>> Announcer: From around the globe, it's theCUBE with coverage of KubeCon + CloudNativeCon Europe 2020 Virtual brought to you by Red Hat, the Cloud Native Computing Foundation and Ecosystem partners. >> Welcome back I'm Stu Miniman, this is theCUBE's coverage of KubeCon + CloudNativeCon Europe 2020, the virtual event. Of course, when we talk about Cloud Native we talk about Kubernetes there's a lot that's happening to modernize the infrastructure but a very important thing that we're going to talk about today is also what's happening up the stack, what sits on top of it and some of the new use cases and applications that are enabled by all of this modern environment and for that we're going to talk about artificial intelligence and machine learning or AI and ML as we tend to talk in the industry, so happy to welcome to the program. We have two first time guests joining us from Red Hat. First of all, we have Abhinav Joshi and Tushar Katarki they are both senior managers, part of the OpenShift group. Abhinav is in the product marketing and Tushar is in product management. Abhinav and Tushar thank you so much for joining us. >> Thanks a lot, Stu, we're glad to be here. >> Thanks Stu and glad to be here at KubeCon. >> All right, so Abhinav I mentioned in the intro here, modernization of the infrastructure is awesome but really it's an enabler. We know... I'm an infrastructure person the whole reason we have infrastructure is to be able to drive those applications, interact with my data and the like and of course, AI and ML are exciting a lot going on there but can also be challenging. So, Abhinav if I could start with you bring us inside your customers that you're talking to, what are the challenges, the opportunities? What are they seeing in this space? Maybe what's been holding them back from really unlocking the value that is expected? >> Yup, that's a very good question to kick off the conversation. So what we are seeing as an organization they typically face a lot of challenges when they're trying to build an AI/ML environment, right? And the first one is like a talent shortage. There is a limited amount of the AI, ML expertise in the market and especially the data scientists that are responsible for building out the machine learning and the deep learning models. So yeah, it's hard to find them and to be able to retain them and also other talents like a data engineer or app DevOps folks as well and the lack of talent can actually stall the project. And the second key challenge that we see is the lack of the readily usable data. So the businesses collect a lot of data but they must find the right data and make it ready for the data scientists to be able to build out, to be able to test and train the machine learning models. If you don't have the right kind of data to the predictions that your model is going to do in the real world is only going to be so good. So that becomes a challenge as well, to be able to find and be able to wrangle the right kind of data. And the third key challenge that we see is the lack of the rapid availability of the compute infrastructure, the data and machine learning, and the app dev tools for the various personas like a data scientist or data engineer, the software developers and so on that can also slow down the project, right? Because if all your teams are waiting on the infrastructure and the tooling of their choice to be provisioned on a recurring basis and they don't get it in a timely manner, it can stall the projects. And then the next one is the lack of collaboration. So you have all these kinds of teams that are involved in the AI project, and they have to collaborate with each other because the work one of the team does has a dependency on a different team like say for example, the data scientists are responsible for building the machine learning models and then what they have to do is they have to work with the app dev teams to make sure the models get integrated as part of the app dev processes and ultimately rolled out into the production. So if all these teams are operating in say silos and there is lack of collaboration between the teams, so this can stall the projects as well. And finally, what we see is the data scientists they typically start the machine learning modeling on their individual PCs or laptops and they don't focus on the operational aspects of the solution. So what this means is when the IT teams have to roll all this out into a production kind of deployment, so they get challenged to take all the work that has been done by the individuals and then be able to make sense out of it, be able to make sure that it can be seamlessly brought up in a production environment in a consistent way, be it on-premises, be it in the cloud or be it say at the edge. So these are some of the key challenges that we see that the organizations are facing, as they say try to take the AI projects from pilot to production. >> Well, some of those things seem like repetition of what we've had in the past. Obviously silos have been the bane of IT moving forward and of course, for many years we've been talking about that gap between developers and what's happening in the operation side. So Tushar, help us connect the dots, containers, Kubernetes, the whole DevOps movement. How is this setting us up to actually be successful for solutions like AI and ML? >> Sure Stu I mean, in fact you said it right like in the world of software, in the world of microservices, in the world of app modernization, in the world of DevOps in the past 10, 15 years, but we have seen this evolution revolution happen with containers and Kubernetes driving more DevOps behavior, driving more agile behavior so this in fact is what we are trying to say here can ease up the cable to EIML also. So the various containers, Kubernetes, DevOps and OpenShift for software development is directly applicable for AI projects to make them move agile, to get them into production, to make them more valuable to organization so that they can realize the full potential of AI. We already touched upon a few personas so it's useful to think about who the users are, who the personas are. Abhinav I talked about data scientists these are the people who obviously do the machine learning itself, do the modeling. Then there are data engineers who do the plumbing who provide the essential data. Data is so essential to machine learning and deep learning and so there are data engineers that are app developers who in some ways will then use the output of what the data scientists have produced in terms of models and then incorporate them into services and of course, none of these things are purely cast in stone there's a lot of overlap you could find that data scientists are app developers as well, you'll see some of app developers being data scientist later data engineer. So it's a continuum rather than strict boundaries, but regardless what all of these personas groups of people need or experts need is self service to that preferred tools and compute and storage resources to be productive and then let's not forget the IT, engineering and operations teams that need to make all this happen in an easy, reliable, available manner and something that is really safe and secure. So containers help you, they help you quickly and easily deploy a broad set of machine learning tools, data tools across the cloud, the hybrid cloud from data center to public cloud to the edge in a very consistent way. Teams can therefore alternatively modify, change a shared container images, machine learning models with (indistinct) and track changes. And this could be applicable to both containers as well as to the data by the way and be transparent and transparency helps in collaboration but also it could help with the regulatory reasons later on in the process. And then with containers because of the inherent processes solution, resource control and protection from threat they can also be very secure. Now, Kubernetes takes it to the next level first of all, it forms a cluster of all your compute and data resources, and it helps you to run your containerized tools and whatever you develop on them in a consistent way with access to these shared compute and centralized compute and storage and networking resources from the data center, the edge or the public cloud. They provide things like resource management, workload scheduling, multi-tendency controls so that you can be a proper neighbors if you will, and quota enforcement right? Now that's Kubernetes now if you want to up level it further if you want to enhance what Kubernetes offers then you go into how do you write applications? How do you actually make those models into services? And that's where... and how do you lifecycle them? And that's sort of the power of Helm and for the more Kubernetes operators really comes into the picture and while Helm helps in installing some of this for a complete life cycle experience. A kubernetes operator is the way to go and they simplify the acceleration and deployment and life cycle management from end-to-end of your entire AI, ML tool chain. So all in all organizations therefore you'll see that they need to dial up and define models rapidly just like applications that's how they get ready out of it quickly. There is a lack of collaboration across teams as Abhinav pointed out earlier, as you noticed that has happened still in the world of software also. So we're talking about how do you bring those best practices here to AI, ML. DevOps approaches for machine learning operations or many analysts and others have started calling as MLOps. So how do you kind of bring DevOps to machine learning, and fosters better collaboration between teams, application developers and IT operations and create this feedback loop so that the time to production and the ability to take more machine learning into production and ML-powered applications into production increase is significant. So that's kind of the, where I wanted shine the light on what you were referring to earlier, Stu. >> All right, Abhinav of course one of the good things about OpenShift is you have quite a lot of customers that have deployed the solution over the years, bring us inside some of your customers what are they doing for AI, ML and help us understand really what differentiates OpenShift in the marketplace for this solution set. >> Yeah, absolutely that's a very good question as well and we're seeing a lot of traction in terms of all kinds of industries, right? Be it the financial services like healthcare, automotive, insurance, oil and gas, manufacturing and so on. For a wide variety of use cases and what we are seeing is at the end of the day like all these deployments are focused on helping improve the customer experience, be able to automate the business processes and then be able to help them increase the revenue, serve their customers better, and also be able to save costs. If you go to openshift.com/ai-ml it's got like a lot of customer stories in there but today I will not touch on three of the customers we have in terms of the different industries. The first one is like Royal Bank of Canada. So they are a top global financial institution based out of Canada and they have more than 17 million clients globally. So they recently announced that they build out an AI-powered private cloud platform that was based on OpenShift as well as the NVIDIA DGX AI compute system and this whole solution is actually helping them to transform the customer banking experience by being able to deliver an AI-powered intelligent apps and also at the same time being able to improve the operational efficiency of their organization. And now with this kind of a solution, what they're able to do is they're able to run thousands of simulations and be able to analyze millions of data points in a fraction of time as compared to the solution that they had before. Yeah, so like a lot of great work going on there but now the next one is the ETCA healthcare. So like ETCA is one of the leading healthcare providers in the country and they're based out of the Nashville, Tennessee. And they have more than 184 hospitals as well as more than 2,000 sites of care in the U.S. as well as in the UK. So what they did was they developed a very innovative machine learning power data platform on top of our OpenShift to help save lives. The first use case was to help with the early detection of sepsis like it's a life-threatening condition and then more recently they've been able to use OpenShift in the same kind of stack to be able to roll out the new applications that are powered by machine learning and deep learning let say to help them fight COVID-19. And recently they did a webinar as well that had all the details on the challenges they had like how did they go about it? Like the people, process and technology and then what the outcomes are. And we are proud to be a partner in the solution to help with such a noble cause. And the third example I want to share here is the BMW group and our partner DXC Technology what they've done is they've actually developed a very high performing data-driven data platform, a development platform based on OpenShift to be able to analyze the massive amount of data from the test fleet, the data and the speed of the say to help speed up the autonomous driving initiatives. And what they've also done is they've redesigned the connected drive capability that they have on top of OpenShift that's actually helping them provide various use cases to help improve the customer experience. With the customers and all of the customers are able to leverage a lot of different value-add services directly from within the car, their own cars. And then like last year at the Red Hat Summit they had a keynote as well and then this year at Summit, they were one of the Innovation Award winners. And we have a lot more stories but these are the three that I thought are actually compelling that I should talk about here on theCUBE. >> Yeah Abhinav just a quick follow up for you. One of the things of course we're looking at in 2020 is how has the COVID-19 pandemic, people working from home how has that impacted projects? I have to think that AI and ML are one of those projects that take a little bit longer to deploy, is it something that you see are they accelerating it? Are they putting on pause or are new project kicking off? Anything you can share from customers you're hearing right now as to the impact that they're seeing this year? >> Yeah what we are seeing is that the customers are now even more keen to be able to roll out the digital (indistinct) but we see a lot of customers are now on the accelerated timeline to be able to say complete the AI, ML project. So yeah, it's picking up a lot of momentum and we talk to a lot of analyst as well and they are reporting the same thing as well. But there is the interest that is actually like ramping up on the AI, ML projects like across their customer base. So yeah it's the right time to be looking at the innovation services that it can help improve the customer experience in the new virtual world that we live in now about COVID-19. >> All right, Tushar you mentioned that there's a few projects involved and of course we know at this conference there's a very large ecosystem. Red Hat is a strong contributor to many, many open source projects. Give us a little bit of a view as to in the AI, ML space who's involved, which pieces are important and how Red Hat looks at this entire ecosystem? >> Thank you, Stu so as you know technology partnerships and the power of open is really what is driving the technology world these days in any ways and particularly in the AI ecosystem. And that is mainly because one of the machine learning is in a bootstrap in the past 10 years or so and a lot of that emerging technology to take advantage of the emerging data as well as compute power has been built on the kind of the Linux ecosystem with openness and languages like popular languages like Python, et cetera. And so what you... and of course tons of technology based in Java but the point really here is that the ecosystem plays a big role and open plays a big role and that's kind of Red Hat's best cup of tea, if you will. And that really has plays a leadership role in the open ecosystem so if we take your question and kind of put it into two parts, what is the... what we are doing in the community and then what we are doing in terms of partnerships themselves, commercial partnerships, technology partnerships we'll take it one step at a time. In terms of the community itself, if you step back to the three years, we worked with other vendors and users, including Google and NVIDIA and H2O and other Seldon, et cetera, and both startups and big companies to develop this Kubeflow ecosystem. The Kubeflow is upstream community that is focused on developing MLOps as we talked about earlier end-to-end machine learning on top of Kubernetes. So Kubeflow right now is in 1.0 it happened a few months ago now it's actually at 1.1 you'll see that coupon here and then so that's the Kubeflow community in addition to that we are augmenting that with the Open Data Hub community which is something that extends the capabilities of the Kubeflow community to also add some of the data pipelining stuff and some of the data stuff that I talked about and forms a reference architecture on how to run some of this on top of OpenShift. So the Open Data Hub community also has a great way of including partners from a technology partnership perspective and then tie that with something that I mentioned earlier, which is the idea of Kubernetes operators. Now, if you take a step back as I mentioned earlier, Kubernetes operators help manage the life cycle of the entire application or containerized application including not only the configuration on day one but also day two activities like update and backups, restore et cetera whatever the application needs. Afford proper functioning that a "operator" needs for it to make sure so anyways, the Kubernetes operators ecosystem is also flourishing and we haven't faced that with the OperatorHub.io which is a community marketplace if you will, I don't call it marketplace a community hub because it's just comprised of community operators. So the Open Data Hub actually can take community operators and can show you how to run that on top of OpenShift and manage the life cycle. Now that's the reference architecture. Now, the other aspect of it really is as I mentioned earlier is the commercial aspect of it. It is from a customer point of view, how do I get certified, supported software? And to that extent, what we have is at the top of the... from a user experience point of view, we have certified operators and certified applications from the AI, ML, ISV community in the Red Hat marketplace. And from the Red Hat marketplace is where it becomes easy for end users to easily deploy these ISVs and manage the complete life cycle as I said. Some of the examples of these kinds of ISVs include startups like H2O although H2O is kind of well known in certain sectors PerceptiLabs, Cnvrg, Seldon, Starburst et cetera and then on the other side, we do have other big giants also in this which includes partnerships with NVIDIA, Cloudera et cetera that we have announced, including our also SaaS I got to mention. So anyways these provide... create that rich ecosystem for data scientists to take advantage of. A TEDx Summit back in April, we along with Cloudera, SaaS Anaconda showcased a live demo that shows all these things to working together on top of OpenShift with this operator kind of idea that I talked about. So I welcome people to go and take a look the openshift.com/ai-ml that Abhinav already referenced should have a link to that it take a simple Google search might download if you need some of that, but anyways and the other part of it is really our work with the hardware OEMs right? And so obviously NVIDIA GPUs is obviously hardware, and that accelerations is really important in this world but we are also working with other OEM partners like HP and Dell to produce this accelerated AI platform that turnkey solutions to run your data-- to create this open AI platform for "private cloud" or the data center. The other thing obviously is IBM, IBM Cloud Pak for Data is based on OpenShift that has been around for some time and is seeing very good traction, if you think about a very turnkey solution, IBM Cloud Pak is definitely kind of well ahead in that and then finally Red Hat is about driving innovation in the open-source community. So, as I said earlier, we are doing the Open Data Hub which that reference architecture that showcases a combination of upstream open source projects and all these ISV ecosystems coming together. So I welcome you to take a look at that at opendatahub.io So I think that would be kind of the some total of how we are not only doing open and community building but also doing certifications and providing to our customers that assurance that they can run these tools in production with the help of a rich certified ecosystem. >> And customer is always key to us so that's the other thing that the goal here is to provide our customers with a choice, right? They can go with open source or they can go with a commercial solution as well. So you want to make sure that they get the best in cloud experience on top of our OpenShift and our broader portfolio as well. >> All right great, great note to end on, Abhinav thank you so much and Tushar great to see the maturation in this space, such an important use case. Really appreciate you sharing this with theCUBE and Kubecon community. >> Thank you, Stu. >> Thank you, Stu. >> Okay thank you and thanks a lot and have a great rest of the show. Thanks everyone, stay safe. >> Thanks you and stay with us for a lot more coverage from KubeCon + CloudNativeCon Europe 2020, the virtual edition I'm Stu Miniman and thank you as always for watching theCUBE. (soft upbeat music plays)

>> Announcer: Live from San Diego, California, it's theCUBE. Covering KubeCon and CloudNativeCon brought to you by Red Hat, the Cloud Native Computing Foundation, and its ecosystem partners. >> Welcome back to theCUBE here in San Diego for KubeCon CloudNativeCon, with John Troyer, I'm Stu Miniman, and happy to welcome to the program two guests, first time guests, I believe. Julio Tapia, who's the director of Cloud BU partner and community with Red Hat and Gou Rao, who's the founder and CEO at Portworx. Gentlemen, thanks so much for joining us. >> Thank you, happy to be here. >> Thanks for having us. >> Alright, let's start with community, ecosystem, it's a big theme we have here at the show. Tell us your main focus, what the team's doing here. >> Sure, so I'm part of a product team, we're responsible for OpenShift, OpenStack and Red Hat virtualization. And my responsibility is to build a partner ecosystem and to do our community development. On the partner front, we work with a lot of different partners. We work with ISVs, we work with OEMs, SIs, COD providers, TelCo partners. And my role is to help evangelize, to help on integrations, a lot of joint solutions, and then do a little bit of go to market as well. And the community side, it's to evangelize with upstream projects or customers with developers, and so forth. >> Alright, so, Gou, actually, it's not luck, but I had a chance to catch up with the Red Hat storage team. Back when I was on the vendor side I partnered with them. Red Hat doesn't sell gear, they're a software company. Everything open-source, and when it comes to data and storage, obviously they're working with partners. So put Portworx into the mix and tell us about the relationship and what you both do together. >> Sure, yeah, we're a Red Hat OpenShift partner. We've been working with them for quite some time now, partner with IBM as well. But yeah, Portworx, we focus on enabling cloud native storage, right? So we complement the OpenShift ecosystem. Essentially we enable people to run stateful services in OpenShift with a lot of agility and we bring DR backup functionality to OpenShift. I'm sure you're familiar with this, but, people, when they deploy OpenShift, they're running fleets of OpenShift clusters. So, multi-cluster management and data accessibility across clusters is a big topic. >> Yeah, if you could, I hear the term cloud native storage, what does that really mean? You know, back a few years ago, containers were stateless, I didn't have my persistent storage, it was super challenging as to how we deal with this. And now we have some options, but what is the goal of what we're doing here? >> There really is no notion of a stateless application, right? Especially when it comes to enterprise applications. What cloud native storage means is, to us at least, it signifies a couple of things. First of all, the consumer of storage is not a machine anymore, right? Typical storage systems are designed to provide storage to either a virtual machine or a hardware server. The consumer of storage is now a container that's running inside of a machine. And in fact, an application is never just one container, it's many containers running on different systems so it's a distributed problem. So what cloud native storage means is the following things. Providing container granular data services, being application aware, meaning that you're providing services to many containers that are running on different systems, and facilitating the data life cycle management of those applications from a Kubernetes way, right? The user experience is now driven through Kubernetes as opposed to a storage admin driving that functionality so it's these three things that make a platform cloud native. >> I want to dig into the operator concept for a little bit here, as it applies to storage. So, first, Operators. I first heard of this a couple years back with the CoreOS folks, who are now part of Red Hat and it's a piece of technology that came into the Kubernetes ecosystem, seems to be very well adopted, they talked about it today on the keynote. And I'd love to hear a little bit more about the ecosystem. But first I want to figure out what it is and in my head, I didn't quite understand it and I'm like, well, okay, automation and life cycle, I get it. There's a bunch of things, Puppet and Chef and Ansible and all sorts of things there. There's also things that know about cloud like Terraform, or Cloudform, or Halloumi, all these sort of things here. But this seems like this is a framework around life cycle, it might be a little higher in the semantic level or knows a little bit more about what's going on inside Kubernetes. >> I'll just touch on this, so Operators, it's a way to codify business logic into the application, so how to manage, how to install, how to manage the life cycle of the application on top of the Kubernetes cluster. So it's a way of automating. >> Right, but-- >> And just to add to that, you mentioned Ansible, Salt, right? So, as engineers, we're always trying to make our lives easier. And so, infrastructure automation certainly is a concept here. What Operators does is it elevates those same needs to more of an application construct level, right? So it's a piece of intelligent software that is watching the entire run-time of an application as opposed to provisioning infrastructure and stepping out of the way. Think of it as a living being, it is constantly running and reacting to what the application is doing and what its needs are. So, on one hand you have automation that sets things up and then the job is done. Here the job is never done, you're sort of, right there as a side car along with the application. >> Nice, but for any sort of life cycle or for any sort of project like this, you have to have code sharing and contributing, right? And so, Julio, can you tell us a little about that? >> What we do is we're obviously all in on Operators. And so we've invested a great deal in terms of documentation and training and workshops. We have certification programs, we're really helping create the ecosystem and facilitate the whole process. You may be familiar, we announced Operator Framework a year ago, it includes Operator SDKs. So we have an Operator SDK for Helm, for Ansible, for Go. We also have announced Operator Life Cycle Manager which does the install, the maintenance and the whole life cycle management process. And then earlier this year we did introduce also, Operatorhub.io which is a community of our Operators, we have about 150 Operators as part of that. >> How does the Operator Framework relate to OpenShare versus upstream Kubernetes? Is it an OpenShift and Red Hat specific thing, or? >> Yes, so, Operatorhub.io is a listing of Operators that includes community Operators. And then we also have certified Operators. And the community Operators run on any Kubernetes instance. The certified Operators make sure that we run on OpenShift specifically. So that's kind of the distinction between those two. >> I remember a Red Hat summit where you talked about some bits. So, give us a little walk around the show, some of the highlights from Operators, the ecosystem, obviously, we've got Portworx here but there's a broad ecosystem. >> Yeah, so we have a huge huge ecosystem. The ISVs play a big part of this. So we've got Operators database partners, security partners, app monitoring partners, storage partners. Yesterday we had an OpenShift commons event, we showcased five of our big Operator partnerships with Couchbase, with MongoDB, with Portworx obviously, with StorageOS and with Dynatrace. But we have a lot of partners in a lot of different areas that are creating these Operators, are certifying them, and they're starting to get a lot of use with customers so it's pretty exciting stuff. >> Gou, I'd love your viewpoint on this because of course, Portworx, good Red Hat partner but you need to work with all the Kubernetes opt-ins out there so, what's the importance of Operators to your business? >> Yeah, you know. OpenShift, obviously, it's one of the leading platforms for Kubernetes out there and so, the reason that is, it's because it's the expectations that it sets to an enterprise customer. It's that Red Hat experience behind it and so the notion of having an Operator that's certified by Red Hat and Red Hat going through the vetting process and making sure that all of the components that it is recommending from its ecosystem that you're putting onto OpenShift, that whole process gives a whole new level of enterprise experience, so, for us, that's been really good, right? Working with Red Hat, going through the process with them and making sure that they are actually double clicking on everything we submit, and there's a real, we iterate with them. So the quality of the product that's put out there within OpenShift is very high. So, we've deployed these Operators now, the Operator that Portworx just announced, right? We have it running in customers' hands so these are real end users, you'll be talking to Ford later on today. Harvard, for example, and so the level of automation that it has provided to them in their platform, it's quite high. >> I was kind of curious to shift maybe to the conference here that you all have a long history. With organizations and both of you personally in the Kubernetes world and cloud native world. We're here at KubeCon CloudNativeCon, North America, 2019. It's pretty big. And I see a lot of folks here, a lot of vendors, a lot of engineers, huge conference, 12,000 people. I mean, any perspective? >> So I've been at Red Hat a little over six years and I was at the very first KubeCon many years ago in San Francisco, I think we had about 200 people there. So this show has really grown over the years. And we're obviously big supporters, we've participated in KubeCon in Shanghai and Barcelona, we're obviously here. We're just super excited about seeing the ecosystem and the whole community grow and expand, so, very exciting. >> Gou? >> Yeah, I mean, like Julio mentioned, right? So, all the way from DockerCon to where we are today and I think last year was 8000 people in Seattle and I think there're probably I've heard numbers like 12? So it's also equally interesting to see the maturity of the products around Kubernetes. And that level of consistency and lack of fracture, right? From mainstream Kubernetes to how it's being adopted in OpenShift, there's consistency across the different Kubernetes platforms. Also, it's very interesting to see how on-prem and public cloud Kubernetes are coexisting. Four years ago we were kind of worried on how that would turn out, but I think it's enabling those hybrid-cloud workloads and I think today in this KubeCon we see a lot of people talking about that and having interest around it. >> That's a really great point there. Julio, want to give you the final word, for people that aren't yet engaged in the ecosystem of Operators, how can they learn more and get involved? >> Yeah, so we're excited to work with everybody, our ecosystem includes customers, partners, contributors, so as long as you're all in on Operators, we're ready to help. We've got tools, we've documentation, we have workshops, we have training, we have certification programs. And we also can help you with go to market. We're very fortunate to have a huge customer footprint, and so for those partners that have solutions, databases, storage solutions, there's a lot of joint opportunities out there that we can participate in. So, really excited to do that. >> Julio, Gou, thank you so much, you have a final word, Gou? >> I was just going to say, so, to follow up on the Operator comment on the certification that Julio mentioned earlier, so the Operator that we have, we were able to achieve level five certification. The level five signifies just the amount of automation that's built into it, so the concept of having Operators help people deploy these complex applications, that's a very important concept in Kubernetes itself. So, glad to be a Red Hat partner. >> That's actually a really good point, we have an Operator maturity model, level one, two, three, four, five. Level one and two are more your installations and upgrades. But the really highly capable ones, the fours and fives, are really to be commended. And Portworx is one of those partners. So we're excited to be here with them. >> That is a powerful statement, we talk about the complexity and how many pieces are in there. Everybody's looking to really help cross that chasm, get the vast majority of people. We need to allow environments to have more automation, more simplicity, a story I heard loud and clear at AnsibleFest earlier this year and through the partner ecosystem. It's good to see progress, so congratulations and thank you both for joining us. >> Thank you, thank you. >> Thank you. >> All right, for John Troyer, I'm Stu Miniman, back with lots more here from KubeCon CloudNativeCon 2019, thanks for watching theCUBE. (electronic music)

>> Announcer: Live from Boston, Massachusetts, it's theCUBE. Covering Red Hat Summit 2019. Brought to you by Red Hat. >> Good to have you back here on theCube we are live in Boston at the Convention Center here. Along with Stu Miniman, I'm John Walls and on theCUBE we're continuing our coverage of Red Hat Summit 2019 in Boston, as I said. Joined now by Reza Shafii, who is the VP of Platform Services at Red Hat. Former CoreOS guy >> That's right. >> Stu actually has his CoreOS socks on, >> He told me. >> Today, yeah, so he came dressed for the occasion. >> Shh, can't see those on camera, John. I can't be wearing vendor here. >> Don't show it to the camera. >> Well I just say they're cool! They're cool. Glad to have you with us, Reza. And first off, your impression, you have a big announcement, right, with OpenShift. OpenShift 4 being launched officially on the keynote stage today. That's some big news, right? >> It's a big deal, it's a big deal. The way I think about it is that it's really a culmination of the efforts that we planned out when we sat down between the CoreOS leadership team and the Red Hat leadership team, when the acquisition was closed. And we planned this out, I remember a meeting we had in the white board room. We planned this out. In terms of bringing the best of OpenShift and CoreOS technology together. And it's really great to see it out there on the keynote, and actually all demoed and working. >> And working, right? Key part. >> Reza, dig in for us a little bit here, because it's one thing to say okay, we got a white board and we put things together. You know, when I looked at both companies, at first both, CoreOS before the acquisition and Red Hat, I mean open source, absolutely as its core. I remember talking to the CoreOS team, I'm like, you guys are gonna build a whole bunch of really cool tools, but what's the business there? Do you guys think you're gonna be the next Red Hat? Come on. Well, now you're part of Red Hat. So, give us a little bit of the insight as to what it took to get from there to the announcements, CoreOS infused in many of the pieces that we heard announced this week. >> Yeah, so the way I like to think about it is that Red Hat's OpenShift's roots, it started with making sure that they create a really nice comfortable surface area for the deaf teams. The deaf teams can go in and start pushing the applications and it just ensures that it's running those applications in the right way. The CoreOS roots came from the operations perspective and the system administrator. We always looked at the world from the system administrator. Yes, you're right, CoreOS had a number of technologies they were working on, etcd, Rocket, clair. I used to joke that there's a constellation of open source services that we're working on, but where is the one product? And, towards the end, right before the acquisition, the one product I think was pretty clear is Tectonic, the Kubernetes software. Now, if you look at Tectonic, the key value difference was automated operations. The core tenants of what Alex Polvi and Brandon Philips said into the mindset of the company was we're outnumbered, the number of machines out there is going to be way more than we can handle, therefore we need to automate all operations. They started that on the operating system itself, with CoreOS, the namesake of the company. And then they brought that to Kubernetes. What you see with OpenShift is, OpenShift 4, you see us bringing that to, not only the Kubernetes core, that's the foundation of OpenShift 4, so all capabilities of running Kubernetes are automated with 20 plus operators now. But you see that apply to all the other value capabilities that are on top of OpenShift as well, and we're bringing that to ISV. I was walking around and a number of ISV's have their operators as the number one thing they're advertising. So you're seeing automated operations really take hold and with OpenShift 4 being a foundation for that. >> You talk about operations or operators, you have Operator Hub that was launched earlier this year, what was the driving force behind that? And then ultimately what are you trying to get out of that in terms of advancement and going forward here? >> Right, I think it means it's worked. Going back a little bit of history on this, the operator pattern was coined at CoreOS as a way to do things on a Kubernetes cluster to automate operations. The right way. You have to expose it as a proper API, you have to use a controller, so on and so forth. Then as the team started doing that we realized well there's a lot of demand for this pattern, we started documenting it, describing it better and so on. But then we realized there's a good case for a framework to help people build these automations. Therefore we announced the operator framework at Cubeacon. I think it was a year and a half ago. What happened then was interesting, suddenly we started seeing hundreds plus operators being built on the operator framework. But, it was hard because you could see five Redis operators, 10 MySQL operators. It was hard for our customers to know where can I find the right set of operators that have the right functionality and how do they compare to each other? OperatorHub.IO is a registry that we launched together with AWS, Google and Microsoft to solve for that problem. Now that we have a way to create operators easily and capture that automated operations, we have sort of created a pattern and a framework around it, where do you go to find the right set of operators. >> It's an interesting point because if you look in the container space, especially Kubernetes, it's like, okay well what's standardized, what works across all of these environments? We always worry, I've probably got some pain from previous projects and foundations as to well what's certified and what's not and how do we do that? So, did I see there's a certification now for operators and how do you balance that we need it to work everywhere, we don't wanna have it's Red Hat's building an open ecosystem not something that's limited to only this? >> Yes. So OperatorHub.IO is a community initiative. And, every operator you find on there should work on any Kubernetes. So in fact as part of the vetting process we make sure that that's the case. And then on the certification we launched today, actually, and you can see a number of, we have already 20 plus operators that are certified. This is where we take it a step further and we work with the vendors to make sure that it works on OpenShift. It's following a number of guidelines that we have, in terms of using, for example, Rail as the basis. They work with us to run the updates through security checks and so on. And that's just to give our enterprise customers more levels of guarantees and validation, if they would like to. >> So what are they getting out of that, out of the certification system? What, I guess, stability and certainty and all those kinds of things that I'm looking for, standardization of some kind, is that what's driving that? >> It's simple, at the end of the day they got three things. They get automated updates that are pushed through the OpenShift update mechanism. So if you are using the Redis one, for example, and it's certified, you're gonna be able to update the Redis operator through the same cluster administration mechanism, then you would apply it to the entire cluster itself. You see updates from Redis come in, you can put it through the same approval work so on, so on. The second is they get support. So they get first line of support from Red Hat. They can call Red Hat, our customers and actually we work with them on that. And the third is that they actually get that security vulnerability scans that we put them through to make sure that they pass certain checks. And actually one last one, they also get Rail as the basis of the operator, so, yup. >> Reza, help bring us into the customer point of view. What does all this mean to them, what are the big challenges, how do they modernize their applications and get more applications moving along this path? >> Yeah, in this case the operator customer is mainly the infrastructure administrators. It's important to point that out. The developers will get some benefit on that in that it's self service, so the provision, but there's other ways to do that as well. You can go to a Helm chart, deploy that Helm chart, you get that level of self service automated provisioning. To go ahead and configure for example, a charted MongoDB database on a Kubernetes cluster, you have to create something like 20 different objects. And then to update that to change the charts, you have to go and modify all those 20 different objects. Let's just stay at that level alone. An operator makes that before different parameters on a yaml file that you change. The operator takes that and applies all these configurations for you. So, it's all about simplifying the life of the infrastructure administrators. I truly believe that operators, human operators, infrastructure administrators are one of the least appreciated personas right now that we have out there. They're not the most important ones, but there is a lot of pain points and challenges that they have we're not really thinking about too much. And I think OpenShift goes a long way and operators go a long way to actually start thinking about their pain point as well. >> So what do you think their reaction was this morning when they're looking, first off, the general announcement, right? And then some of the demonstrations and all those things that are occurring? Is there, do you have or are you talking to customers? Are you getting the sense of relief or of anticipation or expectation? I mean, how would you characterize that? >> Think they're falling into a couple of different buckets. There's the customers we've talked to, for awhile now, that know this stuff, so this is not super new to them, but they're very happy to see it. There's one big automaker that's a customer of us and the main human operator was telling me awhile ago that he does not want any service on the cluster unless it has an operator, this is a year and a half ago. And he kept pushing me well I want a Kafka one and I want an Elasticsearch one, and you know. And we, CoreOS, were too small to try to build that ourselves. Obviously that's not, we can't maintain a Kafka operator and a CoreOS one. Now, he's able to go to our operator APP, he's gonna be able to get a Kafka operator that's maintained by Kafka experts. He's gonna be able to get a Redis operator that's maintained by Redis experts. So that bucket of customers are super happy. And then there's another one that's just starting to understand the power of all this. And I think they're just starting to kick the tires and play around with this. Hopefully they will get to the same point as the first bucket of customers, and be asking for everything to be operator based all the time. >> Convert the tire kickers, you're gonna be okay, right? >> That's right. >> Thank you for the time. >> Thank you. >> We appreciate that and continued success at Red Hat, and, once again, good to see you. >> Thank you, always a pleasure. >> You bet. Live, here on theCUBE, you're watching Red Hat Summit 2019. (upbeat music)