of people. Am I just doing scripts, what do I control, what do I set? Maybe just give us that first grounding of where that automation path is, has come and is going. >> So, there's different kinds of automation right as you said. We've had automation for decades, primarily in IT. Automation was primarily around API to API integration. And that's really hard, right. It requires developers, engineers, it requires them to keep it current. It's expensive and takes a longer time. Along comes the technology, RPA and UiPath, right were you can automate fairly quickly. There's built in recorders and you can do it with a drag and drop, like a flow chart. You can automate a process, and that, that automation is immediately beneficial. Meaning that outcome, is immediate. And, the cost to doing that is small in comparison. And I think, maybe it's the longtail of automation in some ways. It's all of these things that we do around a SAP process. The reality is if you have SAP, or you have Oracle, or you have Workday, the human processes around that involve still a Spreadsheet. It involves PDF documents. A great, one of my favorite examples right now on YouTube with Microsoft is Chevron. Chevron has hundreds of thousands of PDF's that is generated from every oil rig every day. It has all kinds of data in different formats. Tables, different structured and semi-structured data. They would actually extract that data, manually. To be able to process that and analyze that, right. Working with Microsoft AI and UiPath RPA they're able to automate that entire massive process. And now they're on stage talking about it, Microsoft and UiPath events right. And, they call that AI. That's applying AI to a massive problem for them. They need the robot to be completely accurate though. You don't to worry that the data that is being extracted from the PDF's is inaccurate, right. So, Machine Learning goes into that. There's exception management that's a part of that process as well. They call it AI. >> Yeah, some of this is just, people in the industry, the industry watchers is, we get very particular on different terminology. Let's not conflate Artificial Intelligence, or Augmented Intelligence with Machine Learning, because their different environments. I've heard Forester talk about, right, it's a spectrum though, there's an umbrella for some of these. So, we like to get not too pedantic on individual terms itself. >> Right. >> Um - >> Let me give you more examples. I think the term robotic and RPA, yes, it's true that the vast majority of the last couple of years with RPA have been very rules based, right. Because most processes today like in a call center, there's a rule. Do this and this, then this and this. And so, you're automating that same rules based structure. But once that data's flowing through, you can actually then look at the history of that data and then turn a rules based automation into an experience based automation. And how do you do that? You apply Machine Learning algorithms. You apply Data Robot, LMAI, IBM Watson to it, right. But, it's still the RPA platform that is driving that automation, it's just no longer rules based it's experience based. A great example at UiPath Together Dubai recently, was Dubai customs. They had a process where when you declared something, let's say you box of chocolate, they had to open up a binder and find a classification code for that box of chocolate. Well, they use our RPA product and they make a call out to IBM Watson as a part of the automation, and they just write in, pink box of candy filled chocolate. And it takes its Deep Learning, it comes back with a classification code, all part of an automated process. What happens? Dubai customs lines go from being a two hours to a few minutes, right. It's a combination of our RPA capability and our automation board capability and the ability to bring in IBM Watson. Dubai customs says they applied AI now and solved a big problem. >> One of the things I was reading through the recent Gartner Magic Quadrant on RPA, and they had two classifications. One was, kind of the automation does it all, and the other was the people and machines. Things like chatbox, some of the examples you've been giving there seem to be that combination. Where do those two fit together or are those distinctions that you make? >> Yeah, I mean Gartner's interesting. Gartner's a very IT-centric analyst firm, right and IT often in my view are often very conventional thinkers and not the fastest to adopt breakthrough technologies. They weren't the fastest to adopt Cloud, they weren't the fastest to adopt on-demand CRM, and they weren't the fastest to jump onto RPA because they believe, why can't we use API for everything. And the Gartner analysts is kind of, in the beginning of the process of the Magic Quadrant, they spent a lot of time with us and they were trying hard to say that was, you should solve everything with an API. That's just not reality, right? It's not feasible, and it's not affordable, right? But, RPA is just not the automation of a task or process, it's then applying a whole other set of other technologies. We have 700 partners today in our ecosystem. Natural Language processing partners, right. Machine learning partners. Chatbox partners, you mentioned. So we want to be, we want to make it very easy. In a drag and drop way. To be able to apply these great technologies to an automation to solve some big problem. What's fun to me right now is there's a lot of great startups. They come out of say insurance, or they come out of financial services and they've got a great algorithm and they know the business really well. And they probably have one or two amazing customers, and they're stuck. We, for them, this came from a partner of ours, you're becoming, you UiPath, you're becoming our best route to market because you have the data. You have the work flow. Our job I think in some ways, is to make it easy to bring these technologies together to apply them to an automation to make that through a democratized way where a non-engineer can do this, and I think that's what's happening. >> Yeah, those integrations between environments can be very powerful something we see. Every shop has lots of applications, has lots of technical data and they're not just sweeping the floor of everything they have. What are some of the limits of AI and RPA today, what do you see things going? >> I think, Deep Learning we see very little of that. It's probably applied to some kind of science project and things within companies. I think for the vast majority of our customers, they use machine learning within RPA for Computer Vision by default. But, ya know they're still not really at a stage of mass adoption of what algorithms do I want to apply to a process. I think we're trying to make it easier for you to be able to drag and drop AI we call it, to make it easier to apply. But, I think we're in very early days. And as you mentioned, there's market confusion on it. I know one thing from our 90 plus customers that are in our advisory boards. I know from them they say their companies struggles with finding an ROI in AI, and, you know, I think we're helping there cause we're applying to real operations. They say the same thing about Blockchain. I don't know Stu. Do you know of a single example of a Blockchain ROI, great example? >> Yeah, it reminds me, Big Data was one of those, over half of the people failed to get the ROI they want. It's one of those promises of certain technology - >> Right. >> That high-level, you know let's poo-poo Bobby things that actually have tangible results - >> Yeah. >> And get things done. But you weren't following the strict guidelines of the API economy. >> Right, well true, exactly right. What I find amazing is, I mentioned in another one of our talks conversations that 23,000 have come to UiPath events this year. To our own events, not trade events and other shows, that's different. They want to get on stage and talk. They're delighted about this. And their talking about, generally speaking, RPA's helping them go digital. But they're all saying their ambition is to apply AI to make those processes smarter. To learn from - to go from rules based to experience based. I think what's beautiful about UiPath, is that we're a platform that you can get there overtime. You can apply - you can predict perhaps the algorithm 's you're going to want to use in two or three years. We're not going to force you, you can apply any algorithm you want to an automation work going through. I think that flexibility is actually for customers, they find it very comforting. >> It's one of those things I say, most companies have a cloud strategy. That needs to be written in, not etched in stone. You need to revisit it every quarter. Same thing with what happening AI and in your space things are changing so fast and they need to be agile. >> That's right. >> They need to be able to make changes. In October, you're going to have a lot of those customers up on stage talking. Where will this AI discussion fit into UiPath forward in Las Vegas. We talk a lot about our AI fabric, framework it's around document understanding, getting heavy robots getting smarter and smarter, what they see on the screen, what they see on a document, what they see with handwriting, and improving the accuracy of visual understanding. Looking at the, face recognition and other types of images and being able to understand the images. Conversational understanding. The tone of an email. Is this person really upset? How upset? Or, a conversational chatbot. Really evolving from mimicking humans with RPA to augmenting humans and I think that story, both in the innovations, the customer examples on stage, I think you're going to see the sophistication of automation's that are being used through UiPath grow exponentially. >> Okay, so I want to give you the final word on this. And I don't want to talk to the people that might poo-poo or argue RPA and AI and ML and all these things. Bring us inside your customers. What...where, how does that conversation start? Are they coming it from AI, ML, RPA or is there, ya know a business discussion that usually catalyzes this engagement? >> Our customer's are starting with digital. They're trying to go digital. They know they need digital transformation, it's been very, very hard. There's a real outcome that comes quickly from taking a mundane task that is expensive, and automating that. The outcomes are quick, often projects that involve our partners like Accenture and others. The payback period on the entire project with RPA can be 6 months, it's self-funding. What other technologies doing B2B is self-funding in one year? That's part of the incredible adoption birth. But, every single customer doesn't stop there. They say okay, I also want to know that this automation is, I want to know that I can go apply AI to this. It's in every conversation. So there's two big booms with UiPath and our RPA. The first is when you go digital, there's some great outcome. There's productivity gain, it's immediate, right. I guess I said the payback period is quick. The second big one is when you go and turn it from a rules based to an experience based process, or you apply AI to it, there's another set of business benefits down the road. As more algorithms come out and things, you keep applying to it. This is sort of the gift that keeps on giving. I think if we didn't have that connection to Machine Learning or AI, I think the enthusiasm level of the majority of our customers would not be anywhere near what it is today. >> Alright, well Bobby really appreciate digging into the customerality, RPA, AI all the acronym soup that was going on and we look forward to UiPath Forward at the Bellagio in Las Vegas this October. >> It'll be fun. Alright, I'm Stu Miniman, as always thank you so much for watching theCube.