>> Instructor: "theCUBE" presents KubeCon and CloudNativeCon Europe 2022, brought to you by Red Hat, the Cloud Native Computing Foundation and its ecosystem partners. >> Welcome to Valencia, Spain and we're at KubeCon, CloudNativeCon Europe 2022. I'm Keith Townsend, and my co-host, Enrico Signoretti. Enrico's really proud of me. I've called him Enrico instead of Enrique every session. >> Every day. >> Senior IT analyst at GigaOm. We're talking to fantastic builders at KubeCon, CloudNativeCon Europe 2022 about the projects and their efforts. Enrico, up to this point, it's been all about provisioning, insecurity, what conversation have we been missing? >> Well, I mean, I think that we passed the point of having the conversation of deployment, of provisioning. Everybody's very skilled, actually everything is done at day two. They are discovering that, well, there is a security problem. There is an observability problem a and in fact, we are meeting with a lot of people and there are a lot of conversation with people really needing to understand what is happening. I mean, in their cluster work, why it is happening and all the questions that come with it. And the more I talk with people in the show floor here or even in the various sessions is about, we are growing so that our clusters are becoming bigger and bigger, applications are becoming bigger as well. So we need to now understand better what is happening. As it's not only about cost, it's about everything at the end. >> So I think that's a great set up for our guests, Matt Provo, founder and CEO of StormForge and Patrick Brixton? >> Bergstrom. >> Bergstrom. >> Yeah. >> I spelled it right, I didn't say it right, Bergstrom, CTO. We're at KubeCon, CloudNativeCon where projects are discussed, built and StormForge, I've heard the pitch before, so forgive me. And I'm kind of torn. I have service mesh. What do I need more, like what problem is StormForge solving? >> You want to take it? >> Sure, absolutely. So it's interesting because, my background is in the enterprise, right? I was an executive at UnitedHealth Group before that I worked at Best Buy and one of the issues that we always had was, especially as you migrate to the cloud, it seems like the CPU dial or the memory dial is your reliability dial. So it's like, oh, I just turned that all the way to the right and everything's hunky-dory, right? But then we run into the issue like you and I were just talking about, where it gets very very expensive very quickly. And so my first conversations with Matt and the StormForge group, and they were telling me about the product and what we're dealing with. I said, that is the problem statement that I have always struggled with and I wish this existed 10 years ago when I was dealing with EC2 costs, right? And now with Kubernetes, it's the same thing. It's so easy to provision. So realistically what it is, is we take your raw telemetry data and we essentially monitor the performance of your application, and then we can tell you using our machine learning algorithms, the exact configuration that you should be using for your application to achieve the results that you're looking for without over-provisioning. So we reduce your consumption of CPU, of memory and production which ultimately nine times out of 10, actually I would say 10 out of 10, reduces your cost significantly without sacrificing reliability. >> So can your solution also help to optimize the application in the long run? Because, yes, of course-- >> Yep. >> The lowering fluid as you know optimize the deployment. >> Yeah. >> But actually the long-term is optimizing the application. >> Yes. >> Which is the real problem. >> Yep. >> So, we're fine with the former of what you just said, but we exist to do the latter. And so, we're squarely and completely focused at the application layer. As long as you can track or understand the metrics you care about for your application, we can optimize against it. We love that we don't know your application, we don't know what the SLA and SLO requirements are for your app, you do, and so, in our world it's about empowering the developer into the process, not automating them out of it and I think sometimes AI and machine learning sort of gets a bad rap from that standpoint. And so, at this point the company's been around since 2016, kind of from the very early days of Kubernetes, we've always been, squarely focused on Kubernetes, using our core machine learning engine to optimize metrics at the application layer that people care about and need to go after. And the truth of the matter is today and over time, setting a cluster up on Kubernetes has largely been solved. And yet the promise of Kubernetes around portability and flexibility, downstream when you operationalize, the complexity smacks you in the face and that's where StormForge comes in. And so we're a vertical, kind of vertically oriented solution, that's absolutely focused on solving that problem. >> Well, I don't want to play, actually. I want to play the devils advocate here and-- >> You wouldn't be a good analyst if you didn't. >> So the problem is when you talk with clients, users, there are many of them still working with Java, something that is really tough. I mean, all of us loved Java. >> Yeah, absolutely. >> Maybe 20 years ago. Yeah, but not anymore, but still they have developers, they have porting applications, microservices. Yes, but not very optimized, et cetera, cetera, et cetera. So it's becoming tough. So how you can interact with this kind of old hybrid or anyway, not well engineered applications. >> Yeah. >> We do that today. We actually, part of our platform is we offer performance testing in a lower environment and stage and we, like Matt was saying, we can use any metric that you care about and we can work with any configuration for that application. So perfect example is Java, you have to worry about your heap size, your garbage collection tuning and one of the things that really struck me very early on about the StormForge product is because it is true machine learning. You remove the human bias from that. So like a lot of what I did in the past, especially around SRE and performance tuning, we were only as good as our humans were because of what they knew. And so, we kind of got stuck in these paths of making the same configuration adjustments, making the same changes to the application, hoping for different results. But then when you apply machine learning capability to that the machine will recommend things you never would've dreamed of. And you get amazing results out of that. >> So both me and Enrico have been doing this for a long time. Like, I have battled to my last breath the argument when it's a bare metal or a VM, look, I cannot give you any more memory. >> Yeah. >> And the argument going all the way up to the CIO and the CIO basically saying, you know what, Keith you're cheap, my developer resources are expensive, buy bigger box. >> Yeah. >> Yap. >> Buying a bigger box in the cloud to your point is no longer a option because it's just expensive. >> Yeah. >> Talk to me about the carrot or the stick as developers are realizing that they have to be more responsible. Where's the culture change coming from? Is it the shift in responsibility? >> I think the center of the bullseye for us is within those sets of decisions, not in a static way, but in an ongoing way, especially as the development of applications becomes more and more rapid and the management of them. Our charge and our belief wholeheartedly is that you shouldn't have to choose. You should not have to choose between costs or performance. You should not have to choose where your applications live, in a public private or hybrid cloud environment. And so, we want to empower people to be able to sit in the middle of all of that chaos and for those trade offs and those difficult interactions to no longer be a thing. We're at a place now where we've done hundreds of deployments and never once have we met a developer who said, "I'm really excited to get out of bed and come to work every day and manually tune my application." One side, secondly, we've never met, a manager or someone with budget that said, please don't increase the value of my investment that I've made to lift and shift us over to the cloud or to Kubernetes or some combination of both. And so what we're seeing is the converging of these groups, their happy place is the lack of needing to be able to make those trade offs, and that's been exciting for us. >> So, I'm listening and looks like that your solution is right in the middle in application performance, management, observability. >> Yeah. >> And, monitoring. >> Yeah. >> So it's a little bit of all of this. >> Yeah, so we want to be, the intel inside of all of that, we often get lumped into one of those categories, it used to be APM a lot, we sometimes get, are you observability or and we're really not any of those things, in and of themselves, but we instead we've invested in deep integrations and partnerships with a lot of that tooling 'cause in a lot of ways, the tool chain is hardening in a cloud native and in Kubernetes world. And so, integrating in intelligently, staying focused and great at what we solve for, but then seamlessly partnering and not requiring switching for our users who have already invested likely, in a APM or observability. >> So to go a little bit deeper. What does it mean integration? I mean, do you provide data to this, other applications in the environment or are they supporting you in the work that you do. >> Yeah, we're a data consumer for the most part. In fact, one of our big taglines is take your observability and turn it into action ability, right? Like how do you take that, it's one thing to collect all of the data, but then how do you know what to do with it, right? So to Matt's point, we integrate with folks like Datadog, we integrate with Prometheus today. So we want to collect that telemetry data and then do something useful with it for you. >> But also we want Datadog customers, for example, we have a very close partnership with Datadog so that in your existing Datadog dashboard, now you have-- >> Yeah. >> The StormForge capability showing up in the same location. >> Yep. >> And so you don't have to switch out. >> So I was just going to ask, is it a push pull? What is the developer experience when you say you provide developer this resolve ML learnings about performance, how do they receive it? Like, what's the developer experience. >> They can receive it, for a while we were CLI only, like any good developer tool. >> Right. >> And, we have our own UI. And so it is a push in a lot of cases where I can come to one spot, I've got my applications and every time I'm going to release or plan for a release or I have released and I want to pull in observability data from a production standpoint, I can visualize all of that within the StormForge UI and platform, make decisions, we allow you to set your, kind of comfort level of automation that you're okay with. You can be completely set and forget or you can be somewhere along that spectrum and you can say, as long as it's within, these thresholds, go ahead and release the application or go ahead and apply the configuration. But we also allow you to experience the same, a lot of the same functionality right now, in Grafana, in Datadog and a bunch of others that are coming. >> So I've talked to Tim Crawford who talks to a lot of CIOs and he's saying one of the biggest challenges or if not, one of the biggest challenges CIOs are facing are resource constraints. >> Yeah. >> They cannot find the developers to begin with to get this feedback. How are you hoping to address this biggest pain point for CIOs-- >> Yeah.6 >> And developers? >> You should take that one. >> Yeah, absolutely. So like my background, like I said at UnitedHealth Group, right. It's not always just about cost savings. In fact, the way that I look about at some of these tech challenges, especially when we talk about scalability there's kind of three pillars that I consider, right? There's the tech scalability, how am I solving those challenges? There's the financial piece 'cause you can only throw money at a problem for so long and it's the same thing with the human piece. I can only find so many bodies and right now that pool is very small, and so, we are absolutely squarely in that footprint of we enable your team to focus on the things that they matter, not manual tuning like Matt said. And then there are other resource constraints that I think that a lot of folks don't talk about too. Like, you were talking about private cloud for instance and so having a physical data center, I've worked with physical data centers that companies I've worked for have owned where it is literally full, wall to wall. You can't rack any more servers in it, and so their biggest option is, well, I could spend $1.2 billion to build a new one if I wanted to, or if you had a capability to truly optimize your compute to what you needed and free up 30% of your capacity of that data center. So you can deploy additional name spaces into your cluster, like that's a huge opportunity. >> So I have another question. I mean, maybe it doesn't sound very intelligent at this point, but, so is it an ongoing process or is it something that you do at the very beginning, I mean you start deploying this. >> Yeah. >> And maybe as a service. >> Yep. >> Once in a year I say, okay, let's do it again and see if something change it. >> Sure. >> So one spot, one single.. >> Yeah, would you recommend somebody performance test just once a year? Like, so that's my thing is, at previous roles, my role was to do performance test every single release, and that was at a minimum once a week and if your thing did not get faster, you had to have an executive exception to get it into production and that's the space that we want to live in as well as part of your CICD process, like this should be continuous verification, every time you deploy, we want to make sure that we're recommending the perfect configuration for your application in the name space that you're deploying into. >> And I would be as bold as to say that we believe that we can be a part of adding, actually adding a step in the CICD process that's connected to optimization and that no application should be released, monitored, and sort of analyzed on an ongoing basis without optimization being a part of that. And again, not just from a cost perspective, but for cost and performance. >> Almost a couple of hundred vendors on this floor. You mentioned some of the big ones Datadog, et cetera, but what happens when one of the up and comings out of nowhere, completely new data structure, some imaginative way to click to telemetry data. >> Yeah. >> How do, how do you react to that? >> Yeah, to us it's zeros and ones. >> Yeah. >> And, we really are data agnostic from the standpoint of, we're fortunate enough from the design of our algorithm standpoint, it doesn't get caught up on data structure issues, as long as you can capture it and make it available through one of a series of inputs, one would be load or performance tests, could be telemetry, could be observability, if we have access to it. Honestly, the messier the better from time to time from a machine learning standpoint, it's pretty powerful to see. We've never had a deployment where we saved less than 30%, while also improving performance by at least 10%. But the typical results for us are 40 to 60% savings and 30 to 40% improvement in performance. >> And what happens if the application is, I mean, yes Kubernetes is the best thing of the world but sometimes we have to, external data sources or, we have to connect with external services anyway. >> Yeah. >> So, can you provide an indication also on this particular application, like, where the problem could be? >> Yeah. >> Yeah, and that's absolutely one of the things that we look at too, 'cause it's, especially when you talk about resource consumption it's never a flat line, right? Like depending on your application, depending on the workloads that you're running it varies from sometimes minute to minute, day to day, or it could be week to week even. And so, especially with some of the products that we have coming out with what we want to do, integrating heavily with the HPA and being able to handle some of those bumps and not necessarily bumps, but bursts and being able to do it in a way that's intelligent so that we can make sure that, like I said, it's the perfect configuration for the application regardless of the time of day that you're operating in or what your traffic patterns look like, or, what your disc looks like, right. Like 'cause with our low environment testing, any metric you throw at us, we can optimize for. >> So Matt and Patrick, thank you for stopping by. >> Yeah. >> Yes. >> We can go all day because day two is I think the biggest challenge right now, not just in Kubernetes but application re-platforming and transformation, very, very difficult. Most CTOs and EASs that I talked to, this is the challenge space. From Valencia, Spain, I'm Keith Townsend, along with my host Enrico Signoretti and you're watching "theCube" the leader in high-tech coverage. (whimsical music)

>> (Narrator) Live, from Boston, Massachusetts it's theCUBE, covering Red Hat Summit 2017, brought to you by Red Hat. >> Welcome back to Boston everybody, this is Red Hat Summit, and this is theCUBE, the leader in live tech coverage. I'm Dave Vellante, with my cohost Stu Miniman, and John Hodgson is here, he's the Senior Director of IT Program Management at Optum technology. John good to see ya. >> Good, it's good to be here. >> Fresh off the keynote, we were just talking about the large audience, a very large audience here. And Optum, you described a little bit at the keynote what Optum is with healthcare, sort of technology arm. Which is not super common but not uncommon in your world. But describe Optum and where it fits. >> So in the grand scheme of things within UnitedHealth Group you know, we have the parent company, of course, you know the Health Group, our insurance side, that does insurance, whether it's public sector for large corporations, as well as community and state government type work as UnitedHealthcare. They do all that, and then Optum is our technology side. We do really all the development, both for supporting UHC as our main customer, you know, they're truly our focus, but we also do a lot of commercial development as well for UnitedHealthcare's competitors. So big, big group, as I mentioned in the keynote. Over 10,000 developers in the company, lots of spend, I think in the last year our, just internal IT budget was like $1.2 billion in just IT development capital. So it's huge. >> Dave: Mind-boggling. >> John, you've got that internal Optum Cloud, Can you give us just kind of the breadth and depth, you said 1.2 billion, there. What is that make up, what geographies does that span, how many people support that kind of environment? >> As far as numbers of people supporting it, I think we've got a few hundred in our Enterprise Technology Services Group, that supports Optum Cloud. We started Optum Cloud probably a half a dozen years ago, and it's gone through its different iterations. And part of my job right now is all about Enterprise Cloud adoption and migration. So, we started with our own environment, we call it UCI, United, it was supposed to be Converged Infrastructure, but I call it our Cloud Infrastructure, that's really what it is. And we've continued to enhance that. So over the last few years, I think about 3.5, four years ago, we brought in Red Hat and OpenShift. We're on our third iteration of OpenShift. Very, very stable platform for us now. But we also have Azure Stack in there as well, I think even as Paul and those guys mentioned in the keynote there's a lot of different things that you can kind of pull from each one of the technology providers to help support what we're doing, kind of take the best of breed from each one of them, and use them in each solution. >> Organizations are always complaining that they spend all this money on keeping the lights on, and they're trying to make the shift, and obviously Cloud helps them do that, and things like OpenShift, etc. What's that like in your world? How much of your effort is spent on maintenance and keeping the lights on? Sounds like you got a lot of cool, new development activity. Can you describe that dynamic for us? >> Yeah, we've got a really good support staff. Our group, SSMO, when we build an application, they kind of take it back over and run everything. We've got a fabulous support team in the background. And to that end, and it's on both sides, right? We have our UnitedHealthcare applications that we build that have kind of their own feature set, because of what it's doing internally for us, versus what we do on the OptumInsight side, where it's more commercial in nature. So they have some different needs. Some of the things that we're developing, even for Cloud Scaffolding that I mentioned in the keynote. We're kind of working on both sides of the fence, there, to hit the different technologies that each one of them really need to be successful, but doing it in a way that it doesn't if you're on one side of the fence or the other, it's a capability that everybody will be able to use. So if there's a pattern on one side that you want to be able to use for a UHC application, by all means, go ahead and grab it, take it. And a lot of what we're doing now is even kind of crowdsourcing things, and utilizing the really super intelligent people that we have, over 10,000 developers. And so many of them, we've got a lot of legacy stuff. So there's some old-school guys that are still doing their thing, but we've got a lot of new people. And they want to get their hands on the new fresh stuff, and experience that. So there's really a good vibe going on right now, with how things are changing, all the TDP folks that we're bringing in. A lot of fresh college grads and things. And they love to see the new technologies, whether it's OpenShift or whatever. Lot are really getting into DevOps, trying to make that change in a big organization is difficult, we got a little ways to go with that. But that's kind of next up. >> You're an interesting case study, because you've got a lot of the old and a lot of cool innovation going on. And is it, how do you decide when to go, because DevOps is not always the answer. Sometimes waterfall is okay, you know. So, how do you make that determination, and where do you see that going? >> That's a great question, that's actually part of what my team does. So my specific team is all about Cloud adoption and migration, so our charter is really to work across the enterprise. So whether it's OptumInsight, OptumRx, UnitedHealthcare, we are working with them to evaluate their portfolios of applications to figure out legacy applications that we have that are still strategic. They've got life in them, they've got business benefit. And we want to be able to take advantage of that, but at the same time there's some of these monolithic applications that we look at how can we take that application, decompose it down into microservices and APIs, things like that, to make it available to other applications that maybe are just greenfield, are coming out now, but still need that same technology and information. So that's really what my team is doing right now. So we sit down with those teams and go through an analysis, help them develop a road map. And sometimes that road map is two or three years long. Getting to fully cloud from where they're at right now in some of these legacy applications is a journey. And it costs money, right? There's a lot of budget concerns and things like that that go with it. So that's part of what we helped develop is a business case for each one of those applications that we can help support them going back, and getting the necessary capital to do the cloud migrations and the improvements, and really the modernization of their applications. We started the program a couple of years ago and found that if you want to hang your hat on just going from old physical infrastructure, some of the original VMs that we had. And just moving over to cloud infrastructure, and whether that's UCI, OpenShift, Azure, whatever. If you're going to do your business case on that, you're going to be writing a lot of business cases before you get one approved. It's all about modernizing the applications. So if you fold in the move to new infrastructure, cloud infrastructure, along with the ability to modernize that application, get them doing agile development, getting down the DevOps path, looking at automated testing, automated deployment, zero downtime deployments. All of those things, when you add them up together and say, okay, here's what your real benefit looks like. And you're able to present that back to the business, and show them speed to market, speed to value is a new metric that we have. Getting things out there quickly. We used to do quarterly releases, or even biannual releases. And now we're at monthly, weekly, some of our applications that are more relatively new, Health4Me, if you go to the App Store, that's kind of our big app on the App Store. There's updates on a very frequent basis. >> So that's the operating model, really, that you're talking about, essentially, driving business value. We had a practitioner on a couple weeks ago, and he said, "If you just lift and shift to the cloud, "and you don't change your operating model, "you won't get a dime." >> Stu: You're missing the boat. >> Maybe there's something, some value there, a little faster, but you're talking about serious dollars, if you can change the operating model. And that's what you've found? >> Yeah absolutely, and that's the, it's a shift, and you've got to be able to prove it to the business that's there's benefit there, and sometimes that's hard. Some of these cloud concepts and things are a little nebulous, so-- >> It's hard 'cause it's soft. >> It's soft, right, yeah, I mean, you're putting the business case together, the hard stuff is easy to document, but when you're talking about the soft benefits, and you're trying to explain to them the value that they're going to get out of their team switching from a waterfall development over to agile and DevOps, and automated testing and things like that, where I can say, "Hey listen, "you know your team over here that has been, "you know we took them out of the pocket, "from actually doing their day jobs for the last week, "because they needed to test this new version? "If I can take that out of the mix, "and they don't have to do that anymore, "and they can keep on doing what they're doing "and not get a week behind, what value is that for you?" And all of a sudden they're like, "Oh really? "We don't have to do that anymore?" I'm like, "No, we can create test scripts and stuff. "We can automate your deployment. "We can make it zero downtime. "We have," there's an application that we're working on now that has 19,000 individual desktop deployments. And we're going to automate that, turn it into a software as a service application, host it on OpenShift, and completely knock that out. I mean deployments out to 19,000 people take weeks to get done. We only do a couple thousand a week, because there's obviously going to be issues. So now you've got helpdesk tickets, you've got desktop technicians that are going round, trying to fix things, or dialing in, remoting into somebody's desktop to try to help figure that all out. We can do the whole deployment in a day, and everybody logs in the next day, and they've got the new version. That kind of value in creating real cloud-based applications is what's driving the benefit for us. And they're finally starting to really see that. And as we're doing it, more application product owners are going, "Okay, now we're getting some traction. "We heard what you did over here. "Come talk to us, and let's talk "about building a road map and figuring out what we can do." >> John, one of the questions I got from the community after watching you keynote was, they want to understand how you handle security and enforce compliance in this new cloud development model. (laughs) >> That's beyond me, all I can tell you is that we have one of the most secure clouds out there. Our private cloud is beyond secure. We're working right now to try to get the public hybrid cloud space with both AWS and Azure, and working through contracts and stuff right now. But one of the sticking points is our security has to be absolutely top notch, if we're going to do anything that has HIPAA-related data, PHI, PII, PCI, any of that, it has got to be lock-solid secure. And we have a tremendous team led by Robert Booker, he's absolutely fabulous, I mean we're, our whole goal, security-wise, is don't be the next guy on the front page of the Wall Street Journal. >> You mentioned public cloud, how do you make your decisions as to what application, what data can live in which public cloud? You said you've got Azure Stack, and you've got OpenShift. How do you make those platform decisions? >> Well right now, both OpenShift and Azure Stack are on our internal private cloud. So we're in the process of kind of making that shift to move over towards public and hybrid cloud. So I'm working with folks on our team to help develop some of those processes and determine what's actually going to be allowed. And I think in a lot of cases the PHI and protected data is going to stay internal. And we'll be able to take advantage of hosting certain parts of an application on public cloud while keeping other parts of the data really secure and protected behind our private cloud. >> Red Hat made an announcement this morning with AWS, with OpenShift. >> Sounds like that might be of interest to you, would that impact what your doing? >> Absolutely, yeah, in fact I was talking with Jim and Paul back behind the screen this morning. And we were talking about that and I was like wow that is a game changer. With what we're thinking about doing in the hybrid cloud space, having all of the AWS APIs and services and stuff available to us. Part of the objection that I get from some folks now is knowing that we have this move toward public and hybrid cloud internally, and the limitations of our cloud. We're never going to be, our private Optum Cloud is never going to be AWS or Azure, it's just not. I mean they've spent billions of dollars getting those services and stuff in place. Why would we even bother to compete with that? So we do what we do well, and a big portion of that is security. But we want to be able to expand, and take advantage of the things that they have. So that's, this whole announcement of being able to take advantage of those services natively within OpenShift? If we're able to expose that, even internally, on our own private cloud? That's going to take away a lot of the objections, I think, from even our own folks, who are waiting to do the public hybrid cloud piece. >> When the Affordable Care Act hit, did your volume spike? And as things, there's a tug of war now in Washington, it could change again, does that drive changes in your application development in terms of the volume of requests that come in, and compliance things that you have to adhere to? And if so, does having a platform that's more agile, how does that affect your ability to respond? >> Yeah it does, I mean when we first got into the ACA, there was a number of markets that we got into. And there was definitely a ramp-up in development, new things that we had to do on the exchanges. Stuff like that. I mean we even had groups from Optum that were participating directly with the federal government, because some of their exchanges were having issues, and they needed some help from us. So we had a whole team that was kind of embedded with the federal government, helping them out, just based on our experience doing it. And, yeah, having the flexibility, in our own cloud, to be able to able to spin up environments quickly, shut them down, all that, really it's invaluable. >> So the technology business moves so fast, I mean it wasn't that long ago when people saw the first virtualized servers and went Oh my gosh, this is going to change the world. And now it's like, wow we got to do better, and containers. And so you've gone for this amazing transformation, I mean, I think it was 17 developers to 1,600, which is just mind-boggling. Okay, and that's, and you've got technologies that have helped you do that, but five years down the road there's going to be a what's next. So what is next for you? As you break out your telescope, what do you see? >> God, I don't know, I mean I never would have predicted containers. >> Even though they've been around forever, we-- >> Yeah I mean when we first went to VMs, you know back in the day I was a guy in the server room, racking and stacking servers and running cables, and doing all that, so I've seen it go from one extreme to the next. And going from VMs was a huge switch. Building our own private cloud was amazing to be a part of, and now getting into the container side of things, hybrid cloud, I think for us, really, the next big step for us is the hybrid cloud. So we're in the process of getting that, I assume by the end of this year, early next, we'll be a few steps into the hybrid cloud space. And then beyond that, gosh I don't know. >> So that's really extending the operating model into that hybrid cloud notion, bringing that security that you talked about, and that's, you got a lot of work to do. >> John: That's a big task in itself. >> Let's not go too far beyond that, John. Alright well listen, thanks for coming on theCUBE, it was really a pleasure having you. >> Yeah, thanks for having me guys, appreciate it. >> You're welcome, alright keep it right there everybody, Stu and I will be back with our next guest. This is theCUBE, we're live from Red Hat Summit in Boston. We'll be right back. (electronic music)

>> Narrator: Live from Boston, Massachusetts, this is theCUBE, covering Spark Summit East 2017. Brought to you by Databricks. Now, here are your hosts Dave Vellante and George Gilbert. >> Welcome back to Boston, everybody, this is theCUBE, the leader in live tech coverage. We're here in Boston, in snowy Boston. This is Spark Summit. Spark Summit does a East Coast version, they do a West Coast version, they've got one in Europe this year. theCUBE has been a partner with Databricks as the live broadcast partner. Our friend Bill Peterson is here. He's the head of partner marketing at MapR. Bill, good to see you again. >> Thank you, thanks for having me. >> So how's the show going for you? >> It's great. >> Give us the vibe. We're kind of windin' down day two. >> It is. The show's been great, we've got a lot of traffic coming by, a lot of deep technical questions which is-- >> Dave: Hardcore at the show-- >> It is, it is. I spend a lot of time there smiling and going, "Yeah, talk to him." (laughs) But it's great. We're getting those deep technical questions and it's great. We actually just got one on Lustre, which I had to think for a minute, oh, HPC. It was like way back in there. >> Dave: You know, Cray's on the floor. >> Oh, yeah that's true. But a lot of our customers as well. UnitedHealth Group, Wells Fargo, AMEX coming by. Which is great to see them and talk to them, but also they've got some deep technical questions for us. So it's moving the needle with existing customers but also new business, which is great. >> So I got to ask a basic question. What is MapR? MapR started in the early days of Hadoop distro, vendor, one of the big three. When somebody says to you what is MapR, what do you say? My answer today is MapR is an enterprise software company that delivers a converged data platform. That converged data platform consists of a file system, a NoSQL database, a Hadoop distribution, a Spark distribution, and a set of data management tools. And as a customer of MapR, you get all of those. You can turn 'em all on if you'd like. You can just turn on the file system, for example, if you wanted to just use the file system for storage. But the enterprise software piece of that is all the hardening we do behind the scenes on things like snapshots, mirroring, data governance, multi-tenancy, ease of use performance, all of that baked in to the solution, or the platform as we're calling it now. So as you're kind of alluding to, a year ago now we kind of got out of that business of saying okay, lead 100% with Hadoop and then while we have your attention, or if we don't, hey wait, we got all this other stuff in the basket we want to show you, we went the platform play and said we're going to include everything and it's all there and then the baseline underneath is the hardening of it, the file system, the database, and the streaming product, actually, which I didn't mention, which is kind of the core, and everything plays off of there. And that honestly has been really well-received. And it just, I feel, makes it so much easier because-- It happened here, we get the question, okay, how are you different from Cloudera or Hortonworks? And some of it here, given the nature of the attendees, is very technical, but there's been a couple of business users that I've talked to. And when I talk about us as an enterprise software company delivering a plethora of solutions versus just Hadoop, you can see the light going on sometimes in people's eyes. And I got it today, earlier, "I had no idea you had a file system," which, to me, just drives me insane because the file system is pretty cool, right? >> Well you guys are early on in investing in that file system and recovery capabilities and all the-- >> Two years in stealth writing it. >> Nasty, gnarly, hard stuff that was kind of poo-pooed early on. >> Yeah, yeah. MapR was never patient about waiting for the open source community to just figure it out and catch up. You always just said all right, we're going to solve this problem and go sell. >> And I'm glad you said that. I want to be clear. We're not giving up on open source or anything, right? Open source is still a big piece. 50% of our engineers' time is working on open source projects. That's still super important to us. And then back in November-ish last year we announced the MapR Ecosystem Packs, which is our effort to help our customers that are using open source components to stay current. 'Cause that's a pain in the butt. So this is a set of packages that have a whole bunch of components. We lead with Spark and Drill, and that was by customer request, that they were having a hard time keeping current with Spark and Drill. So the packs allow them to come up to current level within the converged data platform for all of their open source components. And that's something we're going to do at dot Level, so I think we're at 2.1 or 2 now. The dot levels will bring you up on everything and then the big ones, like the 3.0s, the 4.0s, will bring Spark and Drill current. And so we're going to kind of leapfrog those. So that's still a really important part of our business and we don't want to forget that part, but what we're trying here to do is, via the platform, is deliver all of that in one entity, right? >> So the converged data platform is relevant presumably because you've got the history of Hadoop, 'cause you got all these different components and you got to cobble 'em together and they're different interfaces and different environments, you're trying to unify that and you have unified that, right? >> Yeah, yeah. >> So what is your customer feedback with regard to the converged data platform? >> Yeah so it's a great question because for existing customers, it was like, ah, thank you. It was one of those, right, because we're listening. Actually, again, glad you said that. This week, in addition to Spark Summit we're doing our yearly customer advisory board so we've got, like a lot of vendors, we've got a 30 plus company customer advisory board that we bring in and we sit down with them for a couple of days and they give us feedback on what we should and shouldn't be doing and where, directional and all that, which is super important. And that's where a lot of this converged data platform came out of is the need for... There was just too much, it's kind of confusing. I'll give the example of streams, right? We came out with our streaming product last year and okay, I'm using Hadoop, I'm using your file system, I'm using NoSQL, now you're adding streams, this is great, but now, like MEP, the Ecosystem Packages, I have to keep everything current. You got to make it easier for me, you got to make my life easier for me. So for existing customers it's a stay current, I like this, the model, I can turn on and off what I want when I want. Great model for them, existing business. For new business it gets us out of that Hadoop-only mode, right? I kind of jokingly call us Hadoop plus plus plus plus. We keep adding solutions and add it to a single, cohesive data platform that we keep updated. And as I mentioned here, talking to new customers or new prospects, our potential new business, when I describe the model you can just see the light going on and they realize wow, there's a lot more to this than I had imagined. I got it earlier today, I thought you guys only did Hadoop. Which is a little infuriating as a marketer, but I think from a mechanism and a delivery and a message and a story point of view, it's really helped. >> More Cube time will help get this out there. (laughs) >> Well played, well played. >> It's good to have you back on. Okay, so Spark comes along a couple years ago and it was like ah, what's going to happen to Hadoop? So you guys embraced Spark. Talk more specifically about Spark, where it fits in your platform and the ecosystem generally. >> Spark, Hadoop, others as a entity to bring data into the converged data platform, that's one way to think about it. Way oversimplified, obviously, but that's a really great way, I think, to think about it is if we're going to provide this platform that anybody can query on, you can run analytics against. We talk a lot about now converged applications. So taking historical data, taking operational data, so streaming data, great example. Putting those together and you could use the Data Lake example if you want, that's fine. But putting them into a converged application in the middle where they overlap, kind of typical Venn diagram where they overlap, and that middle part is the converged application. What's feeding that? Well, Spark could be feeding that, Hadoop could be feeding that. Just yesterday we announced a Docker for containers, that could be feeding into the converged data platform as well. So we look at all of these things as an opportunity for us to manage data and to make data accessible at the enterprise level. And then that enterprise level goes back to what I was talkin' before, it's got to have all of those things, like multi-tenancy and snapshots and mirroring and data governance, security, et cetera. But Spark is a big component of that. All of the customers who came by here that I mentioned earlier, which are some really good names for us, are all using Spark to drive data into the converged data platform. So we look at it as we can help them build new applications within converged data platform with that data. So whether it's Spark data, Hadoop data, container data, we don't really care. >> So along those lines, if the focus of intense interest right now is on Spark, and Spark says oh, and we work with all these databases, data storers, file systems, if you approach a customer who's Spark first, what's the message relative to all the other data storers that they can get to through, without getting too techy, their API? >> Sure, sure. I think as you know, George, we support a whole bunch of APIs. So I guess for us it's the breadth. >> But I'm thinking of Spark in particular. If someone says specifically, I want to run Databricks, but I need something underneath it to capture the data and to manage it. >> Well I think that's the beauty of our file system there. As I mentioned, if you think about it from an architectural point of view, our file system along the bottom, or it could be our database or our streaming product, but in this instance-- >> George: That's what I'm getting at too, all three. >> Picture that as the bottom layer as your storage-- I shouldn't say storage layer but as the bottom layer. 'Cause it's not just storage, it's more than storage. Middle layer is maybe some of your open source tools and the like, and then above that is what I called your data delivery mechanisms. Which would be Spark, for example, one bucket. Another bucket could be Hadoop, and another bucket could be these microservices we're talking about. Let my draw the picture another way using a partner, SAP. One of the things we've had some success with SAP is SAP HANA sitting up here. SAP would love to have you put all your data in HANA. It's probably not going to happen. >> George: Yeah, good luck. >> Yeah, good luck, right? But what if you, hey customer, what if you put zero to two years worth of data, historical data, in HANA. Okay, maybe the customer starts nodding their head like you just did. Hey customer, what if you put two to five years worth of data in Business Warehouse. Guess what, you already own that. You've been an SAP customer for awhile, you already have it. Okay, the customer's now really nodding their head. You got their attention. To your original question, whether it's Spark or whatever, five plus years, put it in MapR. >> Oh, and then like HANA Vora could do the query. >> Drill can query across all of them. >> Oh, right including the Business Warehouse, okay. >> So we're running in the file system. That, to me, and we do this obviously with our joint SAP MapR customers, that to me is kind of a really cool vision. And to your original question, if that was Spark at the top feeding it rather than SAP, sure, right? Why not? >> What can you share with us, Bill, about business metrics around MapR? However you choose to share it, head count, want to give us gross margins by product, that's great, but-- (laughs) >> Would you like revenues too, Dave? >> We know they're very high because you're a software company, so that's actually a bad question. I've already profit-- (laughs) >> You don't have to give us top line revenues-- >> So what are you guys saying publicly about the company, its growth. >> That's fair. >> Give us the latest. >> Fantastic, number one. Hiring like crazy, we're well north of 500 people now. I actually, you want to hear a funny story? I yesterday was texting in the booth, with a candidate from my team, back and forth on salary. Did the salary negotiation on text right there in the booth and closed her, she starts on the 27th, so. >> Dave: Congratulations. >> I'm very excited about that. So moving along on that. Seven, 800 plus customers as we talk about... We just finished our fiscal year on January 31st, so we're on Feb one fiscal year. And we always do a momentum press release, which will be coming out soon. Hiring, again, like crazy, as I mentioned, executive staff is all filled in and built to scale which we're really excited about. We talk a lot about the kind of uptake of-- it used to be of the file system, Hadoop, et cetera on its own, but now in this one the momentum release we'll be doing, we'll talk about the converged data platform and the uplift we've seen from that. So we obviously can't talk revenue numbers and the like, but everything... David, I got to tell you, we've been doin' this a long time, all of that is just all moving in the right direction. And then the other example I'll give you from my world, in the partner world. Last year I rebranded our partner to the converged partner program. We're going with this whole converged thing, right? And we established three levels, elite, preferred, and affiliate with different levels there. But also, there's revenue requirements at each level, so elite, preferred, and affiliate, and there's resell and influence revenues, we have MDF funds, not only from the big guys coming to us, but we're paying out MDF funds now to select partners as well. So all of this stuff I always talk about as the maturity of the company, right? We're maturing in our messaging, we're maturing in the level of people who are joining, and we're maturing in the customers and the deals, the deal sizes and volumes that we're seeing. It's all movin' in the right direction. >> Dave: Great, awesome, congratulations. >> Bill: Thank you, yeah, I'm excited. >> Can you talk about number of customers or number of employees relative to last year? >> Oh boy. Honestly, George, I don't know off the top of my head. I apologize, I don't know the metric, but I know it's north of 500 today, of employees, and it's like seven, 800 customers. >> Okay, okay. >> Yeah, yeah. >> And a little bit more on this partner, elite, preferred, and affiliate. >> Affiliate, yeah. >> What did you call it, the converged partners program? >> Converged-- Yeah, yeah. >> What are some of the details of that? >> Sure. So the elites are invite only, and those are some of the bigger ones. So for us, we're-- >> Dave: Like, some examples. >> Cisco, SAP, AWS, others, but those are some of the big ones. And they were looking at things like resell and influence revenue. That's what I track in my... I always jokingly say at MapR, even though we're kind of a big startup now, I always jokingly say at MapR you have three jobs. You have the job you were hired for, you have your Thursday night job, and you have your Sunday night job. (Dave and George laugh) In the job that I was hired for, partner marketing, I track influence and resell revenue. So at the elite level, we're doing both. Like Cisco resells us, so this S-Series, we're in their SKU, their sales reps can go sell an S-Series for big data workloads or analytical workloads, MapR, on it, off you go. Our job then is cashing checks, which I like. That's a good job to have in this business. At the preferred level it's kind of that next tier of big players, but revenue thresholds haven't moved into the elite yet. Partners in there, like the MicroStrategies of the world, we're doing a lot with them, Tableau, Talend, a lot of the BI vendors in there. And then the affiliates are the smaller guys who maybe we'll do one piece of a campaign during the year with them. So I'll give you an example, Attunity, you guys know those guys right here? >> Sure >> Yeah, yeah. >> Last year we were doing a campaign on DWO, data warehouse offload. We wanted to bring them in but this was a MapR campaign running for a quarter, and we're typical, like a lot of companies, we run four campaigns a year and then my partner in field stuff kind of opts into that and we run stuff to support it. And then corporate marketing does something. Pretty traditional. But what I try and do is pull these partners into those campaigns. So we did a webinar with Attunity as part of that campaign. So at the affiliate level, the lower level, we're not doing a full go-to-market like we would with the elites at the top, but they're being brought into our campaigns and then obviously hopefully, we hope on the other side they're going to pull us in as well. >> Great, last question. What should we pay attention to, what's comin' up? >> Yeah, so-- >> Let's see, we got some events, we got Strata coming up you'll be out your way, or out MapR way. >> As my Twitter handle says, seat 11A. That's where I am. (laughs) Yeah, I mean the Docker announcement we're really excited about, and microservices. You'll see more from us on the whole microservices thing. Streaming is still a big one, we think, for this year. You guys probably agree. That's why we announced the MapR streaming product last year. So again, from a go-to-market point of view and kind of putting some meat behind streaming not only MapR but with partners, so streaming as a component and a delivery model for managing data in CDP. I think that's a big one. Machine learning is something that we're seeing more and more touching us from a number of customers but also from the partner perspective. I see all the partner requests that come in to join the partner program, and there's been an uptick in the machine learning customers that want to come in and-- Excuse me, partners, that want to be talking to us. Which I think is really interesting. >> Where you would be the sort of prediction serving layer? >> Exactly, exactly. Or a data store. A lot of them are looking for just an easy data store that the MapR file system can do. >> Infrastructure to support that, yeah. >> Commodity, right? The whole old promise of Hadoop or just a generic file system is give me easy access to storage on commodity hardware. The machine learning-- >> That works. >> Right. The existing machine learning vendors need an answer for that. When the customer asks them, they want just an easy answer, say oh, we just use MapR FS for that and we're done. Okay, that's fine with me, I'll take that one. >> So that's the operational end of that machine learning pipeline that we call DevOps for data scientists? >> Correct, right. I guess the nice synergy there is the whole, going back to the Docker microservices one, there's a DevOps component there as well. So, might be interesting marrying those together. >> All right, we got to go, Bill, thanks very much, good to see you again. >> All right, thank you. >> All right, George and I will be back to wrap. We're going to part two of our big data forecast right now, so stay with us, right back. (digital music) (synth music)