>> From theCUBE studios in Palo Alto, in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vollante. >> The rise of Kubernetes came about through a combination of forces that were, in hindsight, quite a long shot. Amazon's dominance created momentum for Cloud native application development, and the need for newer and simpler experiences, beyond just easily spinning up computer as a service. This wave crashed into innovations from a startup named Docker, and a reluctant competitor in Google, that needed a way to change the game on Amazon and the Cloud. Now, add in the effort of Red Hat, which needed a new path beyond Enterprise Linux, and oh, by the way, it was just about to commit to a path of a Kubernetes alternative for OpenShift and figure out a governance structure to hurt all the cats and the ecosystem and you get the remarkable ascendancy of Kubernetes. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this breaking analysis, we tapped the back stories of a new documentary that explains the improbable events that led to the creation of Kubernetes. We'll share some new survey data from ETR and commentary from the many early the innovators who came on theCUBE during the exciting period since the founding of Docker in 2013, which marked a new era in computing, because we're talking about Kubernetes and developers today, the hoodie is on. And there's a new two part documentary that I just referenced, it's out and it was produced by Honeypot on Kubernetes, part one and part two, tells a story of how Kubernetes came to prominence and many of the players that made it happen. Now, a lot of these players, including Tim Hawkin Kelsey Hightower, Craig McLuckie, Joe Beda, Brian Grant Solomon Hykes, Jerry Chen and others came on theCUBE during formative years of containers going mainstream and the rise of Kubernetes. John Furrier and Stu Miniman were at the many shows we covered back then and they unpacked what was happening at the time. We'll share the commentary from the guests that they interviewed and try to add some context. Now let's start with the concept of developer defined structure, DDI. Jerry Chen was at VMware and he could see the trends that were evolving. He left VMware to become a venture capitalist at Greylock. Docker was his first investment. And he saw the future this way. >> What happens is when you define infrastructure software you can program it. You make it portable. And that the beauty of this cloud wave what I call DDI's. Now, to your point is every piece of infrastructure from storage, networking, to compute has an API, right? And, and AWS there was an early trend where S3, EBS, EC2 had API. >> As building blocks too. >> As building blocks, exactly. >> Not monolithic. >> Monolithic building blocks every little building bone block has it own API and just like Docker really is the API for this unit of the cloud enables developers to define how they want to build their applications, how to network them know as Wills talked about, and how you want to secure them and how you want to store them. And so the beauty of this generation is now developers are determining how apps are built, not just at the, you know, end user, you know, iPhone app layer the data layer, the storage layer, the networking layer. So every single level is being disrupted by this concept of a DDI and where, how you build use and actually purchase IT has changed. And you're seeing the incumbent vendors like Oracle, VMware Microsoft try to react but you're seeing a whole new generation startup. >> Now what Jerry was explaining is that this new abstraction layer that was being built here's some ETR data that quantifies that and shows where we are today. The chart shows net score or spending momentum on the vertical axis and market share which represents the pervasiveness in the survey set. So as Jerry and the innovators who created Docker saw the cloud was becoming prominent and you can see it still has spending velocity that's elevated above that 40% red line which is kind of a magic mark of momentum. And of course, it's very prominent on the X axis as well. And you see the low level infrastructure virtualization and that even floats above servers and storage and networking right. Back in 2013 the conversation with VMware. And by the way, I remember having this conversation deeply at the time with Chad Sakac was we're going to make this low level infrastructure invisible, and we intend to make virtualization invisible, IE simplified. And so, you see above the two arrows there related to containers, container orchestration and container platforms, which are abstraction layers and services above the underlying VMs and hardware. And you can see the momentum that they have right there with the cloud and AI and RPA. So you had these forces that Jerry described that were taking shape, and this picture kind of summarizes how they came together to form Kubernetes. And the upper left, Of course you see AWS and we inserted a picture from a post we did, right after the first reinvent in 2012, it was obvious to us at the time that the cloud gorilla was AWS and had all this momentum. Now, Solomon Hykes, the founder of Docker, you see there in the upper right. He saw the need to simplify the packaging of applications for cloud developers. Here's how he described it. Back in 2014 in theCUBE with John Furrier >> Container is a unit of deployment, right? It's the format in which you package your application all the files, all the executables libraries all the dependencies in one thing that you can move to any server and deploy in a repeatable way. So it's similar to how you would run an iOS app on an iPhone, for example. >> A Docker at the time was a 30% company and it just changed its name from .cloud. And back to the diagram you have Google with a red question mark. So why would you need more than what Docker had created. Craig McLuckie, who was a product manager at Google back then explains the need for yet another abstraction. >> We created the strong separation between infrastructure operations and application operations. And so, Docker has created a portable framework to take it, basically a binary and run it anywhere which is an amazing capability, but that's not enough. You also need to be able to manage that with a framework that can run anywhere. And so, the union of Docker and Kubernetes provides this framework where you're completely abstracted from the underlying infrastructure. You could use VMware, you could use Red Hat open stack deployment. You could run on another major cloud provider like rec. >> Now Google had this huge cloud infrastructure but no commercial cloud business compete with AWS. At least not one that was taken seriously at the time. So it needed a way to change the game. And it had this thing called Google Borg, which is a container management system and scheduler and Google looked at what was happening with virtualization and said, you know, we obviously could do better Joe Beda, who was with Google at the time explains their mindset going back to the beginning. >> Craig and I started up Google compute engine VM as a service. And the odd thing to recognize is that, nobody who had been in Google for a long time thought that there was anything to this VM stuff, right? Cause Google had been on containers for so long. That was their mindset board was the way that stuff was actually deployed. So, you know, my boss at the time, who's now at Cloudera booted up a VM for the first time, and anybody in the outside world be like, Hey, that's really cool. And his response was like, well now what? Right. You're sitting at a prompt. Like that's not super interesting. How do I run my app? Right. Which is, that's what everybody's been struggling with, with cloud is not how do I get a VM up? How do I actually run my code? >> Okay. So Google never really did virtualization. They were looking at the market and said, okay what can we do to make Google relevant in cloud. Here's Eric Brewer from Google. Talking on theCUBE about Google's thought process at the time. >> One interest things about Google is it essentially makes no use of virtual machines internally. And that's because Google started in 1998 which is the same year that VMware started was kind of brought the modern virtual machine to bear. And so Google infrastructure tends to be built really on kind of classic Unix processes and communication. And so scaling that up, you get a system that works a lot with just processes and containers. So kind of when I saw containers come along with Docker, we said, well, that's a good model for us. And we can take what we know internally which was called Borg a big scheduler. And we can turn that into Kubernetes and we'll open source it. And suddenly we have kind of a cloud version of Google that works the way we would like it to work. >> Now, Eric Brewer gave us the bumper sticker version of the story there. What he reveals in the documentary that I referenced earlier is that initially Google was like, why would we open source our secret sauce to help competitors? So folks like Tim Hockin and Brian Grant who were on the original Kubernetes team, went to management and pressed hard to convince them to bless open sourcing Kubernetes. Here's Hockin's explanation. >> When Docker landed, we saw the community building and building and building. I mean, that was a snowball of its own, right? And as it caught on we realized we know what this is going to we know once you embrace the Docker mindset that you very quickly need something to manage all of your Docker nodes, once you get beyond two or three of them, and we know how to build that, right? We got a ton of experience here. Like we went to our leadership and said, you know, please this is going to happen with us or without us. And I think it, the world would be better if we helped. >> So the open source strategy became more compelling as they studied the problem because it gave Google a way to neutralize AWS's advantage because with containers you could develop on AWS for example, and then run the application anywhere like Google's cloud. So it not only gave developers a path off of AWS. If Google could develop a strong service on GCP they could monetize that play. Now, focus your attention back to the diagram which shows this smiling, Alex Polvi from Core OS which was acquired by Red Hat in 2018. And he saw the need to bring Linux into the cloud. I mean, after all Linux was powering the internet it was the OS for enterprise apps. And he saw the need to extend its path into the cloud. Now here's how he described it at an OpenStack event in 2015. >> Similar to what happened with Linux. Like yes, there is still need for Linux and Windows and other OSs out there. But by and large on production, web infrastructure it's all Linux now. And you were able to get onto one stack. And how were you able to do that? It was, it was by having a truly open consistent API and a commitment into not breaking APIs and, so on. That allowed Linux to really become ubiquitous in the data center. Yes, there are other OSs, but Linux buy in large for production infrastructure, what is being used. And I think you'll see a similar phenomenon happen for this next level up cause we're treating the whole data center as a computer instead of trading one in visual instance is just the computer. And that's the stuff that Kubernetes to me and someone is doing. And I think there will be one that shakes out over time and we believe that'll be Kubernetes. >> So Alex saw the need for a dominant container orchestration platform. And you heard him, they made the right bet. It would be Kubernetes. Now Red Hat, Red Hat is been around since 1993. So it has a lot of on-prem. So it needed a future path to the cloud. So they rang up Google and said, hey. What do you guys have going on in this space? So Google, was kind of non-committal, but it did expose that they were thinking about doing something that was you know, pre Kubernetes. It was before it was called Kubernetes. But hey, we have this thing and we're thinking about open sourcing it, but Google's internal debates, and you know, some of the arm twisting from the engine engineers, it was taking too long. So Red Hat said, well, screw it. We got to move forward with OpenShift. So we'll do what Apple and Airbnb and Heroku are doing and we'll build on an alternative. And so they were ready to go with Mesos which was very much more sophisticated than Kubernetes at the time and much more mature, but then Google the last minute said, hey, let's do this. So Clayton Coleman with Red Hat, he was an architect. And he leaned in right away. He was one of the first outside committers outside of Google. But you still led these competing forces in the market. And internally there were debates. Do we go with simplicity or do we go with system scale? And Hen Goldberg from Google explains why they focus first on simplicity in getting that right. >> We had to defend of why we are only supporting 100 nodes in the first release of Kubernetes. And they explained that they know how to build for scale. They've done that. They know how to do it, but realistically most of users don't need large clusters. So why create this complexity? >> So Goldberg explains that rather than competing right away with say Mesos or Docker swarm, which were far more baked they made the bet to keep it simple and go for adoption and ubiquity, which obviously turned out to be the right choice. But the last piece of the puzzle was governance. Now Google promised to open source Kubernetes but when it started to open up to contributors outside of Google, the code was still controlled by Google and developers had to sign Google paper that said Google could still do whatever it wanted. It could sub license, et cetera. So Google had to pass the Baton to an independent entity and that's how CNCF was started. Kubernetes was its first project. And let's listen to Chris Aniszczyk of the CNCF explain >> CNCF is all about providing a neutral home for cloud native technology. And, you know, it's been about almost two years since our first board meeting. And the idea was, you know there's a certain set of technology out there, you know that are essentially microservice based that like live in containers that are essentially orchestrated by some process, right? That's essentially what we mean when we say cloud native right. And CNCF was seated with Kubernetes as its first project. And you know, as, as we've seen over the last couple years Kubernetes has grown, you know, quite well they have a large community a diverse con you know, contributor base and have done, you know, kind of extremely well. They're one of actually the fastest, you know highest velocity, open source projects out there, maybe. >> Okay. So this is how we got to where we are today. This ETR data shows container orchestration offerings. It's the same X Y graph that we showed earlier. And you can see where Kubernetes lands not we're standing that Kubernetes not a company but respondents, you know, they doing Kubernetes. They maybe don't know, you know, whose platform and it's hard with the ETR taxon economy as a fuzzy and survey data because Kubernetes is increasingly becoming embedded into cloud platforms. And IT pros, they may not even know which one specifically. And so the reason we've linked these two platforms Kubernetes and Red Hat OpenShift is because OpenShift right now is a dominant revenue player in the space and is increasingly popular PaaS layer. Yeah. You could download Kubernetes and do what you want with it. But if you're really building enterprise apps you're going to need support. And that's where OpenShift comes in. And there's not much data on this but we did find this chart from AMDA which show was the container software market, whatever that really is. And Red Hat has got 50% of it. This is revenue. And, you know, we know the muscle of IBM is behind OpenShift. So there's really not hard to believe. Now we've got some other data points that show how Kubernetes is becoming less visible and more embedded under of the hood. If you will, as this chart shows this is data from CNCF's annual survey they had 1800 respondents here, and the data showed that 79% of respondents use certified Kubernetes hosted platforms. Amazon elastic container service for Kubernetes was the most prominent 39% followed by Azure Kubernetes service at 23% in Azure AKS engine at 17%. With Google's GKE, Google Kubernetes engine behind those three. Now. You have to ask, okay, Google. Google's management Initially they had concerns. You know, why are we open sourcing such a key technology? And the premise was, it would level the playing field. And for sure it has, but you have to ask has it driven the monetization Google was after? And I would've to say no, it probably didn't. But think about where Google would've been. If it hadn't open source Kubernetes how relevant would it be in the cloud discussion. Despite its distant third position behind AWS and Microsoft or even fourth, if you include Alibaba without Kubernetes Google probably would be much less prominent or possibly even irrelevant in cloud, enterprise cloud. Okay. Let's wrap up with some comments on the state of Kubernetes and maybe a thought or two about, you know, where we're headed. So look, no shocker Kubernetes for all its improbable beginning has gone mainstream in the past year or so. We're seeing much more maturity and support for state full workloads and big ecosystem support with respect to better security and continued simplification. But you know, it's still pretty complex. It's getting better, but it's not VMware level of maturity. For example, of course. Now adoption has always been strong for Kubernetes, for cloud native companies who start with containers on day one, but we're seeing many more. IT organizations adopting Kubernetes as it matures. It's interesting, you know, Docker set out to be the system of the cloud and Kubernetes has really kind of become that. Docker desktop is where Docker's action really is. That's where Docker is thriving. It sold off Docker swarm to Mirantis has made some tweaks. Docker has made some tweaks to its licensing model to be able to continue to evolve its its business. To hear more about that at DockerCon. And as we said, years ago we expected Kubernetes to become less visible Stu Miniman and I talked about this in one of our predictions post and really become more embedded into other platforms. And that's exactly what's happening here but it's still complicated. Remember, remember the... Go back to the early and mid cycle of VMware understanding things like application performance you needed folks in lab coats to really remediate problems and dig in and peel the onion and scale the system you know, and in some ways you're seeing that dynamic repeated with Kubernetes, security performance scale recovery, when something goes wrong all are made more difficult by the rapid pace at which the ecosystem is evolving Kubernetes. But it's definitely headed in the right direction. So what's next for Kubernetes we would expect further simplification and you're going to see more abstractions. We live in this world of almost perpetual abstractions. Now, as Kubernetes improves support from multi cluster it will be begin to treat those clusters as a unified group. So kind of abstracting multiple clusters and treating them as, as one to be managed together. And this is going to create a lot of ecosystem focus on scaling globally. Okay, once you do that, you're going to have to worry about latency and then you're going to have to keep pace with security as you expand the, the threat area. And then of course recovery what happens when something goes wrong, more complexity, the harder it is to recover and that's going to require new services to share resources across clusters. So look for that. You also should expect more automation. It's going to be driven by the host cloud providers as Kubernetes supports more state full applications and begins to extend its cluster management. Cloud providers will inject as much automation as possible into the system. Now and finally, as these capabilities mature we would expect to see better support for data intensive workloads like, AI and Machine learning and inference. Schedule with these workloads becomes harder because they're so resource intensive and performance management becomes more complex. So that's going to have to evolve. I mean, frankly, many of the things that Kubernetes team way back when, you know they back burn it early on, for example, you saw in Docker swarm or Mesos they're going to start to enter the scene now with Kubernetes as they start to sort of prioritize some of those more complex functions. Now, the last thing I'll ask you to think about is what's next beyond Kubernetes, you know this isn't it right with serverless and IOT in the edge and new data, heavy workloads there's something that's going to disrupt Kubernetes. So in that, by the way, in that CNCF survey nearly 40% of respondents were using serverless and that's going to keep growing. So how is that going to change the development model? You know, Andy Jassy once famously said that if they had to start over with Amazon retail, they'd start with serverless. So let's keep an eye on the horizon to see what's coming next. All right, that's it for now. I want to thank my colleagues, Stephanie Chan who helped research this week's topics and Alex Myerson on the production team, who also manages the breaking analysis podcast, Kristin Martin and Cheryl Knight help get the word out on socials, so thanks to all of you. Remember these episodes, they're all available as podcasts wherever you listen, just search breaking analysis podcast. Don't forget to check out ETR website @etr.ai. We'll also publish. We publish a full report every week on wikibon.com and Silicon angle.com. You can get in touch with me, email me directly david.villane@Siliconangle.com or DM me at D Vollante. You can comment on our LinkedIn post. This is Dave Vollante for theCUBE insights powered by ETR. Have a great week, everybody. Thanks for watching. Stay safe, be well. And we'll see you next time. (upbeat music)

>>all right, We're five minutes after the hour. That's all aboard. Who's coming aboard? Welcome everyone to the tutorial track for our launchpad of them. So for the next couple of hours, we've got a SYRIZA videos and experts on hand to answer questions about our new product, Doctor Enterprise Container Cloud. Before we jump into the videos and the technology, I just want to introduce myself and my other emcee for the session. I'm Bill Milks. I run curriculum development for Mirant us on. And >>I'm Bruce Basil Matthews. I'm the Western regional Solutions architect for Moran Tissue esa and welcome to everyone to this lovely launchpad oven event. >>We're lucky to have you with us proof. At least somebody on the call knows something about your enterprise Computer club. Um, speaking of people that know about Dr Enterprise Container Cloud, make sure that you've got a window open to the chat for this session. We've got a number of our engineers available and on hand to answer your questions live as we go through these videos and disgusting problem. So that's us, I guess, for Dr Enterprise Container Cloud, this is Mirant asses brand new product for bootstrapping Doctor Enterprise Kubernetes clusters at scale Anything. The airport Abu's? >>No, just that I think that we're trying Thio. Uh, let's see. Hold on. I think that we're trying Teoh give you a foundation against which to give this stuff a go yourself. And that's really the key to this thing is to provide some, you know, many training and education in a very condensed period. So, >>yeah, that's exactly what you're going to see. The SYRIZA videos we have today. We're going to focus on your first steps with Dr Enterprise Container Cloud from installing it to bootstrapping your regional child clusters so that by the end of the tutorial content today, you're gonna be prepared to spin up your first documentary prize clusters using documented prize container class. So just a little bit of logistics for the session. We're going to run through these tutorials twice. We're gonna do one run through starting seven minutes ago up until I guess it will be ten fifteen Pacific time. Then we're gonna run through the whole thing again. So if you've got other colleagues that weren't able to join right at the top of the hour and would like to jump in from the beginning, ten. Fifteen Pacific time. We're gonna do the whole thing over again. So if you want to see the videos twice, you got public friends and colleagues that, you know you wanna pull in for a second chance to see this stuff, we're gonna do it all. All twice. Yeah, this session. Any any logistics I should add, Bruce that No, >>I think that's that's pretty much what we had to nail down here. But let's zoom dash into those, uh, feature films. >>Let's do Edmonds. And like I said, don't be shy. Feel free to ask questions in the chat or engineers and boosting myself are standing by to answer your questions. So let me just tee up the first video here and walk their cost. Yeah. Mhm. Yes. Sorry. And here we go. So our first video here is gonna be about installing the Doctor Enterprise Container Club Management cluster. So I like to think of the management cluster as like your mothership, right? This is what you're gonna use to deploy all those little child clusters that you're gonna use is like, Come on it as clusters downstream. So the management costs was always our first step. Let's jump in there >>now. We have to give this brief little pause >>with no good day video. Focus for this demo will be the initial bootstrap of the management cluster in the first regional clusters to support AWS deployments. The management cluster provides the core functionality, including identity management, authentication, infantry release version. The regional cluster provides the specific architecture provided in this case, eight of us and the Elsie um, components on the UCP Cluster Child cluster is the cluster or clusters being deployed and managed. The deployment is broken up into five phases. The first phase is preparing a big strap note on this dependencies on handling with download of the bridge struck tools. The second phase is obtaining America's license file. Third phase. Prepare the AWS credentials instead of the adduce environment. The fourth configuring the deployment, defining things like the machine types on the fifth phase. Run the bootstrap script and wait for the deployment to complete. Okay, so here we're sitting up the strap node, just checking that it's clean and clear and ready to go there. No credentials already set up on that particular note. Now we're just checking through AWS to make sure that the account we want to use we have the correct credentials on the correct roles set up and validating that there are no instances currently set up in easy to instance, not completely necessary, but just helps keep things clean and tidy when I am perspective. Right. So next step, we're just going to check that we can, from the bootstrap note, reach more antis, get to the repositories where the various components of the system are available. They're good. No areas here. Yeah, right now we're going to start sitting at the bootstrap note itself. So we're downloading the cars release, get get cars, script, and then next, we're going to run it. I'm in. Deploy it. Changing into that big struck folder. Just making see what's there. Right now we have no license file, so we're gonna get the license filed. Oh, okay. Get the license file through the more antis downloads site, signing up here, downloading that license file and putting it into the Carisbrook struck folder. Okay, Once we've done that, we can now go ahead with the rest of the deployment. See that the follow is there. Uh, huh? That's again checking that we can now reach E C two, which is extremely important for the deployment. Just validation steps as we move through the process. All right, The next big step is valid in all of our AWS credentials. So the first thing is, we need those route credentials which we're going to export on the command line. This is to create the necessary bootstrap user on AWS credentials for the completion off the deployment we're now running an AWS policy create. So it is part of that is creating our Food trucks script, creating the mystery policy files on top of AWS, Just generally preparing the environment using a cloud formation script you'll see in a second will give a new policy confirmations just waiting for it to complete. Yeah, and there is done. It's gonna have a look at the AWS console. You can see that we're creative completed. Now we can go and get the credentials that we created Today I am console. Go to that new user that's being created. We'll go to the section on security credentials and creating new keys. Download that information media Access key I D and the secret access key. We went, Yeah, usually then exported on the command line. Okay. Couple of things to Notre. Ensure that you're using the correct AWS region on ensure that in the conflict file you put the correct Am I in for that region? I'm sure you have it together in a second. Yes. Okay, that's the key. Secret X key. Right on. Let's kick it off. Yeah, So this process takes between thirty and forty five minutes. Handles all the AWS dependencies for you, and as we go through, the process will show you how you can track it. Andi will start to see things like the running instances being created on the west side. The first phase off this whole process happening in the background is the creation of a local kind based bootstrapped cluster on the bootstrap node that clusters then used to deploy and manage all the various instances and configurations within AWS. At the end of the process, that cluster is copied into the new cluster on AWS and then shut down that local cluster essentially moving itself over. Okay. Local clusters boat just waiting for the various objects to get ready. Standard communities objects here Okay, so we speed up this process a little bit just for demonstration purposes. Yeah. There we go. So first note is being built the best in host. Just jump box that will allow us access to the entire environment. Yeah, In a few seconds, we'll see those instances here in the US console on the right. Um, the failures that you're seeing around failed to get the I. P for Bastian is just the weight state while we wait for a W s to create the instance. Okay. Yes. Here, beauty there. Okay. Mhm. Okay. Yeah, yeah. Okay. On there. We got question. Host has been built on three instances for the management clusters have now been created. We're going through the process of preparing. Those nodes were now copying everything over. See that? The scaling up of controllers in the big Strap cluster? It's indicating that we're starting all of the controllers in the new question. Almost there. Yeah. Yeah, just waiting for key. Clark. Uh huh. Start to finish up. Yeah. No. What? Now we're shutting down control this on the local bootstrap node on preparing our I. D. C. Configuration. Fourth indication, soon as this is completed. Last phase will be to deploy stack light into the new cluster the last time Monitoring tool set way Go stack like to plan It has started. Mhm coming to the end of the deployment Mountain. Yeah, America. Final phase of the deployment. Onda, We are done. Okay, You'll see. At the end they're providing us the details of you. I log in so there's a keeper clogging. You can modify that initial default password is part of the configuration set up with one documentation way. Go Councils up way can log in. Yeah, yeah, thank you very much for watching. >>Excellent. So in that video are wonderful field CTO Shauna Vera bootstrapped up management costume for Dr Enterprise Container Cloud Bruce, where exactly does that leave us? So now we've got this management costume installed like what's next? >>So primarily the foundation for being able to deploy either regional clusters that will then allow you to support child clusters. Uh, comes into play the next piece of what we're going to show, I think with Sean O'Mara doing this is the child cluster capability, which allows you to then deploy your application services on the local cluster. That's being managed by the ah ah management cluster that we just created with the bootstrap. >>Right? So this cluster isn't yet for workloads. This is just for bootstrapping up the downstream clusters. Those or what we're gonna use for workings. >>Exactly. Yeah. And I just wanted to point out, since Sean O'Mara isn't around, toe, actually answer questions. I could listen to that guy. Read the phone book, and it would be interesting, but anyway, you can tell him I said that >>he's watching right now, Crusoe. Good. Um, cool. So and just to make sure I understood what Sean was describing their that bootstrap er knows that you, like, ran document fresh pretender Cloud from to begin with. That's actually creating a kind kubernetes deployment kubernetes and Docker deployment locally. That then hits the AWS a p i in this example that make those e c two instances, and it makes like a three manager kubernetes cluster there, and then it, like, copies itself over toe those communities managers. >>Yeah, and and that's sort of where the transition happens. You can actually see it. The output that when it says I'm pivoting, I'm pivoting from my local kind deployment of cluster AP, I toothy, uh, cluster, that's that's being created inside of AWS or, quite frankly, inside of open stack or inside of bare metal or inside of it. The targeting is, uh, abstracted. Yeah, but >>those air three environments that we're looking at right now, right? Us bare metal in open staff environments. So does that kind cluster on the bootstrap er go away afterwards. You don't need that afterwards. Yeah, that is just temporary. To get things bootstrapped, then you manage things from management cluster on aws in this example? >>Yeah. Yeah. The seed, uh, cloud that post the bootstrap is not required anymore. And there's no, uh, interplay between them after that. So that there's no dependencies on any of the clouds that get created thereafter. >>Yeah, that actually reminds me of how we bootstrapped doctor enterprise back in the day, be a temporary container that would bootstrap all the other containers. Go away. It's, uh, so sort of a similar, similar temporary transient bootstrapping model. Cool. Excellent. What will convict there? It looked like there wasn't a ton, right? It looked like you had to, like, set up some AWS parameters like credentials and region and stuff like that. But other than that, that looked like heavily script herbal like there wasn't a ton of point and click there. >>Yeah, very much so. It's pretty straightforward from a bootstrapping standpoint, The config file that that's generated the template is fairly straightforward and targeted towards of a small medium or large, um, deployment. And by editing that single file and then gathering license file and all of the things that Sean went through, um, that that it makes it fairly easy to script >>this. And if I understood correctly as well that three manager footprint for your management cluster, that's the minimum, right. We always insist on high availability for this management cluster because boy do not wanna see oh, >>right, right. And you know, there's all kinds of persistent data that needs to be available, regardless of whether one of the notes goes down or not. So we're taking care of all of that for you behind the scenes without you having toe worry about it as a developer. >>No, I think there's that's a theme that I think will come back to throughout the rest of this tutorial session today is there's a lot of there's a lot of expertise baked him to Dr Enterprise Container Cloud in terms of implementing best practices for you like the defaulter, just the best practices of how you should be managing these clusters, Miss Seymour. Examples of that is the day goes on. Any interesting questions you want to call out from the chap who's >>well, there was. Yeah, yeah, there was one that we had responded to earlier about the fact that it's a management cluster that then conduce oh, either the the regional cluster or a local child molester. The child clusters, in each case host the application services, >>right? So at this point, we've got, in some sense, like the simplest architectures for our documentary prize Container Cloud. We've got the management cluster, and we're gonna go straight with child cluster. In the next video, there's a more sophisticated architecture, which will also proper today that inserts another layer between those two regional clusters. If you need to manage regions like across a BS, reads across with these documents anything, >>yeah, that that local support for the child cluster makes it a lot easier for you to manage the individual clusters themselves and to take advantage of our observation. I'll support systems a stack light and things like that for each one of clusters locally, as opposed to having to centralize thumb >>eso. It's a couple of good questions. In the chat here, someone was asking for the instructions to do this themselves. I strongly encourage you to do so. That should be in the docks, which I think Dale helpfully thank you. Dale provided links for that's all publicly available right now. So just head on in, head on into the docks like the Dale provided here. You can follow this example yourself. All you need is a Mirante license for this and your AWS credentials. There was a question from many a hear about deploying this toe azure. Not at G. Not at this time. >>Yeah, although that is coming. That's going to be in a very near term release. >>I didn't wanna make promises for product, but I'm not too surprised that she's gonna be targeted. Very bracing. Cool. Okay. Any other thoughts on this one does. >>No, just that the fact that we're running through these individual pieces of the steps Well, I'm sure help you folks. If you go to the link that, uh, the gentleman had put into the chat, um, giving you the step by staff. Um, it makes it fairly straightforward to try this yourselves. >>E strongly encourage that, right? That's when you really start to internalize this stuff. OK, but before we move on to the next video, let's just make sure everyone has a clear picture in your mind of, like, where we are in the life cycle here creating this management cluster. Just stop me if I'm wrong. Who's creating this management cluster is like, you do that once, right? That's when your first setting up your doctor enterprise container cloud environment of system. What we're going to start seeing next is creating child clusters and this is what you're gonna be doing over and over and over again. When you need to create a cluster for this Deb team or, you know, this other team river it is that needs commodity. Doctor Enterprise clusters create these easy on half will. So this was once to set up Dr Enterprise Container Cloud Child clusters, which we're going to see next. We're gonna do over and over and over again. So let's go to that video and see just how straightforward it is to spin up a doctor enterprise cluster for work clothes as a child cluster. Undocumented brands contain >>Hello. In this demo, we will cover the deployment experience of creating a new child cluster, the scaling of the cluster and how to update the cluster. When a new version is available, we begin the process by logging onto the you I as a normal user called Mary. Let's go through the navigation of the U I so you can switch. Project Mary only has access to development. Get a list of the available projects that you have access to. What clusters have been deployed at the moment there. Nan Yes, this H Keys Associate ID for Mary into her team on the cloud credentials that allow you to create access the various clouds that you can deploy clusters to finally different releases that are available to us. We can switch from dark mode to light mode, depending on your preferences, Right? Let's now set up semester search keys for Mary so she can access the notes and machines again. Very simply, had Mississippi key give it a name, we copy and paste our public key into the upload key block. Or we can upload the key if we have the file available on our local machine. A simple process. So to create a new cluster, we define the cluster ad management nodes and add worker nodes to the cluster. Yeah, again, very simply, you go to the clusters tab. We hit the create cluster button. Give the cluster name. Yeah, Andi, select the provider. We only have access to AWS in this particular deployment, so we'll stick to AWS. What's like the region in this case? US West one release version five point seven is the current release Onda Attach. Mary's Key is necessary Key. We can then check the rest of the settings, confirming the provider Any kubernetes c r D r I p address information. We can change this. Should we wish to? We'll leave it default for now on. Then what components? A stack light I would like to deploy into my Custer. For this. I'm enabling stack light on logging on Aiken. Sit up the retention sizes Attention times on. Even at this stage, at any customer alerts for the watchdogs. E consider email alerting which I will need my smart host details and authentication details. Andi Slack Alerts. Now I'm defining the cluster. All that's happened is the cluster's been defined. I now need to add machines to that cluster. I'll begin by clicking the create machine button within the cluster definition. Oh, select manager, Select the number of machines. Three is the minimum. Select the instant size that I'd like to use from AWS and very importantly, ensure correct. Use the correct Am I for the region. I commend side on the route device size. There we go, my three machines obviously creating. I now need to add some workers to this custom. So I go through the same process this time once again, just selecting worker. I'll just add to once again, the AM is extremely important. Will fail if we don't pick the right, Am I for a boon to machine in this case and the deployment has started. We can go and check on the bold status are going back to the clusters screen on clicking on the little three dots on the right. We get the cluster info and the events, so the basic cluster info you'll see pending their listen cluster is still in the process of being built. We kick on, the events will get a list of actions that have been completed This part of the set up of the cluster. So you can see here we've created the VPC. We've created the sub nets on We've created the Internet gateway. It's unnecessary made of us and we have no warnings of the stage. Yeah, this will then run for a while. We have one minute past waken click through. We can check the status of the machine bulls as individuals so we can check the machine info, details of the machines that we've assigned, right? Mhm Onda. See any events pertaining to the machine areas like this one on normal? Yeah. Just watch asked. The community's components are waiting for the machines to start. Go back to Custer's. Okay, right. Because we're moving ahead now. We can see we have it in progress. Five minutes in new Matt Gateway on the stage. The machines have been built on assigned. I pick up the U. S. Thank you. Yeah. There we go. Machine has been created. See the event detail and the AWS. I'd for that machine. Mhm. No speeding things up a little bit. This whole process and to end takes about fifteen minutes. Run the clock forward, you'll notice is the machines continue to bold the in progress. We'll go from in progress to ready. A soon as we got ready on all three machines, the managers on both workers way could go on and we could see that now we reached the point where the cluster itself is being configured. Mhm, mhm. And then we go. Cluster has been deployed. So once the classes deployed, we can now never get around our environment. Okay, Are cooking into configure cluster We could modify their cluster. We could get the end points for alert alert manager on See here The griffon occupying and Prometheus are still building in the background but the cluster is available on you would be able to put workloads on it the stretch to download the cube conflict so that I can put workloads on it. It's again three little dots in the right for that particular cluster. If the download cube conflict give it my password, I now have the Q conflict file necessary so that I can access that cluster Mhm all right Now that the build is fully completed, we can check out cluster info on. We can see that Allow the satellite components have been built. All the storage is there, and we have access to the CPU. I So if we click into the cluster, we can access the UCP dashboard, right? Shit. Click the signing with Detroit button to use the SSO on. We give Mary's possible to use the name once again. Thing is, an unlicensed cluster way could license at this point. Or just skip it on. There. We have the UCP dashboard. You can see that has been up for a little while. We have some data on the dashboard going back to the console. We can now go to the griffon, a data just being automatically pre configured for us. We can switch and utilized a number of different dashboards that have already been instrumented within the cluster. So, for example, communities cluster information, the name spaces, deployments, nodes. Mhm. So we look at nodes. If we could get a view of the resource is utilization of Mrs Custer is very little running in it. Yeah. General dashboard of Cuba navies cluster one of this is configurable. You can modify these for your own needs, or add your own dashboards on de scoped to the cluster. So it is available to all users who have access to this specific cluster, all right to scale the cluster on to add a notice. A simple is the process of adding a mode to the cluster, assuming we've done that in the first place. So we go to the cluster, go into the details for the cluster we select, create machine. Once again, we need to be ensure that we put the correct am I in and any other functions we like. You can create different sized machines so it could be a larger node. Could be bigger disks and you'll see that worker has been added from the provisioning state on shortly. We will see the detail off that worker as a complete to remove a note from a cluster. Once again, we're going to the cluster. We select the node would like to remove. Okay, I just hit delete On that note. Worker nodes will be removed from the cluster using according and drawing method to ensure that your workouts are not affected. Updating a cluster. When an update is available in the menu for that particular cluster, the update button will become available. And it's a simple as clicking the button, validating which release you would like to update to. In this case, the next available releases five point seven point one. Here I'm kicking the update by in the background We will coordinate. Drain each node slowly go through the process of updating it. Andi update will complete depending on what the update is as quickly as possible. Girl, we go. The notes being rebuilt in this case impacted the manager node. So one of the manager nodes is in the process of being rebuilt. In fact, to in this case, one has completed already on In a few minutes we'll see that there are great has been completed. There we go. Great. Done. Yeah. If you work loads of both using proper cloud native community standards, there will be no impact. >>Excellent. So at this point, we've now got a cluster ready to start taking our communities of workloads. He started playing or APs to that costume. So watching that video, the thing that jumped out to me at first Waas like the inputs that go into defining this workload cost of it. All right, so we have to make sure we were using on appropriate am I for that kind of defines the substrate about what we're gonna be deploying our cluster on top of. But there's very little requirements. A so far as I could tell on top of that, am I? Because Docker enterprise Container Cloud is gonna bootstrap all the components that you need. That s all we have is kind of kind of really simple bunch box that we were deploying these things on top of so one thing that didn't get dug into too much in the video. But it's just sort of implied. Bruce, maybe you can comment on this is that release that Shawn had to choose for his, uh, for his cluster in creating it. And that release was also the thing we had to touch. Wanted to upgrade part cluster. So you have really sharp eyes. You could see at the end there that when you're doing the release upgrade enlisted out a stack of components docker, engine, kubernetes, calico, aled, different bits and pieces that go into, uh, go into one of these commodity clusters that deploy. And so, as far as I can tell in that case, that's what we mean by a release. In this sense, right? It's the validated stack off container ization and orchestration components that you know we've tested out and make sure it works well, introduction environments. >>Yeah, and and And that's really the focus of our effort is to ensure that any CVS in any of the stack are taken care of that there is a fixes air documented and up streamed to the open stack community source community, um, and and that, you know, then we test for the scaling ability and the reliability in high availability configuration for the clusters themselves. The hosts of your containers. Right. And I think one of the key, uh, you know, benefits that we provide is that ability to let you know, online, high. We've got an update for you, and it's fixes something that maybe you had asked us to fix. Uh, that all comes to you online as your managing your clusters, so you don't have to think about it. It just comes as part of the product. >>You just have to click on Yes. Please give me that update. Uh, not just the individual components, but again. It's that it's that validated stack, right? Not just, you know, component X, y and Z work. But they all work together effectively Scalable security, reliably cool. Um, yeah. So at that point, once we started creating that workload child cluster, of course, we bootstrapped good old universal control plane. Doctor Enterprise. On top of that, Sean had the classic comment there, you know? Yeah. Yeah. You'll see a little warnings and errors or whatever. When you're setting up, UCP don't handle, right, Just let it do its job, and it will converge all its components, you know, after just just a minute or two. But we saw in that video, we sped things up a little bit there just we didn't wait for, you know, progress fighters to complete. But really, in real life, that whole process is that anything so spend up one of those one of those fosters so quite quite quick. >>Yeah, and and I think the the thoroughness with which it goes through its process and re tries and re tries, uh, as you know, and it was evident when we went through the initial ah video of the bootstrapping as well that the processes themselves are self healing, as they are going through. So they will try and retry and wait for the event to complete properly on. And once it's completed properly, then it will go to the next step. >>Absolutely. And the worst thing you could do is panic at the first warning and start tearing things that don't don't do that. Just don't let it let it heal. Let take care of itself. And that's the beauty of these manage solutions is that they bake in a lot of subject matter expertise, right? The decisions that are getting made by those containers is they're bootstrapping themselves, reflect the expertise of the Mirant ISS crew that has been developing this content in these two is free for years and years now, over recognizing humanities. One cool thing there that I really appreciate it actually that it adds on top of Dr Enterprise is that automatic griffon a deployment as well. So, Dr Enterprises, I think everyone knows has had, like, some very high level of statistics baked into its dashboard for years and years now. But you know our customers always wanted a double click on that right to be able to go a little bit deeper. And Griffon are really addresses that it's built in dashboards. That's what's really nice to see. >>Yeah, uh, and all of the alerts and, uh, data are actually captured in a Prometheus database underlying that you have access to so that you are allowed to add new alerts that then go out to touch slack and say hi, You need to watch your disk space on this machine or those kinds of things. Um, and and this is especially helpful for folks who you know, want to manage the application service layer but don't necessarily want to manage the operations side of the house. So it gives them a tool set that they can easily say here, Can you watch these for us? And Miran tas can actually help do that with you, So >>yeah, yeah, I mean, that's just another example of baking in that expert knowledge, right? So you can leverage that without tons and tons of a long ah, long runway of learning about how to do that sort of thing. Just get out of the box right away. There was the other thing, actually, that you could sleep by really quickly if you weren't paying close attention. But Sean mentioned it on the video. And that was how When you use dark enterprise container cloud to scale your cluster, particularly pulling a worker out, it doesn't just like Territo worker down and forget about it. Right? Is using good communities best practices to cordon and drain the No. So you aren't gonna disrupt your workloads? You're going to just have a bunch of containers instantly. Excellent crash. You could really carefully manage the migration of workloads off that cluster has baked right in tow. How? How? Document? The brass container cloud is his handling cluster scale. >>Right? And And the kubernetes, uh, scaling methodology is is he adhered to with all of the proper techniques that ensure that it will tell you. Wait, you've got a container that actually needs three, uh, three, uh, instances of itself. And you don't want to take that out, because that node, it means you'll only be able to have to. And we can't do that. We can't allow that. >>Okay, Very cool. Further thoughts on this video. So should we go to the questions. >>Let's let's go to the questions >>that people have. Uh, there's one good one here, down near the bottom regarding whether an a p I is available to do this. So in all these demos were clicking through this web. You I Yes, this is all a p. I driven. You could do all of this. You know, automate all this away is part of the CSC change. Absolutely. Um, that's kind of the point, right? We want you to be ableto spin up. Come on. I keep calling them commodity clusters. What I mean by that is clusters that you can create and throw away. You know, easily and automatically. So everything you see in these demos eyes exposed to FBI? >>Yeah. In addition, through the standard Cube cuddle, Uh, cli as well. So if you're not a programmer, but you still want to do some scripting Thio, you know, set up things and deploy your applications and things. You can use this standard tool sets that are available to accomplish that. >>There is a good question on scale here. So, like, just how many clusters and what sort of scale of deployments come this kind of support our engineers report back here that we've done in practice up to a Zeman ia's like two hundred clusters. We've deployed on this with two hundred fifty nodes in a cluster. So were, you know, like like I said, hundreds, hundreds of notes, hundreds of clusters managed by documented press container fall and then those downstream clusters, of course, subject to the usual constraints for kubernetes, right? Like default constraints with something like one hundred pods for no or something like that. There's a few different limitations of how many pods you can run on a given cluster that comes to us not from Dr Enterprise Container Cloud, but just from the underlying kubernetes distribution. >>Yeah, E. I mean, I don't think that we constrain any of the capabilities that are available in the, uh, infrastructure deliveries, uh, service within the goober Netease framework. So were, you know, But we are, uh, adhering to the standards that we would want to set to make sure that we're not overloading a node or those kinds of things, >>right. Absolutely cool. Alright. So at this point, we've got kind of a two layered our protection when we are management cluster, but we deployed in the first video. Then we use that to deploy one child clustering work, classroom, uh, for more sophisticated deployments where we might want to manage child clusters across multiple regions. We're gonna add another layer into our architectural we're gonna add in regional cluster management. So this idea you're gonna have the single management cluster that we started within the first video. On the next video, we're gonna learn how to spin up a regional clusters, each one of which would manage, for example, a different AWS uh, US region. So let me just pull out the video for that bill. We'll check it out for me. Mhm. >>Hello. In this demo, we will cover the deployment of additional regional management. Cluster will include a brief architectures of you how to set up the management environment, prepare for the deployment deployment overview and then just to prove it, to play a regional child cluster. So, looking at the overall architecture, the management cluster provides all the core functionality, including identity management, authentication, inventory and release version. ING Regional Cluster provides the specific architecture provider in this case AWS on the LCN components on the D you speak Cluster for child cluster is the cluster or clusters being deployed and managed? Okay, so why do you need a regional cluster? Different platform architectures, for example aws who have been stack even bare metal to simplify connectivity across multiple regions handle complexities like VPNs or one way connectivity through firewalls, but also help clarify availability zones. Yeah. Here we have a view of the regional cluster and how it connects to the management cluster on their components, including items like the LCN cluster Manager we also Machine Manager were held. Mandel are managed as well as the actual provider logic. Mhm. Okay, we'll begin by logging on Is the default administrative user writer. Okay, once we're in there, we'll have a look at the available clusters making sure we switch to the default project which contains the administration clusters. Here we can see the cars management cluster, which is the master controller. And you see, it only has three nodes, three managers, no workers. Okay, if we look at another regional cluster similar to what we're going to deploy now, also only has three managers once again, no workers. But as a comparison, here's a child cluster This one has three managers, but also has additional workers associate it to the cluster. All right, we need to connect. Tell bootstrap note. Preferably the same note that used to create the original management plaster. It's just on AWS, but I still want to machine. All right. A few things we have to do to make sure the environment is ready. First thing we're going to see go into route. We'll go into our releases folder where we have the kozberg struck on. This was the original bootstrap used to build the original management cluster. Yeah, we're going to double check to make sure our cube con figures there once again, the one created after the original customers created just double check. That cute conflict is the correct one. Does point to the management cluster. We're just checking to make sure that we can reach the images that everything is working. A condom. No damages waken access to a swell. Yeah. Next we're gonna edit the machine definitions. What we're doing here is ensuring that for this cluster we have the right machine definitions, including items like the am I. So that's found under the templates AWS directory. We don't need to edit anything else here. But we could change items like the size of the machines attempts. We want to use that The key items to ensure where you changed the am I reference for the junta image is the one for the region in this case AWS region for utilizing this was no construct deployment. We have to make sure we're pointing in the correct open stack images. Yeah, okay. Set the correct and my save file. Now we need to get up credentials again. When we originally created the bootstrap cluster, we got credentials from eight of the U. S. If we hadn't done this, we would need to go through the u A. W s set up. So we're just exporting the AWS access key and I d. What's important is CAAs aws enabled equals. True. Now we're sitting the region for the new regional cluster. In this case, it's Frankfurt on exporting our cube conflict that we want to use for the management cluster. When we looked at earlier Yeah, now we're exporting that. Want to call the cluster region Is Frank Foods Socrates Frankfurt yet trying to use something descriptive It's easy to identify. Yeah, and then after this, we'll just run the bootstrap script, which will complete the deployment for us. Bootstrap of the regional cluster is quite a bit quicker than the initial management clusters. There are fewer components to be deployed. Um, but to make it watchable, we've spent it up. So we're preparing our bootstrap cluster on the local bootstrap node. Almost ready on. We started preparing the instances at W s and waiting for that bastard and no to get started. Please. The best you nerd Onda. We're also starting to build the actual management machines they're now provisioning on. We've reached the point where they're actually starting to deploy. Dr. Enterprise, this is probably the longest face. Yeah, seeing the second that all the nerds will go from the player deployed. Prepare, prepare. Yeah, You'll see their status changes updates. He was the first night ready. Second, just applying second already. Both my time. No waiting from home control. Let's become ready. Removing cluster the management cluster from the bootstrap instance into the new cluster running the date of the U. S. All my stay. Ah, now we're playing Stockland. Switch over is done on. Done. Now I will build a child cluster in the new region very, very quickly to find the cluster will pick. Our new credential has shown up. We'll just call it Frankfurt for simplicity a key and customs to find. That's the machine. That cluster stop with three managers. Set the correct Am I for the region? Yeah, Do the same to add workers. There we go test the building. Yeah. Total bill of time Should be about fifteen minutes. Concedes in progress. It's going to expect this up a little bit. Check the events. We've created all the dependencies, machine instances, machines, a boat shortly. We should have a working cluster in Frankfurt region. Now almost a one note is ready from management. Two in progress. Yeah, on we're done. Clusters up and running. Yeah. >>Excellent. So at this point, we've now got that three tier structure that we talked about before the video. We got that management cluster that we do strapped in the first video. Now we have in this example to different regional clustering one in Frankfurt, one of one management was two different aws regions. And sitting on that you can do Strap up all those Doctor enterprise costumes that we want for our work clothes. >>Yeah, that's the key to this is to be able to have co resident with your actual application service enabled clusters the management co resident with it so that you can, you know, quickly access that he observation Elson Surfboard services like the graph, Ana and that sort of thing for your particular region. A supposed to having to lug back into the home. What did you call it when we started >>the mothership? >>The mothership. Right. So we don't have to go back to the mother ship. We could get >>it locally. Yeah, when, like to that point of aggregating things under a single pane of glass? That's one thing that again kind of sailed by in the demo really quickly. But you'll notice all your different clusters were on that same cluster. Your pain on your doctor Enterprise Container Cloud management. Uh, court. Right. So both your child clusters for running workload and your regional clusters for bootstrapping. Those child clusters were all listed in the same place there. So it's just one pane of glass to go look for, for all of your clusters, >>right? And, uh, this is kind of an important point. I was, I was realizing, as we were going through this. All of the mechanics are actually identical between the bootstrapped cluster of the original services and the bootstrapped cluster of the regional services. It's the management layer of everything so that you only have managers, you don't have workers and that at the child cluster layer below the regional or the management cluster itself, that's where you have the worker nodes. And those are the ones that host the application services in that three tiered architecture that we've now defined >>and another, you know, detail for those that have sharp eyes. In that video, you'll notice when deploying a child clusters. There's not on Lee. A minimum of three managers for high availability management cluster. You must have at least two workers that's just required for workload failure. It's one of those down get out of work. They could potentially step in there, so your minimum foot point one of these child clusters is fine. Violence and scalable, obviously, from a >>That's right. >>Let's take a quick peek of the questions here, see if there's anything we want to call out, then we move on to our last want to my last video. There's another question here about, like where these clusters can live. So again, I know these examples are very aws heavy. Honestly, it's just easy to set up down on the other us. We could do things on bare metal and, uh, open stack departments on Prem. That's what all of this still works in exactly the same way. >>Yeah, the, uh, key to this, especially for the the, uh, child clusters, is the provision hers? Right? See you establish on AWS provision or you establish a bare metal provision or you establish a open stack provision. Or and eventually that list will include all of the other major players in the cloud arena. But you, by selecting the provision or within your management interface, that's where you decide where it's going to be hosted, where the child cluster is to be hosted. >>Speaking off all through a child clusters. Let's jump into our last video in the Siri's, where we'll see how to spin up a child cluster on bare metal. >>Hello. This demo will cover the process of defining bare metal hosts and then review the steps of defining and deploying a bare metal based doctor enterprise cluster. So why bare metal? Firstly, it eliminates hyper visor overhead with performance boost of up to thirty percent. Provides direct access to GP use, prioritize for high performance wear clothes like machine learning and AI, and supports high performance workloads like network functions, virtualization. It also provides a focus on on Prem workloads, simplifying and ensuring we don't need to create the complexity of adding another opera visor. Lay it between so continue on the theme Why Communities and bare metal again Hyper visor overhead. Well, no virtualization overhead. Direct access to hardware items like F p G A s G p us. We can be much more specific about resource is required on the nodes. No need to cater for additional overhead. Uh, we can handle utilization in the scheduling. Better Onda we increase the performances and simplicity of the entire environment as we don't need another virtualization layer. Yeah, In this section will define the BM hosts will create a new project will add the bare metal hosts, including the host name. I put my credentials I pay my address the Mac address on then provide a machine type label to determine what type of machine it is for later use. Okay, let's get started. So well again. Was the operator thing. We'll go and we'll create a project for our machines to be a member off helps with scoping for later on for security. I begin the process of adding machines to that project. Yeah. So the first thing we had to be in post, Yeah, many of the machine A name. Anything you want, que experimental zero one. Provide the IAP my user name type my password. Okay. On the Mac address for the common interface with the boot interface and then the i p m I i p address These machines will be at the time storage worker manager. He's a manager. Yeah, we're gonna add a number of other machines on will. Speed this up just so you could see what the process looks like in the future. Better discovery will be added to the product. Okay. Okay. Getting back there we have it are Six machines have been added, are busy being inspected, being added to the system. Let's have a look at the details of a single note. Yeah, you can see information on the set up of the node. Its capabilities? Yeah. As well as the inventory information about that particular machine. I see. Okay, let's go and create the cluster. Yeah, So we're going to deploy a bare metal child cluster. The process we're going to go through is pretty much the same as any other child cluster. So we'll credit custom. We'll give it a name, but if it were selecting bare metal on the region, we're going to select the version we want to apply. No way. We're going to add this search keys. If we hope we're going to give the load. Balancer host I p that we'd like to use out of dress range on update the address range that we want to use for the cluster. Check that the sea ideal blocks for the Cuban ladies and tunnels are what we want them to be. Enable disabled stack light. Yeah, and soothe stack light settings to find the cluster. And then, as for any other machine, we need to add machines to the cluster. Here. We're focused on building communities clusters, so we're gonna put the count of machines. You want managers? We're gonna pick the label type manager and create three machines is the manager for the Cuban eighties. Casting Okay thing. We're having workers to the same. It's a process. Just making sure that the worker label host level are I'm sorry. On when Wait for the machines to deploy. Let's go through the process of putting the operating system on the notes validating and operating system deploying doctor identifies Make sure that the cluster is up and running and ready to go. Okay, let's review the bold events waken See the machine info now populated with more information about the specifics of things like storage and of course, details of a cluster etcetera. Yeah, yeah, well, now watch the machines go through the various stages from prepared to deploy on what's the cluster build? And that brings us to the end of this particular demo. You can see the process is identical to that of building a normal child cluster we got our complaint is complete. >>All right, so there we have it, deploying a cluster to bare metal. Much the same is how we did for AWS. I guess maybe the biggest different stepwise there is there is that registration face first, right? So rather than just using AWS financials toe magically create PM's in the cloud. You got a point out all your bare metal servers to Dr Enterprise between the cloud and they really come in, I guess three profiles, right? You got your manager profile with a profile storage profile which has been labeled as allocate. Um, crossword cluster has appropriate, >>right? And And I think that the you know, the key differentiator here is that you have more physical control over what, uh, attributes that love your cat, by the way, uh, where you have the different attributes of a server of physical server. So you can, uh, ensure that the SSD configuration on the storage nodes is gonna be taken advantage of in the best way the GP use on the worker nodes and and that the management layer is going to have sufficient horsepower to, um, spin up to to scale up the the environments, as required. One of the things I wanted to mention, though, um, if I could get this out without the choking much better. Um, is that Ah, hey, mentioned the load balancer and I wanted to make sure in defining the load balancer and the load balancer ranges. Um, that is for the top of the the cluster itself. That's the operations of the management, uh, layer integrating with your systems internally to be able to access the the Cube Can figs. I I p address the, uh, in a centralized way. It's not the load balancer that's working within the kubernetes cluster that you are deploying. That's still cube proxy or service mesh, or however you're intending to do it. So, um, it's kind of an interesting step that your initial step in building this, um and we typically use things like metal L B or in gen X or that kind of thing is to establish that before we deploy this bear mental cluster so that it can ride on top of that for the tips and things. >>Very cool. So any other thoughts on what we've seen so far today? Bruce, we've gone through all the different layers. Doctor enterprise container clouds in these videos from our management are regional to our clusters on aws hand bear amount, Of course, with his dad is still available. Closing thoughts before we take just a very short break and run through these demos again. >>You know, I've been very exciting. Ah, doing the presentation with you. I'm really looking forward to doing it the second time, so that we because we've got a good rhythm going about this kind of thing. So I'm looking forward to doing that. But I think that the key elements of what we're trying to convey to the folks out there in the audience that I hope you've gotten out of it is that will that this is an easy enough process that if you follow the step by steps going through the documentation that's been put out in the chat, um, that you'll be able to give this a go yourself, Um, and you don't have to limit yourself toe having physical hardware on prim to try it. You could do it in a ws as we've shown you today. And if you've got some fancy use cases like, uh, you you need a Hadoop And and, uh, you know, cloud oriented ai stuff that providing a bare metal service helps you to get there very fast. So right. Thank you. It's been a pleasure. >>Yeah, thanks everyone for coming out. So, like I said we're going to take a very short, like, three minute break here. Uh, take the opportunity to let your colleagues know if they were in another session or they didn't quite make it to the beginning of this session. Or if you just want to see these demos again, we're going to kick off this demo. Siri's again in just three minutes at ten. Twenty five a. M. Pacific time where we will see all this great stuff again. Let's take a three minute break. I'll see you all back here in just two minutes now, you know. Okay, folks, that's the end of our extremely short break. We'll give people just maybe, like one more minute to trickle in if folks are interested in coming on in and jumping into our demo. Siri's again. Eso For those of you that are just joining us now I'm Bill Mills. I head up curriculum development for the training team here. Moran Tous on Joining me for this session of demos is Bruce. Don't you go ahead and introduce yourself doors, who is still on break? That's cool. We'll give Bruce a minute or two to get back while everyone else trickles back in. There he is. Hello, Bruce. >>How'd that go for you? Okay, >>Very well. So let's kick off our second session here. I e just interest will feel for you. Thio. Let it run over here. >>Alright. Hi. Bruce Matthews here. I'm the Western Regional Solutions architect for Marantz. Use A I'm the one with the gray hair and the glasses. Uh, the handsome one is Bill. So, uh, Bill, take it away. >>Excellent. So over the next hour or so, we've got a Siris of demos that's gonna walk you through your first steps with Dr Enterprise Container Cloud Doctor Enterprise Container Cloud is, of course, Miranda's brand new offering from bootstrapping kubernetes clusters in AWS bare metal open stack. And for the providers in the very near future. So we we've got, you know, just just over an hour left together on this session, uh, if you joined us at the top of the hour back at nine. A. M. Pacific, we went through these demos once already. Let's do them again for everyone else that was only able to jump in right now. Let's go. Our first video where we're gonna install Dr Enterprise container cloud for the very first time and use it to bootstrap management. Cluster Management Cluster, as I like to describe it, is our mother ship that's going to spin up all the other kubernetes clusters, Doctor Enterprise clusters that we're gonna run our workloads on. So I'm gonna do >>I'm so excited. I can hardly wait. >>Let's do it all right to share my video out here. Yeah, let's do it. >>Good day. The focus for this demo will be the initial bootstrap of the management cluster on the first regional clusters. To support AWS deployments, the management cluster provides the core functionality, including identity management, authentication, infantry release version. The regional cluster provides the specific architecture provided in this case AWS and the Elsom components on the UCP cluster Child cluster is the cluster or clusters being deployed and managed. The deployment is broken up into five phases. The first phase is preparing a bootstrap note on its dependencies on handling the download of the bridge struck tools. The second phase is obtaining America's license file. Third phase. Prepare the AWS credentials instead of the ideas environment, the fourth configuring the deployment, defining things like the machine types on the fifth phase, Run the bootstrap script and wait for the deployment to complete. Okay, so here we're sitting up the strap node. Just checking that it's clean and clear and ready to go there. No credentials already set up on that particular note. Now, we're just checking through aws to make sure that the account we want to use we have the correct credentials on the correct roles set up on validating that there are no instances currently set up in easy to instance, not completely necessary, but just helps keep things clean and tidy when I am perspective. Right. So next step, we're just gonna check that we can from the bootstrap note, reach more antis, get to the repositories where the various components of the system are available. They're good. No areas here. Yeah, right now we're going to start sitting at the bootstrap note itself. So we're downloading the cars release, get get cars, script, and then next we're going to run it. Yeah, I've been deployed changing into that big struck folder, just making see what's there right now we have no license file, so we're gonna get the license filed. Okay? Get the license file through more antis downloads site signing up here, downloading that license file and putting it into the Carisbrook struck folder. Okay, since we've done that, we can now go ahead with the rest of the deployment. Yeah, see what the follow is there? Uh huh. Once again, checking that we can now reach E C two, which is extremely important for the deployment. Just validation steps as we move through the process. Alright. Next big step is violating all of our AWS credentials. So the first thing is, we need those route credentials which we're going to export on the command line. This is to create the necessary bootstrap user on AWS credentials for the completion off the deployment we're now running in AWS policy create. So it is part of that is creating our food trucks script. Creating this through policy files onto the AWS, just generally preparing the environment using a cloud formation script, you'll see in a second, I'll give a new policy confirmations just waiting for it to complete. And there is done. It's gonna have a look at the AWS console. You can see that we're creative completed. Now we can go and get the credentials that we created. Good day. I am console. Go to the new user that's being created. We'll go to the section on security credentials and creating new keys. Download that information media access Key I. D and the secret access key, but usually then exported on the command line. Okay, Couple of things to Notre. Ensure that you're using the correct AWS region on ensure that in the conflict file you put the correct Am I in for that region? I'm sure you have it together in a second. Okay, thanks. Is key. So you could X key Right on. Let's kick it off. So this process takes between thirty and forty five minutes. Handles all the AWS dependencies for you. Um, as we go through, the process will show you how you can track it. Andi will start to see things like the running instances being created on the AWS side. The first phase off this whole process happening in the background is the creation of a local kind based bootstrapped cluster on the bootstrap node that clusters then used to deploy and manage all the various instances and configurations within AWS at the end of the process. That cluster is copied into the new cluster on AWS and then shut down that local cluster essentially moving itself over. Yeah, okay. Local clusters boat. Just waiting for the various objects to get ready. Standard communities objects here. Yeah, you mentioned Yeah. So we've speed up this process a little bit just for demonstration purposes. Okay, there we go. So first note is being built the bastion host just jump box that will allow us access to the entire environment. Yeah, In a few seconds, we'll see those instances here in the US console on the right. Um, the failures that you're seeing around failed to get the I. P for Bastian is just the weight state while we wait for AWS to create the instance. Okay. Yeah. Beauty there. Movies. Okay, sketch. Hello? Yeah, Okay. Okay. On. There we go. Question host has been built on three instances for the management clusters have now been created. Okay, We're going through the process of preparing. Those nodes were now copying everything over. See that scaling up of controllers in the big strapped cluster? It's indicating that we're starting all of the controllers in the new question. Almost there. Right? Okay. Just waiting for key. Clark. Uh huh. So finish up. Yeah. No. Now we're shutting down. Control this on the local bootstrap node on preparing our I. D. C configuration, fourth indication. So once this is completed, the last phase will be to deploy stack light into the new cluster, that glass on monitoring tool set, Then we go stack like deployment has started. Mhm. Coming to the end of the deployment mountain. Yeah, they were cut final phase of the deployment. And we are done. Yeah, you'll see. At the end, they're providing us the details of you. I log in. So there's a key Clark log in. Uh, you can modify that initial default possible is part of the configuration set up where they were in the documentation way. Go Councils up way can log in. Yeah. Yeah. Thank you very much for watching. >>All right, so at this point, what we have we got our management cluster spun up, ready to start creating work clusters. So just a couple of points to clarify there to make sure everyone caught that, uh, as advertised. That's darker. Enterprise container cloud management cluster. That's not rework loans. are gonna go right? That is the tool and you're gonna use to start spinning up downstream commodity documentary prize clusters for bootstrapping record too. >>And the seed host that were, uh, talking about the kind cluster dingy actually doesn't have to exist after the bootstrap succeeds eso It's sort of like, uh, copies head from the seed host Toothy targets in AWS spins it up it then boots the the actual clusters and then it goes away too, because it's no longer necessary >>so that bootstrapping know that there's not really any requirements, Hardly on that, right. It just has to be able to reach aws hit that Hit that a p I to spin up those easy to instances because, as you just said, it's just a kubernetes in docker cluster on that piece. Drop note is just gonna get torn down after the set up finishes on. You no longer need that. Everything you're gonna do, you're gonna drive from the single pane of glass provided to you by your management cluster Doctor enterprise Continue cloud. Another thing that I think is sort of interesting their eyes that the convict is fairly minimal. Really? You just need to provide it like aws regions. Um, am I? And that's what is going to spin up that spending that matter faster. >>Right? There is a mammal file in the bootstrap directory itself, and all of the necessary parameters that you would fill in have default set. But you have the option then of going in and defining a different Am I different for a different region, for example? Oh, are different. Size of instance from AWS. >>One thing that people often ask about is the cluster footprint. And so that example you saw they were spitting up a three manager, um, managing cluster as mandatory, right? No single manager set up at all. We want high availability for doctrine Enterprise Container Cloud management. Like so again, just to make sure everyone sort of on board with the life cycle stage that we're at right now. That's the very first thing you're going to do to set up Dr Enterprise Container Cloud. You're going to do it. Hopefully exactly once. Right now, you've got your management cluster running, and they're gonna use that to spend up all your other work clusters Day today has has needed How do we just have a quick look at the questions and then lets take a look at spinning up some of those child clusters. >>Okay, e think they've actually been answered? >>Yeah, for the most part. One thing I'll point out that came up again in the Dail, helpfully pointed out earlier in surgery, pointed out again, is that if you want to try any of the stuff yourself, it's all of the dogs. And so have a look at the chat. There's a links to instructions, so step by step instructions to do each and every thing we're doing here today yourself. I really encourage you to do that. Taking this out for a drive on your own really helps internalizing communicate these ideas after the after launch pad today, Please give this stuff try on your machines. Okay, So at this point, like I said, we've got our management cluster. We're not gonna run workloads there that we're going to start creating child clusters. That's where all of our work and we're gonna go. That's what we're gonna learn how to do in our next video. Cue that up for us. >>I so love Shawn's voice. >>Wasn't that all day? >>Yeah, I watched him read the phone book. >>All right, here we go. Let's now that we have our management cluster set up, let's create a first child work cluster. >>Hello. In this demo, we will cover the deployment experience of creating a new child cluster the scaling of the cluster on how to update the cluster. When a new version is available, we begin the process by logging onto the you I as a normal user called Mary. Let's go through the navigation of the u I. So you can switch Project Mary only has access to development. Uh huh. Get a list of the available projects that you have access to. What clusters have been deployed at the moment there. Man. Yes, this H keys, Associate ID for Mary into her team on the cloud credentials that allow you to create or access the various clouds that you can deploy clusters to finally different releases that are available to us. We can switch from dark mode to light mode, depending on your preferences. Right. Let's now set up some ssh keys for Mary so she can access the notes and machines again. Very simply, had Mississippi key give it a name. We copy and paste our public key into the upload key block. Or we can upload the key if we have the file available on our machine. A very simple process. So to create a new cluster, we define the cluster ad management nodes and add worker nodes to the cluster. Yeah, again, very simply, we got the clusters tab we had to create cluster button. Give the cluster name. Yeah, Andi, select the provider. We only have access to AWS in this particular deployment, so we'll stick to AWS. What's like the region in this case? US West one released version five point seven is the current release Onda Attach. Mary's Key is necessary key. We can then check the rest of the settings, confirming the provider any kubernetes c r D a r i p address information. We can change this. Should we wish to? We'll leave it default for now and then what components of stack light? I would like to deploy into my custom for this. I'm enabling stack light on logging, and I consider the retention sizes attention times on. Even at this stage, add any custom alerts for the watchdogs. Consider email alerting which I will need my smart host. Details and authentication details. Andi Slack Alerts. Now I'm defining the cluster. All that's happened is the cluster's been defined. I now need to add machines to that cluster. I'll begin by clicking the create machine button within the cluster definition. Oh, select manager, Select the number of machines. Three is the minimum. Select the instant size that I'd like to use from AWS and very importantly, ensure correct. Use the correct Am I for the region. I convinced side on the route. Device size. There we go. My three machines are busy creating. I now need to add some workers to this cluster. So I go through the same process this time once again, just selecting worker. I'll just add to once again the am I is extremely important. Will fail if we don't pick the right. Am I for a Clinton machine? In this case and the deployment has started, we can go and check on the bold status are going back to the clusters screen on clicking on the little three dots on the right. We get the cluster info and the events, so the basic cluster info you'll see pending their listen. Cluster is still in the process of being built. We kick on, the events will get a list of actions that have been completed This part of the set up of the cluster. So you can see here. We've created the VPC. We've created the sub nets on. We've created the Internet Gateway. It's unnecessary made of us. And we have no warnings of the stage. Okay, this will then run for a while. We have one minute past. We can click through. We can check the status of the machine balls as individuals so we can check the machine info, details of the machines that we've assigned mhm and see any events pertaining to the machine areas like this one on normal. Yeah. Just last. The community's components are waiting for the machines to start. Go back to customers. Okay, right. Because we're moving ahead now. We can see we have it in progress. Five minutes in new Matt Gateway. And at this stage, the machines have been built on assigned. I pick up the U S. Yeah, yeah, yeah. There we go. Machine has been created. See the event detail and the AWS. I'd for that machine. No speeding things up a little bit this whole process and to end takes about fifteen minutes. Run the clock forward, you'll notice is the machines continue to bold the in progress. We'll go from in progress to ready. A soon as we got ready on all three machines, the managers on both workers way could go on and we could see that now we reached the point where the cluster itself is being configured mhm and then we go. Cluster has been deployed. So once the classes deployed, we can now never get around. Our environment are looking into configure cluster. We could modify their cluster. We could get the end points for alert Alert Manager See here the griffon occupying and Prometheus are still building in the background but the cluster is available on You would be able to put workloads on it at this stage to download the cube conflict so that I can put workloads on it. It's again the three little dots in the right for that particular cluster. If the download cube conflict give it my password, I now have the Q conflict file necessary so that I can access that cluster. All right, Now that the build is fully completed, we can check out cluster info on. We can see that all the satellite components have been built. All the storage is there, and we have access to the CPU. I. So if we click into the cluster, we can access the UCP dashboard, click the signing with the clock button to use the SSO. We give Mary's possible to use the name once again. Thing is an unlicensed cluster way could license at this point. Or just skip it on. Do we have the UCP dashboard? You could see that has been up for a little while. We have some data on the dashboard going back to the console. We can now go to the griffon. A data just been automatically pre configured for us. We can switch and utilized a number of different dashboards that have already been instrumented within the cluster. So, for example, communities cluster information, the name spaces, deployments, nodes. Um, so we look at nodes. If we could get a view of the resource is utilization of Mrs Custer is very little running in it. Yeah, a general dashboard of Cuba Navies cluster. What If this is configurable, you can modify these for your own needs, or add your own dashboards on de scoped to the cluster. So it is available to all users who have access to this specific cluster. All right to scale the cluster on to add a No. This is simple. Is the process of adding a mode to the cluster, assuming we've done that in the first place. So we go to the cluster, go into the details for the cluster we select, create machine. Once again, we need to be ensure that we put the correct am I in and any other functions we like. You can create different sized machines so it could be a larger node. Could be bigger group disks and you'll see that worker has been added in the provisioning state. On shortly, we will see the detail off that worker as a complete to remove a note from a cluster. Once again, we're going to the cluster. We select the node we would like to remove. Okay, I just hit delete On that note. Worker nodes will be removed from the cluster using according and drawing method to ensure that your workloads are not affected. Updating a cluster. When an update is available in the menu for that particular cluster, the update button will become available. And it's a simple as clicking the button validating which release you would like to update to this case. This available releases five point seven point one give you I'm kicking the update back in the background. We will coordinate. Drain each node slowly, go through the process of updating it. Andi update will complete depending on what the update is as quickly as possible. Who we go. The notes being rebuilt in this case impacted the manager node. So one of the manager nodes is in the process of being rebuilt. In fact, to in this case, one has completed already. Yeah, and in a few minutes, we'll see that the upgrade has been completed. There we go. Great. Done. If you work loads of both using proper cloud native community standards, there will be no impact. >>All right, there. We haven't. We got our first workload cluster spun up and managed by Dr Enterprise Container Cloud. So I I loved Shawn's classic warning there. When you're spinning up an actual doctor enterprise deployment, you see little errors and warnings popping up. Just don't touch it. Just leave it alone and let Dr Enterprises self healing properties take care of all those very transient temporary glitches, resolve themselves and leave you with a functioning workload cluster within victims. >>And now, if you think about it that that video was not very long at all. And that's how long it would take you if someone came into you and said, Hey, can you spend up a kubernetes cluster for development development A. Over here, um, it literally would take you a few minutes to thio Accomplish that. And that was with a W s. Obviously, which is sort of, ah, transient resource in the cloud. But you could do exactly the same thing with resource is on Prem or resource is, um physical resource is and will be going through that later in the process. >>Yeah, absolutely one thing that is present in that demo, but that I like to highlight a little bit more because it just kind of glides by Is this notion of, ah, cluster release? So when Sean was creating that cluster, and also when when he was upgrading that cluster, he had to choose a release. What does that didn't really explain? What does that mean? Well, in Dr Enterprise Container Cloud, we have released numbers that capture the entire staff of container ization tools that will be deploying to that workload costume. So that's your version of kubernetes sed cor DNs calico. Doctor Engineer. All the different bits and pieces that not only work independently but are validated toe work together as a staff appropriate for production, humanities, adopted enterprise environments. >>Yep. From the bottom of the stack to the top, we actually test it for scale. Test it for CVS, test it for all of the various things that would, you know, result in issues with you running the application services. And I've got to tell you from having, you know, managed kubernetes deployments and things like that that if you're the one doing it yourself, it can get rather messy. Eso This makes it easy. >>Bruce, you were staying a second ago. They I'll take you at least fifteen minutes to install your release. Custer. Well, sure, but what would all the other bits and pieces you need toe? Not just It's not just about pressing the button to install it, right? It's making the right decision. About what components work? Well, our best tested toe be successful working together has a staff? Absolutely. We this release mechanism and Dr Enterprise Container Cloud. Let's just kind of package up that expert knowledge and make it available in a really straightforward, fashionable species. Uh, pre Confederate release numbers and Bruce is you're pointing out earlier. He's got delivered to us is updates kind of transparent period. When when? When Sean wanted toe update that cluster, he created little update. Custer Button appeared when an update was available. All you gotta do is click. It tells you what Here's your new stack of communities components. It goes ahead. And the straps those components for you? >>Yeah, it actually even displays at the top of the screen. Ah, little header That says you've got an update available. Do you want me to apply? It s o >>Absolutely. Another couple of cool things. I think that are easy to miss in that demo was I really like the on board Bafana that comes along with this stack. So we've been Prometheus Metrics and Dr Enterprise for years and years now. They're very high level. Maybe in in previous versions of Dr Enterprise having those detailed dashboards that Ravana provides, I think that's a great value out there. People always wanted to be ableto zoom in a little bit on that, uh, on those cluster metrics, you're gonna provides them out of the box for us. Yeah, >>that was Ah, really, uh, you know, the joining of the Miranda's and Dr teams together actually spawned us to be able to take the best of what Morantes had in the open stack environment for monitoring and logging and alerting and to do that integration in in a very short period of time so that now we've got it straight across the board for both the kubernetes world and the open stack world. Using the same tool sets >>warm. One other thing I wanna point out about that demo that I think there was some questions about our last go around was that demo was all about creating a managed workplace cluster. So the doctor enterprise Container Cloud managers were using those aws credentials provisioned it toe actually create new e c two instances installed Docker engine stalled. Doctor Enterprise. Remember all that stuff on top of those fresh new VM created and managed by Dr Enterprise contain the cloud. Nothing unique about that. AWS deployments do that on open staff doing on Parramatta stuff as well. Um, there's another flavor here, though in a way to do this for all of our long time doctor Enterprise customers that have been running Doctor Enterprise for years and years. Now, if you got existing UCP points existing doctor enterprise deployments, you plug those in to Dr Enterprise Container Cloud, uh, and use darker enterprise between the cloud to manage those pre existing Oh, working clusters. You don't always have to be strapping straight from Dr Enterprises. Plug in external clusters is bad. >>Yep, the the Cube config elements of the UCP environment. The bundling capability actually gives us a very straightforward methodology. And there's instructions on our website for exactly how thio, uh, bring in import and you see p cluster. Um so it it makes very convenient for our existing customers to take advantage of this new release. >>Absolutely cool. More thoughts on this wonders if we jump onto the next video. >>I think we should move press on >>time marches on here. So let's Let's carry on. So just to recap where we are right now, first video, we create a management cluster. That's what we're gonna use to create All our downstream were closed clusters, which is what we did in this video. Let's maybe the simplest architectures, because that's doing everything in one region on AWS pretty common use case because we want to be able to spin up workload clusters across many regions. And so to do that, we're gonna add a third layer in between the management and work cluster layers. That's gonna be our regional cluster managers. So this is gonna be, uh, our regional management cluster that exists per region that we're going to manage those regional managers will be than the ones responsible for spending part clusters across all these different regions. Let's see it in action in our next video. >>Hello. In this demo, we will cover the deployment of additional regional management. Cluster will include a brief architectural overview, how to set up the management environment, prepare for the deployment deployment overview, and then just to prove it, to play a regional child cluster. So looking at the overall architecture, the management cluster provides all the core functionality, including identity management, authentication, inventory and release version. ING Regional Cluster provides the specific architecture provider in this case, AWS on the L C M components on the d you speak cluster for child cluster is the cluster or clusters being deployed and managed? Okay, so why do you need original cluster? Different platform architectures, for example AWS open stack, even bare metal to simplify connectivity across multiple regions handle complexities like VPNs or one way connectivity through firewalls, but also help clarify availability zones. Yeah. Here we have a view of the regional cluster and how it connects to the management cluster on their components, including items like the LCN cluster Manager. We also machine manager. We're hell Mandel are managed as well as the actual provider logic. Okay, we'll begin by logging on Is the default administrative user writer. Okay, once we're in there, we'll have a look at the available clusters making sure we switch to the default project which contains the administration clusters. Here we can see the cars management cluster, which is the master controller. When you see it only has three nodes, three managers, no workers. Okay, if we look at another regional cluster, similar to what we're going to deploy now. Also only has three managers once again, no workers. But as a comparison is a child cluster. This one has three managers, but also has additional workers associate it to the cluster. Yeah, all right, we need to connect. Tell bootstrap note, preferably the same note that used to create the original management plaster. It's just on AWS, but I still want to machine Mhm. All right, A few things we have to do to make sure the environment is ready. First thing we're gonna pseudo into route. I mean, we'll go into our releases folder where we have the car's boot strap on. This was the original bootstrap used to build the original management cluster. We're going to double check to make sure our cube con figures there It's again. The one created after the original customers created just double check. That cute conflict is the correct one. Does point to the management cluster. We're just checking to make sure that we can reach the images that everything's working, condone, load our images waken access to a swell. Yeah, Next, we're gonna edit the machine definitions what we're doing here is ensuring that for this cluster we have the right machine definitions, including items like the am I So that's found under the templates AWS directory. We don't need to edit anything else here, but we could change items like the size of the machines attempts we want to use but the key items to ensure where changed the am I reference for the junta image is the one for the region in this case aws region of re utilizing. This was an open stack deployment. We have to make sure we're pointing in the correct open stack images. Yeah, yeah. Okay. Sit the correct Am I save the file? Yeah. We need to get up credentials again. When we originally created the bootstrap cluster, we got credentials made of the U. S. If we hadn't done this, we would need to go through the u A. W s set up. So we just exporting AWS access key and I d. What's important is Kaz aws enabled equals. True. Now we're sitting the region for the new regional cluster. In this case, it's Frankfurt on exporting our Q conflict that we want to use for the management cluster when we looked at earlier. Yeah, now we're exporting that. Want to call? The cluster region is Frankfurt's Socrates Frankfurt yet trying to use something descriptive? It's easy to identify. Yeah, and then after this, we'll just run the bootstrap script, which will complete the deployment for us. Bootstrap of the regional cluster is quite a bit quicker than the initial management clusters. There are fewer components to be deployed, but to make it watchable, we've spent it up. So we're preparing our bootstrap cluster on the local bootstrap node. Almost ready on. We started preparing the instances at us and waiting for the past, you know, to get started. Please the best your node, onda. We're also starting to build the actual management machines they're now provisioning on. We've reached the point where they're actually starting to deploy Dr Enterprise, he says. Probably the longest face we'll see in a second that all the nodes will go from the player deployed. Prepare, prepare Mhm. We'll see. Their status changes updates. It was the first word ready. Second, just applying second. Grady, both my time away from home control that's become ready. Removing cluster the management cluster from the bootstrap instance into the new cluster running a data for us? Yeah, almost a on. Now we're playing Stockland. Thanks. Whichever is done on Done. Now we'll build a child cluster in the new region very, very quickly. Find the cluster will pick our new credential have shown up. We'll just call it Frankfurt for simplicity. A key on customers to find. That's the machine. That cluster stop with three manages set the correct Am I for the region? Yeah, Same to add workers. There we go. That's the building. Yeah. Total bill of time. Should be about fifteen minutes. Concedes in progress. Can we expect this up a little bit? Check the events. We've created all the dependencies, machine instances, machines. A boat? Yeah. Shortly. We should have a working caster in the Frankfurt region. Now almost a one note is ready from management. Two in progress. On we're done. Trust us up and running. >>Excellent. There we have it. We've got our three layered doctor enterprise container cloud structure in place now with our management cluster in which we scrap everything else. Our regional clusters which manage individual aws regions and child clusters sitting over depends. >>Yeah, you can. You know you can actually see in the hierarchy the advantages that that presents for folks who have multiple locations where they'd like a geographic locations where they'd like to distribute their clusters so that you can access them or readily co resident with your development teams. Um and, uh, one of the other things I think that's really unique about it is that we provide that same operational support system capability throughout. So you've got stack light monitoring the stack light that's monitoring the stack light down to the actual child clusters that they have >>all through that single pane of glass that shows you all your different clusters, whether their workload cluster like what the child clusters or usual clusters from managing different regions. Cool. Alright, well, time marches on your folks. We've only got a few minutes left and I got one more video in our last video for the session. We're gonna walk through standing up a child cluster on bare metal. So so far, everything we've seen so far has been aws focus. Just because it's kind of easy to make that was on AWS. We don't want to leave you with the impression that that's all we do, we're covering AWS bare metal and open step deployments as well documented Craftsman Cloud. Let's see it in action with a bare metal child cluster. >>We are on the home stretch, >>right. >>Hello. This demo will cover the process of defining bare metal hosts and then review the steps of defining and deploying a bare metal based doctor enterprise cluster. Yeah, so why bare metal? Firstly, it eliminates hyper visor overhead with performance boost of up to thirty percent provides direct access to GP use, prioritize for high performance wear clothes like machine learning and AI, and support high performance workouts like network functions, virtualization. It also provides a focus on on Prem workloads, simplifying and ensuring we don't need to create the complexity of adding another hyper visor layer in between. So continuing on the theme Why communities and bare metal again Hyper visor overhead. Well, no virtualization overhead. Direct access to hardware items like F p g A s G p, us. We can be much more specific about resource is required on the nodes. No need to cater for additional overhead. We can handle utilization in the scheduling better Onda. We increase the performance and simplicity of the entire environment as we don't need another virtualization layer. Yeah, In this section will define the BM hosts will create a new project. Will add the bare metal hosts, including the host name. I put my credentials. I pay my address, Mac address on, then provide a machine type label to determine what type of machine it is. Related use. Okay, let's get started Certain Blufgan was the operator thing. We'll go and we'll create a project for our machines to be a member off. Helps with scoping for later on for security. I begin the process of adding machines to that project. Yeah. Yeah. So the first thing we had to be in post many of the machine a name. Anything you want? Yeah, in this case by mental zero one. Provide the IAP My user name. Type my password? Yeah. On the Mac address for the active, my interface with boot interface and then the i p m i P address. Yeah, these machines. We have the time storage worker manager. He's a manager. We're gonna add a number of other machines on will speed this up just so you could see what the process. Looks like in the future, better discovery will be added to the product. Okay, Okay. Getting back there. We haven't Are Six machines have been added. Are busy being inspected, being added to the system. Let's have a look at the details of a single note. Mhm. We can see information on the set up of the node. Its capabilities? Yeah. As well as the inventory information about that particular machine. Okay, it's going to create the cluster. Mhm. Okay, so we're going to deploy a bare metal child cluster. The process we're going to go through is pretty much the same as any other child cluster. So credit custom. We'll give it a name. Thank you. But he thought were selecting bare metal on the region. We're going to select the version we want to apply on. We're going to add this search keys. If we hope we're going to give the load. Balancer host I p that we'd like to use out of the dress range update the address range that we want to use for the cluster. Check that the sea idea blocks for the communities and tunnels are what we want them to be. Enable disabled stack light and said the stack light settings to find the cluster. And then, as for any other machine, we need to add machines to the cluster. Here we're focused on building communities clusters. So we're gonna put the count of machines. You want managers? We're gonna pick the label type manager on create three machines. Is a manager for the Cuban a disgusting? Yeah, they were having workers to the same. It's a process. Just making sure that the worker label host like you are so yes, on Duin wait for the machines to deploy. Let's go through the process of putting the operating system on the notes, validating that operating system. Deploying Docker enterprise on making sure that the cluster is up and running ready to go. Okay, let's review the bold events. We can see the machine info now populated with more information about the specifics of things like storage. Yeah, of course. Details of a cluster, etcetera. Yeah, Yeah. Okay. Well, now watch the machines go through the various stages from prepared to deploy on what's the cluster build, and that brings us to the end of this particular do my as you can see the process is identical to that of building a normal child cluster we got our complaint is complete. >>Here we have a child cluster on bare metal for folks that wanted to play the stuff on Prem. >>It's ah been an interesting journey taken from the mothership as we started out building ah management cluster and then populating it with a child cluster and then finally creating a regional cluster to spread the geographically the management of our clusters and finally to provide a platform for supporting, you know, ai needs and and big Data needs, uh, you know, thank goodness we're now able to put things like Hadoop on, uh, bare metal thio in containers were pretty exciting. >>Yeah, absolutely. So with this Doctor Enterprise container cloud platform. Hopefully this commoditized scooping clusters, doctor enterprise clusters that could be spun up and use quickly taking provisioning times. You know, from however many months to get new clusters spun up for our teams. Two minutes, right. We saw those clusters gets better. Just a couple of minutes. Excellent. All right, well, thank you, everyone, for joining us for our demo session for Dr Enterprise Container Cloud. Of course, there's many many more things to discuss about this and all of Miranda's products. If you'd like to learn more, if you'd like to get your hands dirty with all of this content, police see us a training don Miranda's dot com, where we can offer you workshops and a number of different formats on our entire line of products and hands on interactive fashion. Thanks, everyone. Enjoy the rest of the launchpad of that >>thank you all enjoy.

>> From the SiliconANGLE Media Office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone and welcome to this week's episode of theCUBE Insights, powered by ETR. In this Breaking Analysis I want to lay out my 2020 predictions using insights gleaned from theCUBE blended with ETR spending data. You know, 2019 marked our 10th year of doing theCUBE. Over that time we've had the pleasure of covering nearly 1000 events and milestones, including the exit from the great softness of 2008 and 2009. You know theCUBE has extensively tracked a 10 year bull market. We've covered the era of data. We saw the rise and profitless prosperity of the big data and opensource Hadoop movement, where we predicted the practitioners, not vendors, would benefit the most from big data. We've covered many dozens of acquisitions including the 60 billion dollar chess move made by Michael Dell acquiring EMC, and a launch of hundreds of startups in flash, hyper-converged, big data, AI, blockchain, crypto, security and SaaS. There'll be other days to talk about theCUBE and review that, today's all about predicting the future, using spending data and insights from the thousands of interviews we've done on theCUBE. So let's get right into the ETR data and start with the high-level spending. Remember in October, ETR released its survey results and stated that we're coming out of a multiyear investment cycle in digital transformation. Enterprise IT buyers have learned what works, and on which technologies they're going to double down. They're now narrowing their investments on emerging technologies, picking those winners for the next gen tech, and at the same time, they're cutting redundancies from legacy players that they were keeping on as a hedge. Buyers are picking bundled suites from a handful of mega vendors, and solidifying their investments. We're seeing a multi-generational dynamic repeat itself, where buyers are creating a balance between the convenience of packaged offerings, i.e. bundles, and leveraging best of breed technologies to drive innovation. So on balance, the ETR data shows that a contraction in spending and tepid CIO sentiment is impacting both emerging vendors as well as traditional players, and these trends are most pronounced in the very largest organizations, which have always been the best bellwether in ETR's data sets. Let me share with you what one IT executive said recently that I think really sums up the situation quite well. He said, "ETR's findings mirror what we're doing today, "in that we spend most of 2018 bringing in "a lot of the new, core technology. "I believe what you're seeing now is not a lull in spend, "but an operationalization of what we've already purchased. "We're not spending on what's next yet, "because we're still rolling out what we just bought." This is from a VP of global IT at a large public manufacturing company, I said he, it could be a she as well. I think that she's summing it up correctly, and it reflects many of what customers on theCUBE tell us. Now, let's take a look at the macroeconomy. GDP growth is going to come in at about 2.3% this year, give or take. It's not going to hit the Trump administration's goal of 3% plus, but consumers are clearly powering steady growth. At least for now. IT spending should grow at about a point or two above GDP, so let's put that at, say, 4%. We're right in the middle of a Santa Claus rally, and the S&P is above 3200 today. Tech has been a powerful tailwind for stocks, and I think stocks, tech stock's going to take a breath in early 2020, but I expect continued strong growth in the economy and tech spending after a Q1 pause. I could see the S&P flirting with 3700 or even higher in 2020, and I think the tech sector will be a benefactor of that momentum, providing an impetus for continued growth. Here's my thinking on that. So much of 2020 is going to be about the election, and to me the election is going to be really about the economy. And I predict the economy is going to remain steady. And as the IT leader I quoted earlier said, customers will be operationalizing what's been previously purchased. Here's what's different in 2020. Tech projects have historically been very risky investments, and have required higher internal rates of return, IRRs, to get approved by CFOs. But the cloud has altered two factors. One, is that it's allowed more experimentation for way less money. The second is cloud, by shifting CAPEX to OPEX, allows for much more incremental, lower risk investments. So I think you'll see continued steady growth, powered by the cloud, which allows experimentation, and importantly higher hit rates of success. These successful projects will throw off cash for companies, and CFOs are getting on board because they realize it's driving innovation. They also realize that IT does matter, maybe not in the form that Nick Carr envisioned, but a new generation of IT that creates competitive advantage. This brings me to my first main prediction, which is the growth of cloud computing is going to moderate, but the cloud will continue to steal significant share from on-prem spending. Now the narrative that the pendulum is swinging back in my view, is a false narrative. Rather, the pendulum has swung, and the cloud is the underpinning of innovation. Now having said that, I do think we're seeing a bit of an equilibrium in spending, where buyers have identified those workloads that are going to remain on-prem, which is why you see, for example, AWS, Azure, and Google making moves in hybrid. Hybrid slash on-prem offerings. What this chart here shows from ETR, so from 2010 through October '19 survey on cloud spending, I had to block out the 2020 survey as it's currently in the field, I'm not allowed to show that data. The yellow line is market share, which in ETR parlance, as you remember, is pervasiveness, or mentions in their survey. The blue line is spending momentum, measured as net score, which essentially subtracts the percent of customers spending less from those spending more. The long, steady march of cloud, as you can see, continues, and there's no indication that it's going to abate. That said, the penetration of cloud has become much more meaningful, so share gains will be more hard-fought for the cloud guys. Now, you may see this as a non-prediction, or a hedge. It's not, let me be clear. Cloud will continue to steal share from on-prem, but share gains for the cloud vendors will be more difficult. Which brings me to part B of this prediction. What I'm showing in this chart is market share from ETR's January 2016 survey through October '19. And I'm showing spending for three on-prem vendors within AWS, Azure, and Google Cloud accounts. And I'm picking on Oracle, IBM, and Dell EMC as three prominent on-prem proxies, and you can see the steady decline in market share for these companies. And even though there's a bit of an uptick in October, I don't see this as a reversal. What's going to happen is that traditional on-prem vendors are going to step up their cloud strategies. Specifically with multicloud management. This is going to be the case with Dell, who's going to leverage VMware, and in the case of IBM, they'll try to take advantage of Red Hat in that multicloud game. Now both IBM and Oracle, who each have public clouds are going to dig their heels in, they're going to get customers in a headlock, and provide big financial incentives for them to use their captive clouds. All right, so with the high-level spending comments that I made earlier, and that cloud discussion that we just had as a backdrop, the question is, which companies will do well in the coming year? I'm going to call out five companies, that I want to highlight where the ETR data intersects what we're seeing on theCUBE. The prediction is these five players will do well in 2020, they're going to power through any downturn in spending, and they're going to thrive in the face of the cloud share shift. So the chart here shows data from the ETR October 2019 survey, and it lays out net score or spending momentum for these companies, that I am predicting will be winners in 2020 and beyond. And the five companies are UIPath, Snowflake, Databricks, HashiCorp, and Rubrik. Let me start with UIPath. They are the leader in robotic process automation. I think RPA is going to do well even in a downturn, because more companies will be looking to automate and save money, even in a softer climate. Automation Anywhere is another player in this space, they're doing pretty well, and I predict that UIPath will come out on top of this space, but both UIPath and Automation Anywhere can thrive. Next company is Snowflake, they are changing the analytic database market, and I've covered them before in previous Breaking Analysis segments. They are going to continue to grow nicely in my view. They are 100% cloud-based, and they participate in all popular cloud platforms. Now ironically, they compete with AWS RedShift, who continues to copy some of the innovations that Snowflake has popularized. But AWS and Snowflake are strong partners, so there's room for both companies to thrive. Snowflake especially, as they play in clouds other than just AWS. Which brings me to Databricks. We're seeing a new type of workload emerge in the cloud for modern analytic databases, where organizations are taking all this data that they have, lots of it in the cloud, and they're structuring it within a Snowflake database, or RedShift, and they're bringing Databricks tooling to the equation to be able to query and visualize the data in near real time. Now of course, as I say, AWS plays here with RedShift, and they're selling a lot of EC2, so they love Snowflake. All major cloud players are seeing this type of workload enter the mix, and it's going to be a strong area of growth in 2020 and beyond. Next thing I want to talk about is HashiCorp. HashiCorp is capitalizing on this trend toward cloud-native computing. The company provides opensource tooling for developers, and is all about simplifying application deployment independent of the underlying platform, whether it's virtual, container, or cloud. Five years ago, the players in the space that got all the attention on theCUBE were Chef, Puppet, Ansible and Salt, and today, especially again on theCUBE, you hear the most about Hashi and Ansible, and in fact we were at AnsibleFest with theCUBE, and we heard lots about HashiCorp, so they both complement and compete with the older players. To me, this reminds me of Spark within the Hadoop ecosystem. Hashi has raised about 174 million in VC, and as you can see they have very strong spending momentum in the ETR dataset, with a net score, as shown, of 63%. Now finally, I want to talk about Rubrik, which has been a consistent performer in the ETR dataset. They're trying to transform backup into data management as a discipline. They compete with established players in the data protection space, guys like Veritas, Dell EMC, IBM and CommVault. Now Rubrik is not the only new or newish player here, that's doing very well, Cohesity, who's relatively new, Veeam, which has been around for a decade, both doing very well and showing up strong in ETR surveys, especially Veeam, but Rubrik has been a consistently strong performer and has been outpacing the others, so I want to call them out. Look for these five to do very well in 2020, and into the next decade. So that brings me to my next prediction, I want to talk about Kubernetes. This prediction is twofold. Kubernetes is going to continue its strong showing as this data from ETR shows. This is Kubernetes' market share in the October 2019 survey, so Kubernetes spend had a 76% net score. So very very strong. But the other part of the prediction is that Kubernetes will become embedded into virtually every platform, and people will stop thinking about it as a separate market. Already today, there's little discussion of the idea of a Kubernetes distro, I mean Anthos is an example of a Kubernetes stack, but it can be run in the cloud, it can be run on-prem, anywhere. VMware Tanzu, Microsoft Azure Arc are other examples, they're really not stacks, but they're management platforms that can manage anyone's Kubernetes instances. I like to think of this as kind of like flash. You remember when everyone looked at flash storage as a separate market, well today it's just embedded everywhere. And that's kind of what's happening with Kubernetes. So spending momentum is going to continue to be strong, but by 2023, Kubernetes will be ubiquitous, and not really thought of as a separate entity. All right, for my next prediction, I want to talk about cybersecurity. I did a Breaking Analysis earlier this year on security, and I showed this slide. And as you can see, I've added a little something in the red stars for my prediction. So what this chart shows is two views of net score, the left-hand side shows the ranking by net score, and you can see CrowdStrike, Okta, Shape Security, which was just, by the way, bought by F5, that was an announcement. Twistlock, which is now Palo Alto Networks, and you can see the others down that list. On the right-hand side is net score, but it's ranked by shared N, which is a measure of pervasiveness in the ETR dataset. What I've added is the four star companies, that is those companies that have both spending momentum and are pervasive in the ETR survey. So the prediction is 2020 we'll see the four star companies maintain their position and gain strength in 2020. These include established players with portfolios where they can bundle like Microsoft, Cisco, Palo Alto Networks, Splunk, Proofpoint, Fortinet, and CyberArk Software. And then the newer companies like Okta and CrowdStrike are going to continue to gain share faster than the larger players. Now you also may see companies like SailPoint, Illumio, and SentinelOne emerge as four star companies over the next 24 months. Now the one company that's not on this list that is a major player in security is AWS. AWS is the cloud security leader, and is in a category all by itself in many ways. As I said in my security segment earlier this year, the market is incredibly fragmented, and it's going to stay that way. Each year we look back and say "Did we spend more on security?" and "Are we more safe?" And every year the answer is yes, and no. And 2020 will be no different. Now if you look at the various data sources, we spend approximately 120 billion dollars annually on cybersecurity. The worldwide economy is about 85 trillion in dollar terms, so on balance, we spend about .14% on securing our economy, so we're barely scratching the surface. The market is going to remain highly fragmented, the rich will get richer if they have four stars, new players will continue to enter the space, and M&A will continue to be robust. Now if you exclude my long shot that the S&P will break through 3700 next year, that makes nine predictions. For my 10th and final prediction, I don't have hard data from ETR, but I have a strong opinion on this, and that is that the edge will be won by developers, you've heard me talk about this before. Specifically, platforms like Outposts, which are essentially programmable infrastructure which bring a cloud development platform to the edge, is how that space will evolve. It won't be won by shoving traditional servers and storage boxes out to the edge. Rather, it will grow by coders being able to build new applications and workloads on top of infrastructure as code. Okay, that wraps up my 2020 predictions. I'd very much like to hear your opinion, so you can leave your thoughts or your own predictions in the comments sections of this video, or go to my LinkedIn posts. You can reach me @DVellante on Twitter, love to hear your thoughts. And don't forget, this series is available on iTunes, Spotify, and other podcast platforms for your listening pleasure. I'd like to wish everyone a safe and restful holiday season and a prosperous, healthy 2020. Enjoy your families, enjoy this time, this is Dave Vellante, signing out from the latest episode of theCUBE Insights powered by ETR, thanks for watching, everybody. We'll see you next time. (techno music)

(upbeat techo music) >> Covering Kubecon and CloudNativeCon North America 2018 brought to you by RedHat, The Cloud Native Computing Foundation, and its ecosystem partners. (upbeat techno music) >> Okay welcome back everyone, we're live here in Seattle for KubeCon 2018, CloudNativeCon, I'm John Furrier with theCUBE coverage, three days. Our next guest is Chris Rosen who's the Program Director for Offering Management, for Kubernetes, IBM's Kubernetes Service. Chris, welcome to theCUBE thanks for joining us. >> Thank you very much, glad to be here. >> We always love covering IBM. Think is coming up this year. It's going to be in San Francisco. Want to get that out there because we're psyched it's in our backyard. It's always been in Vegas. We've been covering IBM's events for a long time. We've seen the evolution of Cloud, you know, Bluemix, SoftLayer all coming together. Kubernetes, actually the timing of Kubernetes couldn't have been better. >> Absolutely. >> With all the software investments in Bluemix, all the customers that you guys have, now with scale and choice with CNCF. Kind of a perfect storm for you guys, explain kind of what's going on, your role and how it's all kind of clicking together. >> Sure, so it is, you're exactly right it's an exciting time to be there. There's a lot of change. Everyone here at the conference, so excited there is so much new going on. About 2 1/2 years ago, IBM went all in on Kubernetes for our Cloud as well as for on-prem offerings to leverage and provide flexibility, portability, eliminating vendor lock-in, all those things that our customers asked us for and then adding capabilities on top of it. So, we are really excited to kind of grow and participate in the ecosystem. >> So, I hear a lot of people talk about Kubernetes. First of all, we love covering it, but the language around what is Kubernetes, they're even doing children's books, stories, trying to break it down. The rise of Kubernetes kind of has gone mainstream, but I hear things like the Kubernetes stack, the CNCF stack. I mean, it's not necessarily a stack per se. Could you break down, 'cause a lot of people are going to CNCF for a variety of other things. >> Right. >> With Kubernetes, at the core, describe how you talk to customers, how do you explain it. Unpack the positioning of Kubernetes at the core, and the CNCF offerings, or what do people call it? The stack, the CNCF stack? Or, how does this all break down? >> Yeah, so you're right. It's a very complex stack and that's where the complexity comes in that we're trying to eliminate for our customers is to simplify managing that stack. So, at the top of the stack, of course we've got Kubernetes for the orchestration layer. Below that, we've got the engine. We're using containerd now but we also have Prometheus, Fluentd, Calico, it's a very complex stack. And, when you think about managing that and a new version comes out from Kubernetes, how does that effect anything else in that stack? >> Chris, wonder if you can explain a little bit what IBM's doing here because some people I've heard, they've said, ah, there's like over 70 different you know, platforms with Kubernetes, oh they're all trying to sell me a Kubernetes distribution. >> Right. >> I don't believe that's the case. So, maybe you just explain what bakes into your products, what IBM bakes into the community. >> Right. >> And your role, yeah. >> Well, you're exactly right. So we're not forking and doing anything IBM-esk with Kubernetes. >> Right We have core maintainers that live out there. That's their job, is to focus upstream. We think that's very important to be agnostic and to participate in these communities. Now, what we do is, we build our solutions on top of these open source projects, adding value, simplifying the management of those solutions. So, you think about the CNCF conformance testing, IBM participates. We typically are the first public cloud to add support for a new version of Kubernetes. So we're really excited to do that, and the only way we can do that is by actively participating in the community. >> The upstream dynamic is important. Just talk about that for a second because this is, I think why one of the reasons it's been so successful is the upstream contribution is not your IBM perspective, it's just pure contribution for the benefit of the community then downstream, you guys are productizing that piece. >> Right. >> That is kind of, that is the purpose of open source. >> Exactly, exactly, and you hear time and time again at these conferences that the power of the community is so much greater than one individual company. So, let's work together as a community, build that solid foundation at the open source level and then IBM's going to add things that we think are differentiating and unique to our offering. >> What's the number one end-user conversation, problem that's being solved with the evolution of CNCF and Kubernetes at the core? Obviously, choice is one, but when specifically as you talk to customers, what is the big nead? What's the conversations like? Can you share some input into, insight into the customer equation? >> Probably the biggest request is around security, and that's a couple of fronts. One, maybe this is my first step into public cloud, so how do I ensure, in a multi-tenant world, that I am secure in isolation and all of those things. But then also, thinking about maybe I'm just starting with containers and microservices. So, this is a completely different mental paradigm in how I'm developing code, running code, and to explain to them how IBM is helping simplify that security aspect along that entire journey. >> So talk about the auto-scaling security piece, because, again, the touch points, it's interesting about Cloud, the entry point is multiple avenues for a customer could be workload, portability. It could be for a native application in the Cloud. Where's the scale come in? How do you guys see the scale picture developing? >> Right, so again, scaling comes kind of two factors. One, Pod Autoscaling from Kubernetes. So, you can define, let your application scale out when it needs to, but then there is also the Infrastructure side. So, I need to be able to set parameters to scale up when I need to and then scale back down to kind of meet my requirements as well as managing my cost. >> Well IBM Think's coming up on February 15th, just a plug for theCUBE. We'll be there, obviously register but IBM Think is a big conference. How much of Kubernetes will be at the center of IBM Think? >> Kubernetes will be a huge part at Think. We encourage everyone listening to come sign up and join us. There will be a range from hands-on for your Developer focus or your Operators. We'll have much larger business benefits for our C-level participants. So, a lot of activities, a lot of fun, a lot to learn at IBM Think 2019 in San Francisco. >> What's the biggest story here at KubeCon, CloudNative conference for the folks not here, or watching, or maybe are wait-listed in the lobby-con (Chris laughs) that's happening in Seattle? What's the biggest story? >> The biggest story is the vibrant ecosystem. When you look at the amount of people that are here, the chatter, the booths are packed, the sessions are packed, the keynotes are packed. It's great, everyone wants to share a story, learn from each other. It's a fantastic community to be a part of. >> I got to ask you the programmability piece, because, one of the things that people look for is virtual private networks, they're using VPNing, they want to take VPNs to the next level, SD-WAN, super-hot trend that's kicking back up, people want to program networks. >> Right. >> They don't want to have to actually provision networks anymore. this is DevOps but now it's also the network layer. Storage and compute looking good? >> Right. >> Network is evolving, how do you guys see that picture? Can you comment on that, it's a hot area. I just want to get your perspective. >> Yeah, definitely evolving just like the rest of the space. So, we are excited to work with various vendors here. IBM has our own point of view of what virtual private cloud means supporting, bring your own IP, private end-points, private cluster, so that way, if I only want connectivity inside my backbone network, I can configure my networks that way, creating a VPN tunnel back to my resources on-prem, and just have it completely isolated from the rest of the world. >> You see a lot of on-premises activity, Azure stack, Amazon announces this Outposts Cloud Sys supposed to be about a year away, and their whole message is latency. >> Right. >> Workloads need certain things, some of them need low-latency. >> Right. >> Some need more security. Just a, is that just a course of business now, that customers have to have these diverse sets of needs met? >> Absolutely, so IBM has two offerings, IBM Cloud Private for on-prem with multi-cloud manager that's really focused at managing in that hybrid or multi-cloud world. How do we simplify resources that are running on-prem, IBM Cloud, other Clouds, and how do we do so efficiently? So, we definitely see a lot of hybrid, hybrid architectures, whether that's on-prem to IBM Cloud, IBM Cloud to other Clouds, and latency really becomes a minimal factor. >> And what's your to do list on Kubernetes as you look at this event, obviously continuing to grow, the international piece is pretty compelling as well, growth in China, we're seeing that. What's your plans for IBM Kubernetes offering, what's the roadmap look like, what can you share some insight into what's next for you guys? >> Absolutely, so we're definitely focused on security, continues to be paramount, even though we think we are a very secure offering already, but continuing to expand on that. The private endpoints that I mentioned, the private connectivity, isolating network traffic is a huge piece of it, staying compliant and up to date with Kubernetes versions as they come out, making sure that they're scalable, performant, upgradeable, and then making those available to our users. >> IBM continuing to transform obviously the big news we saw with the RedHat acquisition, you know, obviously you've been in the Cloud for a while, everyone knows that with Bluemix, maybe not get to know as much work that went into Bluemix for instance, a lot of great stuff. You guys have built, you know, the Developer side within Cloud. IBM Think is February 15th, it's going to be in San Francisco. theCUBE will be there. Check these guys out. They're going to have a lot of workshops we're excited to see how the evolution of IBM and IBM Cloud continues. Chris coming on theCUBE, appreciate it. >> Thank you very much. >> theCUBE coverage, I'm John Furrier, Stu Miniman, stay with us for more coverage, here in Seattle, after this short break. (upbeat techno music)

>> Narrator: Live from Austin, Texas. It's theCUBE, covering KubeCon and CloudNativeCon 2017. Brought to you by Red Hat, the Linux Foundation, and theCUBE's ecosystem partners. >> Hello everyone. Welcome back to theCUBE's exclusive coverage, here live in Austin, Texas for KubeCon and CloudNativeCon. I'm John Furrier, the co-founder of SiliconANGLE Media with my co-host Stu Miniman, Next is Nigel Poulten, who's the author of the Kubernetes book, also container guru, trainer, been in the business for a long time in the community. Great to have you on for our intro. >> Thank you >> Stu, keynote, let's get down to it. What was the big highlights? >> Yeah, well, first of all John, we've officially entered KubeCon Days here. So CloudNativeCon was yesterday. We've got two more days of KubeCon. Kelsey Hightower, you know, we had him on theCUBE yesterday. Phenomenal speaker, everybody's looking forward to him. Lines to talk to him. Made sure that there was a standing ovation before and after his. Very demo heavy. I mean, you know, this group loves it. There were a lot of, you know, great pithy lines. Arguments over, you know, which is the best language, which is the best way to do things? Knocking on things like YAML. So, it was definitely a fun, geeky discussion. I'm a big Game of Thrones fan. So I loved to see season seven delivered on Kubernetes. >> What was the summary of the keynote? What was the take? >> So I think from my perspective, the summary was Kubernetes is boring. Which translates to us generally, as in it's maturing. It's something that you might want to be able to trust in your production environment, if you're an enterprise. I mean, look, as a technology guy we always think we like to know the details, the weeds. And we like to play with YAML and stuff like that. But at the end of the day, business is down and developers tend not to want to. They want a smooth pipeline. And that's boring, and so boring is good. >> Yeah, and I do want to poke at it a little bit, Nigel, I definitely want your opinion on this, because there are certain technologies we say, "Oh right, it's reached that boring phase", which means it's kind of steady state. Kubernetes is not like One Dot Nine. Coming into the show it was like, how complex it is. Oh my God, there's all these things above and below. Yin gave a really nice keynote showing kind of a layer cake there. >> Yeah. >> I think maybe the Kubernetes layer might be, it's stable enough and used, and people can use it. But this ecosystem by no means is it boring. >> No >> And there's lots of things to make out. What are you seeing? >> Totally, and it's that definition of boring, really. So I would say boring would translate into usable. But you're right, in no way is it boring in any sense. In fact, it's exciting and it's dangerous as well. >> Yeah, and ... >> So I'll give you an example, right. So Kubernetes is massively successful. I think we all grock that at the moment, okay. But it's almost potentially going to be a victim of it's own success. It's always at one of the many summits that was going on before KubeCon and CloudNativeCon started, and it was about networking and there was a bunch of guys here from big carriers and they really want to take this simple networking model that Kubernetes currently has and make it fit their needs, which would make it really complex, dare I say, almost OpenStack Neutron. (laughing) And I think there's so many people here at this conference right now that want to take Kubernetes and use it for their own purposes. And as successful as it is, and as much uptake as it's got, there is a potential danger there, I think, that it explodes out of control, and I don't want to knock OpenStack, but becomes difficult and not what we want it to be, and that's dangerous for them. >> Nigel, you bring up a great point here, because something we've been looking at is every time we abstract or make this new design model, it's "Oh well". We want to make sure the developer doesn't have to worry about that infrastructure. Clayton from Red Hat, we had him on theCUBE, and he talked about it in the keynote, boring means when I write my code I don't have to think about the infrastructure, but networking and storage. Networking some of the basis pieces are done but there's a lot of activity in that space, and storage, we're still arguing over what Container Native Storage should be, what CloudNative storage should be. So it's still to my definition, it's not boring. That's the direction, and I like it. Kind of was where we talked about invisible infrastructure. >> Yeah >> What do you see? You've got a heavy background on that side too. >> So I think I quite like this space that networking is at within Kubernetes. It's simple, and that works for me, right. Storage is certainly, it's still playing catch up there, and I think a lot of decisions still need to be made. The future, in my opinion, is still not clear there. But I think a lot of games have got to be played to say, now how far do we take networking, and how far do we take storage and things like that so that it, in the one sense doesn't balloon out of control, but on the other side you do want it to meet more use cases than just the very basic use cases. So, I mean, that plays back to my idea that that danger aspect of Kubernetes, it seems to have won in the orchestration space at the moment, but I think the road ahead, there still loads of potholes, and there's tight bends, and there's cliff edges and things that we still could fall off, and that's exciting. >> Nigel, your dangerous comment reminds me of some of the early days of V-M-ware. >> Nigel: Right >> You know, people that would get in there, they'd do some really cool things, they'd write it up, share it with the community. And absolutely, it feels like that, almost even bigger. >> Yeah, like the top layer that interfaces with the developers and things like that, that's getting pretty stable. But underneath, I mean, that is a happening place underneath right now, and I imagine it's going to be a happening place for quite a few years. >> What about service meshes and also pluggable architectures? Because that seems to be the answer to the dangerous question. Oh don't worry about it, carriers and what not. You can just build pluggable architectures, no one's going to get hurt. >> Nigel: Yeah >> Not ready for prime time? What's your thoughts? >> So I think service mesh is almost certainly in my opinion, the hot topic of the conference so far. I like this idea of it getting born and stuff, and that's good for the project. But if there's one take away, if it's something that you're not quite clued upon at the moment, go away and look into service mesh. I've got to do a lot of that myself, to be perfectly honest. But this whole idea of running like sidecar containers and what have you, inside of the pods, alongside your application to look at your ingress traffic, your incoming traffic, your outgoing traffic. It's all cool and it can add so much functionality and make it so much more usable to a lot of users. But at the same time there's not ... I don't know, right, look I'm a little bit old fashioned. I remember the days of deploying agents on servers. And we would have server bills that had agent upon agent upon agent. And we have this backlash in the industry of like, you're not bringing your product in vendor x, y or z, okay. If it deploys an agent, we're going fully agentless here. We're sick of managing all these different agents in our stack, and I wonder again, playing to the danger topic here, that like, are we going to end up having loads of these sidecar containers in our pods that are affectively the modern day agents that we then have to manage, and consume resources >> Explain the sidecar generation, it's important. Take a minute to explain the dynamic because containerization has been around for awhile, Google and everyone else knows that. >> Nigel: Yeah. >> But Docker really put it on the map. Now the commoditization of containers with Kubernetes. What's this sidecar thing about? >> Nigel: Okay >> Quick, take a minute to explain to the folks. >> Right, so in the Kubernetes world I guess the atomic unit of deployment, the equivalent of a V-M from the V-M World space would be the pod, which is effectively a container, right? But within that pod you run your application container. And I think for most people you run one container inside of that pod, it's your application, right? What we're starting to see now is, and Kubernetes has always had this ability to run multiple containers inside of a pod. Most people don't do it. And it seems that a lot of the external projects, and a lot of the third party vendors are starting to pick up on this and say, "Alright, well let's run another container "Inside of that pod". It's not your actual application and we call it a sidecar container. And it adds functionality and what have you, but is also potentially eats through resources, it makes your deployments maybe more complicated. I mean it's always a trade off, isn't it? >> Yeah >> You get additional functionality but it's never for free. >> Yeah it's overhead. Alright, talk about the customer guys. What we saw in keynote, we saw HBO on stage. How are customers using Kubernetes? Because I'm trying to put my finger on it. I love Orchestrate, I know what that does, and I understand the benefits, but how are actually people using it today? >> So I think it's a little bit like the whole container thing, right? The early adopters of the Netflix's and the HBOs and the people like that that have got large engineering teams, that have a lot of developers on staff, they're really just comfortable going and taking these new technologies, and rolling them themselves, and they've got this appetite for danger, again within their organization almost. Their risk taking organizations, right. They're all over the containers and the Kubernetes. The more traditional enterprises I think are still kicking the tires. They're still throwing out the occasional new project within the organization and saying, "Let's test the waters with this new feature "That we want to add to our main product", or "We've got something new, "Let's try containers and Kubernetes." They're certain, at least the ones that I speak to, certainly not at the phase where they're taking their legacy apps. >> HBO was using it for like traffic, identifying ingress, you mentioned that earlier, I mean basic stuff. Not a lot of heavy lifting, or is it? >> Well, I think the HBO, I mean ... How much they ran the season seven of Game of Thrones on Kubernetes. I mean, I'm sure there was some non-Kubernetes stuff in there as well, but it seemed like from the presentation pretty much, well, a lot of that stuff was running containers and Kubernetes, and lets be fair, when it comes to HBO, Game of Thrones is like their, it's their killer product at the end of the day, isn't it? And so they've taken a risk there with that. >> Yeah >> But again you know HBO, a rare... >> There's a lot of online viewers, by the way on that too. >> Yeah. >> With HBO Go. >> Oh, an insane number! But I would say compared to a traditional enterprise they're a risk taking organization. They live in the Cloud. They like living on the edge. They're willing to take risks with new technologies to push the product forward. >> Alright, so I want to get your guys' thoughts on a tweet I saw out there. "Think of Kubernetes as the colonel "For modern distributed systems. "It's not about zero ops, it's about op power tools "to unlock developer productivity." Craig McLuckie from Heptio mentioned that on stage. Really kind of rallying around Kubernetes. Thoughts on that quote? What does that mean? >> So I mean John, you know there was for a while people saying, "How do we deprecate? "Or even go to kind of noOps?" Absolutely, many of the keynotes talked about who's deploying them and who's running them. We're not talking about eliminating ops. Even when I can have a voice assistant help roll things out, they're still absolutely a major piece of who needs to run this, but the right things to the right part of the organization. >> Yeah, I think instead of using the word colonel maybe use the word Linux, you know. Looking at Kubernetes as the Linux of the Cloud, and that's not my term, I've heard other people say it. But it's open source for a start like Linux is, it's got a great thriving community of people contributing to it. You can fork it, you can do what ever you want with it, but if you're going to deploy a CloudNative application right now, then Kubernetes is that substrate. You've just got to look at what came out of re:Invent. So A-W-S is now offering a native Kubernetes hosted service, obviously Google does it, Azure does it with Microsoft. They're all picking up on this realizing that people deploying CloudNative apps, they're going to be deploying it on Kubernetes. >> Thoughts about Red Hat. I just saw Gabe Monroy, the keynote, Stu. Red Hat's contribution to hardening Kubernetes cannot be overstated. C-C OpenShift And we had Bryan Gracie on yesterday. I mean OpenShift, what a bet. Microsoft betting heavily on Kubernetes. Google obviously sees this as an opportunity. Multi-Cloud fantasies out there somewhere, but that's what customers are kind of asking for, not yet in tangible product, but this is interesting. You've got Red Hat, the king of the enterprise, OpenSource. >> Nigel: Absolutely, yeah. >> No debate about that. Microsoft and Google, old guard with Microsoft and then new guard in Google. Really if they don't throw a line at the main Cloud trend with Kubernetes, they could be left in the dust. So I see a lot of things at play. How is the Red Hat and the Kubernetes investment paying off? How do you guys see that playing out? Good strategic move, headroom to it? What comments and caller commentary on that? >> Well I think if you compare Red Hat to Microsoft, if you don't mind me doing that, Microsoft has a cash cow in Windows in the past and I think it quickly realized that the cash cow was not going to live forever, and they invested heavily in Azure. Red Hat live a lot, I guess as well, off support contracts and things like that, the Red Hat enterprise Linux. How long of a tail that has, I'm not sure. So certainly they're doing at least, they're looking in the right direction at least by investing heavily in Kubernetes. If they want to go in and be the enterprise's trusted Kubernetes partner, I think they've got a great story. They've contributed a ton to it. They're already in the door at most enterprises, and I think you couple those two things together if the enterprise is going to adopt Kubernetes at some point. I'm not saying they've go the best story, but they've got a pretty decent story. >> Alright, in the last minute I want to ask both you guys this question because it's been kind of on my mind, I've been thinking about it. Maybe I'm overstretching here but three day conference, one day to CloudNative, two days to Kubernetes, KubeCon. Why? More important? Growing community? CloudNative I think, would be probably stronger sessions. Is it because there's more emphasis on the Kubernetes? >> Kubernetes is the core, Kubernetes is what started the C-N-C-F. >> John: Yeah >> All the other projects really build off to it. I think it's pretty... >> It needs more attention. >> Kubernetes, I mean, while there's ... You know I love Kelsey's line this morning. He looked out at the audience he says, "I think everyone that's running Kubernetes "In the globe is here." So, there's jokes about how many people are actually running in production >> Yeah, they're probably here. >> So look, there's still so many people that are getting the Kubernetes 1-0-1. The whole CloudNative, all of these other projects are all building off of it. I think it's really straight forward on there. We even heard, do we call it the C-N-C-F? Do we rename it to something that's a little more Kubernetes focused? Because CloudNative gets talked about some, there's service mesh, absolutely Nigel, it was the buzz coming into the show. I hear those sessions are overflowing here. We didn't even get to talk about, there's like another alternative to Istio that's there. >> And Lou Tucker, by the way, affirmed that same thread yesterday about the service mesh. Nigel, final word for you on this segment. How big order of magnitude and important is Kubernetes? I mean given you've seen, talk about agent-ism in the old days, and all the ways that have come, that's been kind of incremental proving balls been moved down the field here and there. And some big chunk yardage, if you will, use this football analogy. How big, because I've seen Kubernetes just go from here to here. >> Yeah >> Really move the need along the community, it's galvanized. How important is Kubernetes, from an order of magnitude, when we look back a few years from now, what are we going to be saying? "Hey, remember KubeCon in 2017?" How important is Kubernetes? >> Well, can I say I think it's really early days, okay? And I like the analogy that it is the Linux of the Cloud or of CloudNative, okay? But I think there's danger in that as well because the world is changing so fast now. I mean Linux has lived for a very long time, okay. Will Kubernetes live that long or will it be replaced by something else? It probably will be, but I do feel these are early days, and I think it has got a long stretch ahead. A long stretch as in like... >> John: Yeah. >> Good four or five years. And within two to three years, you know, just about every organization in my opinion is going to have some Kubernetes in it. >> And the beginning signs of maturity's coming. Stack Wars too, all the vendors really trying to figure out, strategically it's like a 3-D chess match right now. Open source is kind of like arbiter of this, really good stuff. I think it's going to be super important. Thanks for the commentary. kicking off day two of Cube exclusive coverage here at KubeCon. CloudNativeCon was yesterday. Two days of KubeCon. We'll be back with more live coverage. From theCUBE, I'm John Furrier. Stu Miniman and Nigel Poulten after this short break. (light techno music)

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

(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI/ML Top Startups Building Foundation Model Infrastructure. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem to talk about data and analytics. I'm your host, Lisa Martin and today we're excited to be joined by two guests from Astronomer. Steven Hillion joins us, it's Chief Data Officer and Jeff Fletcher, it's director of ML. They're here to talk about machine learning and data orchestration. Guys, thank you so much for joining us today. >> Thank you. >> It's great to be here. >> Before we get into machine learning let's give the audience an overview of Astronomer. Talk about what that is, Steven. Talk about what you mean by data orchestration. >> Yeah, let's start with Astronomer. We're the Airflow company basically. The commercial developer behind the open-source project, Apache Airflow. I don't know if you've heard of Airflow. It's sort of de-facto standard these days for orchestrating data pipelines, data engineering pipelines, and as we'll talk about later, machine learning pipelines. It's really is the de-facto standard. I think we're up to about 12 million downloads a month. That's actually as a open-source project. I think at this point it's more popular by some measures than Slack. Airflow was created by Airbnb some years ago to manage all of their data pipelines and manage all of their workflows and now it powers the data ecosystem for organizations as diverse as Electronic Arts, Conde Nast is one of our big customers, a big user of Airflow. And also not to mention the biggest banks on Wall Street use Airflow and Astronomer to power the flow of data throughout their organizations. >> Talk about that a little bit more, Steven, in terms of the business impact. You mentioned some great customer names there. What is the business impact or outcomes that a data orchestration strategy enables businesses to achieve? >> Yeah, I mean, at the heart of it is quite simply, scheduling and managing data pipelines. And so if you have some enormous retailer who's managing the flow of information throughout their organization they may literally have thousands or even tens of thousands of data pipelines that need to execute every day to do things as simple as delivering metrics for the executives to consume at the end of the day, to producing on a weekly basis new machine learning models that can be used to drive product recommendations. One of our customers, for example, is a British food delivery service. And you get those recommendations in your application that says, "Well, maybe you want to have samosas with your curry." That sort of thing is powered by machine learning models that they train on a regular basis to reflect changing conditions in the market. And those are produced through Airflow and through the Astronomer platform, which is essentially a managed platform for running airflow. So at its simplest it really is just scheduling and managing those workflows. But that's easier said than done of course. I mean if you have 10 thousands of those things then you need to make sure that they all run that they all have sufficient compute resources. If things fail, how do you track those down across those 10,000 workflows? How easy is it for an average data scientist or data engineer to contribute their code, their Python notebooks or their SQL code into a production environment? And then you've got reproducibility, governance, auditing, like managing data flows across an organization which we think of as orchestrating them is much more than just scheduling. It becomes really complicated pretty quickly. >> I imagine there's a fair amount of complexity there. Jeff, let's bring you into the conversation. Talk a little bit about Astronomer through your lens, data orchestration and how it applies to MLOps. >> So I come from a machine learning background and for me the interesting part is that machine learning requires the expansion into orchestration. A lot of the same things that you're using to go and develop and build pipelines in a standard data orchestration space applies equally well in a machine learning orchestration space. What you're doing is you're moving data between different locations, between different tools, and then tasking different types of tools to act on that data. So extending it made logical sense from a implementation perspective. And a lot of my focus at Astronomer is really to explain how Airflow can be used well in a machine learning context. It is being used well, it is being used a lot by the customers that we have and also by users of the open source version. But it's really being able to explain to people why it's a natural extension for it and how well it fits into that. And a lot of it is also extending some of the infrastructure capabilities that Astronomer provides to those customers for them to be able to run some of the more platform specific requirements that come with doing machine learning pipelines. >> Let's get into some of the things that make Astronomer unique. Jeff, sticking with you, when you're in customer conversations, what are some of the key differentiators that you articulate to customers? >> So a lot of it is that we are not specific to one cloud provider. So we have the ability to operate across all of the big cloud providers. I know, I'm certain we have the best developers that understand how best practices implementations for data orchestration works. So we spend a lot of time talking to not just the business outcomes and the business users of the product, but also also for the technical people, how to help them better implement things that they may have come across on a Stack Overflow article or not necessarily just grown with how the product has migrated. So it's the ability to run it wherever you need to run it and also our ability to help you, the customer, better implement and understand those workflows that I think are two of the primary differentiators that we have. >> Lisa: Got it. >> I'll add another one if you don't mind. >> You can go ahead, Steven. >> Is lineage and dependencies between workflows. One thing we've done is to augment core Airflow with Lineage services. So using the Open Lineage framework, another open source framework for tracking datasets as they move from one workflow to another one, team to another, one data source to another is a really key component of what we do and we bundle that within the service so that as a developer or as a production engineer, you really don't have to worry about lineage, it just happens. Jeff, may show us some of this later that you can actually see as data flows from source through to a data warehouse out through a Python notebook to produce a predictive model or a dashboard. Can you see how those data products relate to each other? And when something goes wrong, figure out what upstream maybe caused the problem, or if you're about to change something, figure out what the impact is going to be on the rest of the organization. So Lineage is a big deal for us. >> Got it. >> And just to add on to that, the other thing to think about is that traditional Airflow is actually a complicated implementation. It required quite a lot of time spent understanding or was almost a bespoke language that you needed to be able to develop in two write these DAGs, which is like fundamental pipelines. So part of what we are focusing on is tooling that makes it more accessible to say a data analyst or a data scientist who doesn't have or really needs to gain the necessary background in how the semantics of Airflow DAGs works to still be able to get the benefit of what Airflow can do. So there is new features and capabilities built into the astronomer cloud platform that effectively obfuscates and removes the need to understand some of the deep work that goes on. But you can still do it, you still have that capability, but we are expanding it to be able to have orchestrated and repeatable processes accessible to more teams within the business. >> In terms of accessibility to more teams in the business. You talked about data scientists, data analysts, developers. Steven, I want to talk to you, as the chief data officer, are you having more and more conversations with that role and how is it emerging and evolving within your customer base? >> Hmm. That's a good question, and it is evolving because I think if you look historically at the way that Airflow has been used it's often from the ground up. You have individual data engineers or maybe single data engineering teams who adopt Airflow 'cause it's very popular. Lots of people know how to use it and they bring it into an organization and say, "Hey, let's use this to run our data pipelines." But then increasingly as you turn from pure workflow management and job scheduling to the larger topic of orchestration you realize it gets pretty complicated, you want to have coordination across teams, and you want to have standardization for the way that you manage your data pipelines. And so having a managed service for Airflow that exists in the cloud is easy to spin up as you expand usage across the organization. And thinking long term about that in the context of orchestration that's where I think the chief data officer or the head of analytics tends to get involved because they really want to think of this as a strategic investment that they're making. Not just per team individual Airflow deployments, but a network of data orchestrators. >> That network is key. Every company these days has to be a data company. We talk about companies being data driven. It's a common word, but it's true. It's whether it is a grocer or a bank or a hospital, they've got to be data companies. So talk to me a little bit about Astronomer's business model. How is this available? How do customers get their hands on it? >> Jeff, go ahead. >> Yeah, yeah. So we have a managed cloud service and we have two modes of operation. One, you can bring your own cloud infrastructure. So you can say here is an account in say, AWS or Azure and we can go and deploy the necessary infrastructure into that, or alternatively we can host everything for you. So it becomes a full SaaS offering. But we then provide a platform that connects at the backend to your internal IDP process. So however you are authenticating users to make sure that the correct people are accessing the services that they need with role-based access control. From there we are deploying through Kubernetes, the different services and capabilities into either your cloud account or into an account that we host. And from there Airflow does what Airflow does, which is its ability to then reach to different data systems and data platforms and to then run the orchestration. We make sure we do it securely, we have all the necessary compliance certifications required for GDPR in Europe and HIPAA based out of the US, and a whole bunch host of others. So it is a secure platform that can run in a place that you need it to run, but it is a managed Airflow that includes a lot of the extra capabilities like the cloud developer environment and the open lineage services to enhance the overall airflow experience. >> Enhance the overall experience. So Steven, going back to you, if I'm a Conde Nast or another organization, what are some of the key business outcomes that I can expect? As one of the things I think we've learned during the pandemic is access to realtime data is no longer a nice to have for organizations. It's really an imperative. It's that demanding consumer that wants to have that personalized, customized, instant access to a product or a service. So if I'm a Conde Nast or I'm one of your customers, what can I expect my business to be able to achieve as a result of data orchestration? >> Yeah, I think in a nutshell it's about providing a reliable, scalable, and easy to use service for developing and running data workflows. And talking of demanding customers, I mean, I'm actually a customer myself, as you mentioned, I'm the head of data for Astronomer. You won't be surprised to hear that we actually use Astronomer and Airflow to run all of our data pipelines. And so I can actually talk about my experience. When I started I was of course familiar with Airflow, but it always seemed a little bit unapproachable to me if I was introducing that to a new team of data scientists. They don't necessarily want to have to think about learning something new. But I think because of the layers that Astronomer has provided with our Astro service around Airflow it was pretty easy for me to get up and running. Of course I've got an incentive for doing that. I work for the Airflow company, but we went from about, at the beginning of last year, about 500 data tasks that we were running on a daily basis to about 15,000 every day. We run something like a million data operations every month within my team. And so as one outcome, just the ability to spin up new production workflows essentially in a single day you go from an idea in the morning to a new dashboard or a new model in the afternoon, that's really the business outcome is just removing that friction to operationalizing your machine learning and data workflows. >> And I imagine too, oh, go ahead, Jeff. >> Yeah, I think to add to that, one of the things that becomes part of the business cycle is a repeatable capabilities for things like reporting, for things like new machine learning models. And the impediment that has existed is that it's difficult to take that from a team that's an analyst team who then provide that or a data science team that then provide that to the data engineering team who have to work the workflow all the way through. What we're trying to unlock is the ability for those teams to directly get access to scheduling and orchestrating capabilities so that a business analyst can have a new report for C-suite execs that needs to be done once a week, but the time to repeatability for that report is much shorter. So it is then immediately in the hands of the person that needs to see it. It doesn't have to go into a long list of to-dos for a data engineering team that's already overworked that they eventually get it to it in a month's time. So that is also a part of it is that the realizing, orchestration I think is fairly well and a lot of people get the benefit of being able to orchestrate things within a business, but it's having more people be able to do it and shorten the time that that repeatability is there is one of the main benefits from good managed orchestration. >> So a lot of workforce productivity improvements in what you're doing to simplify things, giving more people access to data to be able to make those faster decisions, which ultimately helps the end user on the other end to get that product or the service that they're expecting like that. Jeff, I understand you have a demo that you can share so we can kind of dig into this. >> Yeah, let me take you through a quick look of how the whole thing works. So our starting point is our cloud infrastructure. This is the login. You go to the portal. You can see there's a a bunch of workspaces that are available. Workspaces are like individual places for people to operate in. I'm not going to delve into all the deep technical details here, but starting point for a lot of our data science customers is we have what we call our Cloud IDE, which is a web-based development environment for writing and building out DAGs without actually having to know how the underpinnings of Airflow work. This is an internal one, something that we use. You have a notebook-like interface that lets you write python code and SQL code and a bunch of specific bespoke type of blocks if you want. They all get pulled together and create a workflow. So this is a workflow, which gets compiled to something that looks like a complicated set of Python code, which is the DAG. I then have a CICD process pipeline where I commit this through to my GitHub repo. So this comes to a repo here, which is where these DAGs that I created in the previous step exist. I can then go and say, all right, I want to see how those particular DAGs have been running. We then get to the actual Airflow part. So this is the managed Airflow component. So we add the ability for teams to fairly easily bring up an Airflow instance and write code inside our notebook-like environment to get it into that instance. So you can see it's been running. That same process that we built here that graph ends up here inside this, but you don't need to know how the fundamentals of Airflow work in order to get this going. Then we can run one of these, it runs in the background and we can manage how it goes. And from there, every time this runs, it's emitting to a process underneath, which is the open lineage service, which is the lineage integration that allows me to come in here and have a look and see this was that actual, that same graph that we built, but now it's the historic version. So I know where things started, where things are going, and how it ran. And then I can also do a comparison. So if I want to see how this particular run worked compared to one historically, I can grab one from a previous date and it will show me the comparison between the two. So that combination of managed Airflow, getting Airflow up and running very quickly, but the Cloud IDE that lets you write code and know how to get something into a repeatable format get that into Airflow and have that attached to the lineage process adds what is a complete end-to-end orchestration process for any business looking to get the benefit from orchestration. >> Outstanding. Thank you so much Jeff for digging into that. So one of my last questions, Steven is for you. This is exciting. There's a lot that you guys are enabling organizations to achieve here to really become data-driven companies. So where can folks go to get their hands on this? >> Yeah, just go to astronomer.io and we have plenty of resources. If you're new to Airflow, you can read our documentation, our guides to getting started. We have a CLI that you can download that is really I think the easiest way to get started with Airflow. But you can actually sign up for a trial. You can sign up for a guided trial where our teams, we have a team of experts, really the world experts on getting Airflow up and running. And they'll take you through that trial and allow you to actually kick the tires and see how this works with your data. And I think you'll see pretty quickly that it's very easy to get started with Airflow, whether you're doing that from the command line or doing that in our cloud service. And all of that is available on our website >> astronomer.io. Jeff, last question for you. What are you excited about? There's so much going on here. What are some of the things, maybe you can give us a sneak peek coming down the road here that prospects and existing customers should be excited about? >> I think a lot of the development around the data awareness components, so one of the things that's traditionally been complicated with orchestration is you leave your data in the place that you're operating on and we're starting to have more data processing capability being built into Airflow. And from a Astronomer perspective, we are adding more capabilities around working with larger datasets, doing bigger data manipulation with inside the Airflow process itself. And that lends itself to better machine learning implementation. So as we start to grow and as we start to get better in the machine learning context, well, in the data awareness context, it unlocks a lot more capability to do and implement proper machine learning pipelines. >> Awesome guys. Exciting stuff. Thank you so much for talking to me about Astronomer, machine learning, data orchestration, and really the value in it for your customers. Steve and Jeff, we appreciate your time. >> Thank you. >> My pleasure, thanks. >> And we thank you for watching. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem. I'm your host, Lisa Martin. You're watching theCUBE, the leader in live tech coverage. (upbeat music)

(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(bright upbeat music) >> Hello and welcome to theCUBE's coverage of International Women's Day. I'm your host, John Furrier here in Palo Alto, California Studio and remoting is a great guest CUBE alumni, co-founder, technical co-founder and she's also the VP of Product at Platform9 Systems. It's a company pioneering Kubernetes infrastructure, been doing it for a long, long time. Madhura Maskasky, thanks for coming on theCUBE. Appreciate you. Thanks for coming on. >> Thank you for having me. Always exciting. >> So I always... I love interviewing you for many reasons. One, you're super smart, but also you're a co-founder, a technical co-founder, so entrepreneur, VP of product. It's hard to do startups. (John laughs) Okay, so everyone who started a company knows how hard it is. It really is and the rewarding too when you're successful. So I want to get your thoughts on what's it like being an entrepreneur, women in tech, some things you've done along the way. Let's get started. How did you get into your career in tech and what made you want to start a company? >> Yeah, so , you know, I got into tech long, long before I decided to start a company. And back when I got in tech it was very clear to me as a direction for my career that I'm never going to start a business. I was very explicit about that because my father was an entrepreneur and I'd seen how rough the journey can be. And then my brother was also and is an entrepreneur. And I think with both of them I'd seen the ups and downs and I had decided to myself and shared with my family that I really want a very well-structured sort of job at a large company type of path for my career. I think the tech path, tech was interesting to me, not because I was interested in programming, et cetera at that time, to be honest. When I picked computer science as a major for myself, it was because most of what you would consider, I guess most of the cool students were picking that as a major, let's just say that. And it sounded very interesting and cool. A lot of people were doing it and that was sort of the top, top choice for people and I decided to follow along. But I did discover after I picked computer science as my major, I remember when I started learning C++ the first time when I got exposure to it, it was just like a light bulb clicking in my head. I just absolutely loved the language, the lower level nature, the power of it, and what you can do with it, the algorithms. So I think it ended up being a really good fit for me. >> Yeah, so it clicked for you. You tried it, it was all the cool kids were doing it. I mean, I can relate, I did the same thing. Next big thing is computer science, you got to be in there, got to be smart. And then you get hooked on it. >> Yeah, exactly. >> What was the next level? Did you find any blockers in your way? Obviously male dominated, it must have been a lot of... How many females were in your class? What was the ratio at that time? >> Yeah, so the ratio was was pretty, pretty, I would say bleak when it comes to women to men. I think computer science at that time was still probably better compared to some of the other majors like mechanical engineering where I remember I had one friend, she was the single girl in an entire class of about at least 120, 130 students or so. So ratio was better for us. I think there were maybe 20, 25 girls in our class. It was a large class and maybe the number of men were maybe three X or four X number of women. So relatively better. Yeah. >> How about the job when you got into the structured big company? How did that go? >> Yeah, so, you know, I think that was a pretty smooth path I would say after, you know, you graduated from undergrad to grad school and then when I got into Oracle first and VMware, I think both companies had the ratios were still, you know, pretty off. And I think they still are to a very large extent in this industry, but I think this industry in my experience does a fantastic job of, you know, bringing everybody and kind of embracing them and treating them at the same level. That was definitely my experience. And so that makes it very easy for self-confidence, for setting up a path for yourself to thrive. So that was it. >> Okay, so you got an undergraduate degree, okay, in computer science and a master's from Stanford in databases and distributed systems. >> That's right. >> So two degrees. Was that part of your pathway or you just decided, "I want to go right into school?" Did it go right after each other? How did that work out? >> Yeah, so when I went into school, undergrad there was no special major and I didn't quite know if I liked a particular subject or set of subjects or not. Even through grad school, first year it wasn't clear to me, but I think in second year I did start realizing that in general I was a fan of backend systems. I was never a front-end person. The backend distributed systems really were of interest to me because there's a lot of complex problems to solve, and especially databases and large scale distributed systems design in the context of database systems, you know, really started becoming a topic of interest for me. And I think luckily enough at Stanford there were just fantastic professors like Mendel Rosenblum who offered operating system class there, then started VMware and later on I was able to join the company and I took his class while at school and it was one of the most fantastic classes I've ever taken. So they really had and probably I think still do a fantastic curriculum when it comes to distributor systems. And I think that probably helped stoke that interest. >> How do you talk to the younger girls out there in elementary school and through? What's the advice as they start to get into computer science, which is changing and still evolving? There's backend, there's front-end, there's AI, there's data science, there's no code, low code, there's cloud. What's your advice when they say what's the playbook? >> Yeah, so I think two things I always say, and I share this with anybody who's looking to get into computer science or engineering for that matter, right? I think one is that it's, you know, it's important to not worry about what that end specialization's going to be, whether it's AI or databases or backend or front-end. It does naturally evolve and you lend yourself to a path where you will understand, you know, which systems, which aspect you like better. But it's very critical to start with getting the fundamentals well, right? Meaning all of the key coursework around algorithm, systems design, architecture, networking, operating system. I think it is just so crucial to understand those well, even though at times you make question is this ever going to be relevant and useful to me later on in my career? It really does end up helping in ways beyond, you know, you can describe. It makes you a much better engineer. So I think that is the most important aspect of, you know, I would think any engineering stream, but definitely true for computer science. Because there's also been a trend more recently, I think, which I'm not a big fan of, of sort of limited scoped learning, which is you decide early on that you're going to be, let's say a front-end engineer, which is fine, you know. Understanding that is great, but if you... I don't think is ideal to let that limit the scope of your learning when you are an undergrad phrase or grad school. Because later on it comes back to sort of bite you in terms of you not being able to completely understand how the systems work. >> It's a systems kind of thinking. You got to have that mindset of, especially now with cloud, you got distributed systems paradigm going to the edge. You got 5G, Mobile World Congress recently happened, you got now all kinds of IOT devices out there, IP of devices at the edge. Distributed computing is only getting more distributed. >> That's right. Yeah, that's exactly right. But the other thing is also happens... That happens in computer science is that the abstraction layers keep raising things up and up and up. Where even if you're operating at a language like Java, which you know, during some of my times of programming there was a period when it was popular, it already abstracts you so far away from the underlying system. So it can become very easier if you're doing, you know, Java script or UI programming that you really have no understanding of what's happening behind the scenes. And I think that can be pretty difficult. >> Yeah. It's easy to lean in and rely too heavily on the abstractions. I want to get your thoughts on blockers. In your career, have you had situations where it's like, "Oh, you're a woman, okay seat at the table, sit on the side." Or maybe people misunderstood your role. How did you deal with that? Did you have any of that? >> Yeah. So, you know, I think... So there's something really kind of personal to me, which I like to share a few times, which I think I believe in pretty strongly. And which is for me, sort of my personal growth began at a very early phase because my dad and he passed away in 2012, but throughout the time when I was growing up, I was his special little girl. And every little thing that I did could be a simple test. You know, not very meaningful but the genuine pride and pleasure that he felt out of me getting great scores in those tests sort of et cetera, and that I could see that in him, and then I wanted to please him. And through him, I think I build that confidence in myself that I am good at things and I can do good. And I think that just set the building blocks for me for the rest of my life, right? So, I believe very strongly that, you know, yes, there are occasions of unfair treatment and et cetera, but for the most part, it comes from within. And if you are able to be a confident person who is kind of leveled and understands and believes in your capabilities, then for the most part, the right things happen around you. So, I believe very strongly in that kind of grounding and in finding a source to get that for yourself. And I think that many women suffer from the biggest challenge, which is not having enough self-confidence. And I've even, you know, with everything that I said, I've myself felt that, experienced that a few times. And then there's a methodical way to get around it. There's processes to, you know, explain to yourself that that's actually not true. That's a fake feeling. So, you know, I think that is the most important aspect for women. >> I love that. Get the confidence. Find the source for the confidence. We've also been hearing about curiosity and building, you mentioned engineering earlier, love that term. Engineering something, like building something. Curiosity, engineering, confidence. This brings me to my next question for you. What do you think the key skills and qualities are needed to succeed in a technical role? And how do you develop to maintain those skills over time? >> Yeah, so I think that it is so critical that you love that technology that you are part of. It is just so important. I mean, I remember as an example, at one point with one of my buddies before we started Platform9, one of my buddies, he's also a fantastic computer scientists from VMware and he loves video games. And so he said, "Hey, why don't we try to, you know, hack up a video game and see if we can take it somewhere?" And so, it sounded cool to me. And then so we started doing things, but you know, something I realized very quickly is that I as a person, I absolutely hate video games. I've never liked them. I don't think that's ever going to change. And so I was miserable. You know, I was trying to understand what's going on, how to build these systems, but I was not enjoying it. So, I'm glad that I decided to not pursue that. So it is just so important that you enjoy whatever aspect of technology that you decide to associate yourself with. I think that takes away 80, 90% of the work. And then I think it's important to inculcate a level of discipline that you are not going to get sort of... You're not going to get jaded or, you know, continue with happy path when doing the same things over and over again, but you're not necessarily challenging yourself, or pushing yourself, or putting yourself in uncomfortable situation. I think a combination of those typically I think works pretty well in any technical career. >> That's a great advice there. I think trying things when you're younger, or even just for play to understand whether you abandon that path is just as important as finding a good path because at least you know that skews the value in favor of the choices. Kind of like math probability. So, great call out there. So I have to ask you the next question, which is, how do you keep up to date given all the changes? You're in the middle of a world where you've seen personal change in the past 10 years from OpenStack to now. Remember those days when I first interviewed you at OpenStack, I think it was 2012 or something like that. Maybe 10 years ago. So much changed. How do you keep up with technologies in your field and resources that you rely on for personal development? >> Yeah, so I think when it comes to, you know, the field and what we are doing for example, I think one of the most important aspect and you know I am product manager and this is something I insist that all the other product managers in our team also do, is that you have to spend 50% of your time talking to prospects, customers, leads, and through those conversations they do a huge favor to you in that they make you aware of the other things that they're keeping an eye on as long as you're doing the right job of asking the right questions and not just, you know, listening in. So I think that to me ends up being one of the biggest sources where you get tidbits of information, new things, et cetera, and then you pursue. To me, that has worked to be a very effective source. And then the second is, you know, reading and keeping up with all of the publications. You guys, you know, create a lot of great material, you interview a lot of people, making sure you are watching those for us you know, and see there's a ton of activities, new projects keeps coming along every few months. So keeping up with that, listening to podcasts around those topics, all of that helps. But I think the first one I think goes in a big way in terms of being aware of what matters to your customers. >> Awesome. Let me ask you a question. What's the most rewarding aspect of your job right now? >> So, I think there are many. So I think I love... I've come to realize that I love, you know, the high that you get out of being an entrepreneur independent of, you know, there's... In terms of success and failure, there's always ups and downs as an entrepreneur, right? But there is this... There's something really alluring about being able to, you know, define, you know, path of your products and in a way that can potentially impact, you know, a number of companies that'll consume your products, employees that work with you. So that is, I think to me, always been the most satisfying path, is what kept me going. I think that is probably first and foremost. And then the projects. You know, there's always new exciting things that we are working on. Even just today, there are certain projects we are working on that I'm super excited about. So I think it's those two things. >> So now we didn't get into how you started. You said you didn't want to do a startup and you got the big company. Your dad, your brother were entrepreneurs. How did you get into it? >> Yeah, so, you know, it was kind of surprising to me as well, but I think I reached a point of VMware after spending about eight years or so where I definitely packed hold and I could have pushed myself by switching to a completely different company or a different organization within VMware. And I was trying all of those paths, interviewed at different companies, et cetera, but nothing felt different enough. And then I think I was very, very fortunate in that my co-founders, Sirish Raghuram, Roopak Parikh, you know, Bich, you've met them, they were kind of all at the same journey in their careers independently at the same time. And so we would all eat lunch together at VMware 'cause we were on the same team and then we just started brainstorming on different ideas during lunchtime. And that's kind of how... And we did that almost for a year. So by the time that the year long period went by, at the end it felt like the most logical, natural next step to leave our job and to, you know, to start off something together. But I think I wouldn't have done that had it not been for my co-founders. >> So you had comfort with the team as you knew each other at VMware, but you were kind of a little early, (laughing) you had a vision. It's kind of playing out now. How do you feel right now as the wave is hitting? Distributed computing, microservices, Kubernetes, I mean, stuff you guys did and were doing. I mean, it didn't play out exactly, but directionally you were right on the line there. How do you feel? >> Yeah. You know, I think that's kind of the challenge and the fun part with the startup journey, right? Which is you can never predict how things are going to go. When we kicked off we thought that OpenStack is going to really take over infrastructure management space and things kind of went differently, but things are going that way now with Kubernetes and distributed infrastructure. And so I think it's been interesting and in every path that you take that does end up not being successful teaches you so much more, right? So I think it's been a very interesting journey. >> Yeah, and I think the cloud, certainly AWS hit that growth right at 2013 through '17, kind of sucked all the oxygen out. But now as it reverts back to this abstraction layer essentially makes things look like private clouds, but they're just essentially DevOps. It's cloud operations, kind of the same thing. >> Yeah, absolutely. And then with the edge things are becoming way more distributed where having a single large cloud provider is becoming even less relevant in that space and having kind of the central SaaS based management model, which is what we pioneered, like you said, we were ahead of the game at that time, is becoming sort of the most obvious choice now. >> Now you look back at your role at Stanford, distributed systems, again, they have world class program there, neural networks, you name it. It's really, really awesome. As well as Cal Berkeley, there was in debates with each other, who's better? But that's a separate interview. Now you got the edge, what are some of the distributed computing challenges right now with now the distributed edge coming online, industrial 5G, data? What do you see as some of the key areas to solve from a problem statement standpoint with edge and as cloud goes on-premises to essentially data center at the edge, apps coming over the top AI enabled. What's your take on that? >> Yeah, so I think... And there's different flavors of edge and the one that we focus on is, you know, what we call thick edge, which is you have this problem of managing thousands of as we call it micro data centers, rather than managing maybe few tens or hundreds of large data centers where the problem just completely shifts on its head, right? And I think it is still an unsolved problem today where whether you are a retailer or a telecommunications vendor, et cetera, managing your footprints of tens of thousands of stores as a retailer is solved in a very archaic way today because the tool set, the traditional management tooling that's designed to manage, let's say your data centers is not quite, you know, it gets retrofitted to manage these environments and it's kind of (indistinct), you know, round hole kind of situation. So I think the top most challenges are being able to manage this large footprint of micro data centers in the most effective way, right? Where you have latency solved, you have the issue of a small footprint of resources at thousands of locations, and how do you fit in your containerized or virtualized or other workloads in the most effective way? To have that solved, you know, you need to have the security aspects around these environments. So there's a number of challenges that kind of go hand-in-hand, like what is the most effective storage which, you know, can still be deployed in that compact environment? And then cost becomes a related point. >> Costs are huge 'cause if you move data, you're going to have cost. If you move compute, it's not as much. If you have an operating system concept, is the data and state or stateless? These are huge problems. This is an operating system, don't you think? >> Yeah, yeah, absolutely. It's a distributed operating system where it's multiple layers, you know, of ways of solving that problem just in the context of data like you said having an intermediate caching layer so that you know, you still do just in time processing at those edge locations and then send some data back and that's where you can incorporate some AI or other technologies, et cetera. So, you know, just data itself is a multi-layer problem there. >> Well, it's great to have you on this program. Advice final question for you, for the folks watching technical degrees, most people are finding out in elementary school, in middle school, a lot more robotics programs, a lot more tech exposure, you know, not just in Silicon Valley, but all around, you're starting to see that. What's your advice for young girls and people who are getting either coming into the workforce re-skilled as they get enter, it's easy to enter now as they stay in and how do they stay in? What's your advice? >> Yeah, so, you know, I think it's the same goal. I have two little daughters and it's the same principle I try to follow with them, which is I want to give them as much exposure as possible without me having any predefined ideas about what you know, they should pursue. But it's I think that exposure that you need to find for yourself one way or the other, because you really never know. Like, you know, my husband landed into computer science through a very, very meandering path, and then he discovered later in his career that it's the absolute calling for him. It's something he's very good at, right? But so... You know, it's... You know, the reason why he thinks he didn't pick that path early is because he didn't quite have that exposure. So it's that exposure to various things, even things you think that you may not be interested in is the most important aspect. And then things just naturally lend themselves. >> Find your calling, superpower, strengths. Know what you don't want to do. (John chuckles) >> Yeah, exactly. >> Great advice. Thank you so much for coming on and contributing to our program for International Women's Day. Great to see you in this context. We'll see you on theCUBE. We'll talk more about Platform9 when we go KubeCon or some other time. But thank you for sharing your personal perspective and experiences for our audience. Thank you. >> Fantastic. Thanks for having me, John. Always great. >> This is theCUBE's coverage of International Women's Day, I'm John Furrier. We're talking to the leaders in the industry, from developers to the boardroom and everything in between and getting the stories out there making an impact. Thanks for watching. (bright upbeat music)

(upbeat music) >> Hello and welcome to theCube's coverage of International Women's Day. I'm John Furrier, your host of "theCUBE." We've got great two remote guests coming into our Palo Alto Studios, some tech athletes, as we say, people that've been in the trenches, years of experience, Lena Smart, CISO at MongoDB, Cube alumni, and Tara Hernandez, VP of Developer Productivity at MongoDB as well. Thanks for coming in to this program and supporting our efforts today. Thanks so much. >> Thanks for having us. >> Yeah, everyone talk about the journey in tech, where it all started. Before we get there, talk about what you guys are doing at MongoDB specifically. MongoDB is kind of gone the next level as a platform. You have your own ecosystem, lot of developers, very technical crowd, but it's changing the business transformation. What do you guys do at Mongo? We'll start with you, Lena. >> So I'm the CISO, so all security goes through me. I like to say, well, I don't like to say, I'm described as the ones throat to choke. So anything to do with security basically starts and ends with me. We do have a fantastic Cloud engineering security team and a product security team, and they don't report directly to me, but obviously we have very close relationships. I like to keep that kind of church and state separate and I know I've spoken about that before. And we just recently set up a physical security team with an amazing gentleman who left the FBI and he came to join us after 26 years for the agency. So, really starting to look at the physical aspects of what we offer as well. >> I interviewed a CISO the other day and she said, "Every day is day zero for me." Kind of goofing on the Amazon Day one thing, but Tara, go ahead. Tara, go ahead. What's your role there, developer productivity? What are you focusing on? >> Sure. Developer productivity is kind of the latest description for things that we've described over the years as, you know, DevOps oriented engineering or platform engineering or build and release engineering development infrastructure. It's all part and parcel, which is how do we actually get our code from developer to customer, you know, and all the mechanics that go into that. It's been something I discovered from my first job way back in the early '90s at Borland. And the art has just evolved enormously ever since, so. >> Yeah, this is a very great conversation both of you guys, right in the middle of all the action and data infrastructures changing, exploding, and involving big time AI and data tsunami and security never stops. Well, let's get into, we'll talk about that later, but let's get into what motivated you guys to pursue a career in tech and what were some of the challenges that you faced along the way? >> I'll go first. The fact of the matter was I intended to be a double major in history and literature when I went off to university, but I was informed that I had to do a math or a science degree or else the university would not be paid for. At the time, UC Santa Cruz had a policy that called Open Access Computing. This is, you know, the late '80s, early '90s. And anybody at the university could get an email account and that was unusual at the time if you were, those of us who remember, you used to have to pay for that CompuServe or AOL or, there's another one, I forget what it was called, but if a student at Santa Cruz could have an email account. And because of that email account, I met people who were computer science majors and I'm like, "Okay, I'll try that." That seems good. And it was a little bit of a struggle for me, a lot I won't lie, but I can't complain with how it ended up. And certainly once I found my niche, which was development infrastructure, I found my true love and I've been doing it for almost 30 years now. >> Awesome. Great story. Can't wait to ask a few questions on that. We'll go back to that late '80s, early '90s. Lena, your journey, how you got into it. >> So slightly different start. I did not go to university. I had to leave school when I was 16, got a job, had to help support my family. Worked a bunch of various jobs till I was about 21 and then computers became more, I think, I wouldn't say they were ubiquitous, but they were certainly out there. And I'd also been saving up every penny I could earn to buy my own computer and bought an Amstrad 1640, 20 meg hard drive. It rocked. And kind of took that apart, put it back together again, and thought that could be money in this. And so basically just teaching myself about computers any job that I got. 'Cause most of my jobs were like clerical work and secretary at that point. But any job that had a computer in front of that, I would make it my business to go find the guy who did computing 'cause it was always a guy. And I would say, you know, I want to learn how these work. Let, you know, show me. And, you know, I would take my lunch hour and after work and anytime I could with these people and they were very kind with their time and I just kept learning, so yep. >> Yeah, those early days remind me of the inflection point we're going through now. This major C change coming. Back then, if you had a computer, you had to kind of be your own internal engineer to fix things. Remember back on the systems revolution, late '80s, Tara, when, you know, your career started, those were major inflection points. Now we're seeing a similar wave right now, security, infrastructure. It feels like it's going to a whole nother level. At Mongo, you guys certainly see this as well, with this AI surge coming in. A lot more action is coming in. And so there's a lot of parallels between these inflection points. How do you guys see this next wave of change? Obviously, the AI stuff's blowing everyone away. Oh, new user interface. It's been called the browser moment, the mobile iPhone moment, kind of for this generation. There's a lot of people out there who are watching that are young in their careers, what's your take on this? How would you talk to those folks around how important this wave is? >> It, you know, it's funny, I've been having this conversation quite a bit recently in part because, you know, to me AI in a lot of ways is very similar to, you know, back in the '90s when we were talking about bringing in the worldwide web to the forefront of the world, right. And we tended to think in terms of all the optimistic benefits that would come of it. You know, free passing of information, availability to anyone, anywhere. You just needed an internet connection, which back then of course meant a modem. >> John: Not everyone had though. >> Exactly. But what we found in the subsequent years is that human beings are what they are and we bring ourselves to whatever platforms that are there, right. And so, you know, as much as it was amazing to have this freely available HTML based internet experience, it also meant that the negatives came to the forefront quite quickly. And there were ramifications of that. And so to me, when I look at AI, we're already seeing the ramifications to that. Yes, are there these amazing, optimistic, wonderful things that can be done? Yes. >> Yeah. >> But we're also human and the bad stuff's going to come out too. And how do we- >> Yeah. >> How do we as an industry, as a community, you know, understand and mitigate those ramifications so that we can benefit more from the positive than the negative. So it is interesting that it comes kind of full circle in really interesting ways. >> Yeah. The underbelly takes place first, gets it in the early adopter mode. Normally industries with, you know, money involved arbitrage, no standards. But we've seen this movie before. Is there hope, Lena, that we can have a more secure environment? >> I would hope so. (Lena laughs) Although depressingly, we've been in this well for 30 years now and we're, at the end of the day, still telling people not to click links on emails. So yeah, that kind of still keeps me awake at night a wee bit. The whole thing about AI, I mean, it's, obviously I am not an expert by any stretch of the imagination in AI. I did read (indistinct) book recently about AI and that was kind of interesting. And I'm just trying to teach myself as much as I can about it to the extent of even buying the "Dummies Guide to AI." Just because, it's actually not a dummies guide. It's actually fairly interesting, but I'm always thinking about it from a security standpoint. So it's kind of my worst nightmare and the best thing that could ever happen in the same dream. You know, you've got this technology where I can ask it a question and you know, it spits out generally a reasonable answer. And my team are working on with Mark Porter our CTO and his team on almost like an incubation of AI link. What would it look like from MongoDB? What's the legal ramifications? 'Cause there will be legal ramifications even though it's the wild, wild west just now, I think. Regulation's going to catch up to us pretty quickly, I would think. >> John: Yeah, yeah. >> And so I think, you know, as long as companies have a seat at the table and governments perhaps don't become too dictatorial over this, then hopefully we'll be in a good place. But we'll see. I think it's a really interest, there's that curse, we're living in interesting times. I think that's where we are. >> It's interesting just to stay on this tech trend for a minute. The standards bodies are different now. Back in the old days there were, you know, IEEE standards, ITF standards. >> Tara: TPC. >> The developers are the new standard. I mean, now you're seeing open source completely different where it was in the '90s to here beginning, that was gen one, some say gen two, but I say gen one, now we're exploding with open source. You have kind of developers setting the standards. If developers like it in droves, it becomes defacto, which then kind of rolls into implementation. >> Yeah, I mean I think if you don't have developer input, and this is why I love working with Tara and her team so much is 'cause they get it. If we don't have input from developers, it's not going to get used. There's going to be ways of of working around it, especially when it comes to security. If they don't, you know, if you're a developer and you're sat at your screen and you don't want to do that particular thing, you're going to find a way around it. You're a smart person. >> Yeah. >> So. >> Developers on the front lines now versus, even back in the '90s, they're like, "Okay, consider the dev's, got a QA team." Everything was Waterfall, now it's Cloud, and developers are on the front lines of everything. Tara, I mean, this is where the standards are being met. What's your reaction to that? >> Well, I think it's outstanding. I mean, you know, like I was at Netscape and part of the crowd that released the browser as open source and we founded mozilla.org, right. And that was, you know, in many ways kind of the birth of the modern open source movement beyond what we used to have, what was basically free software foundation was sort of the only game in town. And I think it is so incredibly valuable. I want to emphasize, you know, and pile onto what Lena was saying, it's not just that the developers are having input on a sort of company by company basis. Open source to me is like a checks and balance, where it allows us as a broader community to be able to agree on and enforce certain standards in order to try and keep the technology platforms as accessible as possible. I think Kubernetes is a great example of that, right. If we didn't have Kubernetes, that would've really changed the nature of how we think about container orchestration. But even before that, Linux, right. Linux allowed us as an industry to end the Unix Wars and as someone who was on the front lines of that as well and having to support 42 different operating systems with our product, you know, that was a huge win. And it allowed us to stop arguing about operating systems and start arguing about software or not arguing, but developing it in positive ways. So with, you know, with Kubernetes, with container orchestration, we all agree, okay, that's just how we're going to orchestrate. Now we can build up this huge ecosystem, everybody gets taken along, right. And now it changes the game for what we're defining as business differentials, right. And so when we talk about crypto, that's a little bit harder, but certainly with AI, right, you know, what are the checks and balances that as an industry and as the developers around this, that we can in, you know, enforce to make sure that no one company or no one body is able to overly control how these things are managed, how it's defined. And I think that is only for the benefit in the industry as a whole, particularly when we think about the only other option is it gets regulated in ways that do not involve the people who actually know the details of what they're talking about. >> Regulated and or thrown away or bankrupt or- >> Driven underground. >> Yeah. >> Which would be even worse actually. >> Yeah, that's a really interesting, the checks and balances. I love that call out. And I was just talking with another interview part of the series around women being represented in the 51% ratio. Software is for everybody. So that we believe that open source movement around the collective intelligence of the participants in the industry and independent of gender, this is going to be the next wave. You're starting to see these videos really have impact because there are a lot more leaders now at the table in companies developing software systems and with AI, the aperture increases for applications. And this is the new dynamic. What's your guys view on this dynamic? How does this go forward in a positive way? Is there a certain trajectory you see? For women in the industry? >> I mean, I think some of the states are trying to, again, from the government angle, some of the states are trying to force women into the boardroom, for example, California, which can be no bad thing, but I don't know, sometimes I feel a bit iffy about all this kind of forced- >> John: Yeah. >> You know, making, I don't even know how to say it properly so you can cut this part of the interview. (John laughs) >> Tara: Well, and I think that they're >> I'll say it's not organic. >> No, and I think they're already pulling it out, right. It's already been challenged so they're in the process- >> Well, this is the open source angle, Tara, you are getting at it. The change agent is open, right? So to me, the history of the proven model is openness drives transparency drives progress. >> No, it's- >> If you believe that to be true, this could have another impact. >> Yeah, it's so interesting, right. Because if you look at McKinsey Consulting or Boston Consulting or some of the other, I'm blocking on all of the names. There has been a decade or more of research that shows that a non homogeneous employee base, be it gender or ethnicity or whatever, generates more revenue, right? There's dollar signs that can be attached to this, but it's not enough for all companies to want to invest in that way. And it's not enough for all, you know, venture firms or investment firms to grant that seed money or do those seed rounds. I think it's getting better very slowly, but socialization is a much harder thing to overcome over time. Particularly, when you're not just talking about one country like the United States in our case, but around the world. You know, tech centers now exist all over the world, including places that even 10 years ago we might not have expected like Nairobi, right. Which I think is amazing, but you have to factor in the cultural implications of that as well, right. So yes, the openness is important and we have, it's important that we have those voices, but I don't think it's a panacea solution, right. It's just one more piece. I think honestly that one of the most important opportunities has been with Cloud computing and Cloud's been around for a while. So why would I say that? It's because if you think about like everybody holds up the Steve Jobs, Steve Wozniak, back in the '70s, or Sergey and Larry for Google, you know, you had to have access to enough credit card limit to go to Fry's and buy your servers and then access to somebody like Susan Wojcicki to borrow the garage or whatever. But there was still a certain amount of upfrontness that you had to be able to commit to, whereas now, and we've, I think, seen a really good evidence of this being able to lease server resources by the second and have development platforms that you can do on your phone. I mean, for a while I think Africa, that the majority of development happened on mobile devices because there wasn't a sufficient supply chain of laptops yet. And that's no longer true now as far as I know. But like the power that that enables for people who would otherwise be underrepresented in our industry instantly opens it up, right? And so to me that's I think probably the biggest opportunity that we've seen from an industry on how to make more availability in underrepresented representation for entrepreneurship. >> Yeah. >> Something like AI, I think that's actually going to take us backwards if we're not careful. >> Yeah. >> Because of we're reinforcing that socialization. >> Well, also the bias. A lot of people commenting on the biases of the large language inherently built in are also problem. Lena, I want you to weigh on this too, because I think the skills question comes up here and I've been advocating that you don't need the pedigree, college pedigree, to get into a certain jobs, you mentioned Cloud computing. I mean, it's been around for you think a long time, but not really, really think about it. The ability to level up, okay, if you're going to join something new and half the jobs in cybersecurity are created in the past year, right? So, you have this what used to be a barrier, your degree, your pedigree, your certification would take years, would be a blocker. Now that's gone. >> Lena: Yeah, it's the opposite. >> That's, in fact, psychology. >> I think so, but the people who I, by and large, who I interview for jobs, they have, I think security people and also I work with our compliance folks and I can't forget them, but let's talk about security just now. I've always found a particular kind of mindset with security folks. We're very curious, not very good at following rules a lot of the time, and we'd love to teach others. I mean, that's one of the big things stem from the start of my career. People were always interested in teaching and I was interested in learning. So it was perfect. And I think also having, you know, strong women leaders at MongoDB allows other underrepresented groups to actually apply to the company 'cause they see that we're kind of talking the talk. And that's been important. I think it's really important. You know, you've got Tara and I on here today. There's obviously other senior women at MongoDB that you can talk to as well. There's a bunch of us. There's not a whole ton of us, but there's a bunch of us. And it's good. It's definitely growing. I've been there for four years now and I've seen a growth in women in senior leadership positions. And I think having that kind of track record of getting really good quality underrepresented candidates to not just interview, but come and join us, it's seen. And it's seen in the industry and people take notice and they're like, "Oh, okay, well if that person's working, you know, if Tara Hernandez is working there, I'm going to apply for that." And that in itself I think can really, you know, reap the rewards. But it's getting started. It's like how do you get your first strong female into that position or your first strong underrepresented person into that position? It's hard. I get it. If it was easy, we would've sold already. >> It's like anything. I want to see people like me, my friends in there. Am I going to be alone? Am I going to be of a group? It's a group psychology. Why wouldn't? So getting it out there is key. Is there skills that you think that people should pay attention to? One's come up as curiosity, learning. What are some of the best practices for folks trying to get into the tech field or that's in the tech field and advancing through? What advice are you guys- >> I mean, yeah, definitely, what I say to my team is within my budget, we try and give every at least one training course a year. And there's so much free stuff out there as well. But, you know, keep learning. And even if it's not right in your wheelhouse, don't pick about it. Don't, you know, take a look at what else could be out there that could interest you and then go for it. You know, what does it take you few minutes each night to read a book on something that might change your entire career? You know, be enthusiastic about the opportunities out there. And there's so many opportunities in security. Just so many. >> Tara, what's your advice for folks out there? Tons of stuff to taste, taste test, try things. >> Absolutely. I mean, I always say, you know, my primary qualifications for people, I'm looking for them to be smart and motivated, right. Because the industry changes so quickly. What we're doing now versus what we did even last year versus five years ago, you know, is completely different though themes are certainly the same. You know, we still have to code and we still have to compile that code or package the code and ship the code so, you know, how well can we adapt to these new things instead of creating floppy disks, which was my first job. Five and a quarters, even. The big ones. >> That's old school, OG. There it is. Well done. >> And now it's, you know, containers, you know, (indistinct) image containers. And so, you know, I've gotten a lot of really great success hiring boot campers, you know, career transitioners. Because they bring a lot experience in addition to the technical skills. I think the most important thing is to experiment and figuring out what do you like, because, you know, maybe you are really into security or maybe you're really into like deep level coding and you want to go back, you know, try to go to school to get a degree where you would actually want that level of learning. Or maybe you're a front end engineer, you want to be full stacked. Like there's so many different things, data science, right. Maybe you want to go learn R right. You know, I think it's like figure out what you like because once you find that, that in turn is going to energize you 'cause you're going to feel motivated. I think the worst thing you could do is try to force yourself to learn something that you really could not care less about. That's just the worst. You're going in handicapped. >> Yeah and there's choices now versus when we were breaking into the business. It was like, okay, you software engineer. They call it software engineering, that's all it was. You were that or you were in sales. Like, you know, some sort of systems engineer or sales and now it's,- >> I had never heard of my job when I was in school, right. I didn't even know it was a possibility. But there's so many different types of technical roles, you know, absolutely. >> It's so exciting. I wish I was young again. >> One of the- >> Me too. (Lena laughs) >> I don't. I like the age I am. So one of the things that I did to kind of harness that curiosity is we've set up a security champions programs. About 120, I guess, volunteers globally. And these are people from all different backgrounds and all genders, diversity groups, underrepresented groups, we feel are now represented within this champions program. And people basically give up about an hour or two of their time each week, with their supervisors permission, and we basically teach them different things about security. And we've now had seven full-time people move from different areas within MongoDB into my team as a result of that program. So, you know, monetarily and time, yeah, saved us both. But also we're showing people that there is a path, you know, if you start off in Tara's team, for example, doing X, you join the champions program, you're like, "You know, I'd really like to get into red teaming. That would be so cool." If it fits, then we make that happen. And that has been really important for me, especially to give, you know, the women in the underrepresented groups within MongoDB just that window into something they might never have seen otherwise. >> That's a great common fit is fit matters. Also that getting access to what you fit is also access to either mentoring or sponsorship or some sort of, at least some navigation. Like what's out there and not being afraid to like, you know, just ask. >> Yeah, we just actually kicked off our big mentor program last week, so I'm the executive sponsor of that. I know Tara is part of it, which is fantastic. >> We'll put a plug in for it. Go ahead. >> Yeah, no, it's amazing. There's, gosh, I don't even know the numbers anymore, but there's a lot of people involved in this and so much so that we've had to set up mentoring groups rather than one-on-one. And I think it was 45% of the mentors are actually male, which is quite incredible for a program called Mentor Her. And then what we want to do in the future is actually create a program called Mentor Them so that it's not, you know, not just on the female and so that we can live other groups represented and, you know, kind of break down those groups a wee bit more and have some more granularity in the offering. >> Tara, talk about mentoring and sponsorship. Open source has been there for a long time. People help each other. It's community-oriented. What's your view of how to work with mentors and sponsors if someone's moving through ranks? >> You know, one of the things that was really interesting, unfortunately, in some of the earliest open source communities is there was a lot of pervasive misogyny to be perfectly honest. >> Yeah. >> And one of the important adaptations that we made as an open source community was the idea, an introduction of code of conducts. And so when I'm talking to women who are thinking about expanding their skills, I encourage them to join open source communities to have opportunity, even if they're not getting paid for it, you know, to develop their skills to work with people to get those code reviews, right. I'm like, "Whatever you join, make sure they have a code of conduct and a good leadership team. It's very important." And there are plenty, right. And then that idea has come into, you know, conferences now. So now conferences have codes of contact, if there are any good, and maybe not all of them, but most of them, right. And the ideas of expanding that idea of intentional healthy culture. >> John: Yeah. >> As a business goal and business differentiator. I mean, I won't lie, when I was recruited to come to MongoDB, the culture that I was able to discern through talking to people, in addition to seeing that there was actually women in senior leadership roles like Lena, like Kayla Nelson, that was a huge win. And so it just builds on momentum. And so now, you know, those of us who are in that are now representing. And so that kind of reinforces, but it's all ties together, right. As the open source world goes, particularly for a company like MongoDB, which has an open source product, you know, and our community builds. You know, it's a good thing to be mindful of for us, how we interact with the community and you know, because that could also become an opportunity for recruiting. >> John: Yeah. >> Right. So we, in addition to people who might become advocates on Mongo's behalf in their own company as a solution for themselves, so. >> You guys had great successful company and great leadership there. I mean, I can't tell you how many times someone's told me "MongoDB doesn't scale. It's going to be dead next year." I mean, I was going back 10 years. It's like, just keeps getting better and better. You guys do a great job. So it's so fun to see the success of developers. Really appreciate you guys coming on the program. Final question, what are you guys excited about to end the segment? We'll give you guys the last word. Lena will start with you and Tara, you can wrap us up. What are you excited about? >> I'm excited to see what this year brings. I think with ChatGPT and its copycats, I think it'll be a very interesting year when it comes to AI and always in the lookout for the authentic deep fakes that we see coming out. So just trying to make people aware that this is a real thing. It's not just pretend. And then of course, our old friend ransomware, let's see where that's going to go. >> John: Yeah. >> And let's see where we get to and just genuine hygiene and housekeeping when it comes to security. >> Excellent. Tara. >> Ah, well for us, you know, we're always constantly trying to up our game from a security perspective in the software development life cycle. But also, you know, what can we do? You know, one interesting application of AI that maybe Google doesn't like to talk about is it is really cool as an addendum to search and you know, how we might incorporate that as far as our learning environment and developer productivity, and how can we enable our developers to be more efficient, productive in their day-to-day work. So, I don't know, there's all kinds of opportunities that we're looking at for how we might improve that process here at MongoDB and then maybe be able to share it with the world. One of the things I love about working at MongoDB is we get to use our own products, right. And so being able to have this interesting document database in order to put information and then maybe apply some sort of AI to get it out again, is something that we may well be looking at, if not this year, then certainly in the coming year. >> Awesome. Lena Smart, the chief information security officer. Tara Hernandez, vice president developer of productivity from MongoDB. Thank you so much for sharing here on International Women's Day. We're going to do this quarterly every year. We're going to do it and then we're going to do quarterly updates. Thank you so much for being part of this program. >> Thank you. >> Thanks for having us. >> Okay, this is theCube's coverage of International Women's Day. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> theCUBE's live coverage is made possible by funding from Dell Technologies: "Creating technologies that drive human progress." >> Good late afternoon from Barcelona, Spain at the Theater of Barcelona. It's Lisa Martin and Dave Nicholson of "theCUBE" covering MWC23. This is our third day of continuous wall-to-wall coverage on theCUBE. And you know we're going to be here tomorrow as well. We've been having some amazing conversations about the ecosystem. And we're going to continue those conversations next. Honore Labourdette is here, the VP global partner, Ecosystem Success Team, Telco Media and Entertainment at Red Hat. And Tony Jeffries joins us as well, a Senior Director of Product Management, Telecom Systems Business at Dell. Welcome to the theCUBE. >> Thank you. >> Thank you. >> Great to have both of you here. So we're going to be talking about the evolution of the telecom stack. We've been talking a lot about disaggregation the last couple of days. Honore, starting with you, talk about the evolution of the telecom stock. You were saying before we went live this is your 15th at least MWC. So you've seen a lot of evolution, but what are some of the things you're seeing right now? >> Well, I think the interesting thing about disaggregation, which is a key topic, right? 'Cause it's so relative to 5G and the 5G core and the benefits and the features of 5G core around disaggregation. But one thing we have to remember, when you disaggregate, you separate things. You have to bring those things back together again in a different way. And that's predominantly what we're doing in our partnership with Dell, is we're bringing those disaggregated components back together in a cohesive way that takes advantage of the new technology, at the same time taking out the complexity and making it easier for our Telco customers to deploy and to scale and to get much more, accelerate the time to revenue. So the trend now is, what we're seeing is two things I would say. One is how do we solve for the complexity with the disaggregation? And how do we leverage the ecosystem as a partner in order to help solve for some of those challenges? >> Tony, jump on in, talk about what you guys announced last week, Dell and Red Hat, and how it's addressing the complexities that Honore was saying, "Hey, they're there." >> Yeah. You know, our customers, our operators are saying, "Hey, I want disaggregation." "I want competition in the market." But at the same time who's going to support all this disaggregation, right? And so at the end of the day, there's going to be an operator that's going to have to figure this out. They're going to have an SLA that they're going to have to meet. And so they're going to want to go with a best-in-class partner with Red Hat and Dell, in terms of our infrastructure and their software together as one combined engineered system. And that's what we call a Dell Telecom infrastructure block for Red Hat. And so at the end of the day, things may go wrong, and if they do, who are they going to call for that support? And that's also really a key element of an engineered system, is this experience that they get both with Red Hat and with Dell together supporting the customer as one. Which is really important to solve this disaggregated problem that can arise from a disaggregated open network situation, yeah. >> So what is the market, the go to market motion look like? People have loyalties in the IT space to technologies that they've embraced and been successful with for years and years. So you have folks in the marketplace who are diehard, you know, dyed red, Red Hat folks. Is it primarily a pull from them? How does that work? How do you approach that to your, what are your end user joint customers? What does that look like from your perspective? >> Sure, well, interestingly enough both Red Hat and Dell have been in the marketplace for a very long time, right? So we do have the brand with those Telco customers for these solutions. What we're seeing with this solution is, it's an emerging market. It's an emerging market for a new technology. So there's an opportunity for both Red Hat and Dell together to leverage our brands with those customers with no friction in the marketplace as we go to market together. So our field sales teams will be motivated to, you know, take advantage of the solution for their customers, as will the Dell team. And I'll let Tony speak to the Dell, go to market. >> Yeah. You know, so we really co-sell together, right? We're the key partners. Dell will end up fulfilling that order, right? We send these engineered systems through our factories and we send that out either directly to a customer or to a OTEL lab, like an intermediate lab where we can further refine and customize that offer for that particular customer. And so we got a lot of options there, but we're essentially co-selling. And Dell is fulfilling that from an infrastructure perspective, putting Red Hat software on top and the licensing for that support. So it's a really good mix. >> And I think, if I may, one of the key differentiators is the actual capabilities that we're bringing together inside of this pre-integrated solution. So it includes the Red Hat OpenShift which is the container software, but we also add our advanced cluster management as well as our Ansible automation. And then Dell adds their orchestration capability along with the features and functionalities of the platform. And we put that together and we offer capability, remote automation orchestration and management capabilities that again reduces the operating expense, reduces the complexity, allows for easy scale. So it's, you know, certainly it's all about the partnership but it's also the capabilities of the combined technology. >> I was just going to ask about some of the numbers, and you mentioned some of them. Reduction of TCO I imagine is also a big capability that this solution enables besides reducing OpEx. Talk about the TCO reduction. 'Cause I know there's some numbers there that Dell and Red Hat have already delivered to the market. >> Yeah. You know, so these infrastructure blocks are designed specifically for Core, or for RAN, or for the Edge. We're starting out initially in the Core, but we've done some market research with a company called ACG. And ACG has looked at day zero, day one and day two TCO, FTE hours saved. And we're looking at over 40 to 50% TCO savings over you know, five year period, which is quite significant in terms of cost savings at a TCO level. But also we have a lot of numbers around power consumption and savings around power consumption. But also just that experience for our operator that says, hey, I'm going to go to one company to get the best in class from Red Hat and Dell together. That saves a lot of time in procurement and that entire ordering process as well. So you get a lot of savings that aren't exactly seen in the FTE hours around TCO, but just in that overall experience by talking to one company to get the best of both from both Red Hat and Dell together. >> I think the comic book character Charlie Brown once said, "The most discouraging thing in the world is having a lot of potential." (laughing) >> Right. >> And so when we talk about disaggregating and then reaggregating or reintegrating, that means choice. >> Tony: Yeah. >> How does an operator approach making that choice? Because, yeah, it sounds great. We have this integration lab and you have all these choices. Well, how do I decide, how does a person decide? This is a question for Honore from a Red Hat perspective, what's the secret sauce that you believe differentiates the Red Hat-infused stack versus some other assemblage of gear? >> Well, there's a couple of key characteristics, and the one that I think is most prevalent is that we're open, right? So "open" is in Red Hat's DNA because we're an open source technology company, and with that open source technology and that open platform, our customers can now add workloads. They have options to choose the workloads that they want to run on that open source platform. As they choose those workloads, they can be confident that those workloads have been certified and validated on our platform because we have a very robust ecosystem of ISVs that have already completed that process with open source, with Red Hat OpenShift. So then we take the Red Hat OpenShift and we put it on the Dell platform, which is market leader platform, right? Combine those two things, the customers can be confident that they can put those workloads on the combined platform that we're offering and that those workloads would run. So again, it goes back to making it simpler, making it easy to procure, easy to run workloads, easy to deploy, easy to operate. And all of that of course equates to saving time always equates to saving money. >> Yeah. Absolutely. >> Oh, I thought you wanted to continue. >> No, I think Honore sort of, she nailed it. You know, Red Hat is so dominant in 5G, and what they're doing in the market, especially in the Core and where we're going into the RAN, you know, next steps are to validate those workloads, those workload vendors on top of a stack. And the Red Hat leader in the Core is key, right? It's instant credibility in the core market. And so that's one of the reasons why we, Dell, want to partner with with Red Hat for the core market and beyond. We're going to be looking at not only Core but moving into RAN very soon. But then we do, we take that validated workload on top of that to optimize that workload and then be able to instantiate that in the core and the RAN. It's just a really streamlined, good experience for our operators. At the end of the day, we want happy customers in between our mutual customer base. And that's what you get whenever you do that combined stack together. >> Were operators, any operators, and you don't have to mention them by name, involved in the evolution of the infra blocks? I'm just curious how involved they were in helping to co-develop this. I imagine they were to some degree. >> Yeah, I could take that one. So, in doing so, yeah, we can't be myopic and just assume that we nailed it the first time, right? So yeah, we do work with partners all the way up and down the stack. A lot of our engineering work with Red Hat also brings in customer experience that is key to ensure that you're building and designing the right architecture for the Core. I would like to use the names, I don't know if I should, but a lot of those names are big names that are leaders in our industry. But yeah, their footprints, their fingerprints are all over those design best practices, those architectural designs that we build together. And then we further that by doing those validated workloads on top of that. So just to really prove the point that it's optimized for the Core, RAN, Edge kind of workload. >> And it's a huge added value for Red Hat to have a partner like Dell who can take all of those components, take the workload, take the Red Hat software, put it on the platform, and deliver that out to the customers. That's really, you know, a key part of the partnership and the value of the partnership because nobody really does that better than Dell. That center of excellence around delivery and support. >> Can you share any feedback from any of those nameless operators in terms of... I'm even kind of wondering what the catalyst was for the infra block. Was it operators saying, "Ah, we have these challenges here"? Was it the evolution of the Telco stack and Dell said, "We can come in with Red Hat and solve this problem"? And what's been some of their feedback? >> Yeah, it really comes down to what Honore said about, okay, you know, when we are looking at day zero, which is primarily your design, how much time savings can we do by creating that stack for them, right? We have industry experts designing that Core stack that's optimized for different levels of spectrum. When we do that we save a lot of time in terms of FTE hours for our architects, our operators, and then it goes into day one, right? Which is the deployment aspect for saving tons of hours for our operators by being able to deploy this. Speed to market is key. That ultimately ends up in, you know, faster time to revenue for our customers, right? So it's, when they see that we've already done the pre-work that they don't have to, that's what really resonates for them in terms of that, yeah. >> Honore, Lisa and I happen to be veterans of the Cloud native space, and what we heard from a lot of the folks in that ecosystem is that there is a massive hunger for developers to be able to deploy and manage and orchestrate environments that consist of Cloud native application infrastructure, microservices. >> Right. >> What we've heard here is that 5G equals Cloud native application stacks. Is that a fair assessment of the environment? And what are you seeing from a supply and demand for that kind of labor perspective? Is there still a hunger for those folks who develop in that space? >> Well, there is, because the very nature of an open source, Kubernetes-based container platform, which is what OpenShift is, the very nature of it is to open up that code so that developers can have access to the code to develop the workloads to the platform, right? And so, again, the combination of bringing together the Dell infrastructure with the Red Hat software, it doesn't change anything. The developer, the development community still has access to that same container platform to develop to, you know, Cloud native types of application. And you know, OpenShift is Red Hat's hybrid Cloud platform. So it runs on-prem, it runs in the public Cloud, it runs at the edge, it runs at the far edge. So any of the development community that's trying to develop Cloud native applications can develop it on this platform as they would if they were developing on an OpenShift platform in the public Cloud. >> So in "The Graduate", the advice to the graduate was, "Plastics." Plastics. As someone who has more children than I can remember, I forget how many kids I have. >> Four. >> That's right, I have four. That's right. (laughing) Three in college and grad school already at this point. Cloud native, I don't know. Kubernetes definitely a field that's going to, it's got some legs? >> Yes. >> Okay. So I can get 'em off my payroll quickly. >> Honore: Yes, yes. (laughing) >> Okay, good to know. Good to know. Any thoughts on that open Cloud native world? >> You know, there's so many changes that's going to happen in Kubernetes and services that you got to be able to update quickly. CICD, obviously the topic is huge. How quickly can we keep these systems up to date with new releases, changes? That's a great thing about an engineered system is that we do provide that lifecycle management for three to five years through this engagement with our customers. So we're constantly keeping them up with the latest and the greatest. >> David: Well do those customers have that expertise in-house, though? Do they have that now? Or is this a seismic cultural shift in those environments? >> Well, you know, they do have a lot of that experience, but it takes a lot of that time, and we're taking that off of their plate and putting that within us on our system, within our engineered system, and doing that automatically for them. And so they don't have to check in and try to understand what the release certification matrix is. Every quarter we're providing that to them. We're communicating out to the operator, telling them what's coming up latest and greatest, not only in terms of the software but the hardware and how to optimize it all together. That's the beauty of these systems. These are five year relationships with our operators that we're providing that lifecycle management end to end, for years to come. >> Lisa: So last question. You talked about joint GTM availability. When can operators get their hands on this? >> Yes. Yes. It's currently slated for early September release. >> Lisa: Awesome. So sometime this year? >> Yes. >> Well guys, thank you so much for talking with us today about Dell, Red Hat, what you're doing to really help evolve the telecom stack. We appreciate it. Next time come back with a customer, we can dig into it. That'd be fun. >> We sure will, absolutely. That may happen today actually, a little bit later. Not to let the cat out the bag, but good news. >> All right, well, geez, you're going to want to stick around. Thank you so much for your time. For our guests and for Dave Nicholson. This is Lisa Martin of theCUBE at MWC23 from Barcelona, Spain. We'll be back after a short break. (calm music)

(upbeat music) >> Hello, everyone; I'm John Furrier with SiliconANGLE NEWS and host of theCUBE, and welcome to our SiliconANGLE NEWS MWC NEWS UPDATE in Barcelona where MWC is the premier event for the cloud telecommunication industry, and in the news here is Red Hat, Red Hat announcing a collaboration with NVIDIA, Samsung and Arm on Efficient Open Networks. Red Hat announced updates across various fields including advanced 5G telecommunications cloud, industrial edge, artificial intelligence, and radio access networks, RAN, and Efficiency. Red Hat's enterprise Kubernetes platform, OpenShift, has added support for NVIDIA's converged accelerators and aerial SDK facilitating RAND deployments on industry standard service across hybrid and multicloud platforms. This composable infrastructure enables telecom firms to support heavier compute demands for edge computing, AI, private 5G, and more, and just also helps network operators adopt open architectures, allowing them to choose non-proprietary components from multiple suppliers. In addition to the NVIDIA collaboration, Red Hat is working with Samsung to offer a new vRAN solution for service providers to better manage their open RAN networks. They're also working with UK chip designer, Arm, to create new networking solutions for energy efficient Red Hat Open Source Kubernetes-based Efficient Power Level Exporter project, or Kepler, has been donated to the open Cloud Native Compute Foundation, allowing enterprise to better understand their cloud native workloads and power consumptions. Kepler can also help in the development of sustainable software by creating less power hungry applications. Again, Red Hat continuing to provide OpenSource, OpenRAN, and contributing an open source project to the CNCF, continuing to create innovation for developers, and, of course, Red Hat knows what, a lot about operating systems and the telco could be the next frontier. That's SiliconANGLE NEWS. I'm John Furrier; thanks for watching. (monotone music)

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

(intense music) >> Hello, I'm John Furrier with "SiliconANGLE News" and the host of theCUBE here in Palo Alto, with coverage of MWC 2023. theCUBE is onsite in Barcelona, four days of wall to wall coverage. Here is a news update from MWC and in the news here is Google. Google Cloud targets cloud native network transformation for all the carriers or cloud service providers, and the communication service providers. They announced three new products to help communications service providers, also known as CSPs, build, deploy and operate hybrid cloud native networks, as well as collect and manage network data. The new products, when combined with Unified Cloud, enables the CSPs to improve customer experience, artificial intelligence, and data analytics. This is a big move, because 70% of communication service providers are expected to adopt cloud native network functions by the end of this year, making it a big, big wave. One of the key features of Google's products is the telecom network automation. This cloud service accelerates CSPs network and edge deployments through the use of Kubernetes based cloud native automation tools. It's managed by a cloud version of open source Nephio, project that Google founded in 2022. Of course, other key product announcements with Google, the Telecom Data Fabric, a tool that helps CSPs generate insights. That's the data driven piece, to target and optimize their network performance and reliability, works by simplifying the collection, normalization, correlation through an adaptive framework. This is kind of where AI shines. Finally, Google has telecom subscriber insights, a powerful AI tool that enables CSPs to extract insights from existing data sources in a privacy safe environment. Let's see if this is better than Bing search, we'll see. But CSPs are moving to the cloud across all channels. This is a really important trend, as cloud native scale, AI, data, configuration, automation all come to the edge of the network. That's an update from "SiliconANGLE News". Check out the coverage on siliconangle.com. Of course, thecube.net, four days, Dave Vellante and Lisa Martin are there. I'm here in Palo Alto. Thanks for watching. (slow music) (upbeat music)

>> Announcer: theCUBE's live coverage is made possible by funding from Dell Technologies creating technologies that drive human progress. (upbeat music) >> Hey everyone, welcome back to theCube's first day of coverage of MWC 23 from Barcelona, Spain. Lisa Martin here with Dave Vellante and Dave Nicholson. I'm literally in between two Daves. We've had a great first day of coverage of the event. There's been lots of conversations, Dave, on disaggregation, on the change of mobility. I want to be able to get your perspectives from both of you on what you saw on the show floor, what you saw and heard from our guests today. So we'll start with you, Dave V. What were some of the things that were our takeaways from day one for you? >> Well, the big takeaway is the event itself. On day one, you get a feel for what this show is like. Now that we're back, face-to-face kind of pretty much full face-to-face. A lot of excitement here. 2000 plus exhibitors, I mean, planes, trains, automobiles, VR, AI, servers, software, I mean everything. I mean, everybody is here. So it's a really comprehensive show. It's not just about mobile. That's why they changed the name from Mobile World Congress. I think the other thing is from the keynotes this morning, I mean, you heard, there's a lot of, you know, action around the telcos and the transformation, but in a lot of ways they're sort of protecting their existing past from the future. And so they have to be careful about how fast they move. But at the same time if they don't move fast, they're going to get disrupted. We heard some complaints, essentially, you know, veiled complaints that the over the top guys aren't paying their fair share and Telco should be able to charge them more. We heard the chairman of Ericsson talk about how we can't let the OTTs do that again. We're going to charge directly for access through APIs to our network, to our data. We heard from Chris Lewis. Yeah. They've only got, or maybe it was San Ji Choha, how they've only got eight APIs. So, you know the developers are the ones who are going to actually build out the innovation at the edge. The telcos are going to provide the connectivity and the infrastructure companies like Dell as well. But it's really to me all about the developers. And that's where the action's going to be. And it's going to be interesting to see how the developers respond to, you know, the gun to the head. If you want access, you're going to have to pay for it. Now maybe there's so much money to be made that they'll go for it, but I feel like there's maybe a different model. And I think some of the emerging telcos are going to say, you know what, here developers, here's a platform, have at it. We're not going to charge you for all the data until you succeed. Then we're going to figure out a monetization model. >> Right. A lot of opportunity for the developer. That skillset is certainly one that's in demand here. And certainly the transformation of the telecom industry is, there's a lot of conundrums that I was hearing going on today, kind of chicken and egg scenarios. But Dave, you had a chance to walk around the show floor. We were here interviewing all day. What were some of the things that you saw that really stuck out to you? >> I think I was struck by how much attention was being paid to private 5G networks. You sort of read between the lines and it appears as though people kind of accept that the big incumbent telecom players are going to be slower to move. And this idea of things like open RAN where you're leveraging open protocols in a stack to deliver more agility and more value. So it sort of goes back to the generalized IT discussion of moving to cloud for agility. It appears as though a lot of players realize that the wild wild west, the real opportunity, is in the private sphere. So it's really interesting to see how that works, how 5G implemented into an environment with wifi how that actually works. It's really interesting. >> So it's, obviously when you talk to companies like Dell, I haven't hit HPE yet. I'm going to go over there and check out their booth. They got an analyst thing going on but it's really early days for them. I mean, they started in this business by taking an X86 box, putting a name on it, you know, that sounded like it was edged, throwing it over, you know, the wall. That's sort of how they all started in this business. And now they're, you know, but they knew they had to form partnerships. They had to build purpose-built systems. Now with 16 G out, you're seeing that. And so it's still really early days, talking about O RAN, open RAN, the open RAN alliance. You know, it's just, I mean, not even, the game hasn't even barely started yet but we heard from Dish today. They're trying to roll out a massive 5G network. Rakuten is really focused on sort of open RAN that's more reliable, you know, or as reliable as the existing networks but not as nearly as huge a scale as Dish. So it's going to take a decade for this to evolve. >> Which is surprising to the average consumer to hear that. Because as far as we know 5G has been around for a long time. We've been talking about 5G, implementing 5G, you sort of assume it's ubiquitous but the reality is it is just the beginning. >> Yeah. And you know, it's got a fake 5G too, right? I mean you see it on your phone and you're like, what's the difference here? And it's, you know, just, >> Dave N.: What does it really mean? >> Right. And so I think your point about private is interesting, the conversation Dave that we had earlier, I had throughout, hey I don't think it's a replacement for wifi. And you said, "well, why not?" I guess it comes down to economics. I mean if you can get the private network priced close enough then you're right. Why wouldn't it replace wifi? Now you got wifi six coming in. So that's a, you know, and WiFi's flexible, it's cheap, it's good for homes, good for offices, but these private networks are going to be like kickass, right? They're going to be designed to run whatever, warehouses and robots, and energy drilling facilities. And so, you know the economics I don't think are there today but maybe they can be at volume. >> Maybe at some point you sort of think of today's science experiment becoming the enterprise-grade solution in the future. I had a chance to have some conversations with folks around the show. And I think, and what I was surprised by was I was reminded, frankly, I wasn't surprised. I was reminded that when we start talking about 5G, we're talking about spectrum that is managed by government entities. Of course all broadcast, all spectrum, is managed in one way or another. But in particular, you can't simply put a SIM in every device now because there are a lot of regulatory hurdles that have to take place. So typically what these things look like today is 5G backhaul to the network, communication from that box to wifi. That's a huge improvement already. So yeah, my question about whether, you know, why not put a SIM in everything? Maybe eventually, but I think, but there are other things that I was not aware of that are standing in the way. >> Your point about spectrum's an interesting one though because private networks, you're going to be able to leverage that spectrum in different ways, and tune it essentially, use different parts of the spectrum, make it programmable so that you can apply it to that specific use case, right? So it's going to be a lot more flexible, you know, because I presume the needs spectrum needs of a hospital are going to be different than, you know, an agribusiness are going to be different than a drilling, you know, unit, offshore drilling unit. And so the ability to have the flexibility to use the spectrum in different ways and apply it to that use case, I think is going to be powerful. But I suspect it's going to be expensive initially. I think the other thing we talked about is public policy and regulation, and it's San Ji Choha brought up the point, is telcos have been highly regulated. They don't just do something and ask for permission, you know, they have to work within the confines of that regulated environment. And there's a lot of these greenfield companies and private networks that don't necessarily have to follow those rules. So that's a potential disruptive force. So at the same time, the telcos are spending what'd we hear, a billion, a trillion and a half over the next seven years? Building out 5G networks. So they got to figure out, you know how to get a payback on that. They'll get it I think on connectivity, 'cause they have a monopoly but they want more. They're greedy. They see the over, they see the Netflixes of the world and the Googles and the Amazons mopping up services and they want a piece of that action but they've never really been good at it. >> Well, I've got a question for both of you. I mean, what do you think the odds are that by the time the Shangri La of fully deployed 5G happens that we have so much data going through it that effectively it feels exactly the same as 3G? What are the odds? >> That's a good point. Well, the thing that gets me about 5G is there's so much of it on, if I go to the consumer side when we're all consumers in our daily lives so much of it's marketing hype. And, you know all the messaging about that, when it's really early innings yet they're talking about 6G. What does actual fully deployed 5G look like? What is that going to enable a hospital to achieve or an oil refinery out in the middle of the ocean? That's something that interests me is what's next for that? Are we going to hear that at this event? >> I mean, walking around, you see a fair amount of discussion of, you know, the internet of things. Edge devices, the increase in connectivity. And again, what I was surprised by was that there's very little talk about a sim card in every one of those devices at this point. It's like, no, no, no, we got wifi to handle all that but aggregating it back into a central network that's leveraging 5G. That's really interesting. That's really interesting. >> I think you, the odds of your, to go back to your question, I think the odds are even money, that by the time it's all built out there's going to be so much data and so much new capability it's going to work similarly at similar speeds as we see in the networks today. You're just going to be able to do so many more things. You know, and your video's going to look better, the graphics are going to look better. But I think over the course of history, this is what's happening. I mean, even when you go back to dial up, if you were in an AOL chat room in 1996, it was, you know, yeah it took a while. You're like, (screeches) (Lisa laughs) the modem and everything else, but once you were in there- >> Once you're there, 2400 baud. >> It was basically real time. And so you could talk to your friends and, you know, little chat room but that's all you could do. You know, if you wanted to watch a video, forget it, right? And then, you know, early days of streaming video, stop, start, stop, start, you know, look at Amazon Prime when it first started, Prime Video was not that great. It's sort of catching up to Netflix. But, so I think your point, that question is really prescient because more data, more capability, more apps means same speed. >> Well, you know, you've used the phrase over the top. And so just just so we're clear so we're talking about the same thing. Typically we're talking about, you've got, you have network providers. Outside of that, you know, Netflix, internet connection, I don't need Comcast, right? Perfect example. Well, what about the over the top that's coming from direct satellite communications with devices. There are times when I don't have a signal on my, happens to be an Apple iPhone, when I get a little SOS satellite logo because I can communicate under very limited circumstances now directly to the satellite for very limited text messaging purposes. Here at the show, I think it might be a Motorola device. It's a dongle that allows any mobile device to leverage direct satellite communication. Again, for texting back to the 2,400 baud modem, you know, days, 1200 even, 300 even, go back far enough. What's that going to look like? Is that too far in the future to think that eventually it's all going to be over the top? It's all going to be handset to satellite and we don't need these RANs anymore. It's all going to be satellite networks. >> Dave V.: I think you're going to see- >> Little too science fiction-y? (laughs) >> No, I, no, I think it's a good question and I think you're going to see fragments. I think you're going to see fragmentation of private networks. I think you're going to see fragmentation of satellites. I think you're going to see legacy incumbents kind of hanging on, you know, the cable companies. I think that's coming. I think by 2030 it'll, the picture will be much more clear. The question is, and I think it's come down to the innovation on top, which platform is going to be the most developer friendly? Right, and you know, I've not heard anything from the big carriers that they're going to be developer friendly. I've heard "we have proprietary data that we're going to charge access for and developers are going to have to pay for that." But I haven't heard them saying "Developers, developers, developers!" You know, Steve Bomber running around, like bend over backwards for developers, they're asking the developers to bend over. And so if a network can, let's say the satellite network is more developer friendly, you know, you're going to see more innovation there potentially. You know, or if a dish network says, "You know what? We're going after developers, we're going after innovation. We're not going to gouge them for all this network data. Rather we're going to make the platform open or maybe we're going to do an app store-like model where we take a piece of the action after they succeed." You know, take it out of the backend, like a Silicon Valley VC as opposed to an East Coast VC. They're not going to get you in the front end. (Lisa laughs) >> Well, you can see the sort of disruptive forces at play between open RAN and the legacy, call it proprietary stack, right? But what is the, you know, if that's sort of a horizontal disruptive model, what's the vertically disruptive model? Is it private networks coming in? Is it a private 5G network that comes in that says, "We're starting from the ground up, everything is containerized. We're going to go find people at KubeCon who are, who understand how to orchestrate with Kubernetes and use containers in microservices, and we're going to have this little 5G network that's going to deliver capabilities that you can't get from the big boys." Is there a way to monetize that? Is there a way for them to be disrupted, be disruptive, or are these private 5G networks that everybody's talking about just relegated to industrial use cases where you're just squeezing better economics out of wireless communication amongst all your devices in your factory? >> That's an interesting question. I mean, there are a lot of those smart factory industrial use cases. I mean, it's basically industry 4.0 use cases. But yeah, I don't count the cloud guys out. You know, everybody says, "oh, the narrative is, well, the latency of the cloud." Well, not if the cloud is at the edge. If you take a local zone and put storage, compute, and data right next to each other and the cloud model with the cloud APIs, and then you got an asynchronous, you know, connection back. I think that's a reasonable model. I think the cloud guys figured out developers, right? Pretty well. Certainly Microsoft and, and Amazon and Google, they know developers. I don't see any reason why they can't bring their model to the edge. So, and that's really disruptive to the legacy telco guys, you know? So they have to be careful. >> One step closer to my dream of eliminating the word "cloud" from IT lexicon. (Lisa laughs) I contend that it has always been IT, and it will always be IT. And this whole idea of cloud, what is cloud? If AWS, for example, is delivering hardware to the edge where it needs to be, is that cloud? Do we go back to the idea that cloud is an operational model and not a question of physical location? I hope we get to that point. >> Well, what's Apex and GreenLake? Apex is, you know, Dell's as a service. GreenLake is- >> HPE. >> HPE's as a service. That's outposts. >> Dave N.: Right. >> Yeah. >> That's their outpost. >> Yeah. >> Well AWS's position used to be, you know, to use them as a proxy for hyperscale cloud. We'll just, we'll grow in a very straight trajectory forever on the back of net new stuff. Forget about the old stuff. As James T. Kirk said of the Klingons, "let them die." (Lisa laughs) As far as the cloud providers were concerned just, yeah, let, let that old stuff go away. Well then they found out, there came a point in time where they realized there's a lot of friction and stickiness associated with that. So they had to deal with the reality of hybridity, if that's the word, the hybrid nature of things. So what are they doing? They're pushing stuff out to the edge, so... >> With the same operating model. >> With the same operating model. >> Similar. I mean, it's limited, right? >> So you see- >> You can't run a lot of database on outpost, you can run RES- >> You see this clash of Titans where some may have written off traditional IT infrastructure vendors, might have been written off as part of the past. Whereas hyperscale cloud providers represent the future. It seems here at this show they're coming head to head and competing evenly. >> And this is where I think a company like Dell or HPE or Cisco has some advantages in that they're not going to compete with the telcos, but the hyperscalers will. >> Lisa: Right. >> Right. You know, and they're already, Google's, how much undersea cable does Google own? A lot. Probably more than anybody. >> Well, we heard from Google and Microsoft this morning in the keynote. It'd be interesting to see if we hear from AWS and then over the next couple of days. But guys, clearly there is, this is a great wrap of day one. And the crazy thing is this is only day one. We've got three more days of coverage, more news, more information to break down and unpack on theCUBE. Look forward to doing that with you guys over the next three days. Thank you for sharing what you saw on the show floor, what you heard from our guests today as we had about 10 interviews. Appreciate your insights and your perspectives and can't wait for tomorrow. >> Right on. >> All right. For Dave Vellante and Dave Nicholson, I'm Lisa Martin. You're watching theCUBE's day one wrap from MWC 23. We'll see you tomorrow. (relaxing music)

>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (bright upbeat music) >> Good morning everyone. Welcome to theCUBE. We are live at MWC '23 in Barcelona, Spain. I'm Lisa Martin, and I'm going to have a great conversation next with our esteemed CUBE analyst, Dave Nicholson. Dave, great to have you here. Great to be working this event with you. >> Good to be here with you, Lisa. >> So there are, good to be here with you and about 80,000 people. >> Dave: That's right. >> Virtually and and physically. And it's jammed in, and this is the most jammed show I've seen in years. >> Dave: It's crazy. >> So much going on in the telecom industry. What are some of your expectations for what you're going to hear and see at this year's event? >> So, I expect to hear a lot about 5G. Specifically 5G private networks, and the disaggregation of the hardware and software stacks that have driven telecom for decades. So we're at this transition into 5G. From a consumer perspective, we feel like, oh well 5G has been around for years. In terms of where it's actually been deployed, we're just at the beginning stages of that. >> Right, right. Talk about the changing of the stack. You know, the disaggregation. Why now is it too late? And what are the advantages? That it's going to enable telcos to move faster, I imagine? >> Yeah, so it's really analogous to what we see in the general IT industry that we cover so much. The move to cloud, sometimes you're gaining performance. You're always gaining agility and flexibility. A big concern of the legacy telecom providers is going to be maintaining availability, reliability against a backdrop of increasing agility in the direction that they want to go. So that's going to be the conversation. It's going to be the old school folks, who are interested in maintaining primarily availability and performance, excuse me, contrasted with the open source, OpenStack providers, who are going to be saying, hey this is a path to the future. Without that path to the future, things will stagnate. >> Talk about some of those OpenStack providers. I imagine those are some of the folks that we know quite well? >> Sure, sure. Yeah, so someone like Dell, for example. They're perfectly positioned at this sort of crossroads, because Dell has been creating "cloud stacks," that will live sometimes on-premises. And those stacks of infrastructure, based on cots, commercial off-the-shelf components, integrated within an ecosystem can live at the edge, at literally the base of transmitter towers. So when you think about this whole concept of RAN or a radio access network, think of a cellular tower with an antenna and a transmitter. The transmitter might live on that tower, or it might live in pieces at the base of the tower. But there's always at that base of the tower, forget about the acronyms, it's a box of stuff, teleco stuff. All of these things historically have been integrated into single packages. >> Right. >> For good reason. >> Right. >> Think back to a mainframe, where it was utterly, absolutely reliable. We moved, in the general IT space, from the era of the mainframe to the world of client server, through virtualization, containerization. That exact transition is happening in the world of telecom right now. >> Why is it finally happening now? It seems a bit late, given that in our consumer lives, we have this expectation that we could be mobile 24 by seven. >> Right. Well it's because, first of all, we get mad if a call doesn't go through. How often, when you make, when you try to make a cellular call or when you try to send a text, how often does it not work? >> I can count on one hand. >> Right, rarely. >> Right. >> Now, you may be in an area that has spotty coverage. But when you're in an area where you have coverage it just works all of the time. And you expect it to work all of the time. And the miracle of the services that have been delivered to us over the last decade has really kind of blunted the need for next generation stuff. Well, we're at this transition point. And 5G as a technology enables so much more bandwidth. Think of it as, you know, throughput bandwidth latency. It allows the kind of performance characteristics so that things can be delivered that couldn't be delivered in the past. Virtual reality, augmented reality. We're already seeing you know 4K data streams to our phones. So, it's sort of lagged because of our expectations for absolute, rock solid, reliability. >> Yeah. >> The technology is ahead of that area now. And so this question is how do you navigate from utter reliability to awesome openness without sacrificing performance and reliability? >> Well, and also from a stack perspective, from looking at desegregation, and the opportunities there are for the telcos, but also the public cloud providers, are they friends, are they foes? What's the relationship like? >> They're going to be frenemies. >> Lisa: Frenemies? >> Yeah, coopetition is going to be the word of the day again. Yeah because when you think of a cloud, most people automatically think off-premises. >> Lisa: Yes. >> Maybe they even think automatically you know, hyper scale or Azure, GCP, AWS. In this case, it really is a question of cloud as an operating model. Cloud facilitating agility, cloud adopting cloud native architecture from a software perspective, so that you can rapidly deploy net new capabilities into an environment. You can't do that with proprietary closed systems that might use a waterfall development process and take years to develop. You and I have covered the Kubernetes world pretty closely. And what's the big thing that we hear constantly? The hunger, the thirst for human resources, >> Right. >> people who can actually work in this world of containerization. >> Yes, yes. >> Well guess what? In the macroeconomic environment, a lot of folks in the IT space have recently been disrupted. This is a place to look, if you have that skillset. Look at the telecom space, because they need people who are forward thinking in the era of cloud. But this concept of cloud is really, it's going to be, the telcos are both competing and partnering with what we think of as the traditional, hyper scale public cloud providers. >> And what do you think, one of the things that we know at MWC '23 is virtually every industry is represented here. Every vertical is here, whether it's a sports arena, or a retail outlet, or a manufacturer. Every organization, every industry needs to have networks that deliver what they need to do but also enable them to move faster and deliver what the end user wants. What are some of the industries that you think are really ripe for this disruption? And the ability to use private 5G networks, for example? >> Well, so it's interesting, you mentioned private 5G networks. I think a good example of the transition that's underway is this, the move to 4K video. So, you get a high definition television. The first time you see a 720p TV, it's like oh my gosh, amazing. Then we get 1080p, then it's 4K. People get 4K TVs, they bring them home, and there's no content. >> No. >> The first content, was it from your cable provider? No. >> Yeah. >> Was it over the air? ABC, NBC, CBS? No, it was YouTube. YouTube delivered the first reliable 4K content, over the internet. Similarly, everything comes to us now to our mobile devices. So we're not accessing the world around us so much from a desktop or even a laptop. It's mobile. So if you want to communicate with a customer, it's mobile. If you're creating a private 5G network, you now are standing something up that is net new in a greenfield environment. And you can deploy agility and functionality that the large scale telecom providers can't, because of the massive investment they might need. So the irony is, you have a factory that sits on 20 acres and you have folks traveling around, if you create a private 5G network, it might become, it might be more feature rich than what your employees are used to being able to access through their personal mobile devices. >> Wow. >> Yeah, because you're starting net new, you have the luxury of starting greenfield, as opposed to the responsibility and legacy for supporting a massive system that exists already. >> So then, what's in it for the existing incumbent telcos from an advantage opportunity perspective? Because you mentioned frenemies, coopetition. >> Right. >> There's irony there, as you talked about. >> Right, well you could look at it as either opportunity or headache. And it's both. Because they have very, very real SLAs that they need to meet. >> Right. >> Very, very real expectations that have been set in terms of reliability, availability, and performance. So they can't slip off of that. Making that transition is, I think going to be driven by economics, because the idea of having things be open means that there's competition for every part of the stack. There will be a critical role for integration vendors. Folks like Dell, and the ecosystems that they're creating around this will be critical, because often you would prefer to have one back to pat or one throat to choke instead of many. So, you still want to have that centralized entity to go to when something goes wrong. >> Right. >> Or when you want to implement something new. So, for the incumbents, it's a classic example of what you do in the face of disruption. How do you leverage technology? In my role as adjunct faculty at the Wharton CTO Academy, we talk about the CTO mindset. And the idea that your role is to leverage technology, in the service of your organization's mission, whatever that organization and mission is. So from a telecom provider perspective, they need to stay on top of this. >> Yes. >> Or they will be disrupted. >> Right. >> It's fascinating to think of how this disruption's taking place. >> Lisa: They have no choice, if they want to survive. >> No, yeah they have no choice. >> Lisa: In the next few years. >> They have no choice, but they'll come along, kicking and screaming. I'm sure if you had someone sitting here in the industry, they'd say, well, no, no, no, no, no. >> Yeah, of course. >> We love it! It's like, yeah, well but you're going to have to make some painful changes to adopt these things. >> What are some of the opportunities for those folks like Dell that you mentioned, in terms of coming in, being able to disrupt that stack, open things up? Great opportunities for the Dells, and other similar organizations to really start gaining a bigger foothold in the telecom industry, I imagine. >> Well, I look at it through the lens of sort of traditional IT and the transitions that we've been watching for the last couple of decades. It's exactly the same. I mean you, there is a parallel. It is like coming out of the mainframe era to the client server era. So, you know, we went in that transition, it was mainframe operating systems, very, very closed systems to more slightly opened. You know, the worlds of SUN and SGI and HP, and the likes, transitioned to kind of Microsoft based software running with like Dell hardware. >> Yeah. >> And, that stack is now getting deployed into one of the remaining legacy environments which is the telco space. So, the opportunity for Dell is pretty massive because on some fronts they're competing with the move to proper off-premises public cloud. >> Right. >> In this case, they are the future for telecom as opposed to sort of representing legacy, compared to some of the other cloud opportunities that are out there. >> So ultimately, what does a modern telecom network look like? I imagine, cloud native? Distributed? >> Yeah, yeah. So, traditionally, like I said, you've got the tower and the transmitters and the computer hardware that's running it. Those are then networked together. So you can sort of think of it as leaves on a twig, on a branch, on a tree. Eventually it gets into a core network, where there is terrestrial line communication and or communication up to satellites. And that's all been humming along just fine, making the transition from 3G to 4G to 5G. But, the real transition from a cloud perspective is this idea that you're taking these proprietary systems, disaggrevating, disaggrevating them and disaggregating them, carving them up into pieces where now you're introducing virtualization. So there's a VMware play here. Some things are virtualized using that stack. I think more often we're going to be talking about containerized and truly cloud native stacks. So instead of having the proprietary stack, where all the hardware and software is designed together. Now you're going to have Dell servers running some execution layer, orchestration layer, for cloud native, containerized applications and microservices. And that's the way things are going to be developed. >> And who, from a stakeholder perspective is involved here? 'Cause one of the things that I'm hearing is with this disaggregation of the staff, which is a huge change, what you're articulated, that's already happened at enterprise IT, change management is a hard thing to do. If they want to be successful, and well not just survive, they want to thrive. I'm just imagining, who are the stakeholders that are involved in having to push those incumbents to make these decisions, to move faster, to become agile, to compete. >> So, I remember when VMware had the problem that anytime they suggested introducing a hypervisor to to virtualize a physical machine and then run software on top or an operating system on top, and then applications, the big question the customer would have was, well is Microsoft going to support that? What if I can't get support from Microsoft? I dunno if I can do this. Within about a year of those conversations taking place, the question was, can I run this in my production environment? So it was, can I get support in my test environment too? Can I please run this in production? >> Yeah. >> And so, there are folks in the kind of legacy telecom world who are going to be afraid. It's, whatever the dynamic is, there is a no one ever got fired for buying from fill in the blank >> Exactly, yep. >> in the telecom space. >> Yeah, yeah. >> Because they would buy a consolidated, aggregated stack. >> Right. >> And, if something went wrong they could say, boom, blame you. And yeah, that stack doesn't lend itself to the kind of pace of change. >> Right. >> So it doesn't necessarily need the same kind of change management. Or at least it's very, very centralized. >> Okay. Okay. >> We're getting into the brave new world of things where if you let them spin out of control, you can have big problems. And that's where the folks like Dell come in, to make sure that yes, disaggregated, yes best of commercial off-the-shelf stuff, but also the best in terms of performance and reliability and availability. >> Yeah. >> So, that's the execution part, you must execute flawlessly. >> It sounds like from a thematic perspective, the theme of MWC '23 is velocity. But it seems like an underlying theme under that, or maybe an overlying theme is disruption. It's going to be so interesting, we're only on day one. We just started our coverage. Four days of wall to wall coverage on theCUBE. Excited to hear what you're excited about, what you learn over the next few days. We get to host some segments together. >> Yeah. >> But it seems like disruption is the overall theme. And it's going to be so interesting to see how this industry evolves, what the opportunities are, what the coopetition opportunities are. We're going to be learning a lot this week. I'm excited. >> Yeah, and what's fascinating to me about this whole thing is we talk about this, all of this tumultuous, disruptive stuff that's happening. For the average consumer, they're never going to be aware of it. >> Nope. >> Dave: They're just going to see services piled on top of services. >> Which is what we want. >> There are billions of people with mobile devices and the hundreds of billions, I don't know, trillions I guess at some point of connected devices at the edge. >> Lisa: Yes, yes. >> The whole concept of the internet of things. We'll sort of be blissfully unaware of what's happening at the middle. But there's a lot of action there. So we're going to be focusing on that action that's going on. In, you know, in in the middle of it. >> Yeah. >> But there's also some cool consumer stuff out here. >> There is. >> I know I'm going to be checking out the augmented reality and virtual reality stuff. >> Yeah, yeah. Well it's all about that customer experience. We expect things right away, 24/7, wherever we are in the world. And it's enabling that to make that happen. >> Yeah. >> Dave, thank you so much for really sharing what you think you're excited about for the event and some of the trends in telecom. It sounds like it's such an interesting time to be unpacking this. >> It's going to be a great week. >> It is going to be a great week. All right, for Dave Nicholson, I'm Lisa Martin. You're watching theCUBE, the leader in live tech coverage, covering day one of MWC '23. Stick around. We'll be back with our next guest in just a minute. (bright music resumes) (music fades out)

(soft electronic music) >> Hey everyone, welcome to this CUBE conversation as part of the AWS Startup Showcase, season three, episode one, featuring Astronomer. I'm your host, Lisa Martin. I'm in the CUBE's Palo Alto Studios, and today excited to be joined by a couple of guests, a couple of co-founders from Astronomer. Viraj Parekh is with us, as is Paola Peraza-Calderon. Thanks guys so much for joining us. Excited to dig into Astronomer. >> Thank you so much for having us. >> Yeah, thanks for having us. >> Yeah, and we're going to be talking about the role of data orchestration. Paola, let's go ahead and start with you. Give the audience that understanding, that context about Astronomer and what it is that you guys do. >> Mm-hmm. Yeah, absolutely. So, Astronomer is a, you know, we're a technology and software company for modern data orchestration, as you said, and we're the driving force behind Apache Airflow. The Open Source Workflow Management tool that's since been adopted by thousands and thousands of users, and we'll dig into this a little bit more. But, by data orchestration, we mean data pipeline, so generally speaking, getting data from one place to another, transforming it, running it on a schedule, and overall just building a central system that tangibly connects your entire ecosystem of data services, right. So what, that's Redshift, Snowflake, DVT, et cetera. And so tangibly, we build, we at Astronomer here build products powered by Apache Airflow for data teams and for data practitioners, so that they don't have to. So, we sell to data engineers, data scientists, data admins, and we really spend our time doing three things. So, the first is that we build Astro, our flagship cloud service that we'll talk more on. But here, we're really building experiences that make it easier for data practitioners to author, run, and scale their data pipeline footprint on the cloud. And then, we also contribute to Apache Airflow as an open source project and community. So, we cultivate the community of humans, and we also put out open source developer tools that actually make it easier for individual data practitioners to be productive in their day-to-day jobs, whether or not they actually use our product and and pay us money or not. And then of course, we also have professional services and education and all of these things around our commercial products that enable folks to use our products and use Airflow as effectively as possible. So yeah, super, super happy with everything we've done and hopefully that gives you an idea of where we're starting. >> Awesome, so when you're talking with those, Paola, those data engineers, those data scientists, how do you define data orchestration and what does it mean to them? >> Yeah, yeah, it's a good question. So, you know, if you Google data orchestration you're going to get something about an automated process for organizing silo data and making it accessible for processing and analysis. But, to your question, what does that actually mean, you know? So, if you look at it from a customer's perspective, we can share a little bit about how we at Astronomer actually do data orchestration ourselves and the problems that it solves for us. So, as many other companies out in the world do, we at Astronomer need to monitor how our own customers use our products, right? And so, we have a weekly meeting, for example, that goes through a dashboard and a dashboarding tool called Sigma where we see the number of monthly customers and how they're engaging with our product. But, to actually do that, you know, we have to use data from our application database, for example, that has behavioral data on what they're actually doing in our product. We also have data from third party API tools, like Salesforce and HubSpot, and other ways in which our customer, we actually engage with our customers and their behavior. And so, our data team internally at Astronomer uses a bunch of tools to transform and use that data, right? So, we use FiveTran, for example, to ingest. We use Snowflake as our data warehouse. We use other tools for data transformations. And even, if we at Astronomer don't do this, you can imagine a data team also using tools like, Monte Carlo for data quality, or Hightouch for Reverse ETL, or things like that. And, I think the point here is that data teams, you know, that are building data-driven organizations have a plethora of tooling to both ingest the right data and come up with the right interfaces to transform and actually, interact with that data. And so, that movement and sort of synchronization of data across your ecosystem is exactly what data orchestration is responsible for. Historically, I think, and Raj will talk more about this, historically, schedulers like KRON and Oozie or Control-M have taken a role here, but we think that Apache Airflow has sort of risen over the past few years as the defacto industry standard for writing data pipelines that do tasks, that do data jobs that interact with that ecosystem of tools in your organization. And so, beyond that sort of data pipeline unit, I think where we see it is that data acquisition is not only writing those data pipelines that move your data, but it's also all the things around it, right, so, CI/CD tool and Secrets Management, et cetera. So, a long-winded answer here, but I think that's how we talk about it here at Astronomer and how we're building our products. >> Excellent. Great context, Paola. Thank you. Viraj, let's bring you into the conversation. Every company these days has to be a data company, right? They've got to be a software company- >> Mm-hmm. >> whether it's my bank or my grocery store. So, how are companies actually doing data orchestration today, Viraj? >> Yeah, it's a great question. So, I think one thing to think about is like, on one hand, you know, data orchestration is kind of a new category that we're helping define, but on the other hand, it's something that companies have been doing forever, right? You need to get data moving to use it, you know. You've got it all in place, aggregate it, cleaning it, et cetera. So, when you look at what companies out there are doing, right. Sometimes, if you're a more kind of born in the cloud company, as we say, you'll adopt all these cloud native tooling things your cloud provider gives you. If you're a bank or another sort of institution like that, you know, you're probably juggling an even wider variety of tools. You're thinking about a cloud migration. You might have things like Kron running in one place, Uzi running somewhere else, Informatics running somewhere else, while you're also trying to move all your workloads to the cloud. So, there's quite a large spectrum of what the current state is for companies. And then, kind of like Paola was saying, Apache Airflow started in 2014, and it was actually started by Airbnb, and they put out this blog post that was like, "Hey here's how we use Apache Airflow to orchestrate our data across all their sources." And really since then, right, it's almost been a decade since then, Airflow emerged as the open source standard, and there's companies of all sorts using it. And, it's really used to tie all these tools together, especially as that number of tools increases, companies move to hybrid cloud, hybrid multi-cloud strategies, and so on and so forth. But you know, what we found is that if you go to any company, especially a larger one and you say like, "Hey, how are you doing data orchestration?" They'll probably say something like, "Well, I have five data teams, so I have eight different ways I do data orchestration." Right. This idea of data orchestration's been there but the right way to do it, kind of all the abstractions you need, the way your teams need to work together, and so on and so forth, hasn't really emerged just yet, right? It's such a quick moving space that companies have to combine what they were doing before with what their new business initiatives are today. So, you know, what we really believe here at Astronomer is Airflow is the core of how you solve data orchestration for any sort of use case, but it's not everything. You know, it needs a little more. And, that's really where our commercial product, Astro comes in, where we've built, not only the most tried and tested airflow experience out there. We do employ a majority of the Airflow Core Committers, right? So, we're kind of really deep in the project. We've also built the right things around developer tooling, observability, and reliability for customers to really rely on Astro as the heart of the way they do data orchestration, and kind of think of it as the foundational layer that helps tie together all the different tools, practices and teams large companies have to do today. >> That foundational layer is absolutely critical. You've both mentioned open source software. Paola, I want to go back to you, and just give the audience an understanding of how open source really plays into Astronomer's mission as a company, and into the technologies like Astro. >> Mm-hmm. Yeah, absolutely. I mean, we, so we at Astronomers started using Airflow and actually building our products because Airflow is open source and we were our own customers at the beginning of our company journey. And, I think the open source community is at the core of everything we do. You know, without that open source community and culture, I think, you know, we have less of a business, and so, we're super invested in continuing to cultivate and grow that. And, I think there's a couple sort of concrete ways in which we do this that personally make me really excited to do my own job. You know, for one, we do things like we organize meetups and we sponsor the Airflow Summit and there's these sort of baseline community efforts that I think are really important and that reminds you, hey, there just humans trying to do their jobs and learn and use both our technology and things that are out there and contribute to it. So, making it easier to contribute to Airflow, for example, is another one of our efforts. As Viraj mentioned, we also employ, you know, engineers internally who are on our team whose full-time job is to make the open source project better. Again, regardless of whether or not you're a customer of ours or not, we want to make sure that we continue to cultivate the Airflow project in and of itself. And, we're also building developer tooling that might not be a part of the Apache Open Source project, but is still open source. So, we have repositories in our own sort of GitHub organization, for example, with tools that individual data practitioners, again customers are not, can use to make them be more productive in their day-to-day jobs with Airflow writing Dags for the most common use cases out there. The last thing I'll say is how important I think we've found it to build sort of educational resources and documentation and best practices. Airflow can be complex. It's been around for a long time. There's a lot of really, really rich feature sets. And so, how do we enable folks to actually use those? And that comes in, you know, things like webinars, and best practices, and courses and curriculum that are free and accessible and open to the community are just some of the ways in which I think we're continuing to invest in that open source community over the next year and beyond. >> That's awesome. It sounds like open source is really core, not only to the mission, but really to the heart of the organization. Viraj, I want to go back to you and really try to understand how does Astronomer fit into the wider modern data stack and ecosystem? Like what does that look like for customers? >> Yeah, yeah. So, both in the open source and with our commercial customers, right? Folks everywhere are trying to tie together a huge variety of tools in order to start making sense of their data. And you know, I kind of think of it almost like as like a pyramid, right? At the base level, you need things like data reliability, data, sorry, data freshness, data availability, and so on and so forth, right? You just need your data to be there. (coughs) I'm sorry. You just need your data to be there, and you need to make it predictable when it's going to be there. You need to make sure it's kind of correct at the highest level, some quality checks, and so on and so forth. And oftentimes, that kind of takes the case of ELT or ETL use cases, right? Taking data from somewhere and moving it somewhere else, usually into some sort of analytics destination. And, that's really what businesses can do to just power the core parts of getting insights into how their business is going, right? How much revenue did I had? What's in my pipeline, salesforce, and so on and so forth. Once that kind of base foundation is there and people can get the data they need, how they need it, it really opens up a lot for what customers can do. You know, I think one of the trendier things out there right now is MLOps, and how do companies actually put machine learning into production? Well, when you think about it you kind of have to squint at it, right? Like, machine learning pipelines are really just any other data pipeline. They just have a certain set of needs that might not not be applicable to ELT pipelines. And, when you kind of have a common layer to tie together all the ways data can move through your organization, that's really what we're trying to make it so companies can do. And, that happens in financial services where, you know, we have some customers who take app data coming from their mobile apps, and actually run it through their fraud detection services to make sure that all the activity is not fraudulent. We have customers that will run sports betting models on our platform where they'll take data from a bunch of public APIs around different sporting events that are happening, transform all of that in a way their data scientist can build models with it, and then actually bet on sports based on that output. You know, one of my favorite use cases I like to talk about that we saw in the open source is we had there was one company whose their business was to deliver blood transfusions via drone into remote parts of the world. And, it was really cool because they took all this data from all sorts of places, right? Kind of orchestrated all the aggregation and cleaning and analysis that happened had to happen via airflow and the end product would be a drone being shot out into a real remote part of the world to actually give somebody blood who needed it there. Because it turns out for certain parts of the world, the easiest way to deliver blood to them is via drone and not via some other, some other thing. So, these kind of, all the things people do with the modern data stack is absolutely incredible, right? Like you were saying, every company's trying to be a data-driven company. What really energizes me is knowing that like, for all those best, super great tools out there that power a business, we get to be the connective tissue, or the, almost like the electricity that kind of ropes them all together and makes so people can actually do what they need to do. >> Right. Phenomenal use cases that you just described, Raj. I mean, just the variety alone of what you guys are able to do and impact is so cool. So Paola, when you're with those data engineers, those data scientists, and customer conversations, what's your pitch? Why use Astro? >> Mm-hmm. Yeah, yeah, it's a good question. And honestly, to piggyback off of Viraj, there's so many. I think what keeps me so energized is how mission critical both our product and data orchestration is, and those use cases really are incredible and we work with customers of all shapes and sizes. But, to answer your question, right, so why use Astra? Why use our commercial products? There's so many people using open source, why pay for something more than that? So, you know, the baseline for our business really is that Airflow has grown exponentially over the last five years, and like we said has become an industry standard that we're confident there's a huge opportunity for us as a company and as a team. But, we also strongly believe that being great at running Airflow, you know, doesn't make you a successful company at what you do. What makes you a successful company at what you do is building great products and solving problems and solving pin points of your own customers, right? And, that differentiating value isn't being amazing at running Airflow. That should be our job. And so, we want to abstract those customers from meaning to do things like manage Kubernetes infrastructure that you need to run Airflow, and then hiring someone full-time to go do that. Which can be hard, but again doesn't add differentiating value to your team, or to your product, or to your customers. So, folks to get away from managing that infrastructure sort of a base, a base layer. Folks who are looking for differentiating features that make their team more productive and allows them to spend less time tweaking Airflow configurations and more time working with the data that they're getting from their business. For help, getting, staying up with Airflow releases. There's a ton of, we've actually been pretty quick to come out with new Airflow features and releases, and actually just keeping up with that feature set and working strategically with a partner to help you make the most out of those feature sets is a key part of it. And, really it's, especially if you're an organization who currently is committed to using Airflow, you likely have a lot of Airflow environments across your organization. And, being able to see those Airflow environments in a single place and being able to enable your data practitioners to create Airflow environments with a click of a button, and then use, for example, our command line to develop your Airflow Dags locally and push them up to our product, and use all of the sort of testing and monitoring and observability that we have on top of our product is such a key. It sounds so simple, especially if you use Airflow, but really those things are, you know, baseline value props that we have for the customers that continue to be excited to work with us. And of course, I think we can go beyond that and there's, we have ambitions to add whole, a whole bunch of features and expand into different types of personas. >> Right? >> But really our main value prop is for companies who are committed to Airflow and want to abstract themselves and make use of some of the differentiating features that we now have at Astronomer. >> Got it. Awesome. >> Thank you. One thing, one thing I'll add to that, Paola, and I think you did a good job of saying is because every company's trying to be a data company, companies are at different parts of their journey along that, right? And we want to meet customers where they are, and take them through it to where they want to go. So, on one end you have folks who are like, "Hey, we're just building a data team here. We have a new initiative. We heard about Airflow. How do you help us out?" On the farther end, you know, we have some customers that have been using Airflow for five plus years and they're like, "Hey, this is awesome. We have 10 more teams we want to bring on. How can you help with this? How can we do more stuff in the open source with you? How can we tell our story together?" And, it's all about kind of taking this vast community of data users everywhere, seeing where they're at, and saying like, "Hey, Astro and Airflow can take you to the next place that you want to go." >> Which is incredibly- >> Mm-hmm. >> and you bring up a great point, Viraj, that every company is somewhere in a different place on that journey. And it's, and it's complex. But it sounds to me like a lot of what you're doing is really stripping away a lot of the complexity, really enabling folks to use their data as quickly as possible, so that it's relevant and they can serve up, you know, the right products and services to whoever wants what. Really incredibly important. We're almost out of time, but I'd love to get both of your perspectives on what's next for Astronomer. You give us a a great overview of what the company's doing, the value in it for customers. Paola, from your lens as one of the co-founders, what's next? >> Yeah, I mean, I think we'll continue to, I think cultivate in that open source community. I think we'll continue to build products that are open sourced as part of our ecosystem. I also think that we'll continue to build products that actually make Airflow, and getting started with Airflow, more accessible. So, sort of lowering that barrier to entry to our products, whether that's price wise or infrastructure requirement wise. I think making it easier for folks to get started and get their hands on our product is super important for us this year. And really it's about, I think, you know, for us, it's really about focused execution this year and all of the sort of core principles that we've been talking about. And continuing to invest in all of the things around our product that again, enable teams to use Airflow more effectively and efficiently. >> And that efficiency piece is, everybody needs that. Last question, Viraj, for you. What do you see in terms of the next year for Astronomer and for your role? >> Yeah, you know, I think Paola did a really good job of laying it out. So it's, it's really hard to disagree with her on anything, right? I think executing is definitely the most important thing. My own personal bias on that is I think more than ever it's important to really galvanize the community around airflow. So, we're going to be focusing on that a lot. We want to make it easier for our users to get get our product into their hands, be that open source users or commercial users. And last, but certainly not least, is we're also really excited about Data Lineage and this other open source project in our umbrella called Open Lineage to make it so that there's a standard way for users to get lineage out of different systems that they use. When we think about what's in store for data lineage and needing to audit the way automated decisions are being made. You know, I think that's just such an important thing that companies are really just starting with, and I don't think there's a solution that's emerged that kind of ties it all together. So, we think that as we kind of grow the role of Airflow, right, we can also make it so that we're helping solve, we're helping customers solve their lineage problems all in Astro, which is our kind of the best of both worlds for us. >> Awesome. I can definitely feel and hear the enthusiasm and the passion that you both bring to Astronomer, to your customers, to your team. I love it. We could keep talking more and more, so you're going to have to come back. (laughing) Viraj, Paola, thank you so much for joining me today on this showcase conversation. We really appreciate your insights and all the context that you provided about Astronomer. >> Thank you so much for having us. >> My pleasure. For my guests, I'm Lisa Martin. You're watching this Cube conversation. (soft electronic music)